In-silico identification of SNPs associated with breast cancer for disclosing pathogenetic processes and therapeutic candidates

Abstract

Breast cancer (BC) is a leading cause of premature death in women. However, researcher’s understanding of BC development, progression and its treatment strategies has not yet reached a satisfactory level. To address these issues, first, it is necessary to accurately identify the key molecular signatures associated with BC. This study aimed to identify genetic variants associated with BC through meta-analysis of multiple genome-wide association studies (GWAS), as meta-analysis of multiple GWAS datasets increases statistical power and improves the reliability of identified genetic associations. At first, we have identified 728 genetic variants also known as single nucleotide polymorphisms (SNPs) associated with BC using METAL software tool. These SNPs were then mapped to the corresponding genes using FUMA web-tools, and found 68 genes. These genes were analyzed via independent PPI networks. By taking the intersection of top hub genes and filtering for combined annotation dependent depletion (CADD) scores and GWAS significance, we identified 5 Key Genes (KGs) associated with BC. We have identified top-ranked functionally significant SNPs for each of KGs as rs11571833 T/C in BRCA2 , rs186430430 T/C in CHEK2 , rs4252685A/C and rs4252686 A/G in MDM4 , rs6940919 T/G, rs35240111 C/G in ESR1 , rs4751844 T/G and rs17542768 A/G in FGFR2 . Top-ranked three transcription factors FOXC1 , GATA2 and E2F1 and two miRNAs hsa-miR-34a-5p and hsa-let-7i-5p were identified as the transcriptional and post-transcriptional regulators of KGs. Functional enrichment analysis of KGs with GO-terms and KEGG-pathways revealed some crucial biological processes, molecular functions, cellular components and signaling pathways that might be associated with the BC. Finally, KG-guided drug prioritization identified five top-ranked compounds, including three FDA-approved drugs (Imatinib, Nilotinib, and Lynparza), one investigational drug (Masitinib), and NVP-BHG712 as a computationally prioritized repurposing candidate with limited evidence in the breast cancer literature. Experimental validation is required to confirm these computational predictions.