ArticlePeerJ2022
Autophagy-related prognostic signature for survival prediction of triple negative breast cancer.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Integration of Single-cell and Bulk Transcriptome Analyses Unravels a Macrophage-based Gene Signature for Prognostication and Treatment in Triple-negative Breast Cancer.International journal of medical sciences · 2026Article
- A novel prognostic tool for triple-negative breast cancer: creating and testing a parthanatos-related gene model.Translational cancer research · 2025Article
- Autophagy-Related Proteins in Triple-Negative Breast Cancer: From Molecular Insights to Therapeutic Applications.International journal of molecular sciences · 2025Review
- LCP1 correlates with immune infiltration: a prognostic marker for triple-negative breast cancer.BMC immunology · 2024Article
- Beyond self‑eating: Emerging autophagy‑independent functions for the autophagy molecules in cancer (Review).International journal of oncology · 2024Review
- Construction and validation of predictive model based on endoplasmic reticulum stress-related genes for triple-negative breast cancer.Cell cycle (Georgetown, Tex.)Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTriple-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.
methodsmRNA 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.
resultsA signature-based risk score model was established for eight genes (
conclusionAn 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.