Open access peer-reviewed chapter - ONLINE FIRST

APC and MSH2 mRNA Quantitative Gene Expression and Bayesian Analysis of Proband in Hereditary Colorectal Carcinoma

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Tjahjadi Robert Tedjasaputra, Mochammad Hatta, Muhammad Nasrum Massi, Rosdiana Natzir, Ilhamjaya Patellongi, Marcellus Simadibrata, Rina Masadah, Muhammad Luthfi Parewangi, Prihantono, Andi Asadul Islam, Agussalim Bukhari, Rinda Nariswati, Shirly Elisa Tedjasaputra, Vincent Tedjasaputra and Jonathan Salim

Submitted: December 27th, 2021 Reviewed: January 21st, 2022 Published: March 31st, 2022

DOI: 10.5772/intechopen.102800

IntechOpen
Gene Expression Edited by Fumiaki Uchiumi

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Gene Expression [Working Title]

Dr. Fumiaki Uchiumi

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Abstract

Heterozygote relatives have approximately 80% lifetime colorectal cancer (CRC) risk. mRNA gene expression and Bayesian theorem can calculate CRC’s family risk through the initial pedigree proportion appended with conditional information. The study is the first to report such an application. The present cross-sectional and translational investigation tracked CRC patients’ tissue and blood measurement of adenomatous polyposis coli (APC) and MutS homolog (MSH)2 mRNA quantitative gene expressions, control matching, and ancestral analysis by pedigree and Bayesian theorem. Among 40 CRC patients, mean tissue level and hereditary cutoff of APC are 13,261 (670) fold-change (fc) and 12,195 fc, while 12,219 (756) fc and 11,059 fc for MSH2. A quarter of the CRC patients had a history of familial CRC. Meanwhile, four CRC patients and 10 probands were evaluated for recurrence risk via pedigree, quantitative PCR, and Bayesian analysis. We determined a cutoff point for hereditary mRNA quantitative expression. APC and MSH2 levels in the CRC subjects were significantly lower than controls. The Bayesian analysis builds ways to calculate relative risk in CRC patients’ family members and application in clinical practice.

Keywords

  • hereditary CRC
  • APC gene
  • MSH gene
  • Bayesian analysis

1. Introduction

The continuing morbidity and lethality of colorectal cancer (CRC) do not always stop at the diseased person. In fact CRC holds the top place in familial inherited case prevalence [1]. Hereditary CRC with clear-cut forms overall can be divided into lynch syndrome (LS) or hereditary non-polyposis colorectal cancer (HNPCC) and familial adenomatous polyposis (FAP), which are inherited by autosomal dominant pattern [2]. Those who related to parents or grandparents with an autosomal dominant trait has at least an 80% chance for lifetime risk of CRC incidence [3].

Screening for cancers is dependent on every individual. Those without any familial cancer history can start colonoscopy for CRC screening at 50 years old. Nevertheless, the age is smaller by a magnitude if you have a CRC first-degree relative. A CRC individual will raise your risk by two to three times more than normal; however, more relatives with the disease may equal to an exponential risk increase [4].

Familial characteristics such as age, disease onset, size, and health history often pose precarious conditions to the internist and gastroenterologist who did CRC hereditary screening by the Amsterdam and Bethesda criteria. These were illustrated in the low guidelines’ performances from both Revised Bethesda Guideline and Amsterdam II Criteria against molecular tumor analysis with 50 and 25% sensitivity and 7 and 38% positive predictive values [5]. We accordingly need a more swift and stable method to screen for hereditary CRC, such as with the implementation of family history, molecular expression, and Mendel inheritance concept [6, 7].

The Mendel hereditary concept is well-performed in screening or determining autosomal and gonadal patterns risks, as it can compute recurrence probability; however, it cannot be quickly adjusted for mutation, external factors, and coverage changes, since it focused more on empirical recurrences. Yet the application of only such analysis is questionable, as most traits are not generalizable. Hence, family members’ recurrence risk should be calculated with prior Mendelian risk and geared with personal genotyping and environmental conditional probability [8, 9, 10, 11].

