Association of SNPs in Long Intergenic Noncoding RNA 00511 (LINC00511) With Breast Cancer Among the Egyptian Population

Overview

Long non-coding RNAs (lncRNAs) play an important role in different types of cancer, including breast cancer, through regulation of gene expression and epigenetic signatures. Genetic variations such as single nucleotide polymorphisms (SNPs) in lncRNAs have been found to be associated with cancer. Our aim was to provide information about the role of LINC00511 SNPs (rs11657109 or rs17780195 or rs9906859, rs4432291 and rs1558535) in breast cancer susceptibility in the Egyptian population.

1. Introduction 1.1. Background: Breast cancer (BC) is one of the most common types of cancer in women nowadays as it is estimated that 1.6 million BC cases occur around the world each year. There were approximately 500000 women die due to BC annually making it a leading cause of cancer mortality among females. It represents 23% of the total cancer cases and 14% of the cancer deaths in females. It represents 52% of BC cases and 62% of deaths in economically developing countries. In Egypt, BC is reported to be the most frequent cancer in females (38.8%) and the age adjusted rate of BC is 49.6 per 100.000 population. Breast cancer can be classified based on the hormones and HER2 status to Luminal A BC (Estrogen Receptor(ER) + , Progesterone Receptor(PR) +/- , Human epidermal growth factor receptor 2 (HER2) -) , Luminal B BC (ER+, PR+/-, HER2 +), HER2 BC (ER-, PR- and HER2+) and Triple negative BC (TNBC) (ER-, PR- and HER2-).TNBC is an aggressive cancer due to its recurrence and fewer targeted medicines. BC can become a metastatic cancer and transfer to distant organs such as bones, lung and brain, which is the cause of its incurability. Early diagnosis of the disease leads to good prognosis and increasing the survival rate. Traditional prognostic factors, such as tumor size, tumor grade, and lymph node metastasis status, are the most important prognostic factors for BC. However, including the genetic information is needed in prognosis. As a typical cancer, BC occurs because of the interaction of genetic and nongenetic factors. It has been reported that long non-coding RNAs (lncRNAs) play an important role in different types of cancer, including BC, through regulation of gene expression and epigenetic signatures. LncRNAs are greater than 200 nucleotides in length. LncRNAs undergo different biological actions, such as regulating RNA stability, transcriptional regulation, acting as a scaffold, RNA enhancer, miRNA sequestration and guiding protein-DNA interaction. LncRNAs are implicated in gene expression regulation at many levels, including alternative splicing, and changing of protein localization, chromatin modification, transcription, and post-transcriptional processing. Moreover, lncRNAs are involved in several hallmarks of cancer, including uncontrolled proliferation, angiogenesis, evading cell death and metastasis. It is noteworthy to mention that, abnormal expression of lncRNAs contribute significantly to cancer susceptibility and progression in BC cases. Long intergenic non-coding RNA 00511 (LINC00511) is a 2265 bp ncRNA and is located on chromosome 17q24. Previous studies found that it exerts an oncogenic function in many cancers, such as BC, non-small cell lung cancer, ovarian cancer and glioma. In BC cases, being oncogenic, LINC00511 promotes tumor growth by accelerating the G1/S transition and inhibiting apoptosis. It has been demonstrated that there is an association between LINC00511 and BC growth and invasion, where competitive binding between LINC00511 and the microRNA-185 (miR-185) affects BC prognosis and progression. LINC00511 sponges miR-185-3p preventing this miRNA from binding to its target mRNA, hence, free mRNA is there, with more expression of the transcription factor E2F1, which eventually, promotes BC proliferation and progression. Genetic variations such as single nucleotide polymorphisms (SNPs) in lncRNAs have been found to be associated with cancer. They affect the function of target genes, through the alteration of the process of splicing and stability of mRNA conformation, leading to the modification of their downstream interacting partners. Mutant variants can affect the expression and secondary structure of lncRNAs, which may affect the status of the binding site(s) for miRNAs, furthermore, altering the interaction between miRNAs and mRNAs. SNPs in lncRNAs may increase or reduce the risk of cancer, depending on the function of lncRNA, as it may act as oncogene or tumor suppressor gene, a hypothesis to be explored. Moreover, SNPs in lncRNAs may increase or reduce the risk of cancer, depending on the location of these SNPs, if in a noncoding area or not. Identifying such loci, if mutant or not, involved in BC progression or prevention, will be an important issue for understanding BC pathogenesis as well as for discovering new targets for cancer diagnosis, prevention, and/or treatment. 