ArticleJournal of translational medicine2020
Development of an autophagy-related gene expression signature for prognosis prediction in prostate cancer patients.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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68 citing papers in PubMed.
- Targeted inhibition of RGS19 alleviates renal fibrosis by restoring autophagy and modulating immune cell infiltration.Journal of nanobiotechnology · 2026Article
- Rab24 protein levels show dynamic changes in mouse tissues and human cancers.Cell and tissue research · 2026Article
- Identification of autophagy-related immune targets for enhancing immunotherapy in pancreatic cancer aggressiveness.Discover oncology · 2025Article
- Development of a prostate cancer biochemical recurrence risk signature using machine learning and motor protein-related genes.PloS one · 2025Article
- RGS19 activates the MYH9/β-catenin/c-Myc positive feedback loop in hepatocellular carcinoma.Experimental & molecular medicine · 2024Article
- Prognostic biomarker NRG2 correlates with autophagy and epithelial‑mesenchymal transition in breast cancer.Oncology letters · 2024Article
- Four centrosome-related genes to predict the prognosis and drug sensitivity of patients with colon cancer.World journal of gastrointestinal oncology · 2024Article
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- Oncogenic lncRNA FAM215A promotes the malignant cell phenotypes of acute myeloid leukemia (AML) cell lines.Journal of molecular histology · 2024Article
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- Identification of basement membrane-related signatures for estimating prognosis, immune infiltration landscape and drug candidates in pancreatic adenocarcinoma.Journal of Cancer · 2024Article
- Integrative analysis of autophagy-related genes reveals that CAPNS1 is a novel prognostic biomarker and promotes the malignancy of melanoma via Notch signaling pathway.American journal of cancer research · 2024Article
- Prognostic model and immune-infiltrating cell landscape based on differentially expressed autophagy-related genes in TP53-mutated multiple myeloma.Archives of medical science : AMS · 2024Article
- Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients.Discover oncology · 2023Article
- Establishment of cancer-associated fibroblasts-related subtypes and prognostic index for prostate cancer through single-cell and bulk RNA transcriptome.Scientific reports · 2023Article
- Construction and validation of N6-methyladenosine long non-coding RNAs signature of prognostic value for early biochemical recurrence of prostate cancer.Journal of cancer research and clinical oncology · 2023Article
- Correlation analysis between immune-related genes and cell infiltration revealed prostate cancer immunotherapy biomarkers linked to T cells gamma delta.Scientific reports · 2023Article
8 more citing papers are in PubMed but not listed here.
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7 authors.
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Abstract
backgroundProstate cancer (PCa) is one of the most prevalent cancers that occur in men worldwide. Autophagy-related genes (ARGs) may play an essential role in multiple biological processes of prostate cancer. However, ARGs expression signature has rarely been used to investigate the association between autophagy and prognosis in PCa. This study aimed to identify and assess prognostic ARGs signature to predict overall survival (OS) and disease-free survival (DFS) in PCa patients.
methodsFirst, a total of 234 autophagy-related genes were obtained from The Human Autophagy Database. Then, differentially expressed ARGs were identified in prostate cancer patients based on The Cancer Genome Atlas (TCGA) database. The univariate and multivariate Cox regression analysis was performed to screen hub prognostic ARGs for overall survival and disease-free survival, and the prognostic model was constructed. Finally, the correlation between the prognostic model and clinicopathological parameters was further analyzed, including age, T status, N status, and Gleason score.
resultsThe OS-related prognostic model was constructed based on the five ARGs (FAM215A, FDD, MYC, RHEB, and ATG16L1) and significantly stratified prostate cancer patients into high- and low-risk groups in terms of OS (HR = 6.391, 95% CI = 1.581- 25.840, P < 0.001). The area under the receiver operating characteristic curve (AUC) of the prediction model was 0.84. The OS-related prediction model values were higher in T3-4 than in T1-2 (P = 0.008), and higher in Gleason score > 7 than ≤ 7 (P = 0.015). In addition, the DFS-related prognostic model was constructed based on the 22 ARGs (ULK2, NLRC4, MAPK1, ATG4D, MAPK3, ATG2A, ATG9B, FOXO1, PTEN, HDAC6, PRKN, HSPB8, P4HB, MAP2K7, MTOR, RHEB, TSC1, BIRC5, RGS19, RAB24, PTK6, and NRG2), with AUC of 0.85 (HR = 7.407, 95% CI = 4.850-11.320, P < 0.001), which were firmly related to T status (P < 0.001), N status (P = 0.001), and Gleason score (P < 0.001).
conclusionsOur ARGs based prediction models are a reliable prognostic and predictive tool for overall survival and disease-free survival in prostate cancer patients.
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