List of NSCLC-associated lncRNA biomarkers identified in different researches.
\r\n\tWith the discovery of more unconventional heavier crude and alternative hydrocarbon sources, primary upgrading or cracking of the oil into lighter liquid fuel is critical. With increasing concern for environmental sustainability, the regulations on fuel specifications are becoming more stringent. Processing and treating crude oil into a cleaner oil with better quality is equally important. Hence, there has been a relentless and continuous effort to develop new crude upgrading and treating technologies, such as various catalytic systems for more economical and better system performance, as well as cleaner and higher-quality oil.
\r\n\r\n\tThis edited book aims to provide the reader with an overview of the state-of-the-art technologies of crude oil downstream processing which include the primary and secondary upgrading or treating processes covering desulfurization, denitrogenation, demetallation, and evidence-based developments in this area.
",isbn:"978-1-80356-681-8",printIsbn:"978-1-80356-680-1",pdfIsbn:"978-1-80356-682-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"808b0ddfb3b92e0636ae44a83ef7dbd9",bookSignature:"Dr. Ching Thian Tye",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11542.jpg",keywords:"Crude Oil Properties, Hydrocracking, Catalytic Cracking, Coking, Visbreaking, Thermal Cracking, Hydroprocessing, Hydrodesulfurization, Desulfurization, Denitrogenation, Demetallation, Dearomatization",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 22nd 2022",dateEndSecondStepPublish:"April 19th 2022",dateEndThirdStepPublish:"June 18th 2022",dateEndFourthStepPublish:"September 6th 2022",dateEndFifthStepPublish:"November 5th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Associate professor at the School of Chemical Engineering in Universiti Sains Malaysia and dedicated researcher in fuel-related catalytic process and chemical reaction engineering. 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She has been involved in projects to improve catalysis activities, system efficiency, as well as products quality via different upgrading and treating paths that are related to petroleum and unconventional oil such as heavy oil, used motor oil, spent tire pyrolysis oils as well as renewable resources like palm oil. 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According to the estimation of National Cancer Institute, USA, the estimated new cases for lung cancer in 2016 will be 224,390, accounting 13.3% of all new cancer cases, and the estimated deaths of lung cancer in 2016 will be 158,080 accounting 26.5% of all new cancer cases [1]. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer which accounts for nearly 80–85% of diagnosed lung cancers [2]. NSCLC can be further histologically classified into three major subtypes: lung adenocarcinoma (ADC), lung squamous cell carcinoma (SCC), and large cell carcinoma. Much attention has been paid to the clinical diagnosis and treatment of NSCLC, however, the 5-year overall survival rate of NSCLC is still less than 19% in these days [1]. This may attribute to the advanced stage of the disease at the time of diagnosis for many patients. The predominantly late diagnosis of NSCLC has limited the therapy options. The low-dose computed tomography (CT) scan can detect NSCLC early and has become the dominant detection approach. However, the high cost and the risk of false positive has overshadowed the benefits of swift diagnosis [3]. Thus, it is important to develop novel early detection approach with high sensitivity and specificity.
\nBiomarker is a powerful approach for cancer detection and treatment. It is defined as an indicator of biologic processes, pathogenic processes, or pharmacologic responses to therapeutic interventions. Traditional protein biomarkers such as CEA, SSC, CY211, and CA125 are classic tumour biomarkers commonly used in the diagnoses of NSCLC patients [4]. However, the current lack of diagnostic sensitivity and specificity has limited their usefulness in early detection of NSCLC. The occurrence of NSCLC always comes with the genetic changes. A thorough understanding of the genetic aberrations that contribute to NSCLC would assist in identifying biomarkers that could aid in earlier diagnoses and serve as drug targets, thus increasing treatment efficacy. Considerable efforts have been made to achieve great progress in understanding genomic landscape of NSCLC and the molecular mechanisms involved in tumorigenesis, several cancer-related genes such as TP53, EGFR, and KRAS, have been identified which play a vital role in cancer-related pathways [5–7]. Identification and characterization of specific driver mutations has transformed the diagnosis and treatment of NSCLC.
\nWith the development of sequencing technology and bioinformatics databases, researchers have identified that more than 90% of genome is transcribed; of these transcripts, most are non-coding RNAs with little or no protein-coding potentials [8]. The enormous number and complex kinds of non-coding RNAs have drawn peoples’ attention to their roles in biological processes. MicroRNA (miRNA) is a well-studied small non-coding RNA of 18–25 nucleotides [9]. The functions of miRNAs can be summarized as mediating gene silencing by interfering with translational process or inducing mRNA degradation [10, 11]. miRNAs can be classified into oncomiRNAs and tumour suppressor miRNAs in relation to their function in carcinogenic processes; meanwhile, some of them show both oncogenic and suppressive activities under different situations [12]. Another advantage for miRNAs is their high stability and easy detection in tissue and blood [13, 14]. Several studies have reported the deregulation of various miRNAs in NSCLC [13, 15]. Screening studies have uncovered the potential of miRNAs as biomarkers in the diagnosis and prognosis of NSCLC [16, 17]. As the researches for novel biomarkers and therapy targets go further, another class of non-coding RNA molecules, long non-coding RNAs (lncRNAs) with longer length and more complex biological functions have drawn people’s attentions and become a new star in the RNA world.
\nThe development of microarray and high-throughput sequencing technologies have enabled us to explore the RNA world. As the understanding of the heterogeneous RNA molecules goes deeper, the functional RNA molecules gain increasing attention again, among these, lncRNAs play a major role in the centre stage. Long non-coding RNAs can be loosely defined as a class of non-coding RNA, which are longer than 200 nucleotides. Different from the small non-coding RNAs, although lncRNAs have little or no protein coding potentials, several common features are still shared with mRNAs. Most of the lncRNAs are transcribed by RNA polymerase II, subsequent post-transcriptional processing including alternative splicing, 5′-capping, and polyadenylation are prevalently found in many lncRNAs [18]. Like mRNAs, the expression of lncRNAs is also under the regulation of transcriptional and epigenetic factors. Active or repressive histone marks that indicate the transcription status can also be found around the transcription start site of the lncRNAs [19]. On the other hand, lncRNAs have their own characters. LncRNAs have shorter median transcript length (2453 nucleotides for mRNAs and 592 nucleotides for lncRNAs) and less median exons number (8 exons for mRNAs and 3 exons for lncRNAs) than mRNAs [18]. Most lncRNAs are located in the nucleus, as most of them are functioned as regulation factors. The expression level of lncRNAs is always lower in cells than mRNAs, but with higher tissue specificities [18]. On the epigenetic level, the transcription start sites of lncRNAs have a higher density of DNA methylation compare with the mRNAs, however, this high methylation density is independent of their expression status [19].
\nThe various features associated with mRNAs imply the complex origin and functions of lncRNAs. Study on the origin of lncRNAs are relatively scant, several hypotheses of the emergence of lncRNAs have been put forward. Some lncRNAs, such as
According to the current version of GENCODE (encyclopaedia of genes and gene variants), 15,767 long non-coding RNA genes encoding 27,692 long non-coding loci RNA have been identified based on manual curation, computational analysis, and experimental validation [35]. Along with it is the plethora of deregulated lncRNAs that are found in plenty of high-throughput lncRNA screen works. However, compared with the numerous screened lncRNAs, only few lncRNAs are well characterized and validated, the roles of most deregulated lncRNAs in diseases still remained unknown, and the data on the mechanism are scarce. In this section, some well characterized lncRNAs with reported deregulation and associated pathophysiological functions in NSCLC are reviewed.
\nHOX transcript antisense RNA (HOTAIR) is a 2158 bps long antisense lncRNA transcribed from human HOXC locus in chromosome 12q13 [36]. As one of the most well-studied lncRNA implicated in cancer, HOTAIR is mainly involved in the epigenetic regulation as a molecular scaffold. HOTAIR can interact with polycomb repressive complex 2 (PRC2) and lysine-specific demethylase 1 (LSD1) in its 5′- and 3′-domain, respectively, and recruits PCR2 and LSD1 to the HOXD locus located on chromosome 2, inducing H3K27 methylation and H3K4 demethylation, thus silences a gene cluster involved in metastasis suppression.
\nHOTAIR was first reported to be highly overexpressed in primary breast cancer and metastatic breast cancer tissues. The high expression level of HOTAIR in breast cancer was closely associated with the metastasis [37]. A further study in the breast carcinoma cells showed that the enforced overexpression of HOTAIR led to the methylation of H3K27 [37]. Other researches also reported the up-regulated HORAIR as a negative prognostic predictor in hepatocellular carcinoma [38], colorectal carcinoma [39], pancreatic cancer [40], oesophageal carcinoma [41], lung cancer [42], and gastric cancer [43, 44]. In gastric cancer, HOTAIR was also reported as an endogenous sponge of miR-331-3p, thus abolishing repression of target gene HER2 [44].
