Implication of Genetic Variations in Long Intergenic Non-coding RNA 00511 (LINC00511) in Colorectal Cancer

1. Introduction 1.1. Background Colorectal cancer (CRC) represents the 7th most common cancer in Egypt. The global burden of CRC is expected to increase 60%, by 2030, in terms of new cases and deaths. Traditional treatments including radio- and chemo-therapies are associated with various undesirable side-effects. Meanwhile, the 5-year survival rate for CRC is ~ 64% but drops to 12% for metastatic CRC. Therefore, development of reliable and accurate prognostic markers is necessary. Long non-coding RNAs (lncRNAs) play an important role in different types of cancer through regulation of gene expression, protein synthesis, being epigenetic signatures. Long intergenic ncRNA 00511 (LINC00511) is a 2265 bp ncRNA that exerts an oncogenic function in many cancers, such as glioma, ovarian cancer and CRC. Single nucleotide polymorphisms (SNPs) in lncRNAs have been found to be associated with cancer. Such genetic variants may increase or reduce the risk of cancer depending on the location of these SNPs. Recently, LINC00511 SNPs were associated with breast cancer (BC) risk in Chinese population and currently, the chief supervisor is studying LINC00511 SNPs in Egyptian BC. Moreover, to address implication of LINC00511 SNPs in CRC, being not studied yet, might be useful for understanding CRC pathogenesis, linking LINC00511 SNPs to disease severity, as well as for discovering new target for CRC prevention and/or treatment. 1.2. Aim of the work Investigation of the association between LINC00511 SNPs (rs17780195 or rs9906859 and rs1558535) and CRC susceptibility and/or pathogenesis in addition to finding out the interaction between these SNPs and clinicopathological factors such as histopathological type, tumor size, lymph node metastasis and tumor grade.
2. Subjects Study Participants; will be Classified into two main groups 2.1. Group I CRC Cases: 200 CRC patients were recruited from Mansoura University Hospitals, previously signed an informed consent to participate in the study after obtaining the required institutional research ethics committee (REC) review board approval, Sample size was calculated according to the same SNPs studied in BC in a Chinese study, according to the relevant statistical methods. Exclusion Criteria; patients with HBV, schistosomiasis, HIV, alcohol intake, thyroid dysfunction, inflammatory diseases, diabetes mellitus and cardiovascular disorders. Subjects receiving any chemotherapy or radiotherapy, or had undergone a GIT surgical operation, patients with blood disorder diseases, any cancer other than CRC, patients with neuronal diseases, respiratory diseases, uterine diseases, kidney diseases, cirrhosis of the liver, prolonged use of corticosteroids or sex hormones. Additionally, patients with incomplete data or histopathology diagnosis. Clinico-pathological Criteria: Clinical data will be obtained from medical records and the original pathology reports. These data to be compiled in an excel file. The following clinical data to be recorded and assessed as in the attached excel file.

• Full family history will be recorded for all participants, -Individual cancer history and the tumor clinical assessment done using the tumor-node- metastasis (TNM) classification of American Joint Committee on Cancer (AJCC),
• The characteristics of the participants with regards to body mass index (BMI), inflammation marker(s), platelets count, CBC, and liver function tests, -Cancer histopathological type to be determined, tumor size, as well as clinico- pathological biomarkers carcinoembryonic antigen (CEA) and carcinoma antigen 19.9 (CA19.9), Ki-67 or PCNA (if any) data will be collected from patient files for further correlations and statistical analysis,
• For the metastatic patients' sub-group (if any, or if follow up will be done) treatment type and the site of metastatic destination, disease-free survival (DFS), overall survival (OS), the duration of patient survival from the time of treatment initiation, will be considered as a universally accepted direct measure of clinical benefit. 2.2. Control Group: apparently healthy, age and sex matched, 200 volunteers, not suffering from any disease or taking any medication. Control subjects with normal kidney functions and liver enzyme levels, absence of any clinical or laboratory evidence of CRC, were recruited during routine checkup examinations for themselves or their relatives or from the Chronic Diseases Screening National Presidential Program.

1. Methodology

3.1. In silico databases access:

• The HUGO Gene Nomenclature Committee (HGNC) supported by the National Human Genome Research Institute (NHGRI) provided a report https://www.genenames.org/ (accessed on April, 2022) and identification of LINC00511 gene using gene card identification (accessed on April, 2022). In addition, LINC00511 gene expression was obtained from RNA-seq using data unit TPM, from 53 human tissue samples from the Genotype-Tissue Expression (GTEx) Project from Expression Atlas Gene expression across species and biological conditions https://www.ebi.ac.uk/gxa/home (accessed Jan. 3rd, 2022).
• SNP Selection: Based on the previous study by Han et al. who studied several LINC00511 SNPs in breast cancer and after screening for a minor allele frequency (MAF) above 0.05= ≥ 5%. MAF was obtained from the International Genome Sample Resource (IGSR) Supporting open human variation data https://www.internationalgenome.org/ in 1000 genomes data https://www.ensembl.org/ 1000GENOMES:phase_3.

SNPinfo Web Server http://snpinfo.niehs.nih.gov/cgi- bin/snpinfo/snpfunc.cgi was accessed to obtain SNP info in DNA sequence and to predict one SNP effect on potential neighbors.

