4.6 Article

Autophagy-related prognostic signature for survival prediction of triple negative breast cancer

Journal

PEERJ
Volume 10, Issue -, Pages -

Publisher

PEERJ INC
DOI: 10.7717/peerj.12878

Keywords

Corresponding authors; Autophagy gene; Prognosis; Predict model; Triple negative breast cancer; TCGA

Funding

  1. Zhejiang Traditional Chinese Medicine Project [2020ZB031]
  2. Zhejiang Medical and Science project [2020KY008]

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An eight-gene autophagic signature model was developed in this study to predict the survival risk for triple-negative breast cancer (TNBC) patients, and autophagy genes associated with clinical stage of TNBC were identified.
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 ARGtranscription 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.

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