\nIn non-small cell lung cancer, Liu et al. analysed the HOTAIR expression level in 42 NSCLC tissues and 4 NSCLC cell lines and reported the high expression of HOTAIR in both NSCLC samples and cell lines compared with corresponding normal counterparts. Results showed that high expression level of HOTAIR was correlated with advanced disease stage, metastasis, and short disease free interval. Furthermore, knockdown of HOTAIR decreased the migration and invasion of NSCLC cells
In summary, various studies have worked on illuminating the mechanism of HOTAIR deregulation and function in NSCLCs. HOTAIR is widely involved in the chromatin modifications, and can also interact with various molecules like miRNAs and proteins. Although the facts that HOTAIR promotes cancer progress and drug resistance in NSCLC cells have been revealed, there are still many unclarified details in the mechanisms. Hence, more analyses on HOTAIR regulation and modes of action are needed.
\nMetastasis-associated lung adenocarcinoma transcript 1 (MALAT1), also known as nuclear-enriched abundant transcript 2 (NEAT2), is an 8 kbs nuclear lncRNA expressed in chromosome 11q13 [49]. The mature MALAT1 transcript is generated through the procession by RNase P and RNase Z from the primary transcript [50]. MALAT1 is located in the nuclear speckles, and is mainly involved in alternative splicing process [51, 52]. MALAT1 exists widely and conservatively in lung, pancreas, and other healthy organs, the abundant amount of MALAT1 in these organs suggests significant functions for MALAT1 [49].
\nAs one of the earliest identified cancer-associated lncRNAs, MALAT1 was firstly regarded as a high-risk predictor for metastasis in early stage NSCLC patients [49]. Since then, accumulating evidences confirmed the negative prognostic factor of MALAT1 in various cancers. Overexpression of MALAT1 was identified in pancreatic cancers and colorectal cancers, the high expression level was correlated with clinical progression and poor prognosis [53, 54]. Upregulation of MALAT1 was reported to promote the proliferation and metastasis of osteosarcoma and gallbladder cancer via different pathways such as PI3K/AKT and ERK/MAPK pathways [55, 56]. In oesophageal squamous cell carcinoma, silencing the MALAT1 expression resulted in the inhabitation of proliferation, migration, and invasion [57, 58]. Other studies also reported the deregulation of MALAT1 in bladder cancer and renal cancers [59, 60].
\nHigh expression of MALAT1 was identified in both early stage lung adenocarcinomas and squamous cell lung cancers [49, 61]. The overexpression of MALAT1 in NSCLC was reported to be associated with poor prognosis, shorter overall survival, and metastasis development. An RNAi-mediated suppression of MALAT1 performed in A549 cells led to the suppression of cell migration and clonogenic growth. Reversely, enforced upregulation of MALAT1 resulted in an increased NSCLC cell growth and colony formation
Numbers of studies have been performed on elucidating the mechanism of MALAT1 regulation. Alternative splicing is one of the major topics in the MALAT1 functions. One example was that MALAT1 could interact with some alternative splicing factors such as Serine/Arginine (SR) proteins, thus affected the gene expression [52]. Another example in human diploid fibroblasts cell lines demonstrated the depletion of MALAT1 might led to the aberrant alternative splicing of the pre-mRNA of oncogenic transcription factor B-MYB, thus reduced the expression of it [63]. Another major topic is the gene expression regulation. MALAT1 displayed a strong association with genes involved in cellular growth, movement, proliferation, signalling, and immune regulation [61]. In lung cancer, MALAT1 could activate the expression of some metastasis-associated genes without affecting the alternative splicing [62]. A study involved in the epigenetic field reported that MALAT1 could interact with demethylated Polycomb 2 protein (Pc2), and controlled the re-localization of growth control genes between polycomb bodies and interchromatin granules [64].
\nIn summary, the poor prognostic role and multiple functions of MALAT1 indicate its potential as predictable biomarker and therapy target. However, detailed studies are still needed as the mechanisms of MALAT1 regulation differ in various situations.
\nH19 is a paternally imprinted and maternally expressed gene localized on human chromosome 11p15 [65]. Beside the H19 transcripts, the H19 locus also harbours miR-675, an antisense transcript, and an antisense protein-encoding transcript [66]. H19 was first reported as a tumour suppressor gene in mice [65]. However, later studies point out the oncogenic potential of H19.
\nLoss of imprinting (LOI) in paternal allele and the resulting overexpression of H19 was found in various cancers including lung cancer [67], oesophagus cancer [68], osteosarcoma [69], and bladder cancer [70]. H19 upregulation was found related to a range of risk factors such as smoking, carcinogens exposuring, and hypoxia. Cigarette smoking could induce a LOI-independent upregulation of H19 by activating the H19 maternal allele [71]. This observation was also confirmed in a later
In NSCLC, the expression levels of lncRNA H19 in tissues and cells were significantly higher than adjacent tissues and normal cells, overexpression and knockdown of c-Myc could change the H19 expression level significantly. Moreover the higher expression of H19 was positively correlated with advanced tumour-node-metastasis (TNM) stage and tumour size [76].
\nIn summary, the expression level of H19 is related to many risk factors including smoking, which is an important lung cancer factor. Overexpression of H19 may contribute to the cell proliferative in many cancers and is associated with poor prognosis. Since the deregulation of H19 expression may occur from different mechanism, future studies should focus on the different functions of H19 in physiological and pathological processes and evaluate the potential of H19 as biomarkers and therapy targets under different situations.
\nLncRNAs cancer-associated region long non-coding RNA 5 (CARLo-5) is transcribed from the (−) strand of the 8q24.21 genomic region, where two other transcripts, sharing significant overlap in their sequences, colon cancer-associated transcript 1 (CCAT1) and CCAT1 long isoform (CCAT1-L) are also transcribed [66].
\nCARLo-5 was originally reported to be overexpressed in colorectal cancer patient tissues [77]. Later study revealed the overexpression of CARLo-5 in NSCLC and in some other cancers such as gastric cancer [78, 79]. In NSCLC patients, high CARLo-5 expression level was associated with advanced pathological stage and lymph node metastasis and was a significant predictor of shorter overall survival. An
The regulatory mechanism of CARLo-5 may be related to the adjacent region of the cancer-associated variant rs6983267, as the region including rs6983267 has enhancer activity and can interact with the proto-oncogene MYC [80]. Evidences were provided in a colon cancer study that demonstrated a strong connection between the cancer-associated variant rs6983267 and the expression of CARLo-5. The chromosome conformation capture method revealed the MYC enhancer region could physically interact with the active regulatory region of the CARLo-5 promoter and enhanced the expression of CARLo-5, this finding suggested there was a long-range interaction of MYC enhancer with the CARLo-5 promoter [77]. Since CARLo-5 is proved to have an oncogenic function, further studies focus on elucidating the mechanism of CARLo-5 regulation may provide potential therapy target for cancer treatment.
\nLncRNA colon cancer-associated transcript 2 (CCAT2) is expressed from a highly conserved MYC enhancer region within chromosome 8q24.21. It was initially reported to be involved in metastatic progress and chromosome instability in colorectal cancer [81]. Further studies reported its association with poor prognosis in various cancers including NSCLC [82]. CCAT2 was significantly overexpressed in NSCLC tissues, in particular, the overexpression of CCAT2 was associated with adenocarcinomas specifically but not with squamous cell carcinoma. Silencing CCAT2 by siRNA led to the inhibition of proliferation and invasion in NSCLC cell lines
Growth arrest-specific transcript 5 (GAS5) is expressed from human chromosome 1q25 [83]. GAS5 was found significantly downregulated in NSCLC tissues, which was correlated with advanced TNM stage and increased tumour size [84]. GAS5 could compete with the glucocorticoid response elements (GRE) on DNA by directly interacting with the DNA-binding domain of glucocorticoid receptor (GR), thus prevented the activation of glucocorticoid-responsive genes. This competition resulted in the reduction of cell growth and metabolism, while sensitizing cells to apoptosis [85]. Recent study demonstrated that downregulation of GAS5 was associated with cisplatin resistance in NSCLC. GAS5 could inhibit autophagy and therefore enhance cisplatin sensitivity in NSCLC cells [86]. Another study found that GAS5 overexpression was inversely correlated with EGFR pathway and the expression of IGF-1R proteins in human ADC cell line, indicating its role in reversing EGFR-TKIs resistance [87]. These findings indicate the tumour suppressor lncRNA GAS5 may represent a potential biomarker for diagnosis and therapy target for NSCLC intervention.
\nSRY-box containing gene 2 overlapping transcript (SOX2OT) locates in the chromosome region 3q26.33, and is transcribed form the same orientation of gene SOX2 [88]. SOX2OT was reported upregulated in NSCLC, along with the upregulation of SOX2, meanwhile, the expression level was significantly higher in lung SCCs than ADCs [89]. Further study found high SOX2OT expression predicted poor survival in lung cancer patients. In lung cancer cell lines, knocking down SOX2OT inhibited the cell proliferation. These finding suggest the oncogenic SOX2OT may be prognostic indicator for NSCLC [89].
