Open access peer-reviewed chapter

Long Non-Coding RNA in Non-Small Cell Lung Cancers

Written By

Zule Cheng and Hongju Mao

Submitted: 06 June 2016 Reviewed: 24 October 2016 Published: 01 March 2017

DOI: 10.5772/66487

From the Edited Volume

A Global Scientific Vision - Prevention, Diagnosis, and Treatment of Lung Cancer

Edited by Marta Adonis

Chapter metrics overview

1,531 Chapter Downloads

View Full Metrics


Non-small cell lung cancer (NSCLC) accounts for nearly 80% of diagnosed lung cancers. Due to the predominantly late diagnosis of NSCLC and drug resistance in the targeted therapy approaches, the 5-year overall survival rate is still less than 19%. Thus, novel diagnosis and treatment approaches are needed. Many efforts have been made to achieve great progress in understanding the genomic landscape of NSCLC and the molecular mechanisms involved in tumorigenesis. Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nucleotides with little or no protein-coding potential. They are encoded across the genome and are involved in a wide range of cellular and biological processes. Dysregulation of lncRNAs is associated with a number of cancer-related processes, including epigenetic regulation, microRNA silencing, and DNA damage. Furthermore, lncRNAs have been reported to have the potential as biomarker for diagnosis and prognosis, as well as the therapy targets. Here in this chapter, we review some well-characterized lncRNAs associated with NSCLCs and the potential of lncRNAs as biomarkers in the diagnosis and prognosis of NSCLCs.


  • lncRNA
  • diagnosis
  • prognosis
  • epigenetic

1. Introduction

Lung cancer is the leading cause of cancer-related deaths worldwide. 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.

Biomarker 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 [57]. Identification and characterization of specific driver mutations has transformed the diagnosis and treatment of NSCLC.

With 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.


2. Long non-coding RNAs in non-small cell lung cancer

2.1. Long non-coding RNAs

The 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].

The 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 Xist lncRNA, are believed to originate by undergoing a metamorphosis from erstwhile protein-coding gene while incorporating transposable sequence [20]. Other studies report the lncRNA can also originate from chromosome’s rearrangement, duplication of a non-coding gene by retro-transposition, neighbouring repeat, or transposable elements insertion [21, 22]. Along with the complex originations, lncRNAs also have heterogeneous groups with multiple classifications. The most prevalent classification method is based on the genomic location and context. According to this method, lncRNAs can be defined as: (1) sense lncRNAs; (2) antisense lncRNAs that are transcribed from the sense or antisense strands, respectively, overlapping one or more exons of protein-coding gene on the same or opposite strand; (3) bidirectional lncRNAs, whose transcription and neighbouring coding transcript on the opposite strand is initiated in close genomic proximity; (4) intronic lncRNA that are transcribed entirely from introns of protein-coding genes; (5) intergenic lncRNA that lies within the genomic interval between two genes [20, 23, 24]. Functions of lncRNAs may differ from each category. In general, through molecular mechanisms like signalling, decoying, guiding, and scaffolding [25], lncRNAs are widely involved in gene progresses like chromatin modification, transcriptional regulation, and post-transcriptional regulation [2629]. As the exploration of lncRNAs goes further, growing evidences demonstrate that lncRNAs play important roles in various cellular processes [25, 30, 31]. Studies have identified the aberrant expression of lncRNAs in various cancers [32, 33] including non-small cell lung cancer [34]. The deregulation of some specific functional lncRNAs is proved to be important drivers implicated in tumour initialization and malignant transformation. Thus, lncRNAs have the potential as cancer biomarkers in the diagnosis and prognosis, as well as the targets for cancer therapy.

2.2. Functional lncRNAs in NSCLC

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.

2.2.1. HOX transcript antisense RNA (HOTAIR)

HOX 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.

HOTAIR 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].

In 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 vitro and impeded cell metastasis in vivo, without altering cell vitality, which suggests a potential therapeutic role of lncRNA targeted therapies [42]. Nakagawa et al. examined the expression of HOTAIR in 77 NSCLCs and 6 brain metastases. They confirmed the negative prognostic effects of HOTAIR high expression and pointed out the overexpression of HOTAIR enhanced the invasion of NSCLC cells [45]. In lung adenocarcinoma (ADC) cells, the upregulation of HOTAIR contributed to the cisplatin resistance of ADC cells through the regulation of p21 expression, and a silence of HOTAIR resulted in the increase of chemosensitivity, which further led to the inhibition of cell proliferation, induction of G0/G1 cell-cycle arrest and enhancement of apoptosis [46]. Another study of lung ADC found that HOTAIR was an important mediator for the ratio of FOXA1 and FOXA2, which might infect the migration and invasion [47]. A tumour micro-environment study performed in a type I collagen (Col-1) supplemented three-dimensional organotypic culture model found that the expression of HOTAIR could be upregulated by the tumour-promoting Col-1 in lung cancer cells. This finding provided a deeper insight into the mechanism of HOTAIR regulation [48].

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.

2.2.2. Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1)

Metastasis-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].

As 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].