3.2. Blood sampling 5 ml collected from controls and CRC patients, on EDTA anticoagulant vacutainers and stored at -80º C, until biochemical assessment at the Advanced Biochemistry Research Lab (ABRL), Faculty of pharmacy, Ain Shams University (research setting).

3.3. DNA Extraction from blood using a QIAmp DNA Blood Mini extraction kit from 200 μL whole blood, according to the manufacturer's instructions. DNA Quantification was done by NanoDrop 2000 (Thermo Fisher Scientific, UK).

3.4. SNPs Genotyping using TaqMan® SNP genotyping assay will be used to perform the genotyping for LINC00511 SNPs (rs17780195, rs9906859 and rs1558535) through using the TaqMan Universal Master Mix No UNG (Thermo Fisher Scientific, USA) and StepOnePlus Thermal Cycler (Applied Biosystems, USA).

3.5. Statistical analysis The statistical analysis was performed in R software (version 4.2.0). First, genotype frequencies for each studied SNP were checked to be in concordance with the Hardy-Weinberg Equilibrium (HWE) using Pearson's χ 2 Chi-squared tests.

Student's t-test and the χ2 test were used to compare quantitative and qualitative variables between the CRC and control groups, respectively. After adjusting for demographic factors (age, sex, BMI, family history of cancer in first degree relatives), an unconditional logistic regression applied to explore the independent risk factors for CRC among the examined LINC00511 SNPs using the co-dominant, dominant, over-dominant, and recessive genotypic models, then being compared using measures of model fit and prediction (the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Deviance Information Criterion (DIC), Pseudo R2 (McFadden's, Cox and Snell's, and Nagelkerke's)), and the area-under-receiver operating characteristics (AUC) curve. The additive/co-dominant model was superior to all models based on these criteria. Sensitivity (SN), specificity (SP), positive predictive value (PPV), and negative predicted value (NPV) were reported for the model with the best fit and predictive power (not for mere disease diagnosis). The optimal cut-offs for age, CA19-9, and CEA categorization of LINC00511 SNPs were determined using ROC analysis. BMI was categorized based on the standard definitions of overweight and obese individuals (subjects with BMI ≥ 25 kg/m2 were considered overweight or obese). SNPs genotypes were analyzed using the snpReady library in R (Granato et al., 2018) [missing genotypes were imputed using Wright's method (based on Wright's equilibrium) and missing baseline demographic and clinical data were imputed using the predictive mean matching method implemented in R (van Buuren \& Groothuis-Oudshoorn, 2011)].

Multiple logistic regression analysis was done to examine the association between each SNP alleles, genotypes, haplotypes, and CRC prevalence, stage, and grade, while adjusting for baseline covariates (age, BMI, and additional risk factors for tumor stage and tumor grade, patients' cancer family history, tumor site, history of IBD), classical tumor markers (CEA and CA19.9), and the presence or absence of vascular infiltration.

Firth's logistic regression was implemented in the case of quasi-separation in one or more variables. Haploview software version 2.0 was used to calculate r2 and D' as the measurements of linkage disequilibrium extent between pairwise SNP combinations blocks of different genotypes were determined using the SHEsis software http://analysis.bio-x.cn/myAnalysis.php. A stratified analysis and the SHEsis plus online software http://shesisplus.bio-x.cn/SHEsis.html applied to further evaluate the association between LINC00511 SNPs and CRC susceptibility as well as haplotypes frequency as a measurement of genetic distribution was directly calculated in CRC and healthy control groups. Further, Epistasis was analyzed by Multifactor Dimensionality Reduction (MDR) package software in R https://ritchielab.org/software/mdr-download for carrying out SNP-SNP or gene-gene and SNP-Environment or gene-environment interaction analysis, applied to evaluate the interactive role of genetic and demographic factors (false Discovery rate was controlled by adjusting the significance level using Benjamini and Hochberg, Benjamini and Yekutieli, Holm step-down, Sidak step-down, and Sidak single-step p-value adjustment procedures). When comparing the predictive ability of the models using the pseudo-R-squared measures and the SN, SP, PPV, and NPV by ROC curve, in addition to the MDR part, aids for LINC00511 SNPs rs prediction. Prediction method LD values were calculated by a pairwise estimation between LINC00511 SNPs genotyped in the same sample and within a given window. An established method was used to estimate the maximum likelihood of the proportion that each possible haplotype contributed to the double heterozygote.

To confirm differences between obtained results in the patient group were not by chance, a Bonferroni calculation was applied to the data.

Finally, the investigators analyzed the overall survival (OS) for 2 years in the CRC patients (n=200) with recording the date of death or last contact with the Clinician as the follow-up end point. For disease-free survival (DFS), as the event free survival (EFS), in patients with non-metastatic CRC at the time of diagnosis, date of relapse or last contact with the Clinician was the follow-up end point. The non-parametric Kaplan-Meier method (PROC LIFETEST) were done for OS curves and EFS. The survival probability calculator generates the Kaplan-Meier curve with 95% CI, using log-rank test using the Chi square distribution, for comparison of more than of two groups.

The relative risk of disease relapse was estimated as hazard ratio (HR). Univariable survival analyses were done for gender, MBI, tumor site, family history of cancer, lymph node involvement, TNM stage and tumor grades separately, for the CRC patients' group as well as for LINC00511 SNPs genotypes.

Level of significance. A two-sided value of P ≤ 0.05 was deemed as a sign of statistical significance.