title: Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen.
abstract: This study aims to identify effective gene networks and prognostic biomarkers associated with estrogen receptor positive (ER+) breast cancer using human mRNA studies. Weighted gene coexpression network analysis was performed with a complex ER+ breast cancer transcriptome to investigate the function of networks and key genes in the prognosis of breast cancer. We found a significant correlation of an expression module with distant metastasis-free survival (HR = 2.25; 95% CI .21.03-4.88 in discovery set; HR = 1.78; 95% CI = 1.07-2.93 in validation set). This module contained genes enriched in the biological process of the M phase. From this module, we further identified and validated 5 hub genes (CDK1, DLGAP5, MELK, NUSAP1, and RRM2), the expression levels of which were strongly associated with poor survival. Highly expressed MELK indicated poor survival in luminal A and luminal B breast cancer molecular subtypes. This gene was also found to be associated with tamoxifen resistance. Results indicated that a network-based approach may facilitate the discovery of biomarkers for the prognosis of ER+ breast cancer and may also be used as a basis for establishing personalized therapies. Nevertheless, before the application of this approach in clinical settings, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed.
authors.affiliation: a Department of Clinical Pharmacology; Xiangya Hospital; Central South University ; Changsha , China.
authors.forename: Rong
authors.initials: R
authors.lastname: Liu
authors.forename: Cheng-Xian
authors.initials: CX
authors.lastname: Guo
authors.forename: Hong-Hao
authors.initials: HH
authors.lastname: Zhou
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism*; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics / Q000378:metabolism* / Q000401:mortality* / Q000473:pathology; D016000:Cluster Analysis; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D053208:Kaplan-Meier Estimate; D008875:Middle Aged; D060787:Neoplasm Grading; D009367:Neoplasm Staging; D011379:Prognosis; D011960:Receptors, Estrogen / Q000235:genetics / Q000378:metabolism*; D015203:Reproducibility of Results; D013629:Tamoxifen / Q000494:pharmacology* / Q000627:therapeutic use
chemical_list: D014408:Biomarkers, Tumor; D011960:Receptors, Estrogen; D013629:Tamoxifen
keywords: CI, confidence interval; ER+, estrogen receptor positive; GS, gene significance; HER2, human epidermal growth factor 2; ME, module eigengene; MS, module significance; PCC, Pearson's correlation coefficient; PR, progesterone receptor; TOM, topologic overlap measure; WGCNA, weighted gene co-expression network analysis; biomarker; breast cancer; gene expression profiling; k.in, intramodular connectivity; k.total, Network connectivity; systems biology; tamoxifen resistance