High 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 in vitro [61]. In a later study, a highly efficient knockdown of MALAT1 in a NSCLC cell line, through zinc finger nuclease-based technique, confirmed the MALAT1 positive influence on the cell metastasis in vitro and in vivo without affecting cell proliferation. Furthermore, blocking MALAT1 by antisense oligonucleotides (ASO) prevented the tumour metastasis formation in a tumour implanted mouse xenograft module, which provides a potential therapeutic approach to prevent lung cancer metastasis [62].

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].

In 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.

2.2.3. H19

H19 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.

Loss 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 vitro study, which reported the increased expression of H19 in human respiratory epithelial cells treated with cigarette smoking condensate [72]. Hypoxia could also induce upregulation of H19 in cell lines with mutated p53, through a critical factor HIF1-alpha [73]. H19 was also directly induced by MYC oncogene in different cell types including fibroblast cells. A study demonstrated that c-Myc selectively increased H19 transcription from the maternally derived allele, and downregulated the reciprocally imprinted gene insulin-like growth factor 2 (IGF2) at the H19/IGF2 locus. This study indicated that c-Myc and H19 expression had strong association in lung carcinomas [74]. The mineral dust-induced gene (MDIG) could conduct the demethylation of H3K9me3 in the promoter region of H19 and activate the H19 expression. High expression of MDIG and H19 were found correlated with poorer survival of the lung cancer patients [75].

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].

In 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.

2.2.4. Cancer-associated region long non-coding RNA 5 (CARLo-5)

LncRNAs 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].

CARLo-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 in vitro knockdown experiment showed a significant inhibition of proliferation in tumour cells, mainly due to the induction of the G0/G1 arrest [77, 78]. Furthermore, silencing CARLo-5 could result in the inhibitory effects in the cell invasion and migration, possibly by modulating the EMT process [78].

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.

2.2.5. Other functional lncRNAs in NSCLC

LncRNA 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 in vitro, supporting the role of CCAT2 in the metastatic progression. Further analysis found that CCAT2 combined with CEA could predict lymph node metastasis. These findings implied the potential of CCAT2 as a specific ADC biomarker for lymph node metastasis.

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.

SRY-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].

BRAF-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 in vitro. These inhibitory effects were reported to be associated with EMT [91]. Interestingly, although BANCR was downregulated in NSCLC, some studies reported the significant upregulation of BANCR in other cancers, which suggested a tissue-dependent regulation mechanism of BANCR [66].

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 in vitro, partially through activating the p53 signalling pathway [94].


3. Long noncoding RNA as novel NSCLC biomarkers

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.

As 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.

With 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 ( Table 1 ) 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].

LncRNA Regulation Region p-Value AUC Sensitivity (%) Specificity (%) Type of sample Reference
BC034684 Up Chr1:203,148,063–203,148,611 1.486E−06 0.719 79.4 60.3 Tissue Wang et al. [99]
RP11-1008C21.2 Down Chr15:38,363,827–38,364,884 1.193E−07 0.843 81 79.4
AK094413 Down Chr9:104,235,441–104,237,132 6.634E−08 0.821 85.2 62.4
RP11-598F7.5 Down Chr12:273,829–275,487 4.108E−11 0.882 79.4 84.1
VNN2 Down Chr6:133,065,008–133,079,022 1.063E−05 0.835 77.8 79.4
Combination 0.987 92 98
SPRY4-IT1 Up Chr5:142,317,620–142,318,322 <0.01 0.603 / / Plasma Hu et al. [102]
ANRIL Up Chr9:21,994,791–22,120,646 <0.001 0.798 / /
NEAT1 Up Chr11:65,422,800–65,423,368 <0.001 0.693 / /
Combination 0.876 88 81
MALAT1 Up Chr11:65,497,762–65,506,469 <0.0001 0.79 56 96 Peripheral blood Weber et al. [101]

Table 1.

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 ( Table 1 ), 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 ( Table 1 ). Combination with the three factors indicated a high power of discrimination (AUC, 0.876; sensitivity, 82.8%; and specificity, 92.3%) [102].


4. Conclusion

Since 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.

The 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 cis or trans manner through directly or indirectly binding to DNA or protein molecules. Thus, the occurrences of diseases including cancers are always along with the dysregulation of lncRNAs. In this chapter, we have discussed several lncRNAs that have been reported to be associated with NSCLCs. Some of them are well-characterized and also identified in other cancers, while some still remain to be studied. Most of these lncRNAs have the potential as biomarkers in diagnosis and prognosis of NSCLCs. However, these lncRNAs lack NSCLC tissue specificity, thus great efforts are still needed to identify NSCLC tissue-specific lncRNAs.

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.

Considering 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.


