Development of a 15-Gene Signature Model as a Prognostic Tool in Sex Hormone-Dependent Cancers.

Abstract

Sex hormone dependence is associated with tumor progression and prognosis. Here, we explored the molecular basis of luminal A-like phenotype in sex hormone-dependent cancers. RNA-sequencing data from 8 cancer types were obtained from The Cancer Genome Atlas (TCGA). We investigated the enrichment function of differentially expressed genes (DEGs) in luminal A breast cancer (BRCA). Weighted coexpression network analysis (WGCNA) was used to identify gene modules associated with the luminal A-like phenotype, and we calculated the module's preservation in 8 cancer types. Module hub genes screened using least absolute shrinkage and selection operator (LASSO) were used to construct a gene signature model for the luminal A-like phenotype, and we assessed the model's relationship with prognosis, enriched pathways, and immune infiltration using bioinformatics approaches. Compared to other BRCA subtypes, the enrichment functions of upregulated genes in luminal A BRCA were related to hormone biological processes and receptor activity, and the downregulated genes were associated with the cell cycle and nuclear division. A gene module significantly associated with luminal A BRCA was shared by uterine corpus endometrial carcinoma (UCEC), leading to a similar phenotype. Fifteen hub genes were used to construct a gene signature model for the assessment of the luminal A-like phenotype, and the corrected C-statistics and Brier scores were 0.986 and 0.023, respectively. Calibration plots showed good performance, and decision curve analysis indicated a high net benefit of the model. The 15-gene signature model was associated with better overall survival in BRCA and UCEC and was characterized by downregulation of DNA replication, cell cycle and activated CD4 T cells. In conclusion, our study elucidated that BRCA and UCEC share a similar sex hormone-dependent phenotype and constructed a 15-gene signature model for use as a prognostic tool to quantify the probability of the phenotype.

• Authors: Zhi Xia, Jian Xiao, Aibin Liu, Qiong Chen
• Journal: BioMed research international (Biomed Res Int) Vol. 2021 Pg. 3676107 ( 2021) ISSN: 2314-6141 [Electronic] United States
• PMID: 34869761 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Zhi Xia et al.

Chemical References

• Gonadal Steroid Hormones

Topics

• Breast Neoplasms (genetics, pathology)
• CD4-Positive T-Lymphocytes (pathology)
• Cell Cycle (genetics)
• Computational Biology
• DNA Replication (genetics)
• Down-Regulation (genetics)
• Female
• Gene Expression Profiling (methods)
• Gene Expression Regulation, Neoplastic (genetics)
• Gene Regulatory Networks (genetics)
• Gonadal Steroid Hormones (genetics)
• Humans
• Phenotype
• Prognosis
• Up-Regulation (genetics)