Open AccessAutophagy-related prognostic signature for survival prediction of triple negative breast cancer
Author(s) -
Qiong Yang,
Kaiqi Sun,
Wenjie Xia,
Ying Li,
Miaochun Zhong,
Kefeng Lei
Publication year - 2022
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.12878
Subject(s) - triple negative breast cancer , autophagy , signature (topology) , breast cancer , oncology , triple negative , medicine , gene signature , cancer , biology , cancer research , computational biology , gene , genetics , gene expression , mathematics , apoptosis , geometry
Background Triple-negative breast cancer (TNBC) is a highly aggressive type of cancer with few available treatment methods. The aim of the current study was to provide a prognostic autophagy-related gene (ARG) model to predict the outcomes for TNBC patients using bioinformatic analysis. Methods mRNA expression data and its clinical information for TNBC samples obtained from The Cancer Genome Atlas (TCGA) and Metabric databases were extracted for bioinformatic analysis. Differentially expressed autophagy genes were identified using the Wilcoxon rank sum test in R software. ARGs were downloaded from the Human Autophagy Database. The Kaplan–Meier plotter was employed to determine the prognostic significance of the ARGs. The sample splitting method and Cox regression analysis were employed to establish the risk model and to demonstrate the association between the ARGs and the survival duration. The corresponding ARG-transcription factor interaction network was visualized using the Cytoscape software. Results A signature-based risk score model was established for eight genes ( ITGA3 , HSPA8 , CTSD , ATG12 , CLN3 , ATG7 , MAP1LC3C , and WIPI1 ) using the TCGA data and the model was validated with the GSE38959 and Metabric datasets, respectively. Patients with high risk scores had worse survival outcomes than those with low risk scores. Of note, amplification of ATG12 and reduction of WIPI were confirmed to be significantly correlated with the clinical stage of TNBC. Conclusion An eight-gene autophagic signature model was developed in this study to predict the survival risk for TNBC. The genes identified in the study may favor the design of target agents for autophagy control in advanced TNBC.