  1. 1. National Cancer Institute. SEER Stat fact sheets: lung and bronchus cancer [Internet]. 2016 [Updated: 2016]. Available from:
  2. 2. Davidson MR, Gazdar AF, Clarke BE. The pivotal role of pathology in the management of lung cancer. Journal of Thoracic Disease. 2013; 5(4):S463–S478. DOI: 10.3978/j.issn. 2072-1439. 2013.08.43
  3. 3. Hasan N, Kumar R, Kavuru MS. Lung cancer screening beyond low-dose computed tomography: the role of novel biomarkers. Lung. 2014; 192(5):639–648. DOI: 10.1007/ s00408-014-9636-z
  4. 4. Molina R, Filella X, Augé JM, Fuentes R, Bover I, Rifa J, et al. Tumor markers (CEA, CA 125, CYFRA 21-1, SCC and NSE) in patients with non-small cell lung cancer as an aid in histological diagnosis and prognosis. Comparison with the main clinical and pathological prognostic factors. Tumour Biology. 2003; 24(4):209–218. DOI: 74432
  5. 5. Mogi A, Kuwano H. TP53 mutations in nonsmall cell lung cancer. BioMed Research International. 2011; 2011(1):50–56. DOI: 10.1155/2011/583929
  6. 6. Paez JG, Jänne PA, Lee JC, Tracy S, Greulich H, Gabriel S, et al. EGFR mutations in lung cancer: correlation with clinical. Science. 2004; 304 (5676):1497–1500. DOI: 10.1126/science.1099314
  7. 7. Tam IY, Chung LP, Suen WS, Wang E, Wong MC, Ho KK, et al. Distinct epidermal growth factor receptor and KRAS mutation patterns in non-small cell lung cancer patients with different tobacco exposure and clinicopathologic features. Clinical Cancer Research. 2006; 12(5): 1647–1653. DOI: 10.1158/1078-0432.CCR-05-1981
  8. 8. Ponting CP, Belgard TG. Transcribed dark matter: meaning or myth? Human Molecular Genetics. 2010; 15(19):162–168. DOI: 10.1093/hmg/ddq362
  9. 9. Bartel DP. MicroRNAs: target recognition and regulatory. Cell. 2009; 136(2):215–233. DOI: 10.1016/j.cell.2009.01.002
  10. 10. Chen X, Ba Y, Ma L, Cai X, Yin Y, Wang K, Guo J, et al. Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases. Cell Research. 2008; 18(10):997–1006. DOI: 10.1038/cr.2008.282
  11. 11. Russell RB, Cohen SM. Principles of microRNA-target recognition. PLoS Biology. 2005; 3(3):e85. DOI: 10.1371/journal.pbio.0030085
  12. 12. O’Carroll D, Mecklenbrauker I, Das PP, Santana A, Koenig U, Enright AJ, et al. Slicer-independent role for Argonaute 2 in hematopoiesis and the microRNA pathway. Genes & Development. 2007; 21(16):1999–2004. DOI: 10.1101 /gad. 1565607
  13. 13. Ji Q, David M. MicroRNAs and lung cancers: from pathogenesis to clinical implications. Frontiers of Medicine. 2012; 6(2):134–155. DOI: 10.1007/s11684-012-0188-4
  14. 14. Mattia B, Carla V, Davide C, Luca R, Piergiorgio M, Federica F, et al. MicroRNA signatures in tissues and plasma predict development and prognosis of computed tomography detected lung cancer. PNAS. 2010; 108(9):3713–3718. DOI: 10.1073/pnas. 1100048108
  15. 15. Nozomu Y, Natasha C, Elise B, Masahiro S, Kensuke K, Ming Y, et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell. 2006; 9(3):189–198. DOI: 10.1016/j.ccr.2006.01.025
  16. 16. Wang P, Yang D, Zhang H, Wei X, Ma T, Cheng Z, et al. Early detection of lung cancer in serum by a panel of microRNA biomarkers. Clinical Lung Cancer. 2015; 16(4):313–319. DOI: 10.1016/j.cllc.2014.12.006
  17. 17. Li X, Shi Y, Yin Z, Xue X, Zhou B. An eight-miRNA signature as a potential biomarker for predicting survival in lung adenocarcinoma. Journal of Translational Medicine. 2014; 4(12):159. DOI: 10.1186/1479-5876-12-159
  18. 18. Derrien T, Johnson R, Bussotti G, Tanzer A, Djebali S, Tilgner H, et al. The GENCODE v7 catalog of human long noncoding RNAs: analysis of their gene structure, evolution, and expression. Genome Research. 2012; 22(9):1775–1789. DOI: 10.1101/ gr.132159.111
  19. 19. Sati S, Ghosh S, Jain V, Scaria V, Sengupta S. Genome-wide analysis reveals distinct patterns of epigenetic features in long non-coding RNA loci. Nucleic Acids Research. 2012; 40(20):10018–10031. DOI: 10.1093/nar/gks776
  20. 20. Elisaphenko EA, Kolesnikov NN, Shevchenko AI, Rogozin IB, Nesterova TB, Brockdorff N, et al. A dual origin of the Xist gene from a protein-coding gene and a set of transposable elements. PLoS One. 2008; 3(6):e2521. DOI: 10.1371/journal.pone.0002521