Genetic studies are becoming more present recently with research around genetic matters like DNA sequencing or polymorphism [12]. They open up a new horizon for disease susceptibility and inheritance analysis, including malignancy. However, RNA study was still rare as mistakes in the nucleotide base or elsewhere will be quickly dealt by the proofreading and the mismatch repair (MMR) genes [13].

Adenomatous polyposis coli (APC) gene exhibited a unique causal relationship to the incidence of hereditary FAP from mutation on the fifth chromosome’s second region and first band. LS conversely rises from mutations in the second, third, and seventh chromosome of several different genes, including human MutL homolog 1, human MutS homolog 2 and 6, as well as human post-meiotic segregation 1 and 2 (hMLH1, hMSH2, hMSH6, hPMS1, and hPMS2) [14].

In commencing the current study, research operators or the authors need to be more aware of their surroundings. This time, huge complex calculations and uncommon Bayesian prior and posterior analysis were implemented. There is no former report on the APCand MSH2genomic RNA expressions to CRC risk with modified Bayesian estimation per the authors’ knowledge [15, 16, 17]. We hoped the current study was able to officialize an adequate hereditary measure through gene expression and the families able to incorporate Bayesian into their risk of CRC.

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2. Materials and methods

2.1 Study design

The current translational study adopted a cross-sectional design in assessing 71 subjects from May 2018 to December 2019. Medical Ethics Committee of Hasanuddin University ensured the research commencement had followed Helsinki declaration and institutional review board (IRB) standards with certification of 884/H4.8.45.31/PP31-Komite/2018. Every subject had understood and agreed to participate as shown by the signed informed consent form. The author priorly measured the minimum sample size by 5% alpha and 80% power.

2.2 Subject enrollment

Subjects were consecutively gathered from Tarakan General Hospital in Jakarta and Siloam Hospitals Lippo Village in Tangerang. The case group broadly enrolled all 41 CRC patients who had undergone a biopsy in either hospital. Gastroenterologists and oncologists made the CRC diagnosis based on the clinical symptoms, physical examinations, and supporting investigations (i.e., endoscopy and histopathological findings). We contrarily enlisted normal patients or CRC patients’ relatives who had been matched by age, sex, and body mass index to the control group.

Exclusion of patients from either group may happen at any time of the study if they had: (1) presence or history of other malignancies or inflammatory bowel disease, (2) ever done chemotherapy or radiotherapy, (3) illnesses that inhibit communication, and (4) refuse to participate.

2.3 Data and sample collection

The current study investigated APCand MSH2quantitative genotypic expressions as well as hereditary possibilities. 0.3 ml of blood samples were taken from all 71 subjects using one cc syringes, yet only CRC subjects did colonoscopy biopsy. Each of the blood and tissue samples was then laced into separated sample tubes containing L6 buffer preservative, specifically created by Hasanuddin University from a slightly modified version of the buffer in the Boom RNA extraction method. Figure 1 showed the complete RNA extraction techniques by the NucleoSpin technique (#740200.50) [18]. The isolated extraction results were subsequently amplified using a real-time PCR (RT-PCR) and then measured with a Bio-Rad CFX Manager 3.1 (Bio-Rad Laboratories, Hercules, USA) [19, 20, 21]. After that, we also applied the Bayesian probability analysis on the probands’ age, APC, and MSH2data to yield CRC risk estimations.

Figure 1.

RNA extraction technique. Step by step pathway of the RNA extraction with NucleoSpin technique (#740200.50) [18].

2.4 Polymerase chain reaction (PCR)

The present study used RT-PCR to detect the mRNA expression of MSH2and APCgenes with the following primers (Table 1). First, they entered the initial denaturing phase with 94°C for 3 minutes. Then the process continued with 38 cycles of annealing stage in 54°C for 30 s and extension stage in 72°C for 30–40 s [19, 20, 21]. Note that each gene has its unique amplicon length. For example, APCis 89 bp long, 81 bp for GAPDH, 215 bp for MSH2, and 109 bp for ß-actin [23].

GeneOrientationSequencePrimer [19, 22]
Specific primer targeta
MSH2Forward5′ to 3′CATCCAGGCATGCTTGTGTTGA
ReverseGCAGTCCACAATGGACACTTC
APCForwardTGTCCCTCCGTTCTTATGGAA
ReverseTCTTGGAAATGAACCCATAGGAA
Internal control genesa
ß-actinForward5′ to 3′ACAGAGCCTCGCCTTTGCCGAT
ReverseCTTGCACATGCCGGAGCCGTT
GAPDHForwardCGCTCTCTGCTCCTCCTGTT
ReverseCCATGGTGTCTGAGCGATGT

Table 1.

