Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39331250", "kind": "science-record", "revision": 3, "content_hash": "sha256:ad9f6e631dce271e00dc59c5a512177ed27cef8ce73adfc8ee38d8d479f80923", "created_at": "2026-11-01T20:29:31Z", "updated_at": "2026-11-01T22:12:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis.", "doi": "10.1007/s12672-024-01277-6", "pmid": "39331250", "authors": ["Yuni Wu", "Ran Xu", "Jing Wang", "Zhibin Luo"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: Prostate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.\nMETHODS: This study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.\nRESULTS: An ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.\nCONCLUSIONS: This study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.", "claims": [{"id": "claim-method", "statement": "We conducted univariate and multivariate Cox regression analyses to determine if risk scores could serve as independent prognostic indicators.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39331250", "locator": "sections[4] \"Prognostic analysis and column line plot construction\" paragraph 2", "quote": "We conducted univariate and multivariate Cox regression analyses to determine if risk scores could serve as independent prognostic indicators."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets employed in this study can be accessed through the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and the TCGA portal (https://portal.gdc.cancer.gov/). Additionally, the raw data files, code files, and images supporting this research are available for download via the following link: https://www.jianguoyun.com/p/DY7CFbEQkeKyCxiQvaAFIAA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39331250", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets employed in this study can be accessed through the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and the TCGA portal (https://portal.gdc.cancer.gov/). Additionally, the raw data files, code files, and images supporting this research are available for download via the following link: https://www.jianguoyun.com/p/DY7CFbEQkeKyCxiQvaAFIAA."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39331250", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39331250"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE116918"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE116918"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39331250", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01277-6", "sha256": "sha256:2e24a18898d5955dce679afae4c1908cf6aa4a9f70afc29c8c1d0ffe427f5f17", "bytes": 38383, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1007/s12672-024-01277-6", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11436555", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE116918", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE116918", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE116918", "version": null, "note": "The datasets employed in this study can be accessed through the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and the TCGA portal (https://portal.gdc.cancer.gov/). 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE70768", "GSE116918", "GSE70769"], "resolved_accessions": ["GSE70768", "GSE116918", "GSE70769"], "statements": ["The datasets employed in this study can be accessed through the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) and the TCGA portal (https://portal.gdc.cancer.gov/). Additionally, the raw data files, code files, and images supporting this research are available for download via the following link: https://www.jianguoyun.com/p/DY7CFbEQkeKyCxiQvaAFIAA."], "basis": "The paper names GSE70768, GSE116918, GSE70769 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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