  21. 21. Chris PP, Peter LO, Wolf R. Evolution and functions of long noncoding RNAs. Cell. 2009; 136(4):629–641. DOI: 10.1016/j.cell.2009.02.006
  22. 22. Conley AB, Miller WJ, Jordan IK. Human cis natural antisense transcripts initiated by transposable elements. Trends in Genetics 2008; 24(2):53–56. DOI: 10.1016/j.tig.2007. 11. 008
  23. 23. St Laurent G, Wahlestedt C, Kapranov P. The landscape of long noncoding RNA classification. Trends in Genetics. 2015; 31(5):239–251. DOI: 10.1016/j.tig.2015.03.007
  24. 24. Ma L, Bajic VB, Zhang Z. On the classification of long non-coding RNAs. RNA Biology. 2013; 10(6):925–933. DOI: 10.4161/rna.24604
  25. 25. Wang KC, Chang HY. Molecular mechanisms of long noncoding RNAs. Molecular Cell. 2011;43(6):904–914. DOI: 10.1016/j.molce2011.08.018
  26. 26. John LR, Michael K, Jordon KW, Sharon LS, Xiao X, Samantha A, et al. Functional demarcation of active and silent chromatin domains in human HOX loci by non-coding RNAs. Cell. 2007; 129(7):1311–1323. DOI: 10.1016/j.cell.2007.05.022
  27. 27. Ogawa Y, Sun BK, Lee JT. Intersection of the RNA interference and X-inactivation pathways. Science. 2008; 320(5881):1336–1341. DOI: 10.1126/science.1157676
  28. 28. Wang X, Arai S, Song X, Reichart D, Du K, Pascual G, et al. Induced ncRNAs allosterically modify RNA-binding proteins in cis to inhibit transcription. Nature. 2008; 454(7200):126–130. DOI: 10.1038/nature06992
  29. 29. Manuel B, Isabel P, Cristina P, José MG, Ana BÁ, Raúl P, et al. A natural antisense transcript regulates Zeb2/Sip1 gene expression during Snail1-induced epithelial–mesenchymal transition. Genes & Development. 2008; 22(6):756–769.DOI: 10.1101/gad. 455708
  30. 30. Batista PJ, Chang HY. Long noncoding RNAs: cellular address codes in development and disease. Cell. 2013; 152(6):1298–1307. DOI: 10.1016/j.cell.2013.02.012
  31. 31. Tim RM, Marcel ED, John SM. Long non-coding RNAs: insights into functions. Nature Reviews Genetics. 2009; 10(3):155–159. DOI: 10.1038/nrg2521
  32. 32. Tsai MC, Spitale RC, Chang HY. Long intergenic non-coding RNAs—new links in cancer progression. Cancer Research. 2011; 71(1):3–7. DOI: 10.1158/0008-5472.CAN-10-2483
  33. 33. Yang G, Lu X, Yuan L. LncRNA: a link between RNA and cancer. Biochimica et Biophysica Acta. 2014; 1839(11):1097–1109. DOI: 10.1016/j.bbagrm.2014.08.012
  34. 34. Yang Y, Li H, Hou S, Hu B, Liu J, Wang J. The noncoding RNA expression profile and the effect of lncRNA AK126698 on cisplatin resistance in non-small-cell lung cancer cell. PLoS One. 2013; 8(5):e65309. DOI: 10.1371/journal.pone.0065309
  35. 35. The GENCODE Project. Encyclopaedia of genes and gene variants. Statistics about the current human GENCODE release (version 25) [Internet]. 2016 freeze, GRCh38. Available from:
  36. 36. Rinn JL, Kertesz M, Wang JK, Squazzo SL, Xu X, Brugmann SA, et al. Functional demarcation of active and silent chromatin domains in human HOX loci by noncoding RNAs. Cell. 2007; 129(7):1311–1323. DOI: 10.1016/j.cell.2007.05.022
  37. 37. Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, et al. Long noncoding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 2010; 464(7291):1071–1076. DOI: 10.1038/nature08975
  38. 38. Yang Z, Zhou L, Wu LM, Lai MC, Xie HY, Zhang F, et al. Overexpression of long non-coding RNA HOTAIR predicts tumor recurrence in hepatocellular carcinoma patients following liver transplantation. Annals of Surgical Oncology. 2011; 18(5):1243–1250. DOI: 10.1245/s10434-011-1581-y
  39. 39. Svoboda M, Slyskova J, Schneiderova M, Makovicky P, Bielik L, Levy M, et al. HOTAIR long non-coding RNA is a negative prognostic factor not only in primary tumors, but also in the blood of colorectal cancer patients. Carcinogenesis. 2014; 35(7): 1510–1515. DOI: 10.1093/carcin/bgu055
  40. 40. Kim K, Jutooru I, Chadalapaka G, Johnson G, Frank J, Burghardt R, et al. HOTAIR is a negative prognostic factor and exhibits pro-oncogenic activity in pancreatic cancer. Oncogene. 2013; 32(13):1616–1625. DOI: 10.1038/onc.2012.193
