Transcriptomics and Prognosis Analysis to Identify Critical Biomarkers in Invasive Breast Carcinoma

Abstract

Objective: Invasive breast cancer (BRCA) is one of the prevalent types of invasive tumors with high mortality worldwide. Due to the lack of effective treatment to control the recurrence of distant metastases, the prognosis of BRCA is still very unsatisfactory. We aimed to find some biomarkers by bioinformatics analysis for survival prediction. Methods: Differentially expressed genes (DEGs) were screened out based on tumor group and normal group. Then, the weighted gene correlation network analysis (WGCNA) was employed to identify the clinically associated gene sets. Meanwhile, the enrichment analyses were performed for the functional annotation of the critical genes. The Kaplan Meier analysis calculated the essential genes’ prognostic value. Results: After threshold screening, 1655 DEGs were obtained for subsequent analysis. 51 out of 1655 DEGs were significantly associated with BRCA patients’ estrogen receptor status via WGCNA. Three genes (FABP7, CXCL3, and LOC284578) out of the 51 genes were associated with overall survival, and 3 genes were relapse-free survival associated. Finally, we obtained 5 essential prognostic associated genes (FABP7, CXCL3, LOC284578, CAPN6, and NRG2), which could be used as prognostic factors for BRCA. Conclusion: Our findings obtained a gene module associated with BRCA clinical trait and several key genes that acted as essential components in the prognostic of cancer, which may improve its treatment.

Identification of modules and key genes associated with breast cancer subtypes through network analysis

“…For the Luminal B subtype, none of the gene hubs identified in this work have been proposed as potential biomarkers in other studies. However, Wu et al 159 suggest the capn6 gene as a prognostic marker for BC. While it is not the main gene hub correlating with this SBC in our analysis, it is present in the skyblue module.…”