Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer

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

Automated summary

This study integrates bioinformatics and network-based approaches to explore the molecular and genetic mechanisms of cervical cancer. Transcriptomic analysis identifies 218 differentially expressed genes related to cervical cancer. Protein-protein interaction network analysis identifies 20 hub genes, and survival analysis validates CDC45, MCM2, PCNA, and TOP2A as biomarkers for cervical cancer. Furthermore, transcriptional factors and post-transcriptional regulators are detected, and a drug-gene relationship analysis identifies PD0325901 and Selumetinib as potential candidate drugs for cervical cancer treatment.