  41. 41. Li X, Wu Z, Mei Q, Guo M, Fu X, Han W, et al. Long non-coding RNA HOTAIR, a driver of malignancy, predicts negative prognosis and exhibits oncogenic activity in oesophageal squamous cell carcinoma. British Journal of Cancer. 2013; 109(109):2266–2278. DOI: 10.1038/bjc.2013.548
  42. 42. Liu XH, Liu ZL, Sun M, Liu J, Wang ZX, De W. The long non-coding RNA HOTAIR indicates a poor prognosis and promotes metastasis in non-small cell lung cancer. BMC Cancer. 2013; 13(1):1–10. DOI: 10.1186/1471-2407-13-464
  43. 43. Hiroyuki E, Takeharu S, Takayuki N, Misa Y, Keiichi T, Hideaki Y, et al. Enhanced expression of long non-coding RNA HOTAIR is associated with the development of gastric cancer. PLoS One. 2013; 8(10):e77070. DOI: 10.1371/journal.pone.0077070
  44. 44. Liu XH, Sun M, Nie FQ, Ge YB, Zhang EB, Yin DD, et al. Lnc RNA HOTAIR functions as a competing endogenous RNA to regulate HER2 expression by sponging miR-331-3p in gastric cancer. Molecular Cancer. 2014; 13(1):2739–2748. DOI: 10.1186/1476-4598-13-92
  45. 45. Nakagawa T, Endo H, Yokoyama M, Abe J, Tamai K, Tanaka N, et al. Large noncoding RNA HOTAIR enhances aggressive biological behavior and is associated with short disease-free survival in human non-small cell lung cancer. Biochemical and Biophysical Research Communications. 2013; 436(2):319–324. DOI: 10.1016/ j.bbrc.2013. 05.101
  46. 46. Liu Z, Sun M, Lu K, Liu J, Zhang M, Wu W, et al. The long noncoding RNA HOTAIR contributes to cisplatin resistance of human lung adenocarcinoma cells via downregualtion of p21 (WAF1/CIP1) expression. PLoS One. 2013; 8(10):e77293. DOI: 10.1371/journal.pone.0077293
  47. 47. Wang R, Shi Y, Chen L, Jiang Y, Mao C, Yan B, et al. The ratio of FoxA1 to FoxA2 in lung adenocarcinoma is regulated by LncRNA HOTAIR and chromatin remodeling factor LSH. Scientific Reports. 2015; 5(17):826. DOI: 10.1038/srep17826
  48. 48. Zhuang Y, Wang X, Nguyen HT, Zhuo Y, Cui X, Fewell C, et al. Induction of long intergenic non-coding RNA HOTAIR in lung cancer cells by type I collagen. Journal of Hematology & Oncology. 2013; 6(35). DOI: 10.1186/1756-8722-6-35
  49. 49. Ji P, Diederichs S, Wang W, Böing S, Metzger R, Schneider PM, et al. MALAT-1, a novel noncoding RNA, and thymosin beta4 predict metastasis and survival in early-stage non-small cell lung cancer. Oncogene. 2003; 22(39): 8031–8041. DOI: 10.1038 /sj. onc.1206 928
  50. 50. Wilusz JE, Freier SM, Spector DL. 3′ end processing of a long nuclear-retained noncoding RNA yields a tRNA-like cytoplasmic RNA. Cell. 2008; 135(5):919–932. DOI: 10.1016/j.cell.2008.10.012
  51. 51. Hutchinson JN, Ensminger AW, Clemson CM, Lynch CR, Lawrence JB, Chess A. A screen for nuclear transcripts identifies two linked noncoding RNAs associated with SC35 splicing domains. BMC Genomics. 2007; 8(39):1–16. DOI: 10.1186/1471-2164-8-39
  52. 52. Tripathi V, Ellis JD, Shen Z, Song DY, Pan Q, Watt AT, et al. The nuclear-retained noncoding RNA MALAT1 regulates alternative splicing by modulating SR splicing factor phosphorylation. Molecular Cell. 2010; 39(6):925–938. DOI: 10.1016/j.molcel.2010. 08.011
  53. 53. Pang EJ, Yang R, Fu XB, Liu YF. Overexpression of long non-coding RNA MALAT1 is correlated with clinical progression and unfavorable prognosis in pancreatic cancer. Tumour Biol: J Int Soc Oncodevelopmental Biol Med. 2014; 36(4):2403–2407. DOI: 10.10 07/s13277-014-2850-8
  54. 54. Zheng HT, Shi DB, Wang YW, Li XX, Xu Y, Tripathi P, et al. High expression of lncrna malat1 suggests a biomarker of poor prognosis in colorectal cancer. International Journal of Clinical and Experimental Pathology. 2014; 7(6):3174–3181.
  55. 55. Dong Y, Liang G, Yuan B, Yang C, Gao R, Zhou X. Malat1 promotes the proliferation and metastasis of osteosarcoma cells by activating the PI3K/AKT pathway. Tumour Biology: The Journal of the International Society for Oncodevelopmental Biology & Medicine. 2014; 36(3):1477–1486. DOI: 10.1007/s13277-014-2631-4.
