Identifying condition specific key genes from basal-like breast cancer gene expression data. (February 2019) - Maind, Ankush; Raut, Shital
Graphical abstract: Highlights: Proposed a new method for the identification of conditions specific key genes from the BLBC dataset. Extracted the functionally coherent gene biclusters from the BLBC gene expression data and performed gene set enrichment analysis for verifying the biological significance of the extracted biclusters. Constructed gene co-expression network (GCN) for each extracted significant bicluster using difference matrix, gene correlation matrix and gene network. Identified conditions specific key genes from each and every GCN and validated the identified key genes by using the literature and pathway analysis. Compared the results of proposed approach with WGCNA based approach and summarized how our approach is better than the WGCNA. Demonstrated the entire process of conditions specific key gene identification with the help of example. Abstract: Mining patterns of co-expressed genes across the subset of conditions help to narrow down the search space for the analysis of gene expression data. Identifying conditions specific key genes from the large-scale gene expression data is a challenging task. The conditions specific key gene signifies functional behavior of a group of co-expressed genes across the subset of conditions and can be act as biomarkers of the diseases. In this paper, we have propose a novel approach for identification of conditions specific key genes from Basal-Like Breast Cancer (BLBC) disease using biclustering algorithm and Gene Co-expression Network (GCN). The proposed approach is a two-stage approach. In the first stage, significant biclusters have been extracted with the help of 'runibic' biclustering algorithm. The second stage identifies conditions specific key genes from the extracted significant biclusters with the help of GCN. By using difference matrix and gene correlation matrix, we have constructed biologically meaningful and statistically strong GCN. Also, presented the proposed approach with the help of a process diagram and demonstrated the procedure with an example of bicluster number 93 (Bic93). From the experimental results, we observed that 95% and 85% of the extracted biclusters are found to be biologically significant at the p-values less than 0.05 and 0.01 respectively. We have compared proposed approach with the Weighted Gene Co-expression Network Analysis (WGCNA) based approach. From the comparison, our approach has performed effectively and extracted biologically significant biclusters. Also, identified conditions specific key genes which cannot be extracted using the WGCNA based approach. Some of the important identified known key genes are PIK3CA, SHC3, ERBB2, SHC4, PTOV1, STAG1, ZNF215 etc. These key genes can be used as a diagnostic and prognostic biomarker for the BLBC disease after the rigorous analysis. The identified conditions specific key genes can be helpful to reduce the analysis time and increase the accuracy of further research such as biomarker identification, drug target discovery etc.