\nBRAF-activated non-coding RNA (BANCR) is a 693 bps lncRNA located on (−) strand of chromosome 9q21, which is initially found as a tumour suppressor factor involved in melanoma cell migration [90]. BANCR expression level was reported to be significantly decreased in NSCLC tumour tissues samples, the reduction of BANCR was related to the larger tumour size, advanced TNM stage, metastasis development, and shorter overall survival. BANCR was also an independent poor prognostic predictor of poor survival for NSCLC. An investigation on the mechanisms of tissue-specific expression revealed that histone deacetylase might be involved in the repression of BANCR. Furthermore, upregulation of BANCR inhibited NSCLC cell viability, migration, and invasion, while promoting the apoptosis process. Reversely, knockdown of BANCR promoted migration and invasion of NSCLC cells
Maternally expressed gene 3 (MEG3) is expressed in chromosome 14q32.3 with a full length of 1.6 kb nucleotides [92]. Alternative splicing process was found associated with the gene MEG3, which consisted of 10 exons and could generate multiple transcripts [93]. It was reported that the expression level of MEG3 in NSCLC tissues was significantly lowered than normal tissues, which might due to the higher methylation rate of MEG3-DMR in NSCLC cells. Downregulation of MEG3 in NSCLC patients was associated with poor prognosis. In addition, overexpression of MEG3 by transfecting exogenous pCDNA-MEG3 into NSCLC cells inhibited cell proliferation and induced cell apoptosis
The high mortal rate of NSCLC may be mainly attributed to the late diagnosis and tumour metastasis. In addition, the heterogeneity of disease also increases the difficulty in the diagnosis and treatment, the molecular characters are different from each subtypes. Early detection, precise diagnosis, and treatment may increases the survival rate of NSCLC. To meet these ends, it is of great importance to identify novel NSCLC biomarkers.
\nAs a new class of functional RNA molecules, lncRNAs are involved in a wide range of cellular and biological processes. Dysregulation of lncRNAs is associated with many cancer-related processes. In addition, the expression of lncRNA can be very tissue specific. These advantageous features imply a potential role of lncRNAs in cancer detection and treatment. Reduced BANCR expression was found to be an independent prognostic factor for NSCLC [91]. Huang et al. found small amount of lncRNAs (3.36%) in circulating vesicles [95]. Later research detected lncRNA HOTAIR, MALAT1, and H19 in the plasma of patients with gastric cancer and identified the expression level of plasma H19 was significantly higher than normal samples, furthermore, plasma H19 level was reduced in postoperative samples, which suggested H19 might be a biomarkers for gastric cancer [96]. Ren et al. identified fragments of lncRNA MALAT1 in plasma of prostate cancer (Pca) and named one of them as MALAT1-derived miniRNA (MD-miniRNA). Researchers then evaluated the diagnostic performance of MD-miniRNA in plasma samples of 192 patients. The results showed a sensitivity of 58.6% and specificity of 84.8% for discriminating PCa from non-PCa [97]. Although the functional lncRNAs mentioned above have been well-characterized, only few of them have been evaluated as biomarkers for diagnosis and prognosis in NSCLC, further validations is still need.
\nWith the development of high-throughput technology, an increasing number of previously unidentified lncRNAs have been found. More and more researchers started to explore novel biomarkers from these unidentified lncRNAs. MiTranscriptome is a database, which derived from computational analysis of high-throughput RNA sequencing (RNA-Seq) data comprising 6500 samples spanning diverse cancer and tissue types. In database, 1128 ADC-related lncRNAs and 1309 lung SCC-related lncRNAs are identified, among these, 4 lncRNAs in ADC and 11 lncRNAs in lung SCC are predicted to be tissue specific, indicating that lncRNAs can discriminate not only between tumour and normal samples, but also between different subtypes [98]. Although most of these lncRNAs remain to be annotated and validated, the large number of cancer-related lncRNAs provides great hope for further screening of biomarkers and therapy targets. Some groups have investigated the potential of lncRNAs as biomarkers in early detection of NSCLCs. Wang et al. examined the expression of lncRNAs in three pairs of early stage ADC samples by high-throughput microarray technology and identified 1170 differentially expressed lncRNAs (DE-lncRNAs) between early stage ADC tissues and their adjacent normal tissues. Further analysis identified 20 candidates of lncRNAs from 1170 DE-lncRNAs through a screening pipeline, the pipeline could be summarized briefly as follows: if an lncRNA’s average inter-group difference between tumour group and normal group was 10 times bigger than the inner group difference, it would be selected as a candidate. These 20 candidates were then validated by real-time quantitative PCR (qPCR) on a total of 102 pairs of early stage ADC samples. A panel of five lncRNAs (\nTable 1\n) was finally identified which can distinguish early stage adenocarcinoma samples from normal samples with high sensitivity (97%) and specificity (92%) [99]. Another study, which integrated two NSCLC microarray datasets comprising 165 and 90 patients, reported a list of 64 significantly deregulated lncRNAs in NSCLC tumours compared with normal lung tissues and a panel of 181 lncRNAs that were specific to histological subtypes of NSCLC [100].
\nLncRNA | \nRegulation | \nRegion | \n\n | \nAUC | \nSensitivity (%) | \nSpecificity (%) | \nType of sample | \nReference | \n
---|---|---|---|---|---|---|---|---|
BC034684 | \nUp | \nChr1:203,148,063–203,148,611 | \n1.486E−06 | \n0.719 | \n79.4 | \n60.3 | \nTissue | \nWang et al. [99] | \n
RP11-1008C21.2 | \nDown | \nChr15:38,363,827–38,364,884 | \n1.193E−07 | \n0.843 | \n81 | \n79.4 | \n||
AK094413 | \nDown | \nChr9:104,235,441–104,237,132 | \n6.634E−08 | \n0.821 | \n85.2 | \n62.4 | \n||
RP11-598F7.5 | \nDown | \nChr12:273,829–275,487 | \n4.108E−11 | \n0.882 | \n79.4 | \n84.1 | \n||
VNN2 | \nDown | \nChr6:133,065,008–133,079,022 | \n1.063E−05 | \n0.835 | \n77.8 | \n79.4 | \n||
Combination | \n0.987 | \n92 | \n98 | \n|||||
SPRY4-IT1 | \nUp | \nChr5:142,317,620–142,318,322 | \n<0.01 | \n0.603 | \n/ | \n/ | \nPlasma | \nHu et al. [102] | \n
ANRIL | \nUp | \nChr9:21,994,791–22,120,646 | \n<0.001 | \n0.798 | \n/ | \n/ | \n||
NEAT1 | \nUp | \nChr11:65,422,800–65,423,368 | \n<0.001 | \n0.693 | \n/ | \n/ | \n||
Combination | \n0.876 | \n88 | \n81 | \n|||||
MALAT1 | \nUp | \nChr11:65,497,762–65,506,469 | \n<0.0001 | \n0.79 | \n56 | \n96 | \nPeripheral blood | \nWeber et al. [101] | \n
List of NSCLC-associated lncRNA biomarkers identified in different researches.
An ideal biomarker should be of high sensitivity and specificity, and it should be easy to detect, better with non-invasive methods from body liquids. Weber et al. detected the expression level of MALAT1 in the cellular fraction of blood of a small NSCLC patients group (\nTable 1\n), they found that MALAT1 was detectable in peripheral human blood and the expression level between cancer patients and cancer-free controls was different, the sensitivity and specificity for discrimination was 56% and 96%, respectively [101]. Another study reported circulating SPRY4-IT1, ANRIL, and NEAT1 were significantly increased in plasma samples of NSCLC patients (\nTable 1\n). Combination with the three factors indicated a high power of discrimination (AUC, 0.876; sensitivity, 82.8%; and specificity, 92.3%) [102].
\nSince the researchers have identified that most of the genome is actively transcribed, while only small part of the human genome has the coding potential as protein-coding genes, the roles of non-coding RNAs have been transferred from transcriptional noises to the important functional molecules. This finding has led the classical view of the central dogma, which considers that the RNA functions mainly as an intermediate bridge between DNA sequences and protein synthesis, into a deeper understanding.
\nThe roles of lncRNAs in the upstream of whole cellular signal system indicate that lncRNAs are closely associated with cellular differentiation, mitosis, and apoptosis. In the view of epigenetics, the functions of lncRNAs are mainly involved in three levels, including chromatin remodelling, transcriptional control, and post-transcriptional processes. LncRNA can act as transcriptional regulators and modulate the expression of protein-coding genes in
Despite the high performance of these lncRNA panels in diagnosis, most of them are identified from statistical analysis, which means the biological meanings of lncRNAs have not been taken into consideration. In addition, the candidate screening methods are mainly based on p-value, fold change, absolute expression level, and PAM method, outcomes of the candidates may differs for different methods [103]. Thus, a better candidate screening method combining the biological meaning of lncRNAs and robust statistical pipeline is need for future studies. Also, up to now, most of the samples in these studies are collected from patient tissues through invasive methods, more works are still needed to explore circulating lncRNA expressions in blood plasma, urine, or sputum, which can meet the non-invasive demands.
\nConsidering the sophisticated functions and large number of lncRNAs, we have now identified just the tip of the lncRNA iceberg. Lots of questions are waiting to be clarified, for example, what does the classification of lncRNAs looks like, and what the mechanistic basis of their functions is. Huge gaps are still in front of us in understanding the big picture of the lncRNA world. Fortunately, new technology such as the third generation sequencing, which allows the longer read length, are now providing more reliable and accurate information of lncRNAs. In future, we believe that understanding the lncRNA world will bring us new answers to old questions in evolution, development, and the understanding of NSCLCs. There may be a long way before the clinical application of lncRNAs in NSCLC, however, fast progressing in the lncRNA filed opens up numerous opportunities for diagnosis and therapeutic intervention against NSCLC.