  56. 56. Wu XS, Wang XA, Wu WG, Hu YP, Li ML, Ding Q, et al. Malat1 promotes the proliferation and metastasis of gallbladder cancer cells by activating the ERK/MAPK pathway. Cancer Biology & Therapy, 2014; 15(6): 806–814. DOI: 10.4161/cbt.28584
  57. 57. Hu L, Wu Y, Tan D, Meng H, Wang K, Bai Y, et al. Up-regulation of long noncoding RNA MALAT1 contributes to proliferation and metastasis in esophageal squamous cell carcinoma. Journal of Experimental & Clinical Cancer Research. 2015; 34(7). DOI: 10.1186/s13046-015-0123-z.
  58. 58. Wang X, Li M, Wang Z, Han S, Tang X, Ge Y, et al. Silencing of long noncoding RNA malat1 by mir-101 and mir-217 inhibits proliferation, migration, and invasion of esophageal squamous cell carcinoma cells. Journal of Biological Chemistry. 2015; 290(7): 3925–3935. DOI: 10.1074/jbc.M114.596866
  59. 59. Fan Y, Shen B, Tan M, Mu X, Qin Y, Zhang F, Liu Y. TGF-β-induced upregulation of malat1 promotes bladder cancer metastasis by associating with suz12. Clinical Cancer Research. 2014; 20(6):1531–1541. DOI: 10.1158/1078-0432.CCR-13-1455
  60. 60. Hirata H, Hinoda Y, Shahryari V, Deng G, Nakajima K, Tabatabai ZL, et al. Long noncoding RNA MALAT1 promotes aggressive renal cell carcinoma through Ezh2 and interacts with miR-205. Cancer Research. 2015; 75(7):1322–1331. DOI: 10.1158/0008-5472.CAN-14-2931
  61. 61. Schmidt LH, Spieker T, Koschmieder S, Humberg J, Jungen D, Bulk E, et al. The long noncoding MALAT-1 RNA indicates a poor prognosis in non-small cell lung cancer and induces migration and tumor growth. Journal of Thoracic Oncology. 2011; 6(12):1984–1992. DOI: 10.1097/JTO.0b013e3182307eac
  62. 62. Gutschner T, Hämmerle M, Eissmann M, Hsu J, Kim Y, Hung G, et al. The non-coding RNA MALAT1 is a critical regulator of the metastasis phenotype of lung cancer cells. Cancer Research. 2013; 73(3):1180–1189. DOI: 10.1158/0008-5472.CAN-12-2850
  63. 63. Tripathi V, Shen Z, Chakraborty A, Giri S, Freier SM, Wu X, et al. Long noncoding RNA MALAT1 controls cell cycle progression by regulating the expression of oncogenic transcription factor B-MYB. PLoS Genetics. 2013; 9(3):e1003368. DOI: 10.1371/journal. pgen.1003368
  64. 64. Yang L, Lin C, Liu W, Zhang J, Ohgi K, Grinstein J, et al. NcRNA- and Pc2 methylation-dependent gene relocation between nuclear structures mediates gene activation programs. Cell. 2011; 147(4):773–788. DOI: 10.1016/j.cell.2011.08.054
  65. 65. Gabory A, Jammes H, Dandolo L. The H19 locus: role of an imprinted non-coding RNA in growth and development. Bioessays: News & Reviews in Molecular Cellular & Developmental Biology. 2010; 32(6):473–480. DOI: 10.1002/bies.200900170
  66. 66. Roth A, Diederichs S. Long noncoding RNAs in lung cancer. Curent Topics in Microbiology & Immunolog. 2016; 394:57–110. DOI: 10.1007/82_2015_444
  67. 67. Kondo M, Suzuki H, Ueda R, Osada H, Takagi K, Takahashi T, et al. Frequent loss of imprinting of the H19 gene is often associated with its overexpression in human lung cancers. Oncogene. 1995; 10(6):1193–1198.
  68. 68. Gao T, He B, Pan Y, Gu L, Chen L, Nie Z, et al. H19 dmr methylation correlates to the progression of esophageal squamous cell carcinoma through igf2 imprinting pathway. Clinical and Translational Oncology. 2014; 16(4):410–417. DOI: 10.1007/s12094-013-1098-x
  69. 69. Ulaner GA, Vu TH, Li T, Hu JF, Yao XM, Yang Y, et al. Loss of imprinting of IGF2 and H19 in osteosarcoma is accompanied by reciprocal methylation changes of a CTCF-binding site. Human Molecular Genetics. 2003; 12(5):535–549. DOI: 10.1093/hmg/ddg034
  70. 70. Byun HM, Wong HL, Birnstein EA, Wolff EM, Liang G, Yang AS. Examination of IGF2 and H19 loss of imprinting in bladder cancer. Cancer Research. 2007; 67(22):10753–10758. DOI: 10.1158/0008-5472.CAN-07-0329
  71. 71. Kaplan R, Luettich K, Heguy A, Hackett NR, Harvey BG, Crystal RG. Monoallelic up-regulation of the imprinted H19 gene in airway epithelium of phenotypically normal cigarette smokers. Cancer Research. 2003; 63(7):1475–1482.
