ArticleJournal of cellular and molecular medicine2025
Autophagy Associated Genes (ARGs) -Based Predictive Model AIDPS for Prostate Cancer.
Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of autophagy in ocular health: mechanisms, pathologies, and therapeutic strategies.Biology direct · 2026Pooled it
- Deciphering Angiogenic Drivers in Hepatocellular Carcinoma: From Prognostic Signature Construction to Genistein-Mediated Inhibition.Journal of cellular and molecular medicine · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) is one of the most common cancers in men worldwide. Autophagy-related genes (ARGs) may play an important role in various biological processes of PCa. The aim of this study was to identify and evaluate autophagy-related features to predict clinical outcomes in patients with PCa. Single-cell sequencing data and RNA sequencing data was included from public GEO and TCGA databases. Cells were clustered and annotated by dimension reduction cluster analysis. Epithelial cells, T cells and fibroblasts were isolated to explore their heterogeneity. Autophagy-related genes were obtained from the HADb database. Survival analysis was conducted by K-M curve, and prognostic risk model was established using 101 machine learning algorithms. In addition, we performed gene colocalisation analysis and Mendelian randomisation analysis. Univariate Cox analysis was used to screen out prognostic genes from DEGs and ARGs in each dataset. Risk model was generated by artificial intelligence-derived prognostic signature (AIDPS), which showed better prognostic performance in every dataset than other published models for PCa. The disease-free period (DFS) of patients in the high-risk group was significantly worse than that in the low-risk group (all p < 0.05). The best model is the Ridge (C-index 0.726). We found significant differences in IC50 values of the Dactinomycin_1811, Dactolisib_1057, Luminespib_1559 and Paclitaxel_1080 between groups. In SNP sites rs2743987 and rs7768988, there was a significant correlation between prostate hyperplasia and prostate cancer. Our ARG-based predictive model AIDPS is a reliable and effective tool for prognosis and treatment of prostate cancer.
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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 OpenQuestion graph.