Genetic primers.

Designed by Macrogen (Seoul, Korea).


We procured the RT-PCR materials from Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, USA). Additionally, we used CFX Connect real-time PCR system from Bio-Rad Laboratories for the measurement [22, 23].

2.5 Bayesian probability

The current investigation quantified the CRC risk among family members through Bayesian analysis of the Mendelian hereditary, genetic, and direct mutations data [4, 16]. We derived a posterior probability equation (Eq. (1)) to estimate the family CRC risk from the coupling of the conditional and prior probability theorems.

pA|B=pB|ApApBandEβ=βpβ|γ|,ϑdβ
pβ|γ|,n=pγ,β,|ϑpγ|ϑ=pγ,β|ϑpγ,u|ϑdu=fγ|βϖβ|ϑfγ|uϖu|ϑduE1

2.6 Statistical analysis

Statisticians used descriptive statistics to univariately portray the subjects’ demographic characteristics and histological findings, as opposed to Shapiro-Wilk for determining the numeric data normality. Further bivariate analysis of the parametric numeric variables used t-test, while non-parametric used Mann-Whitney. Meanwhile, they tested categorical variables by either x2 or Fisher exact. Bayesian analysis was employed last for adjusted estimation of the CRC risk in the probands (i.e., relatives of the hereditary CRC patients).

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3. Results

Forty CRC patients and 31 healthy controls had a 100% participation rate within the research period. Those with CRC on average live 5.19 years longer with 1.21 kg/m2 lower body mass index (BMI) than the counterpart. The sex difference was also apparent with a 1.11:1 vs. 0.72:1 male to female ratio among the case and control groups, respectively. We also determined the cellular differentiation levels among the biopsied CRC subjects, with results in Table 2.

VariableSubject groupp
CRC (n= 40)Control (n= 31)
Age (year)a56.80 (8.40)51.61 (13.44)
Body mass index (kg/m2)a22.41 (3.29)23.62 (3.41)
Sexb>0.05
  • Male

21 (52.5)13 (41.9)
  • Female

19 (47.5)18 (58.1)
Cellular differentiationb
  • Adenocarcinoma

  • Well

26 (65.0)
  • Fair

6 (15.0)
  • Poor

7 (17.5)
  • Neuroendocrine carcinoma

1 (2.5)

Table 2.

Baseline characteristics.

Mean (standard deviation).


n(%).


There was a significant difference in blood APClevels between CRC and control subjects. There was a lower mean value of MSH2in CRC but no substantial difference between CRC and control subjects because of the outlier (Table 3).

GeneSubject group (fold-change)p
CRC (n= 40)Control (n= 31)
Blood sample
APC
  • Median (range)

12,156.5 (5848–15,035)13,260 (12,080–14,376)0.014
  • Mean (SD)

11,578.68 (2638.23)13,261.74 (670.56)0.014
MSH2
  • Median (range)

12,554.5 (4230–14,559)12,146 (11,029–13,633)0.116
  • Mean (SD)

11,411.05 (2912.45)12,219.87 (756.87)0.465
Tissue sample
APC
  • Median (range)

8337.0 (5060–13,087)
  • Mean (SD)

8147.78 (1875.12)
MSH2
  • Median (range)

7485.0 (4174–14,218)
  • Mean (SD)

7475.20 (1946.24)

Table 3.

APCand MSH2gene expression between groups.

Hereditary screening of the CRC subjects came next. Analyzing the CRC risk with Bayesian Analysis is futile if the disease is not hereditary in the first place. There are however no prior validated data on cutoff amounts for CRC hereditary trait from APCand MSH2gene expression. Hence, we established the required cutoff values through the fifth percentile technique.

Table 4 showed the percentiles distribution of both APCand MSH2quantitative expressions among the control group. The fifth percentile of both genes adequately fits to be hereditary cutoff values since it had no significant difference to the first and third percentile. Henceforth, hereditary CRC was very likely in those with over 12,195.80 fc APCor 11,059.60 fc MSH2.

