Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer

• Published in: Computers in biology and medicine Vol. 145; p. 105508
• Main Authors: Alam, Md Shahin, Rahaman, Md Matiur, Sultana, Adiba, Wang, Guanghui, Mollah, Md Nurul Haque
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-06-2022; Elsevier Limited
• Subjects: Biological activity; Breast cancer; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Computational Biology - methods; Datasets; Diagnosis; Disease; Drug development; Drug repositioning; Drugs; Female; Functional and pathway enrichment analysis; Gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genes; Health risk assessment; Humans; Identification; Inhibitor drugs; Integrated statistics and network-based approaches; Key genes (KGs); Medical prognosis; Molecular docking; Molecular Docking Simulation; Molecular modelling; Network analysis; Pharmaceutical industry; Post-transcription; Prognosis; Protein interaction; Proteins; Regulatory network analysis; Risk factors; Statistical analysis; Statistical methods; Targeted cancer therapy; Transcriptomics analysis; Tumors; Key genes (KGs); Transcriptomics analysis; Integrated statistics and network-based approaches; Regulatory network analysis; Drug repositioning; Breast cancer; Functional and pathway enrichment analysis
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.105508

Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.
•Transcriptomics study through the statistical methods revealed significant DEGs that unfolded new molecular mechanisms of BC.•The PPI network of DEGs identified KGs that might be played the vital roles for BC diagnosis, prognosis and therapies.•The network analysis identified some TFs and miRNAs as the key transcriptional and post-transcriptional regulators of KGs.•The DEGs-set enrichment analysis provided the BC-causing key biological processes, molecular function and pathways.•Drug-KGs enrichment analysis predicted some highly potential small molecular agents against BC.