\nInformation Technology (IT) has already transformed into a business enabler and an intentional reason [1] in firms to date. However, the IT presence should get improved administration to cause more values [2] such as effective and efficient business processes, and profit growth [3]. Consequently, IT should replace from a business enabler to a business transformer as per IT ecosystem to convey the approach of IT services [4], in which IT does not only behave a driving instrument but also lets businesses innovate and disrupt customs to revitalize its presence inside the firm. In sequence, this revival will allow the firm to sustain its competitive advantage remains efficient [5].
Additionally, this chapter intends to verify the IT presence in terms of a business transformer in the firm operation [6, 7, 8] improving its performance. To do so, it needs a method to engineer the IT position in transforming the business. Also, the method is to show up the IT capitals bringing up more values. In other words, the method should involve a systems engineering viewpoint, which discloses prime thoughts of the systems approach such as holism, synthesis, interrelationships, along with the engineering-project-based estimates of system life cycle and requirements [9]. Likewise, the systems engineering utilizes an engineering design containing problem-solving, alternative solutions, solution selection, detailed model, model guard, and validated model [10].
Also, validating the chapter, the studies on the strength of resources on performance [11, 12, 13] turns into an essential theory to analyze IT systems as components of business completeness [14] because the studies emphasize on the resources an organization owns to promote its performance. This is in line with the intent of this chapter that also emphasizes that the business performance runs over IT resources owned [15, 16, 17]. Likewise, this chapter applies the Partial Adjustment Valuation (PAV) theory approach in congregating valuation methods among system components as promoted by [18, 19, 20]. Equally for the use of PAV in this chapter is the ability to relate between IT resources and the organizational performance mathematically. Thus, it is easier to trace the relationship of each component or subsystem for further synthesis.
Additionally, this chapter has continued the earlier studies addressing the IT value model from the ontological approach towards IT value engineering [21], the IT value model using a variance-based structural equation modeling (SEM) towards IT value engineering [22], engineering IT value in IT-based industries using PAV and RBV (Resource-Based View) approach [23], valuation methodology of IT value in the IT-based business [24], IT value engineering model and its optimum performance [25, 26], and hybrid configuration in IT value models [27]. The investigation leads IT to be valuable resources of the firm to revitalize the IT’s role through the IT value engineering model. Formerly, those studies had associated with a number of studies discussing the relationship between IT resources to business performance, such as [11, 12, 13, 17, 18, 19, 20, 28, 29]. Meanwhile, the IT value defined in this chapter is the added value in the form of a currency, which can also be expressed as the index ratio, generated due to the IT spending presence.
This chapter problem relates to past studies, which most of them had talked over the relationship between IT and the organization performance. Researchers identified the types of conclusion [8] about the relationship, where numerous conclusions show that the relationship might be positive [12, 13, 30], negative [3], and even neutral [11, 31], see Figure 1. Additionally, the positive relationship means that IT has a positive correlation to the organization to increase its business value and at that moment the negative is otherwise. The negative relationship shows in early empirical studies explaining the association between IT investment and organizational performance; they set off the controversy of the IT productivity paradox as Brynjolfsson’s (1993) conclusion [32].
The state of the art of ITVE.
In addition, the neutral relationship explicates that IT and business performance have no relationship in between as Strassman (1997) argued that there was no clear relationship between IT investment and a few measures of firm profitability, including return on asset, return on equity, and economic value added [11]. This chapter, further, addresses the positive relationship, although there are unfortunate situations where IT may have a negative impact as well [33].
The earlier studies, especially about the positive conclusion, have not yet talked about how to engineer the value of IT to deliver more benefits to the organization. On one hand, this issue is a complement study to the previous one because the chapter topic is a continual study of the past studies. On the other hand, also, this chapter may strengthen to close the past study, especially in terms of positive relationships between IT and the business performance, therefore, this chapter is to enrich and develop this domain further.
Additionally, the past studies seem like passive research, meaning that the work performed in the IT-equipped organization before. Consequently, the study has been simply conducted in conventional organizations and it has been less hard if it has maneuvered in a planned system. Therefore, the IT value engineering model chapter tries to offer a new approach to studying the role of IT within an organization. The approach initiates from RBV theory mentioning that the firm performance should root on the resources the firm owns, as the most famous fundamental theory in studying IT and organizational performance [13]. Then it explores sources of values of IT such that it can carry out a relationship formulation between IT value and the organization.
Figure 1 confirms to place this chapter among the others, which grounded on the earlier ones, nevertheless, with a different approach. For instance, from the subsystems point of views, this chapter solely resembles the Ravichandran’s model, which also took in four subsystems, namely IT resources, IS capabilities, IT support for core competencies, and firm performance [12]. Likewise, [34] exhausted four subsystems, although their nomenclature is different from Ravichandran’s. As for the other studies such as [11, 13] used three subsystems, namely IT resources, IT capability, and firm performance. However, [35] directly studied to link up between IT competency to firm performance, also [30, 36] simply studied between the data management capability to the performance as well as [18, 19, 20, 37, 38] researched the relationship between IT investment and public presentation. This chapter applies four subsystems based on RBV theory with their nomenclature as follows firm performance (FP), firm competence (FCC), firm capability (FC), and IT resource (ITR).
Furthermore, from an engineering point of views, the earlier studies generally exploited forward engineering, which begins from resources towards firm performance, while this chapter proposes reverse engineering for a serial configuration (see Figure 1), which begins from the required firm performance, afterwards, estimate the firm core competencies and firm capabilities to get IT resources composition. Also, from a methodological point of views, the earlier studies generally benefitted statistical approach or structural equation modeling (SEM) and RBV approach, excluding [18, 19, 20, 37, 38] who used PAV. This chapter appears with a different approach, whereas combining the earlier approaches such as RBV, PAV, and systems engineering at once. In other words, this chapter also carries the different final goal from the earlier ones, in which this chapter is to engineer IT resource about the required firm performance to let it performs at the lower cost.
Additionally, the chapter on the IT value was also conducted by the researchers such as [33] directing that the IT is an integrally part of a system of interrelated organizational factors, [39], who concentrated on the potential and realized IT values estimated by DEA (data envelopment analysis). Similarly, [6] estimated the IT business value by Cobb–Douglass function. Furthermore, [28, 40, 41] generally addressed the IT business value.
Meanwhile, to complete the chapter, the PAV [20] applies to correlate the subsystem input to its output. Additionally, the PAV usually operates with a static speed of adjustment or with a dynamic speed of adjustment in a researched object, on the contrary, in this chapter both the static and the dynamic speed of adjustment work together at a time in the PAV experiment. Likewise, thus far the PAV has applied in the country level study such as [18, 19, 20], however, this chapter tries to use the PAV at the firm level as the other study of the IT investment correlation to the firm productivity [8]. As for the chapter analysis, system engineering is to find this chapter because the IT value engineering essentially consists of subsystems such as electrical, computer, and value engineering. Likewise, the system engineering approach analyzes the chapter from both system engineering life cycle and model point of views.
In turn, the chapter result may turn away to become a framework to design an IT-based governing body by looking at several factors either internal or outside factors, including business environment ones. In other words, this chapter is an active chapter using its result, it can plan an organization as well as develop an established organization. Consequently, this chapter encompasses a broader domain of IT-based organization: established and planned systems.
Discussing a value means that it is addressing usefulness, worth, benefit. Furthermore, the value may disclose if there is an interaction between two or added systems or subsystems, in which one system works with the other one and vice versa, or the system works due to the other systems. Why would the systems mutually function? There is an energy that urges them to work, which is latterly called the value, usefulness, worth, benefit, competitive advantage, or other terms. In other words, this construction can facilitate accomplishing the stage of value creation by benefiting system processes.
There are various types of values such as normative value, realist value, and perceived value. Consecutively, the normative value relates to the required value as planned previously, the realist value pertains to the resulted value that comes from an accomplishment, and the perceived value is what consumer relatively perceives [16]. Additionally, if comprehended from cost management perspectives, the other types of values are the use value, meaning the value of the required function associated with the cost. Afterwards, the cost value, namely all cost values, dedicated to result in the item; the esteem value, means that the value of surplus cost to pay the additional items; the exchange value, namely the value of an item to exchange something else [42, 43].
Moreover, as a fundamental nature of the value definition of this research, the equation definition below bases further studies. This equation technically articulates a value (V) as an index resulted from a function (F) division by cost (C) as proposed by [44] as follows:
According to the formula, several efforts to bring the value gaining are:
For a similar function (F), diminish the cost (C) or
The cost (C) is stable, improve the function (F) or
The function (F) slightly reduced, the cost (C) significantly decreased or
The cost (C) a slight increase, the function (F) has increased significantly or
The function (F) increases while the cost (C) decreases.
It appears that by adjusting function and or cost, the system can control the value to ascend or descend consistent with what the purpose is, although, in practice, there are several considerations to essentially prepare in implementations [44].
As mentioned, the IT value may come from an estimate of the real worth, utility, or the IT system’s significance. This definition does not limit from what the worth, the utility, or the significance come from, thus, it does not prevent the multiple perspectives possibility. There is the stakeholder expectation such this, in turn, it influences the IT value achievement. In substance, value stems from the IT system to support the stakeholder aims attainment. For example, a debit card system that removes the requisite for cashiers to manually count cash may present cashiers with value since it lessens tension on their hands [45].
