Integrated bioinformatics analysis identifies core genes in breast cancer

Abstract

Background Breast cancer (BRCA) is one of the most common malignancies in women. To reveal the molecular mechanism of the BRCA, the core genes associated with BRCA was studied by the integrated bioinformatics methods in the current study. Results A total of 1181 DEGs, 84 DEGs, and 190 DEGs were detected in GSE7904, GSE10797, and GSE103512, respectively. In addition, 7 hub genes overlapped from 3 datasets were selected, and ANNXA1, MYH11, IGFBP6, FOS, FOSB, KIT, and WLS might be the core genes of BRCA. The Go function analysis showed that the DEGs were enriched in chromosome segregation, gland development, leukocyte migration, extracellular structure organization and so on. The KEGG pathway analysis showed that the most DEGs were enriched in Pathways in cancer, Focal adhesion, ECM-receptor interaction, PPAR signaling pathway, etc. Additionally, ANXA1 was associated with worse overall survival, and the other 6 hub genes were associated with better overall survival. Conclusions ANNXA1, MYH11, IGFBP6, FOS, FOSB, KIT, and WLS might be the core genes of BRCA. WLS, IGFBP6, and FOSB might be a new biomarker of the breast tumor and potential new drug targets. This study provided new evidence for the future genomic individualized treatment of BRCA.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

“…Revisiting a given experimental observation is scientifically essential for maximum conclusion extraction as new and powerful statistical and computational methods are introduced. Numerous bioinformatic studies analyzing high-dimensional datasets of various modalities [ 25 , 26 , 27 ] have produced significant knowledge for BrCa biology, whereas applications of ML approaches have recently become spearheads for building powerful classifiers with major advantages towards diagnostic clinical applications [ 9 , 28 , 29 ]. Here, our ambition has been to exploit genome-wide BrCa methylation datasets through bioinformatic analysis using readily available tools in order to identify DMGs, to reveal pathophysiological implications by functional analysis and most importantly to build accurate and simple predictive signatures by means of feature selection, to be exploited in personalized BrCa management.…”