Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies. Communicated by Ramaswamy H. Sarma.
Keywords: Bioinformatics; cervical cancer; cheminformatics; gene ontology; molecular docking; pathway enrichment analysis; transcriptomic analysis.
Transcriptomic analysis through bioinformatics revealed 218 significant differentially expressed genes (DEGs) that unfolded new molecular pathways responsible for cervical cancer (CC); The PPI network sorted major hub-genes that can be accounted as potential biomarkers with prominent roles in CC progression and helpful for its diagnosis, prognosis and therapies;TFs-DEGs and miRNAs-DEGs regulatory network assessment detected transcriptional and post-transcriptional elements;The gene-set enrichment provided gene ontological terms and pathway enrichment analysis shared biological relevance of CC development;Integrated statistics and cheminformatics approaches predicted some highly potential candidate drugs against CC;All the outcomes of the study were cross-validated through survival analyses, molecular docking and literature review.