Abstract
Breast cancer (BC) is the most common leading cause of cancer-related death in women worldwide. Gene expression profiling analysis for human BCs has been studied previously. However, co-expression analysis for BC cell lines is still devoid to date. The aim of the study was to identify key pathways and hub genes that may serve as a biomarker for BC and uncover potential molecular mechanism using weighted correlation network analysis. We analyzed microarray data of BC cell lines (GSE 48213) listed in the Gene Expression Omnibus database. Gene co-expression networks were used to construct and explore the biological function in hub modules using the weighted correlation network analysis algorithm method. Meanwhile, Gene ontology and KEGG pathway analysis were performed using Cytoscape plug-in ClueGo. The network of the key module was also constructed using Cytoscape. A total of 5000 genes were selected, 28 modules of co-expressed genes were identified from the gene co–expression network, one of which was found to be significantly associated with a subtype of BC lines. Functional enrichment analysis revealed that the brown module was mainly involved in the pathway of the autophagy, spliceosome, and mitophagy, the black module was mainly enriched in the pathway of colorectal cancer and pancreatic cancer, and genes in midnightblue module played critical roles in ribosome and regulation of lipolysis in adipocytes pathway. Three hub genes CBR3, SF3B6, and RHPN1 may play an important role in the development and malignancy of the disease. The findings of the present study could improve our understanding of the molecular pathogenesis of breast cancer.
Highlights
Breast cancer (BC) is one of the most common types of cancer in women [1]
We found that the luminal phenotype highly correlated with the brown module (Spearmen correlation coefficients: 0.86, P
BC cell lines may mirror many of the molecular characteristics of the tumors from which they were derived, and are a useful preclinical model in which to explore strategies for predictive marker development
Summary
Breast cancer (BC) is one of the most common types of cancer in women [1]. This disease largely affects women in their 40–60s. The morbidity and mortality of BC are continuously growing. BC is a complex disease with respect to molecular alterations, cellular composition, and clinical outcome six subtypes were defined approximately a decade ago based on transcriptional characteristics, and were designated luminal A, luminal B, ERBB2-enriched, basal-like, Claudin-low, and normal-like [4]. Growing body studies indicated that BC is heterogeneous cancer in various aspects including clinical-pathological, molecular, and cellular heterogeneity [5]. In order to improve the prognosis and decrease mortality and morbidity of BC, diagnostic biomarkers are critical for early detection and risk stratification of BC, which could help us to choose the proper treatment
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
