Genome-scale meta-analysis of breast cancer datasets identifies promising targets for drug development

Abstract

Background Because of the highly heterogeneous nature of breast cancer, each subtype differs in response to several treatment regimens. This has limited the therapeutic options for metastatic breast cancer disease requiring exploration of diverse therapeutic models to target tumor specific biomarkers. Methods Differentially expressed breast cancer genes identified through extensive data mapping were studied for their interaction with other target proteins involved in breast cancer progression. The molecular mechanisms by which these signature genes are involved in breast cancer metastasis were also studied through pathway analysis. The potential drug targets for these genes were also identified. Results From 50 DEGs, 20 genes were identified based on fold change and p-value and the data curation of these genes helped in shortlisting 8 potential gene signatures that can be used as potential candidates for breast cancer. Their network and pathway analysis clarified the role of these genes in breast cancer and their interaction with other signaling pathways involved in the progression of disease metastasis. The miRNA targets identified through miRDB predictor provided potential miRNA targets for these genes that can be involved in breast cancer progression. Several FDA approved drug targets were identified for the signature genes easing the therapeutic options for breast cancer treatment. Conclusion The study provides a more clarified role of signature genes, their interaction with other genes as well as signaling pathways. The miRNA prediction and the potential drugs identified will aid in assessing the role of these targets in breast cancer.

A meta-analysis of genome-wide gene expression differences identifies promising targets for type 2 diabetes mellitus

“…The differentially expressed genes were identified using the array platform and the annotation platform hgu133plus2 probes. For the computational analysis, the R programming and the Bioconductor packages, affy QC Report, affy, annotate, AnnotationDbi, limma, Biobase, AffyRNAdegradation, hgu133plus2cdf, and hgu133a2cdf, were used ( 11 ).…”

“…The shortlisting of DEGs was done on the basis of the resulting scores and p -values that were then ranked. The cutoff values for shortlisting the genes were p -value ≤0.05, false discovery rate (FDR) <0.05, and absolute log fold change logFC >1 ( 11 ).…”

“…Various databases including OMIM, MeSH, and PMC were used to recognize the role of target genes in type 2 diabetes mellitus, and the associated gene signatures cause a pathological phenotype by dysregulation. The online tool Database for Annotation Visualization and Integrated Discovery (DAVID) and the functional enrichment tool FunRich were used to study the physiological functions of signature genes including functional annotation and gene ontology ( 11 , 12 ).…”

Cytotoxic Evaluation, Molecular Docking, and 2D-QSAR Studies of Dihydropyrimidinone Derivatives as Potential Anticancer Agents

“…The target proteins identified through system biology approach were used in order to study the protein-ligand interaction of these proteins with the synthesized compounds [ 20 ]. The differentially expressed breast cancer genes were identified through extensive data mapping, and functional enrichment analysis was performed to screen the differentially expressed genes between breast tumor cells and treated tissues.…”

“…The compounds were screened for their anticancer activities. The anticancer activities of these synthesized compounds were evaluated against the breast cancer target proteins identified through system biology approach [ 20 ]. The system biology approach has helped in identifying several gene targets in better management of diseases [ 21 ].…”