PercentileGene expression (n= 31)
APC(fc)MSH2(fc)
First12,080.0011,029.00
Third12,080.0011,029.00
Fifth12,195.8011,059.60

Table 4.

Gene expression percentile distribution.

The CRC subjects were then distributed nicely into either the hereditary or sporadic category with the determined cutoff. Gene expressions equal to and above the cutoff positively correspond to a hereditary status. A majority proportion (52.5%) of the 40 people with colorectal cancer had hereditary nature based on both APCand MSH2cutoffs. Nonetheless, the hereditary rate decreased by 2.5% and 20.0% if only using cutoff from either one (Table 5).

Cutoff geneHereditary (n(%))Sporadic (n(%))
APC20 (50.0)20 (50.0)
MSH213 (32.5)27 (67.5)
APCand MSH221 (52.5)19 (47.5)

Table 5.

CRC subjects’ hereditary distribution.

Complete pedigree analysis of the family age, health, gender, and family history of diseases is essential for the estimation of CRC risk. There were merely eight subjects with positive CRC in the family and even then, half were dropped because of vague recollection or retracted permission. We consequently extracted only 10 probands from the four CRC families for Bayesian analysis (Figure 2AD).

Figure 2.

Hereditary CRC subjects’ family pedigree. The pedigrees symbols correspond to the standardized human pedigree nomenclature [17], where a circle denoted a woman, a square for a man, a straight line for a relationship, and a diagonal strikethrough line for death. Colors also exhibited a similar trend. Red represented the proband, yellow for malignancy other than CRC, and light green for CRC subjects. A: Pedigree of 67-year-old man CRC, B: Pedigree of 44-years-old woman with early-onset CRC, C: Pedigree of elderly 62-years-old woman with CRC, D: Pedigree of 47-years-old woman with CRC.

Table 6 described the process of CRC risk estimation among the probands. Bayesian analysis conditionally tweaked each proband’s initial risk (‘prior’ column) with his or her age and gene expressions to yield adjusted odds (‘phi’ column) in developing or carrying CRC (‘Y’ column).

ProbandPriorAge (year)APC(fc)MSH2(fc)Yphi
10.5307290975311.000
20.07413,83214,20900.932
30.5498727956710.504
40.523975710,32010.505
50.52014,52413,07300.500
60.54311,67610,67310.500
70.06414,02013,65300.500
80.5526884707310.500
90.54514,34113,29500.500
100.52814,60913,42600.500

Table 6.

Proband Bayesian CRC risk estimation.

The combination of the pedigree in Figure 2 and Bayesian estimation in Table 6 gave rise to the complete story of CRC risks in 10 selected probands relative to the four subjects with CRC. A 67-year-old man with CRC of late-onset had a son with almost 100% CRC risk carrier or development (Figure 2A). Meanwhile, a 44-year-old woman with early-onset CRC and paternal death due to CRC had a mother with 93% unlikely, a son with 50.51% likely, and another son with 50% unlikely carrier or incidence of CRC (Figure 2B). An elderly woman with paternal death of unknown origin and CRC death of the aunt had an adult daughter with 50% of the CRC risk (Figure 2C). On the other side, a 47-year-old CRC diseased woman with a huge family and paternal death due to prostate cancer had a 50% likely risk on her sister, but three 50% unlikely probability on her mother, younger sister, and female offspring (Figure 2D).

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4. Discussion

Several autosomal dominant diseases may appear to be majorly asymptomatic until adulthood or beyond puberty. Given the notion, sophisticated comprehension of the hereditary risk of neoplasm is critical. Direct genomic examination and molecular diagnosis of nucleic acids from the blood, tissue, or other bodily fluids have become more prominent as a screening and investigation standard [4].

DNA is the building block of every living cell in the world. In doing its job, DNA is often destringed into a single-stranded DNA (ssDNA) for DNA replication or mRNA transcription. The first case goes by attachment of complementary nucleic acid base pairs to the corresponding one in both the leading and lagging strand of the partly unzipped ssDNA by DNA polymerase. This created an exact copy of the source DNA. While for the latter case, the RNA polymerase enzyme works separately on the sense and antisense part of the ssDNA. Each strand of ssDNA produced a single mRNA, thus there will be two mRNAs for every transcription of a DNA. These mRNA then moved to the ribosome for translocations into amino acids and eventually proteins.

