Bioinformatics and In silico approaches to identify novel biomarkers and key pathways for cancers that are linked to the progression of female infertility: A comprehensive approach for drug discovery

Abstract

Despite modern treatment, infertility remains one of the most common gynecologic diseases causing severe health effects worldwide. The clinical and epidemiological data have shown that several cancerous risk factors are strongly linked to Female Infertility (FI) development, but the exact causes remain unknown. Understanding how these risk factors affect FI-affected cell pathways might pave the door for the discovery of critical signaling pathways and hub proteins that may be targeted for therapeutic intervention. To deal with this, we have used a bioinformatics pipeline to build a transcriptome study of FI with four carcinogenic risk factors: Endometrial Cancer (EC), Ovarian Cancer (OC), Cervical Cancer (CC), and Thyroid Cancer (TC). We identified FI sharing 97, 211, 87 and 33 differentially expressed genes (DEGs) with EC, OC, CC, and TC, respectively. We have built gene-disease association networks from the identified genes based on the multilayer network and neighbour-based benchmarking. Identified TNF signalling pathways, ovarian infertility genes, cholesterol metabolic process, and cellular response to cytokine stimulus were significant molecular and GO pathways, both of which improved our understanding the fundamental molecular mechanisms of cancers associated with FI progression. For therapeutic intervention, we have targeted the two most significant hub proteins VEGFA and PIK3R1, out of ten proteins based on Maximal Clique Centrality (MCC) value of cytoscape and literature analysis for molecular docking with 27 phytoestrogenic compounds. Among them, sesamin, galangin and coumestrol showed the highest binding affinity for VEGFA and PIK3R1 proteins together with favourable ADMET properties. We recommended that our identified pathway, hub proteins and phytocompounds may be served as new targets and therapeutic interventions for accurate diagnosis and treatment of multiple diseases.

Selected Flavonols in Breast and Gynecological Cancer: A Systematic Review

“…Molecular docking studies confirm the potential effect of GAL on VEGFA and PIK3R1 and indicate its possible use in the treatment of endometrial cancer [184].…”

Decoding common genetic alterations between Barrett's esophagus and esophageal adenocarcinoma: A bioinformatics analysis

“…In this regard, by using experimentally validated microRNAs for constructing ceRNA network and determining the regulatory elements of the top module genes, we tried to partially compensate for this limitation of our study. Furthermore, it is worth noting that in silico and bioinformatics approaches have proven to be valuable in gaining insights into complex biological processes and have led to significant discoveries in the field of genomics and cancer research [ 81 , 82 ].…”

Identification of Molecular Mechanisms of Ameloblastoma and Drug Repositioning by Integration of Bioinformatics Analysis and Molecular Docking Simulation

“… 6 In silico approaches, such as bioinformatic analysis, have been performed to investigate signaling pathways, protein interactions, gene-drug prediction signature, and gene expression to obtain the best understanding of the pathological mechanisms of several diseases. 7 The computational method is an important tool to understand molecular aspects of oral pathology and medicine. 8 This study aimed to comprehensively investigate and analyze the differentially expressed genes (DEGs), biological function alterations, disease targets, and existing drugs for therapeutic strategies for AM through bioinformatics analysis.…”