In addition, to explore the value of IT needs to investigate some scales reflecting these values. Accordingly, the metrics development is a necessity to measure IT values, however, there are certain criteria for the metric development as proposed by [45]. These criteria depart from selected questions that might be considered as follows:
What is the evidence to evaluate?
Where must valuation occur?
When must valuation occur?
How must valuations be interpreted?
It has been completely recognized that the IT value systems can manifest as a complex system consisting of various subsystems, components, subcomponents, and parts. Furthermore, as measuring the IT value, it is valuable to think about measurements that concentrate on. Definitely, the building of the metrics as a means to evaluate values results in a variety of problems, which necessitates doing so with care [45].
On the other point of view, the IT value study has to involve two sections: (i) IT variable, IT management variable or manifestation, and (ii) endogenous variable with IT economic impact [46]. Doing IT valuation involves complex issues, including social accomplishment so it requires over a period of time. Thus, this study should perform in an inclusive fashion such that the IT value research corresponds to an imperative flow of work that leads to business value. Likewise, there are economic associates of IT and its manifestations, and by itself, the scope of the research should restrict to examine the IT value to engineering it at the organizational level.
In the meantime, the IT resources that are subsequently delivering their capabilities can not create value for themselves within the organization. They need interaction with a business environment such that each will complement one another. As a result, IT infrastructures and organizational factors appear to work in a synergistic way, where these factors are part of the IT-based system consisting of IT human resources and IT management skills, rules, and policies. This is as the organizational system that comprises non-IT human resources and management skills, business procedures, information benefits, affiliation benefits, way of life, organization, and rules. In reality, IT is production machines, therefore, it generates value in the output configuration resulting in benefits due to business processing. In other forms, the value is apparent by itself in the form of process improvements such as saving time, process effectiveness, profitability, such as a higher return on assets, on investment, and consumer surplus such as higher customer satisfaction.
Furthermore, [34] stated that there are numerous factors in terms of the IT value creation chain that is essential and required conditions. Included in these conditions are the IS-strategy configuration, organizational restructuring, business process accomplishment, knowledge sharing, and IT management among others. Accordingly, those are critical in terms of the encouraging of the transformation process and renovation of the effectiveness of IT advantages. Additionally, there are four foremost subjects to demonstrate how IT value is shifting to describe, quantify and show it. The four subjects are (1) value IT-based co-creation, (2) IT embeddedness, (3) information approach, and (4) value extension.
The following stage of the IT value creation should concentrate on the co-creation of value by means of IT instead of IT value itself, further, it is called IT-based co-creation of value. While, the co-creation stands for the thought that (a) IT value cannot manifest in an isolated environment, it is progressively more being formed and accomplished due to actions of numerous parties, (b) value comes from strong joint associations among organizations, and (c) configurations and encouragements for the parties to contribute in and equitably assign emergent values are essential to keeping up co-creation. Moreover, IT embeddedness relates to the condition in which the IT is a central part of the process such that it turns into identical to the product. For example, the IT in a bank’s industry of instant credit check is intensely embedded in the loan endorsement process and hard to separate out. In other words, IT embeddedness is a fundamental model that attached to value co-creation, information mindset, and value expansion. Thus, it is plausible that preferred business capabilities drive IT embeddedness. Therefore, the effective convergence of preferred business capabilities and IT capabilities is a precondition “to realizing capabilities among organizations (co-creation), creating information value (information mindset), and ultimately realizing a wide repertoire of value (value expansion)” [34].
Tohidi (2011) stated that the idea of value engineering is to employ the projects, strengthen accomplishment and diminish costs in all life cycles of the projects. In this case, the lifetime value of the engineering project with the productivity increment can result in the value of the project, namely the output to the input ratio. Hence, the value engineering application is boiling down to performance improvement of inputs and outputs, by applying a theoretical approach of value engineering processes together with project management, project analysis, value analysis, and value management. Additionally, he mentioned that value engineering is constantly dealing with the growth of technology, reducing the unnecessary costs that do not relate to improving the products or services quality. Reducing costs in conventional point of views do not associate with creativity, it only refers to familiarities, feelings, and practices. On the contrary, in value engineering, the usage of knowledge, the problem recognition, the method of problem-solving, the development of the creative solutions could combine to develop comprehensive approaches [47].
Additionally, value engineering is a structured method to investigate the systems function and its completeness in dealing with a systems fundamental function accomplishment at the lowest cost. However, the functions of systems consistently keep up a better performance, trustworthiness, quality, and security [48]. Consequently, the value engineering process arrives at success if it is to discover opportunities to diminish needless costs and at the same time it is able to keep up and raise quality, consistency, accomplishment, and other customer needs on products or services. In terms of IT value, the IT generally boils down on the effectiveness and efficiency of processes, including achieving the best organizational performance. In other words, IT should disseminate value-added advantages through strategic alignment with the organizations.
Essentially, the IT presence within an organization is the norm for the era that is so, there is no one business organization that does not exploit the IT, where the simple difference is the amount of IT capacity. Empirically, such circumstances are something that is unquestionable, but the problem now is how to place the IT position within the business organization with the aim of its presence increasingly contribute enormous weights to the organization performance. Thus far, the IT inclusion in organizations is due to the demands of the times as technology-driven instead of market-driven, it is more emphasis on administrative rather than business development activities. Therefore, this chapter attempts to reposition IT as a means to improve competitive advantages of firms as indicated in Figure 2, which appears that IT should set it on the layer where is as the engineering processes culmination that preceded by electrical, computer, software, systems, and a complex system engineering to lead generating an IT value engineering.
IT value engineering position in an organizational environment.
Intrinsically, IT value engineering positions the IT at the more well-organized since it can go through an engineering process to create additional significant values as a continuation value generated on the preceding layers (see Figure 2). In other words, IT value engineering is the added value due to the engineering of the systems consisting of value, software, computer, electrical engineering. Meanwhile, the organizational environment is a circumstance where a firm should perform its business here, which are competitive forces including risks due to the business activities. The organizational environment should controllable to continue firm’s existence in business turbulence to sustain its competitive advantages, which are consisting of six categories, namely cost, differentiation, focus, execution, knowledge, and maneuverability advantages [5]. In this study, the competitive advantage that becomes a highlight is a cost-competitive advantage, which can result from the IT value engineering through a system optimization effort.
Furthermore, the IT value engineering has presented in an IT-based firm (see Figure 2), which is a firm that its core business has two wide-ranging groups of products and services, namely lifespan application development and support services and production processes [5] or industrial products and services that catch, transmit and display data and information by electronic means [49]. In the meantime, using RBV theory, this chapter departs from firm performance towards IT resources instead of the regular RBV, which originates from the resources to the firm performance in terms of the serial configuration.
This chapter proposes to structure the four subsystems of the RBV-based result, namely firm performance (FP), firm competence (FCC), firm capability (FC), and IT resource (ITR) to accomplish the rigorous IT value engineering concept by considering the nature of VRIN (valuable, rare, inimitable, non-substitutable) IT resources. In this case, each subsystem needs to identify its measures, which facilitate to determine the characteristics of the subsystem to build relationships with other subsystems or between the subsystem input and output [50]. Therefore, the FP typically addresses financial and efficiency performance, which manifests in, such as time-to-market and mass customization [51], profitability containing return on investment, return on asset, return on equity [16, 52]. While the FCC emphasizes to a firm’s core competence as the learning process ability to manage various resources and technology within the firm [53], consisting of three components: IT knowledge, IT operations, and IT objects. IT knowledge is the extent to which a firm possesses a body of technical knowledge about objects such as computer-based systems, while IT operations are the extent to which a firm utilizes IT to manage market and customer information. IT objects represent computer-based hardware, software, and support personnel [35].
Moreover, FC focuses on the assembling and installing IT-based resource capabilities to work together with the other resources in the firm [11] controlled by IT infrastructures, managed IT skills, and collaboration between IT and business [54]. The three measures combination can result in the firm capability, hence, it can create the VRIN IT resources, which also consist of IT infrastructure as tangible resources, human IT resources representing technical and managerial IT skills, and intangible IT-enabled resources such as knowledge assets, customer orientation, and synergy [11].
Preferably, to construct an IT spending model system, each measure or component of the subsystems relates one to another is not only qualitatively rational, but also quantitatively plausible as issued by [8]. However, to quantitatively plausible, the subsystems should also have complete measures that can manifest in a mathematical model. As proposed above, the mathematical formula to construct the relationship in this research is the partial adjustment valuation theory [20], which involves Cobb–Douglas production transformation as the input function.