Cancerous cell arises due to mutations or faulty repair of the nucleic acid bases. Even one deletion, addition, or translocation of the bases drastically changes the transcribed mRNA, codon, and hence the protein. Commonly, uncontrolled proliferation of cells happened if the mistakes occurred on oncogenes or tumor suppressor genes. Constantly activated oncogenes or inhibited tumor suppressor genes direct the cell cycle to bypass checkpoints and not return to the resting phase.

Familial adenomatous polyposis and Lynch syndrome are the top two subtypes of hereditary colorectal cancer with the most incidence count. FAP is almost solely generated because of mistakes in the tumor suppressor gene of APC. Whilst a lot of MMR genes can be responsible for LS or HNPCC (e.g., MLH1, MSH2, MSH6, PMS2, and epithelial cell adhesion molecule) [24]. The present study accordingly chose APC and one of the repair genes to accommodate both hereditary subtypes of CRC. We inevitably selected MSH2among the other mismatch repair (MMR) genes due to the prevalence and missenses amount. Kim et al. stated that approximately 90% of the mutations in the MMR occurred in either MLH1, MSH2, or both genes [25]. Furthermore, a 13% increase of missenses (i.e., a type of gene mutation which renders genotypic reading and interpretation to be considerably harder) was measured on MLH1as opposed to MSH2[26]. MLHis also more often found in sporadic colon cancer [14]. Next, a three countries assessment on lynch syndrome also gathered that MSH2had a substantially higher 10-year-risk of severe adenoma (∆ = 10.1%) and tumor pathogenic variants (11.4 vs. 11.3%) over MLH1[27].

Observation among the CRC versus the control group displayed a lesser mean mRNA level of APCgene expression than the control group (∆ = 1683.06 fc, p= 0.014). However, the reverse is true for the MSH2gene expression (∆ = 808.82 fc, p= 0.465). The minute discrepancy can be because of many gene mutations also somatically involved APC. Engel et al. confirmed that from MSH2, MSH6, and MLH1tumor variants, somatic mutations of APChappened in 75, 100, and 11% cases [27].

The current study and its prior version in the Indonesian Medical Journal [28], swiftly acted on a novel proposition to determine APCand MSH2gene expressions cutoff for hereditary cancer classification. Looking over the percentile distribution of healthy controls, the measure of the first to fifth percentiles only had negligible insignificant differences. Following the wrapped Cauchy distribution of circular data techniques with M, D, and A statistics [29], we officialize the fifth percentile mark as the hereditary cutoff (APC= 12,195.80 fc and MSH2= 11,059.60 fc).

Interesting hereditary proportions had been exhibited by the 40 CRC subjects. Hereditary using only APCgene enlisted 50%, while MSH2gene gathered 32.5%, and both genes combination enticed 52.5% of the subjects. Only the one with MSH2cutoff showed akin prevalence to the 20–30% global familial CRC [30].

The brief, simple, yet informative presentation of the family medical history can be conveniently reflected through a visual pedigree. Taking accurate information on family history should be standard medical practice. The pedigree will subtly enhance oncology prevention, diagnosis, and treatment together with recent genomics advancements. Family history can substantially alter not only genetic testing results but also oncology prevention, including digestive cancer [16]. For instance, a study found that cancer occurrence in an individual relative to a bowel cancer was dependent on familial cancer prevalence, duration of onset, and closeness to the diseased [14]. The current study employs a family pedigree to analyze the health status of CRC patients’ relatives. The diagram clearly outlines familial relationships; thus, it will be easier for recognition and interpretation of the inheritance patterns [16, 17].

Individualized medical care has been on the rise of attention. Instead of general medicine for a certain disease, therapeutic care needs to start keenly observing the patient and prescribing medicine that has been tailored for that particular individual [31]. One of the ways in achieving such goals is through personal genetic and biological factors consideration. Particularly, extra attention should be imposed on hereditary disorders like malignancy.

Relatives of a confirmed cancer patient may not experience any cancer-predisposing syndrome from a clinical viewpoint although having an increased risk of developing one. Decreased penetrance and onset age must be factored into consideration especially for autosomal dominant hereditary disorders like colorectal cancer [4, 7, 8, 32].

