Identification of chemoresistance-associated microRNAs and hub genes in breast cancer using bioinformatics analysis

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment

Breast cancer (BC) is the most widespread cancer worldwide. Over 2 million new cases of BC were identified in 2020 alone. Despite previous studies, the lack of specific biomarkers and signaling pathways implicated in BC impedes the development of potential therapeutic strategies. We employed several RNAseq datasets to extract differentially expressed genes (DEGs) based on the intersection of all datasets, followed by protein-protein interaction network construction. Using the shared DEGs, we also identified significant gene ontology (GO) and KEGG pathways to understand the signaling pathways involved in BC development. A molecular docking simulation was performed to explore potential interactions between proteins and drugs. The intersection of the four datasets resulted in 146 DEGs common, including AURKB, PLK1, TTK, UBE2C, CDCA8, KIF15, and CDC45 that are significant hub-proteins associated with breastcancer development. These genes are crucial in complement activation, mitotic cytokinesis, aging, and cancer development. We identified key microRNAs (i.e., hsa-miR-16-5p, hsa-miR-1-3p, hsa-miR-147a, hsa-miR-195-5p, and hsa-miR-155-5p) that are associated with aggressive tumor behavior and poor clinical outcomes in BC. Notable transcription factors (TFs) were FOXC1, GATA2, FOXL1, ZNF24 and NR2F6. These biomarkers are involved in regulating cancer cell proliferation, invasion, and migration. Finally, molecular docking suggested Hesperidin, 2-amino-isoxazolopyridines, and NMS-P715 as potential lead compounds against BC progression. We believe that these findings will provide important insight into the BC progression as well as potential biomarkers and drug candidates for therapeutic development.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

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

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.