Network-based approach to identify biomarkers predicting response and prognosis for HER2-negative breast cancer treatment with taxane-anthracycline neoadjuvant chemotherapy

Objective This study aims to identify effective gene networks and biomarkers to predict response and prognosis for HER2-negative breast cancer patients who received sequential taxane-anthracycline neoadjuvant chemotherapy. Materials and Methods Transcriptome data of training dataset including 310 HER2-negative breast cancer who received taxane-anthracycline treatment and an independent validation set with 198 samples were analyzed by weighted gene co-expression network analysis (WGCNA) approach in R language. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were performed for the selected genes. Module-clinical trait relationships were analyzed to explore the genes and pathways that associated with clinicopathological parameters. Log-rank tests and COX regression were used to identify the prognosis-related genes. Results We found a significant correlation of an expression module with distant relapse–free survival (HR = 0.213, 95% CI [0.131–0.347], P  = 4.80E−9). This blue module contained genes enriched in biological process of hormone levels regulation, reproductive system, response to estradiol, cell growth and mammary gland development as well as pathways including estrogen, apelin, cAMP, the PPAR signaling pathway and fatty acid metabolism. From this module, we further screened and validated six hub genes (CA12, FOXA1, MLPH, XBP1, GATA3 and MAGED2), the expression of which were significantly associated with both better chemotherapeutic response and favorable survival for BC patients. Conclusion We used WGCNA approach to reveal a gene network that regulate HER2-negative breast cancer treatment with taxane-anthracycline neoadjuvant chemotherapy, which enriched in pathways of estrogen signaling, apelin signaling, cAMP signaling, the PPAR signaling pathway and fatty acid metabolism. In addition, genes of CA12, FOXA1, MLPH, XBP1, GATA3 and MAGED2 might serve as novel biomarkers predicting chemotherapeutic response and prognosis for HER2-negative breast cancer.