Investigation on the hereditary risk of diseases in relatives and families accordingly requires a lot of extra information and tests. The natural history of the disease firstly should be made clear (i.e., what is the diagnosis, how does it spread, how does it inherit, and what about its epidemiology). Next, focus on medical examinations or observations that include genetic data (i.e., marker, expression, mutation, and then clinical data). Lastly, we can examine the family pedigree and make objective evidence-based inferences for analysis.

Bayesian analysis of the pedigree from the familial and genetic factors displayed an interesting finding. It was obvious that the majority of probands on the next generation of the current CRC patients had a higher likely probability of CRC incidence or carrier. Probands below the current CRC patients’ generation (i.e., the first, fourth, fifth, sixth, and tenth probands) on average had a 60.1% likelihood of CRC risk. Those in the same generation had 50.1% risk, while those above (i.e., the second and seventh probands) had 28.4% risk. These observations were moderately suitable with the autosomal dominant disease hereditary pattern.

The current study bridged the knowledge gaps of CRC hereditary cutoff, gene expressions risk of CRC, and Bayesian pedigree analysis. Nevertheless, some limitations are still presented. First, a small sample size increased the chance for a biased result. The sample selection in the study is also confined to a segmental niche of Indonesian urban citizens, hence the result may not be generalizable to rural or foreign populations. There were also no immunohistochemical or DNA sequencing mutation tests for objective comparisons. Lastly, the method of selecting cutoff from a fifth percentile has not broadly checked for its credibility yet.

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5. Conclusion

The current study explored the relationship between APCand MSH2gene expressions to colorectal cancer risk assessment. Bayesian analysis computed that downregulation of the mRNA gene expression will induce a higher risk of developing or exacerbating CRCs. Yet only APChad significance while MSH2did not. Therefore, the study establishes the foundation of utilizing APCand MSH2gene expressions for CRCs risk indicators. Future novel or multiplicity studies should consider family pedigree as a part of CRC prevention strategy among the patient’s relatives with expanded cohorts and sample pool, including more profound Bayesian analysis and application with other essential hereditary genes.

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Acknowledgments

The authors appreciate all of the participants in this study.

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Conflict of interest

The authors declare no conflict of interest. The authors acknowledge that although Vincent Tedjasaputra is currently an American Association for the Advancement of Science (AAAS) Science and Technology Policy Fellow; yet, the current paper is not affiliated with AAAS nor is the product of his position at the National Science Foundation.

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Declarations

Please note that an earlier version of this manuscript has been published in the Indonesian Biomedical Journal volume 12 on 2020 with a digital object identifier (DOI) of 10.18585/inabj.v12i4.1329 [28].

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Abbreviation summary

APC

adenomatous polyposis coli

BMI

body mass index

bp

base pair

CKD

chronic kidney disease

CRC

colorectal cancer

DNA

deoxyribose nucleic acid

EDTA

ethylenediaminetetraacetic acid

EPCAM

epithelial cell adhesion molecule

FAP

familial adenomatous polyposis

fc

fold-change

GAPDH

glyceraldehyde 3-phosphate dehydrogenase

HNPCC

hereditary non-polyposis colon cancer

IRB

Institutional Review Board

LS

Lynch syndrome

MI

myocardial infarct

MLH

MutL homolog

MMR

mismatch repair

MSH

MutS homolog

PMS

post-meiotic segregation

RNA

ribose nucleic acid

RT-PCR

real-time-polymerase chain reaction

SD

standard deviation

ss

single-stranded

TA

traffic accident

TACSTD

tumor-associated calcium signal transducer

y.o.

years old

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Written By

Tjahjadi Robert Tedjasaputra, Mochammad Hatta, Muhammad Nasrum Massi, Rosdiana Natzir, Ilhamjaya Patellongi, Marcellus Simadibrata, Rina Masadah, Muhammad Luthfi Parewangi, Prihantono, Andi Asadul Islam, Agussalim Bukhari, Rinda Nariswati, Shirly Elisa Tedjasaputra, Vincent Tedjasaputra and Jonathan Salim

Submitted: December 27th, 2021 Reviewed: January 21st, 2022 Published: March 31st, 2022