ArticleJournal of cellular and molecular medicine2020
Comprehensive analysis of autophagy-related prognostic genes in breast cancer.
Article in Journal of cellular and molecular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 39 citations in OpenAlex.
- Proteomic analysis of breast cancer based on immune subtypes.Clinical proteomics · 2024Article
- A Prognostic Risk Signature of Two Autophagy-Related Genes for Predicting Triple-Negative Breast Cancer Outcomes.Breast cancer (Dove Medical Press) · 2024Article
- Comprehensive evaluation of cell death-related genes as novel diagnostic biomarkers for breast cancer.Heliyon · 2023Article
- Article
- NOL12 as an Oncogenic Biomarker Promotes Hepatocellular Carcinoma Growth and Metastasis.Oxidative medicine and cellular longevity · 2022Article
- Searching for essential genes and drug discovery in breast cancer and periodontitis via text mining and bioinformatics analysis.Anti-cancer drugs · 2021Article
- CDKN1C as a prognostic biomarker correlated with immune infiltrates and therapeutic responses in breast cancer patients.Journal of cellular and molecular medicine · 2021Article
- Identification and development of an independent immune-related genes prognostic model for breast cancer.BMC cancer · 2021Article
- Review
- Codon Usage Bias in Autophagy-Related Gene 13 in Eukaryotes: Uncovering the Genetic Divergence by the Interplay Between Nucleotides and Codon Usages.Frontiers in cellular and infection microbiology · 2021Article
- A Novel Transcription Factor-Based Prognostic Signature in Endometrial Cancer: Establishment and Validation.OncoTargets and therapy · 2021Article
- A novel autophagy-related lncRNA prognostic risk model for breast cancer.Journal of cellular and molecular medicine · 2021Article
- Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature.Journal of translational medicine · 2020Article
- Pathogenic Single Nucleotide Polymorphisms on Autophagy-Related Genes.International journal of molecular sciences · 2020Review
- Comprehensive analysis of autophagy-related prognostic genes in breast cancer.Journal of cellular and molecular medicine · 2020Article
- Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accumulating evidence revealed that autophagy played vital roles in breast cancer (BC) progression. Thus, the aim of this study was to investigate the prognostic value of autophagy-related genes (ARGs) and develop a ARG-based model to evaluate 5-year overall survival (OS) in BC patients. We acquired ARG expression profiling in a large BC cohort (N = 1007) from The Cancer Genome Atlas (TCGA) database. The correlation between ARGs and OS was confirmed by the LASSO and Cox regression analyses. A predictive model was established based on independent prognostic variables. Thus, time-dependent receiver operating curve (ROC), calibration plot, decision curve and subgroup analysis were conducted to determine the predictive performance of ARG-based model. Four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were identified using the LASSO and multivariate Cox regression analyses. A ARG-based model was constructed based on the four ARGs and two clinicopathological risk factors (age and TNM stage), dividing patients into high-risk and low-risk groups. The 5-year OS of patients in the low-risk group was higher than that in the high-risk group (P < 0.0001). Time-dependent ROC at 5 years indicated that the four ARG-based tool had better prognostic accuracy than TNM stage in the training cohort (AUC: 0.731 vs 0.640, P < 0.01) and validation cohort (AUC: 0.804 vs 0.671, P < 0.01). The mutation frequencies of the four ARGs (ATG4A, IFNG, NRG1 and SERPINA1) were 0.9%, 2.8%, 8% and 1.3%, respectively. We built and verified a novel four ARG-based nomogram, a credible approach to predict 5-year OS in BC, which can assist oncologists in determining effective therapeutic strategies.
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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 OpenQuestion graph.