Dedrick et al. (2003) stated that the failure of the subject area of the relationship between IT spending and the output performance at the firm level occurred due to the difficulty of quantifying measurement between these quantities [8]. Therefore, the chapter tries to do so by PAV. In this case, Nerlove (1958) was a developer of the origin of PAV theory and further developed by the researchers as it is today. The theory tells that the change in real output of a production process generally does not precisely fit the desired output alteration. The alteration measurement is in the present (t), compared with the previous period (t-1) for the real alteration and the desired alteration, which it is clear that there must be a coefficient bridging the relationship between the two alterations called a constant speed of adjustment [19, 20]. Therefore, if written in a mathematical formula, the theory manifests as follows:
It seems that yt is the real output of a production process unit, for example, a firm, in time t, as for yt-1 is the real output of the equal production process unit at time t – 1. While yt* is the desired output of the production process unit at time t, and μ is the coefficient depicting a constant speed of adjustment [20]. In an estimation process, an old-fashioned random error symbolized by ϵt needs to consider completing the formula. Consequently, Eq. (2) manifests as follows:
Whereas ϵt = conventional error. It appears that the real output is equal to the weighted average of the current desired output – with the weights μ – and the real output at a past time, with weights 1-μ. Furthermore, Lin and Kao (2014) suggested that μ in Eq. (2) and (3) can vary and be dynamic, therefore, μ may convert to μt where t represents fluctuations in time for the dynamic and μ for the constant or static. This scheme aims to provide more meaning of μ, for instance, the dynamic μ represents the speed of adjustment behavior in connecting the real output alteration with alterations in the desired one. In other words, these two alterations in output comprehend the dynamic nature of μ. Later, the scheme also exhibits the other signification of the state for further exploration [20].
At that time, the writing the equation above can turn to the subsequent Eqs. (4) and (5) [19, 20]:
Here f(Xt,β) is the alternate function of the desired output (yt*), which manifests in the form of a production function [8, 18, 19, 37, 38]. Accordingly, Xt could consist of a vector of production such as the regular capital (Kt), the regular labor expense (Lt), and the technology spending, in this study related to IT spending (It). For the benefit of variable estimation of the production function, it may consist of two compositions. The first is K, L, and I combination to accommodate the factors of capital, labor, and IT spending immediately, and the second is K and L combination that accommodates the factors of capital and labor. Thus, there are two models: Xt = (Kt, Lt, It) and Xt = (Kt, Lt) while β is the unknown parameters [19, 20].
Meanwhile, the function μt = g(St; γ) represents a dynamic speed of adjustment that accommodates variables, which fluctuate along with the different fluctuations of the required output such as return on equity (ROE). The magnitude of μt or μ is in the range of 0 and 1 [20], where the value of 0 means that the real output at time t is precisely equal to the real output of the previous period, t-1. While if 1 indicates that the real output is equivalent to the desired output. Conversely, μt is a St function, a vector of the variable, affecting the speed of adjustment of a firm, and γ is the unknown parameters. Therefore, to return to the original PAV theory, Eq. (4) is as follows:
Essentially the production function of the Eq. (4), namely f(Xt,β), can originate from various production functions such as the Cobb–Douglas (CD), the Box–Cox, the Box–Tidwell, the translog, and the constant elasticity of substitution functions [18, 19, 20, 37, 38]. The work may select all or a number of them as a test target. For that reason, this study just exploits the CD production function to substitute f(Xt,β) in Eq. (4). While, the CD equation is equally in the Eq. (7) below [18]:
The Eq. (7) presents the CD function with Xt consisting of production factors Kt, Lt, and It. Kt is the regular capital, Lt is the regular labor expense, and It is IT capital over time. In other words, Eq. (7) takes into account the IT capital inclusion. Meanwhile α, β1, β2, and β3 are the unknown parameters and vt ∼ N(0, σv2), and ut ∼ |N(0, σv2)|. In addition, to estimate these parameters performs in an estimation process. Equally for the CD function without It presence is as follows in Eq. (8):
Justification of the Eq. (8) is equivalent to the Eq. (7), apart just the It absence. While the Eq. (5), the speed of adjustment, can display as in Eq. (9) [20]:
Here μt is the dynamic speed of adjustment, and St is the dynamic factor that can manipulate the dynamics of μt suitable to the time-varying. Likewise, it may show as variances between the actual and the estimable variables of the firm. Furthermore, researchers provide a number of measures to fill these factors with various variables St, for example, return on equity, interest rate, firm size, growth option, economic value-added, and Tobin q [19, 20]. While γ1 and γ2 are the unknown parameters.
Moreover, if the Eqs. (7) and (9) substitute components of the Eq. (6), it produces an Eq. (10) as follows:
The Eq. (10) is for the three-factor production function, namely Kt, Lt, and It. It looks that the equation above is analogous to the Eq. (6), except that the production function, namely f (Xt, β), has converted to the Cobb–Douglas function [see Eq. (7)] and the speed of adjustment μt replaced by the Eq. (9). The variables and parameters justification of the equation is equivalent to the preceding equations, which substitute it. Meanwhile, for the two-factor function [Eqs. (7) and (8) substituted into the Eq. (6)], the equation becomes Eq. (11) as follows:
The equation justification is also analogous to the Eq. (10), except just the It absence. Furthermore, the Eqs. (10) and (11) are non-linear equations, their solution must also exploit a non-linear least square (NLS) application [20].
The first method of the ITVE is the meta-analysis approach, where the study concerns with the previous results in the analogous context, namely the relationship between IT resources and business performance. The method enriches the study since various validated hypotheses provide the researcher with strengthening the topic justification, therefore, the study can lead to conclude towards the objective of the chapter qualitatively [13]. In addition, this technique authorizes authors to study several papers addressing the IT value to the business performance relationship from the RBV point of view. Consequently, based on a number of the previous papers, particular topics such as IT resources, firm capabilities, firm core competencies and firm performance are categorically recognizable, where each group has to have relationship one to another for what this relationship leads to a means to link one category to another to construct a model of the IT value. Essentially, the resulting model is not only based on the meta-analysis approach, but also based on the RBV theory.
This method addresses PAV theory, which is linked components of each subsystem to investigate the correlation between IT resources and business performance. This section first reviews the PAV utilization in this chapter through the theory experimentally of the real facts to measure several IT-based firms using the PAV approach to examine the level of the IT value within each firm.
According to the meta-analysis, to create conceptual models, which involves logical and mathematical relationships, this method facilitates to develop two types of the model. The first model of IT value or the three-factor model is through substituting the Eq. (6) by the Eq. (7) and (9). Hence, the first model changes to the Eq. (10) above, which is the partial adjustment with Cobb–Douglas (CD) production function is inside and implying Kt (regular capital), Lt (labor expense), and It (IT spending) factors.
Meanwhile, through substituting the Eq. (6) by the Eq. (8) and (9) can create the second model of IT value (Kt and Lt) or the two-factor model as comprehended in the Eq. (11) above. Once again, both the Eqs. (10) and (11) are non-linear equations, thus, to estimate them must also use a non-linear least square (NLS) [20].
This study selects the return on equity (ROE) [20] as a component of the dynamic factor of the speed of adjustment since the ROE has an adjacent relationship to the regular capital (K), which is the firm equity, thus, the ROE can seem more representative as a dynamic factor (St) in this study than the others. Also, in order for the fluctuation rhythm of the K to compensate by the ROE fluctuation. While the ROE is a gain for the year of the parent firm divided by total equity of the parent firm at year-end December. Thus, the ROE becomes a dynamic factor (St, t is a period of time) of μt (the speed of adjustment) function to signify the dynamics of the speed of adjustment as comprehended in the Eq. (5) or (9). As for the static speed of adjustment, the Eq. (5) or (9) is equal to a constant, which is estimable in the non-linear least squares (NLS) estimation process. Moreover, the production function of the Eq. (4) devotes to the Cobb–Douglas (CD) function as explicated in the Eq. (7) and (8) above due to its simplicity and familiarity in production function transformations [18, 55].
For the purpose of assessment to separate the presence (with Iit) and the absence (without Iit) of the IT capital in the PAV approach, the estimate works on both Xit = (Kit, Lit, Iit) and Xit = (Kit, Lit). Here i = 1, …, r = 8, for example, for the number of testing firms, and t = 1,…, s = 11, for example, for the period of testing data, such as from 2004 to 2014. It is a time-varying, hence, the system is dynamic, therefore, for that reason, the study models can apply both Eqs. (10) and (11), however, caused the equations to overparameterize due to nonlinear, the estimate also needs the nonlinear least squares (NLS) application [20].
The Eqs. (10) and (11) estimation results in the unknown parameters, including γ1 and γ2 of the Eq. (9) for the dynamic speed of adjustment, while the static speed of adjustment is constant for all periods t. Therefore, the dynamic speed of adjustment as in the Eq. (9) is estimable to assess the dynamics of the μit. In addition, due to covering a period of time, the μit has the average speed of adjustment (ASA) as well. Assume γi estimate as
At this point, g(Sit;
In order to evaluate the change of the firm performance due to IT spending, Lin and Kao (2014) proposed the performance measures (PM) of the dynamic (μit) and static (μi) speeds of partial adjustment evaluate the performance change of the processing unit tested. This measurement manifests in Eq. (13) below [19, 20]:
To estimate the parameter γ and β, both parameters further designated to become
In this case, PVit is performance values of the processing unit or the firm. If averaged, the Eq. (14) results in:
Both the Eqs. (14) and (15) result in the currency value, however, that is further common, it would be superior if presented in the form of an index ratio. Consequently, PVit should be divided by the real output (yit), instead of a “devisor” (
The average value (APR) of Eq. (16) appears as using the subsequent formula:
Using this method, it is plausible to consider the amount of value between the IT capital presence and its absence within a capital expenditure of the firm. In other words, the IT value model using the PAV guides the study to comprehend the value of IT.
