ArticleDiscover oncology2025
Comprehensive analysis of cholesterol metabolism-related genes in prostate cancer: integrated analysis of single-cell and bulk RNA sequencing.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Reprogrammed Lipid Metabolism-Associated Therapeutic Vulnerabilities in Prostate Cancer.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundCholesterol metabolism plays a significant role in cancer progression, including prostate adenocarcinoma (PRAD), making it a promising target for therapeutic intervention. This study aimed to construct and validate a cholesterol metabolism gene (CMG)-related prognostic signature to predict prognosis in PRAD patients, while exploring its biological, clinical, and therapeutic implications.
methodsCMGs were retrieved through comprehensive searches in public databases. Prognostic CMGs were determined using univariate Cox regression analysis on The Cancer Genome Atlas (TCGA) PRAD dataset. Patients were classified into subgroups using consensus clustering. Functional enrichment and Gene Set Enrichment Analysis (GSEA) were applied to explore the potential pathways. Importantly, a prognostic signature based on CMGs was constructed using the least absolute shrinkage and selection operator (LASSO) method, with performance evaluated through Kaplan-Meier (KM) analyses and receiver operating characteristic (ROC) curves. The model was validated in three external cohorts, and its clinical relevance was assessed through nomogram construction and drug sensitivity analysis. Immune landscape analysis was also performed to evaluate the PRAD immune microenvironment. Single-cell RNA sequencing analysis was conducted using Seurat package.
results18 CMGs were identified to establish the prognostic signature. The risk score derived from this signature demonstrated robust prognostic performance in survival analysis and was significantly associated with key clinical variables, including N-stage, T-stage, and Gleason Score. The risk score of CMG signature was recognized as an independent prognostic parameter, and a nomogram was created to estimate 1-, 3-, and 5-year prognosis in PRAD patients. Additionally, the analysis of drug sensitivity identified variations in responses to commonly used drugs (such as camptothecin, CDK9 inhibitors, docetaxel, mitoxantrone, paclitaxel, and sepantronium bromide) between the two risk groups. Furthermore, immune landscape and single-cell sequencing analyses indicated that biological pathways were significantly correlated with the risk score.
conclusionsThe CMG-based prognostic model effectively predicts prognosis in PRAD patients and is linked to distinct biological pathways, immune landscapes, and drug sensitivities. This signature has the robust potential to guide personalized therapy and improve prognosis in PRAD.
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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.