  72. 72. Liu F, Killian JK, Yang M, Walker RL, Hong JA, Zhang M, et al. Epigenomic alterations and gene expression profiles in respiratory epithelia exposed to cigarette smoke condensate. Oncogene. 2010; 29(25):3650–3664. DOI: 10.1038/onc.2010.129
  73. 73. Matouk IJ, Mezan S, Mizrahi A, Ohana P, Abu-Lail R, Fellig Y, et al. The oncofetal h19 rna connection: hypoxia, p53 and cancer. Biochimica et Biophysica Acta, Molecular Cell Research. 2010; 1803(4):443–451. DOI: 10.1016/j.bbamcr.2010.01.010
  74. 74. Barsyte-Lovejoy D, Lau SK, Boutros PC, Khosravi F, Jurisica I, Andrulis IL, et al. The c-Myc oncogene directly induces the H19 noncoding RNA by allele-specific binding to potentiate tumorigenesis. Cancer Research. 2006; 66(10):5330–5337. DOI: 10.1158/0008-5472.CAN-06-0037
  75. 75. Chen B, Yu M, Chang Q, Lu Y, Thakur C, Ma D, et al. Mdig de-represses h19 large intergenic non-coding rna (lincrna) by down-regulating h3k9me3 and heterochromatin. Oncotarget. 2013; 4(9):1427-1437. DOI: 10.18632/oncotarget.1155
  76. 76. Cui J, Mo J, Luo M, Yu Q, Zhou S, Li T, et al. c-Myc-activated long non-coding RNA H19 downregulates miR-107 and promotes cell cycle progression of non-small cell lung cancer. International Journal of Clinical and Experimental Pathology. 2015; 8(10):12400–12409.
  77. 77. Kim T, Cui R, Jeon YJ, Lee JH, Sim H, Park JK, et al. Long-range interaction and correlation between MYC enhancer and oncogenic long noncoding RNA CARLo-5. PNAS. 2014; 111(11):4173–4178. DOI: 10.1073/pnas.1400350111
  78. 78. Luo J, Tang L, Zhang J, Ni J, Zhang HP, Zhang L, et al. Long non-coding RNA CARLo-5 is a negative prognostic factor and exhibits tumor pro-oncogenic activity in non-small cell lung cancer. Tumour Biology. 2014; 35(11):11541–11549. DOI: 10.1007/ s13277-014-2442-7
  79. 79. Zhang Y, Ma M, Liu W, Ding W, Yu H. Enhanced expression of long noncoding RNA CARLo-5 is associated with the development of gastric cancer. International Journal of Clinical and Experimental Pathology. 2014; 7(12):8471–8479.
  80. 80. Pomerantz MM, Ahmadiyeh N, Jia L, Herman P, Verzi MP, Doddapaneni H, et al. The 8q24 cancer risk variant rs6983267 shows long-range interaction with MYC in colorectal cancer. Nature Genetics. 2009; 41(8):882–884. DOI: 10.1038/ng.403
  81. 81. Ling H, Spizzo R, Atlasi Y, Nicoloso M, Shimizu M, Redis RS, et al. CCAT2, a novel noncoding RNA mapping to 8q24, underlies metastatic progression and chromosomal instability in colon cancer. Genome Research. 2013; 23(9):1446–1461. DOI: 10.1101/gr. 152942.112
  82. 82. Qiu M, Xu Y, Yang X, Wang J, Hu J, Xu L, et al. CCAT2 is a lung adenocarcinoma-specific long non-coding RNA and promotes invasion of non-small cell lung cancer. Tumour Biology: The Journal of the International Society for Oncodevelopmental Biology & Medicine. 2014;35(6):5375–5380. DOI: 10.1007/s13277-014-1700-z
  83. 83. Coccia EM, Cicala C, Charlesworth A, Ciccarelli C, Rossi GB, Philipson L, et al. Regulation and expression of a growth arrest-specific gene (gas5) during growth, differentiation, and development. Molecular and Cellular Biology. 1992; 12(8):3514–3521.
  84. 84. Shi X, Sun M, Liu H, Yao Y, Kong R, Chen F. et al. A critical role for the long non-coding RNA GAS5 in proliferation and apoptosis in non-small-cell lung cancer. Molecular Carcinogenesis. 2013; 54(S1):E1–E12. DOI: 10.1002/mc.22120
  85. 85. Kino T, Hurt DE, Ichijo T, Nader N, Chrousos GP. Noncoding RNA gas5 is a growth arrest- and starvation-associated repressor of the glucocorticoid receptor. Science Signaling. 2010; 3(107):692–702. DOI: 10.1126/scisignal.2000568
  86. 86. Zhang N, Yang GQ, Shao XM, Wei L. GAS5 modulated autophagy is a mechanism modulating cisplatin sensitivity in NSCLC cells. European Review for Medical and Pharmacological Sciences. 2016; 20(11):2271–2277.
  87. 87. Dong S, Qu X, Li W, Zhong X, Li P, Yang S, et al. The long non-coding RNA GAS5 enhances gefitinib-induced cell death in innate EGFR tyrosine kinase inhibitor-resistant lung adenocarcinoma cells with wide-type EGFR via downregulation of the IGF-1R expression. Journal of Hematology & Oncology. 2015; 8(1):1–13. DOI: 10.1186/s13045-015-0140-6
