Prediction of précised biomarkers by expression-network-survival-based approach for breast cancer bone metastasis

Background: About 70% of Breast Cancer (BCa) patients have Bone Metastasis (BM), the prediction of précised biomarkers for BM from BCa would guide focused treatment by early interventions to prevent or delay BM. Method: In this study, the datasets (GSE103357, GSE55715, GSE2034, GSE14776 and GSE137842) were retrieved from the Gene Expression Omnibus (GEO). These datasets comprise of gene expression patterns of 232 samples of tumor cells from BCa and 84 samples of metastatic tumor cells from BM to understand molecular mechanism underlying in development of BCa-BM. Common differentially expressed genes (DEGs) were identified by performing meta-analysis implying ImaGEO. A protein-protein interaction (PPI) network was constructed from high throughput experiments using STRING (Search Tool for the Retrieval of Interacting Genes). Analysis of the PPI-Interactome and their sorted hub genes was performed using centrality parameters viz., degree, clustering coefficient, closeness, and betweenness centrality which are statistically and biologically significant plug-ins added in Cytoscape 3.9.1. To understand the likely course of the progression of BCa to BM survival analysis was performed using GEPIA2 and validation was done by TCGA (The Cancer Genome Atlas). To characterize the précised potential biomarkers the functional enrichment analysis, Pathway analysis and Gene Ontology (GO) studies was performed by Funrich 3.1.3. Result: 90 common DEGs were acquired from our study among all 5 datasets, out of which 28 and 62 constitute down- and up-regulated genes respectively. Where among these 90 DEGs, 18 genes were showing unambiguous connections with each other. 12 genes were identified significant through the topological analysis performed by the above said centrality parameters of interactome and were also showing worse survival outcome in disease free survival analysis. Five genes (RACGAP1, PPP1CC, RAD23A, PSMD1 and RPL26L1) among the 12 genes were identified as hub genes and were validated by TCGA, 3 genes (RPL26L1, PSMD1, and RAD23A) were identified to show poorer disease-free survival, also RPL26L1 was identified to show worst overall survival across all samples. 3 genes (RACGAP1, PPP1CC, and RAD23A) were literature reviewed potential diagnostic biomarkers in cancer progression, including breast cancer bone metastasis. RPL26L1 is significant potential biomarker identified with upregulation in BCa-BM samples with worse overall survival. These reported and significantly identified genes intersect notably cell growth, skeletal muscle development, and cell communication Conclusion: Our expression-based network-based approach successfully prioritized précised biomarkers for breast cancer bone metastasis.