In order to evaluate the change in the firm performance due to IT spending, Lin and Kao (2014) proposed the performance measures (PM) of the dynamic (μit) and static (μi) speeds of partial adjustment to evaluate the performance change of the processing unit tested. This measurement manifests in Eq. (18) below [19, 20]:
To estimate the parameter γ and β, both parameters further designated to be
In this case, PVit is the performance values of the processing unit or the firm. If averaged, the Eq. (19) results in:
Both the Eqs. (19) and (20) result in the currency value, however, that is further common, it would be superior if presented in the form of an index ratio. Consequently, PVit should be divided by the real output (yit), instead of a “devisor” (
The average value (APR) of Eq. (21) is calculated using the subsequent formula:
Using this method, it is plausible to consider the amount of value between the IT capital presence and the absence of it within a capital expenditure of the firm. In other words, the IT value model using the PAV guides the study to comprehend the value of IT.
In essence, the applied method in this chapter is identical to the abovementioned method, namely starting from the structure of the conceptual model of IT value consisting of two types of models: three and two-factor models up until valuation of performance measures. However, the difference is simply on the goal, namely the earlier method aims to examine the PAV theory using the real facts to make sure that the IT inclusion in the business organization is material and valuable, while, this subchapter is to validate the resulted experiment data in several IT-based firms to certify that the PAV theory encounters the criteria of system measurements from a statistical point of views [8] to identify the level of the IT value of each firm. The validation is through model data examinations.
Here, the exploited data have been covering the period, for example, from 2004 to 2014, collected from the audited financial statements and the published annual reports. To compare between the presence (with It) and absence (without It) of the IT capital in the PAV approach [20], the estimation involves both Xt = (Kt, Lt, It), and Xt = (Kt, Lt) where t = 1…, 11 at the time of confirmed data from 2004 to 2014 for both static and dynamic speed of adjustment.
In reality, the adopted chapter method respects with the exposure of systems engineering processes offered by [50, 56], which is afterwards packaged in the method sequences as depicted below [10].
As mentioned, the primary problem of this chapter is how to carry out the need of worthy performance of the IT-based business organization to sustain competitive advantages by optimal costs, especially IT costs. Since this problem involves a variety of factors such as functional subsystems of RBV point of views, financial systems, competitive forces, business performance, risk management, resource management, and so forth. Accordingly, to solve this problem needs a systems engineering approach integrating various components into a unity solving the needed values.
In order to solve the problem, various alternative solutions could be a means to undo. Examples of the alternatives are with increasing the firm performance while the IT capital is constant, improving the IT competency and capability of the organization, and cost optimization by encouraging innovation, restructuring, IT cost-saving/ efficiency, and effective IT procurement. Indeed, each alternative has advantages and disadvantages, therefore, the preferred solution is all alternatives combinations to compile in a systems engineering process.
In the meantime, the preferred solution selected based on the five criteria that Kosky et al. (2013) initiated, namely “minimize information content, maintain the independence of functional requirements, ease of manufacture, robustness, and design for adjustability” [10].
According to [56], the systems engineering life cycle phases and the systems engineering method merges, which denotes that for each engineering phase of a horizontal nature, is vertically explored using these engineering models. This step is for concept development and engineering development phases, including each block of the phases. Meanwhile, the post-development phase is beyond this study. Consequently, the analytical results separated into two tables.
Furthermore, the information technology value engineering model exists to develop three types of models: parallel, serial, and hybrid ITVE. Likewise, their validation takes place to certify that the model is reasonable philosophically and technically.
The parallel model is in Figure 3 [25, 26]. This figure explicates that the principal subsystems of the model consist of firm performance (FP), firm core competence (FCC), firm capability (FC), and IT resource (ITR), which each subsystem links one to another in a parallel fashion. In a mathematical relationship, the parallel connection manifests an add operation (see Figure 3). It implies that the input (yt*) is proportionally divided into four sub-inputs, i.e. y*1t, y*2t, y*3t, and y*4t or yt* = y*1t + y*2t + y*3t + y*4t. Each subsystem has each speed of adjustment (μit, i = 1,2,3,4 and t = period), i.e. FP has μ1t, FCC has μ2t, FC has μ3t, and ITR has μ4t, whether static (constant) or dynamic [20]. Likewise, the output consists of four sub outputs, i.e. y1t, y2t, y3t, and y4t, which can appear as yt = y1t + y2t + y3t + y4t.
IT value engineering model in a parallel relationship [
Using the partial adjustment valuation approach [see the Eq. (3)], each subsystem could be mathematically revealed as follows [25, 26], see Figure 3:
Firm Performance (FP):
Firm Core Competence (FCC):
Firm Capability (FC):
Information Technology Resource (ITR):
If Eq. (24), Eq. (26), Eq. (28), and Eq. (30) are together added would result in Eq. (31) [25, 26]:
Where yt = the real output of period t, y1t = the real output of FP at period t, y*1t = the desired output (input) of FP, y1t-1 = the real output of the previous period (t-1), and μ1 = the constant speed of adjustment of FP. Similarly, y2t = the real output of FCC at period t, y*2t = the desired output (input) of FCC at period t, y2t-1 = the real output of the previous period (t-1), and μ2 = the constant speed of adjustment of FCC. Afterwards, y3t = the real output of FC at period t, y*3t = the desired output (input) of FC at period t, y3t-1 = the real output of the previous period (t-1), and μ3 = the constant speed of adjustment of FC. Finally, y4t = the real output of ITR at period t, y*4t = the desired output (input) of ITR period t, y4t-1 = the real output of the previous period (t-1), and μ4 = the constant speed of adjustment of ITR.
Instead of the parallel fashion, the serial ITVEM appears, in which to do so, suppose the Eq. (24), the Eq. (26), the Eq. (28), and the Eq. (30) exhibit in a serial relationship (see Figure 4), with an assumption that each output of a subsystem fully becomes an input of the subsequent ones, the end result is as Eq. (32) [25, 26].
IT value engineering model in a serial relationship [
As for the explanation of the symbols is equal to the parallel ITVE.
The hybrid configuration [27] is an option for structuring each subsystem in the chapter. Figure 5 explicates that the principal subsystems of the model consist of ITR, FC, FCC, and FP. It appears that the resources are the ITR consisting of the regular capital (Kt), the regular labor expense (Lt), and the technology spending, in this chapter related to IT spending (It). Furthermore, the resources become inputs of the FC subsystem as Kcap, Lcap, and Icap to be processed in resulting the FC output, viz. wmt (m = 1,2,3) or w1t, w2t, and w3t, see Figure 5. Likewise, the resources also become inputs of the FCC subsystem as Kcom, Lcom, and Icom to be processed in resulting the FC output, viz. vjt (j = 1,2,3) or v1t, v2t, and v3t, see Figure 5. Moreover, the output of both FC and FCC turn into the input of the FP. In other words, (w1t, w2t, w3t) and (v1t,v2t, v3t) appear as inputs of the FP.
IT value in the hybrid configuration [
Therefore, the PAV model of the hybrid configuration (see Figure 5) is as follows [27]:
Firm Capabilities “cap” (FC):
or
Where wmt = the real output of FC at period t, μm = the constant speed of adjustment of FC, αm = a constant of Cobb–Douglas function; β1, β2, and β3 are input elasticity of production factors regarding the regular capital (K), the labor expense (L), and the IT capital (I), and wmt-1 = the real output of the previous period (t-1). Hence, if the FC consists of three variables (m = 1, 2, and 3), viz. IT infrastructures, IT managerial skills, and Collaboration [54], thus each variable has output as follows [27].
IT infrastructures (w1t):
IT managerial skills (w2t):
Collaboration (w3t):
Firm Core Competence “com” (FCC):
or
Where vjt = the real output of FCC at period t, λj = the constant speed of adjustment of FCC, λj = a constant of Cobb–Douglas function; σ1,σ2, and σ3 are input elasticity of production factors regarding the regular capital (K), the labor expense (L), and the IT capital (I), and vjt-1 = the real output of the previous period (t-1). Hence, if the FCC consists of three variables (j = 1, 2, and 3), viz. IT knowledge, IT operations, and IT objects [35], thus each variable has output as follows [27].
IT knowledge (v1t):
IT managerial skills (v2t):
Collaboration (v3t):
Firm Performance “per” (FP):
or
Where znt = the real output of FP at period t, ηn = the constant speed of adjustment of FC, γn = a constant of Cobb–Douglas function; ϕ1 and ϕ2 are input elasticity of production factors regarding the FC output (w1t,w2t,w3t) and the FCC output (v1t,v2t,v3t), and znt-1 = the real output of the previous period (t-1). Hence, if the FP consists of three variables (n = 1, 2, and 3), viz. ROE, ROA, and Revenue [16], thus each variable has output as follows [27].