  88. 88. Amaral PP, Neyt C, Wilkins SJ, Askarian-Amiri ME, Sunkin SM, Perkins A, et al. Complex architecture and regulated expression of the SOX2OT locus during vertebrate development. RNA. 2009; 15(11):2013–2027. DOI: 10.1261/rna.1705309
  89. 89. Hou Z, Zhao W, Zhou J, Shen L, Zhan P, Xu C. et al. A long noncoding RNA Sox2ot regulates lung cancer cell proliferation and is a prognostic indicator of poor survival. The International Journal of Biochemistry & Cell Biology. 2014; 53(8):380–388. DOI: 10.1016/j.biocel.2014.06.004
  90. 90. Flockhart RJ, Webster DE, Qu K, Mascarenhas N, Kovalski J, Kretz M, et al. BRAFV600E remodels the melanocyte transcriptome and induces BANCR to regulate melanoma cell migration. Genome Research. 2012; 22(6):1006–1014. DOI: 10.1101/gr. 140061.112
  91. 91. Sun M, Liu XH, Wang KM, Nie FQ, Kong R, Yang JS, et al. Downregulation of braf activated non-coding rna is associated with poor prognosis for non-small cell lung cancer and promotes metastasis by affecting epithelial-mesenchymal transition. Molecular Cancer. 2014; 13(1):1–12. DOI: 10.1186/1476-4598-13-68
  92. 92. Miyoshi N, Wagatsuma H, Wakana S, Shiroishi T, Nomura M, Aisaka K, et al. Identification of an imprinted gene, Meg3/Gtl2 and its human homologue MEG3, first mapped on mouse distal chromosome 12 and human chromosome 14q. Genes to Cells. 2000; 5(3):211–220.
  93. 93. Zhou Y, Zhong Y, Wang Y, Zhang X, Batista DL, Gejman R, et al. Activation of p53 by meg3 non-coding RNA. Journal of Biological Chemistry. 2007; 282(34):24731–24742. DOI: 10.1074/jbc.M702029200
  94. 94. Lu KH, Li W, Liu XH, Sun M, Zhang ML, Wu WQ, et al. Long non-coding RNA MEG3 inhibits NSCLC cells proliferation and induces apoptosis by affecting p53 expression. BMC Cancer. 2013; 13(1):1–11. DOI: 10.1186/1471-2407-13-461
  95. 95. Huang X, Yuan T, Tschannen M, Sun Z, Jacob H, Du M, et al. Characterization of human plasma-derived exosomal RNAs by deep sequencing. BMC Genomics. 2013; 14(319):1–14. DOI: 10.1186/1471-2164-14-319
  96. 96. Arita T, Ichikawa D, Konishi H, Komatsu S, Shiozak, A, Shoda, K, et al. Circulating long non-coding RNAs in plasma of patients with gastric cancer. Anticancer Research. 2013; 33(8):3185–3193.
  97. 97. Ren S, Wang F, Shen J, Sun Y, Xu W, Lu J, et al. Long non-coding RNA metastasis associated in lung adenocarcinoma transcript 1 derived miniRNA as a novel plasma-based biomarker for diagnosing prostate cancer. European Journal of Cancer. 2013; 49 (13):2949–2959. DOI: 10.1016/j.ejca.2013.04.026
  98. 98. Iyer MK, Niknafs YS, Malik R, Singhal U, Sahu A, Hosono Y, et al. The landscape of long noncoding RNAs in the human transcriptome. Nature Genetics. 2015; 47(3):199–208. DOI: 10.1038/ng.3192
  99. 99. Wang P, Lu SH, Mao HH, Bai YN, Ma TL, Cheng ZL, et al. Identification of biomarkers for the detection of early stage lung adenocarcinoma by microarray profiling of long noncoding RNAs. Lung Cancer. 2015; 88(2):147–153. DOI: 10.1016/j.lungcan.2015.02.009
  100. 100. Yu H, Xu Q, Liu F, Ye X, Wang J, Meng X. Identification and validation of long noncoding RNA biomarkers in human non-small-cell lung carcinomas. Journal of Thoracic Oncology. 2015; 10(4):645–654. DOI: 10.1097/JTO.0000000000000470.
  101. 101. Weber DG, Johnen G, Casjens S, Bryk O, Pesch B, Jöckel KH, et al. Evaluation of long noncoding RNA MALAT1 as a candidate blood-based biomarker for the diagnosis of non-small cell lung cancer. BMC Research Notes. 2013; 6(518):1–9. DOI: 10.1186/1756-0500-6-518.
  102. 102. Hu X, Bao J, Wang Z, Zhang Z, Gu P, Tao F, et al. The plasma lncRNA acting as fingerprint in non-small-cell lung cancer. Tumour Biology. 2016; 37(3):3497–3504. DOI: 10.1007/s13277-015-4023-9
  103. 103. Tibshirani R, Hastie T, Narasimhan B, Chu G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proceedings of the National Academy of Sciences of the United States of America. 2002; 99(10):6567–6572. DOI: 10.1073/pnas.082099299

Written By

Zule Cheng and Hongju Mao

Submitted: 06 June 2016 Reviewed: 24 October 2016 Published: 01 March 2017