1.2 Aim of the Work Providing information about the role of LINC00511 SNPs (rs11657109 or rs17780195 or rs9906859, rs4432291 and rs1558535) in BC susceptibility. 1.3 Previous Studies Findings Chong et al. found that there was an association between LINC00511 SNPs and BC in the Chinese population. we studied the same associations but in our Egyptian population. 2. Subjects 2.1 Ethics Statement An ethical approval was obtained from Ain Shams University, Faculty of Pharmacy's review board Research Ethical Committee approval (REC ID 6, date: November 11, 2020). The study was conducted in accordance to the Declaration of Helsinki Guidelines. A written informed consent was taken from all participants. 2.2 Power Analysis and Sample Size Calculations Sample size calculation was done using PS: Power and Sample Size Calculations software, version 3.0.11 for MS Windows (William D. Dupont and Walton D., Vanderbilt University, Nashville, Tennessee, USA). The α-error level was fixed at 0.05, the power was set at 80%. The minimum optimum sample size should be 85 participants for each SNP group. 2.3 Study participants Will be Classified into two main groups * Cases group n= 267, BC female patients from National cancer institute (NCI), Cairo, Egypt. * Controls group n= 150, healthy female volunteers. Clinical data was obtained from medical records and the original pathology reports. The following data parameters were recorded and assessed: age of the patient, tumor size (defined by mammography or magnetic resonance techniques diameter (mm or cm) on diagnosis), initial tumor stage, BIRADs classification according to the American College of Radiology and nodal status according to the TNM classification of American Joint Committee on Cancer (AJCC). 3. Methods 3.1 Blood sampling Blood samples (5 ml) were collected from controls and BC patients, on EDTA anticoagulant vacutainers and stored at -20º C until biochemical assessment. 3.2 Biochemical Assessment was performed in the pharmacology and Biochemistry Department Lab at the Faculty of Pharmacy, The British University in Egypt and Advanced biochemistry research lab at the faculty of pharmacy, Ain Shams University. 3.3 DNA Extraction A DNA Extraction Kit (QIAamp DNA Blood Mini Kit) (Cat. No. 51104; Zymo Research, USA) was used to extract DNA from whole blood collected on EDTA anticoagulant vacutainers, according to the manufacturer's instructions. 3.4 DNA Quantification It was done using a Quawell UV-Vis spectrophotometer Q5000 (USA). 3.5 SNPs Genotyping TaqMan® SNP genotyping assay was used to perform genotyping for LINC00511 polymorphisms (rs11657109 or rs17780195 or rs9906859 or rs4432291 and rs1558535) through using the TaqMan Universal Master Mix No UNG (Thermo Fisher Scientific, USA) and StepOnePlus™ qPCR system (Applied Biosystems, USA). 3.6 Statistical analysis Statistical Package for the Social Sciences (SPSS) v.23.0 software and SHEsis software were used to perform all statistical analyses. Student's t-test and χ 2 test were used to compare quantitative and qualitative variables between cases and control groups, respectively. Logistic regression was applied to find out the association between LINC00511 SNPs and BC susceptibility. A stratified analysis was applied to further investigate the relationship between LINC00511 SNPs and BC susceptibility. To find out more about the relationship between BC susceptibility and LINC00511 SNPs, a stratified analysis was used. SHEsis software was used to do the haplotype analysis to test the combined effect of the studied SNPs. Analyses with P value less than or equal to 0.05 were considered statistically significant.

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