ROE (z1t):
ROA (z2t):
Revenue (z3t):
The ITVE optimization involves the cost minimization in accordance with the major problem of this research to raise the firm performance at optimal cost [26]. To do so, it needs several assumptions [57] along with the optimization process. For example, the Cobb–Douglas production function [20] replaces each the desired output (the starred y*it, i = 1, 2, 3, 4 and t = 1, …, 11, for example) of subsystems. The Cobb-Douglass function is as follows:
Whereas y*it = the desired output with i = subsystem and t = period, Kit = the regular capital, Lit = the labor expense, Iit = the IT capital, α = total factor productivity, and β1, β2, β3 = the output elasticity of the regular capital, the labor expense, and the IT capital. Therefore, the partial adjustment for each subsystem is as follows (to simplify, i is disappearing):
Whereas μt is the static speed of adjustment and yt-1 is the revenue in the earlier period. Additionally, for cost minimization, the partial derivatives of the Eq. (48) should fulfill these conditions [58, 59]:
If the Eq. (49) is mathematically derived to K, L, and I, it respectively results in the following equations (whereas p1, p2, and p3 are added to the equations as unit prices of the regular capital (K), the labor expense (L), and the IT capital (I):
Using the Eq. (50) prerequisites, the Eq. (51) = the Eq. (52) = the Eq. (53), further equations arise as follows:
If the Eq. (49) is substituted by the Eq. (54) such that the new equation appears in the regular capital (K) variable, the equation is as Eq. (55) and afterwards simplified to become Eq. (56).
Furthermore, the Eq. (56) becomes K variable as in Eq. (57) and afterwards simplified as in Eq. (58) as follows:
Using the equivalent way, the variable L and I can become as follows:
If K, L, and I are multiplying p1, p2, and p3 as unit prices respectively, then it appears as follows:
Moreover, the Eqs. (61), (62), and (63) substituted into Eq. (64), the total cost of yielding y units in the low-cost technique manifest as the Eq. (64) and (65).
Where B:
Whereas p1, p2, and p3 is unit prices of the regular capital (Kt), the labor expense (Lt), and the IT capital (It) respectively, yt is the real output of period t, yt-1 is the real output of earlier period t-1, and C is the total cost [26].
The significant problem surrounding this study is to sustain superior firm performance as desired at optimal costs due to the IT presence, which has inevitably become a need for running the business world. Numerous studies on the relationship of the firm performance of the IT resource were more focused on a statistical method that links between components using survey data. In essence, this study undertakes an analogous study, but with a different approach, namely, the systems engineering approach combined with RBV theory, systems engineering, the theory of partial adjustment, including the CD production function, which, in turn, lead to creating the ITVE. Furthermore, to create the ITVE, the followed stages are to build the conceptual model of the IT value based on the RBV theory, model experiment using PAV, validate PAV, model the ITVE, confirm the ITVE and study managerial impacts of the model.
The conceptual model of IT value has logically exemplified the relationship between ITR, FC, FCC, and FP in terms of competitive advantages. The theory of partial adjustment links logically the model, which formulates it in two types of models. Explicitly, the first model addresses PAV with the IT capital presence (with It) inside of its production function, and the second model with the IT capital absence (without It). The applied production function is the CD function while the dynamic factor component of the speed of adjustment is the ROE. However, it may be replaced by other dynamic factors.
The principal problem of this chapter is how to achieve the optimal resources, for instance, IT resource costs, for required business performance. By benefiting the earlier studies, namely the systems engineering methodology, the conceptual model of IT value, the RBV theory, and the PAV theory can solve this problem so that the solution results in the IT value engineering. Furthermore, using the analysis results, a synthesis work leads to composing a block diagram, which depicts a model in terms of the systems engineering of IT value engineering framework, which ultimately results in serial, parallel, and hybrid configurations. Likewise, by benefiting CD production function involved within PAV, the optimal cost of the required firm performance occurs. For that reason, it should surely be an experiment as a simulation on work mechanisms of the model. Consequently, the ITVE technically appears as a framework to study IT value models. However, in practice, this model contributes to managerial implications, which should reinforce the match between techniques and practices.
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\n\n\r\n\tThis book series will offer a comprehensive overview of recent research trends as well as clinical applications within different specialties of dentistry. Topics will include overviews of the health of the oral cavity, from prevention and care to different treatments for the rehabilitation of problems that may affect the organs and/or tissues present. The different areas of dentistry will be explored, with the aim of disseminating knowledge and providing readers with new tools for the comprehensive treatment of their patients with greater safety and with current techniques. Ongoing issues, recent advances, and future diagnostic approaches and therapeutic strategies will also be discussed. This series of books will focus on various aspects of the properties and results obtained by the various treatments available, whether preventive or curative.
",coverUrl:"https://cdn.intechopen.com/series/covers/3.jpg",latestPublicationDate:"May 13th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:8,editor:{id:"419588",title:"Ph.D.",name:"Sergio",middleName:"Alexandre",surname:"Gehrke",slug:"sergio-gehrke",fullName:"Sergio Gehrke",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038WgMKQA0/Profile_Picture_2022-06-02T11:44:20.jpg",biography:"Dr. Sergio Alexandre Gehrke is a doctorate holder in two fields. The first is a Ph.D. in Cellular and Molecular Biology from the Pontificia Catholic University, Porto Alegre, Brazil, in 2010 and the other is an International Ph.D. in Bioengineering from the Universidad Miguel Hernandez, Elche/Alicante, Spain, obtained in 2020. In 2018, he completed a postdoctoral fellowship in Materials Engineering in the NUCLEMAT of the Pontificia Catholic University, Porto Alegre, Brazil. He is currently the Director of the Postgraduate Program in Implantology of the Bioface/UCAM/PgO (Montevideo, Uruguay), Director of the Cathedra of Biotechnology of the Catholic University of Murcia (Murcia, Spain), an Extraordinary Full Professor of the Catholic University of Murcia (Murcia, Spain) as well as the Director of the private center of research Biotecnos – Technology and Science (Montevideo, Uruguay). Applied biomaterials, cellular and molecular biology, and dental implants are among his research interests. He has published several original papers in renowned journals. In addition, he is also a Collaborating Professor in several Postgraduate programs at different universities all over the world.",institutionString:null,institution:{name:"Universidad Católica San Antonio de Murcia",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:2,paginationItems:[{id:"1",title:"Oral Health",coverUrl:"https://cdn.intechopen.com/series_topics/covers/1.jpg",isOpenForSubmission:!0,editor:{id:"173955",title:"Prof.",name:"Sandra",middleName:null,surname:"Marinho",slug:"sandra-marinho",fullName:"Sandra Marinho",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRGYMQA4/Profile_Picture_2022-06-01T13:22:41.png",biography:"Dr. Sandra A. Marinho is an Associate Professor and Brazilian researcher at the State University of Paraíba (Universidade Estadual da Paraíba- UEPB), Campus VIII, located in Araruna, state of Paraíba since 2011. She holds a degree in Dentistry from the Federal University of Alfenas (UNIFAL), while her specialization and professional improvement in Stomatology took place at Hospital Heliopolis (São Paulo, SP). Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). Dr. Marinho's experience in Dentistry mainly covers the following subjects: oral diagnosis, oral radiology; oral medicine; lesions and oral infections; oral pathology, laser therapy and epidemiological studies.",institutionString:null,institution:{name:"State University of Paraíba",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null},{id:"2",title:"Prosthodontics and Implant Dentistry",coverUrl:"https://cdn.intechopen.com/series_topics/covers/2.jpg",isOpenForSubmission:!0,editor:{id:"179568",title:"Associate Prof.",name:"Wen Lin",middleName:null,surname:"Chai",slug:"wen-lin-chai",fullName:"Wen Lin Chai",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHGAQA4/Profile_Picture_2022-05-23T14:31:12.png",biography:"Professor Dr. Chai Wen Lin is currently a lecturer at the Department of Restorative Dentistry, Faculty of Dentistry of the University of Malaya. She obtained a Master of Dental Science in 2006 and a Ph.D. in 2011. Her Ph.D. research work on the soft tissue-implant interface at the University of Sheffield has yielded several important publications in the key implant journals. She was awarded an Excellent Exchange Award by the University of Sheffield which gave her the opportunity to work at the famous Faculty of Dentistry of the University of Gothenburg, Sweden, under the tutelage of Prof. Peter Thomsen. In 2016, she was appointed as a visiting scholar at UCLA, USA, with attachment in Hospital Dentistry, and involvement in research work related to zirconia implant. In 2016, her contribution to dentistry was recognized by the Royal College of Surgeon of Edinburgh with her being awarded a Fellowship in Dental Surgery. She has authored numerous papers published both in local and international journals. She was the Editor of the Malaysian Dental Journal for several years. Her main research interests are implant-soft tissue interface, zirconia implant, photofunctionalization, 3D-oral mucosal model and pulpal regeneration.",institutionString:null,institution:{name:"University of Malaya",institutionURL:null,country:{name:"Malaysia"}}},editorTwo:{id:"479686",title:"Dr.",name:"Ghee Seong",middleName:null,surname:"Lim",slug:"ghee-seong-lim",fullName:"Ghee Seong Lim",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003ScjLZQAZ/Profile_Picture_2022-06-08T14:17:06.png",biography:"Assoc. Prof Dr. Lim Ghee Seong graduated with a Bachelor of Dental Surgery from University of Malaya, Kuala Lumpur in 2008. He then pursued his Master in Clinical Dentistry, specializing in Restorative Dentistry at Newcastle University, Newcastle, UK, where he graduated with distinction. He has also been awarded the International Training Fellowship (Restorative Dentistry) from the Royal College of Surgeons. 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He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry"}}},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. 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Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. 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Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. 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