Comprehensive analysis of fatty acid metabolism-related gene signatures for predicting prognosis in patients with prostate cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36643625", "kind": "science-record", "revision": 3, "content_hash": "sha256:8ae43d6357f7fef00c09ea1c57c76c1faae5cbd4cc3eacd215c2bdb5e7d1ce78", "created_at": "2026-11-01T19:01:12Z", "updated_at": "2026-11-01T22:08:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of fatty acid metabolism-related gene signatures for predicting prognosis in patients with prostate cancer.", "doi": "10.7717/peerj.14646", "pmid": "36643625", "authors": ["Hongbo Wang", "Zhendong Liu", "Yubo Wang", "Dali Han", "Yuelin Du", "Bin Zhang", "Yang He", "Junyao Liu", "Wei Xiong", "Xingxing Zhang", "Yanzheng Gao", "Panfeng Shang"], "journal": "PeerJ", "year": 2023, "abstract": "Fatty acid metabolism (FAM) is an important factor in tumorigenesis and development. However, whether fatty acid metabolism (FAM)-related genes are associated with prostate cancer (PCa) prognosis is not known. Therefore, we established a novel prognostic model based on FAM-related genes to predict biochemical recurrence in PCa patients. First, PCa sequencing data were acquired from TCGA as the training cohort and GSE21032 as the validation cohort. Second, a prostate cancer prognostic model containing 10 FAM-related genes was constructed using univariate Cox and LASSO. Principal component analysis and t-distributed stochastic neighbour embedding analysis showed that the model was highly effective. Third, PCa patients were divided into high- and low-risk groups according to the model risk score. Survival analysis, ROC curve analysis, and independent prognostic analysis showed that the high-risk group had short recurrence-free survival (RFS), and the risk score was an independent diagnostic factor with diagnostic value in PCa patients. External validation using GSE21032 also showed that the prognostic model had high reliability. A nomogram based on a prognostic model was constructed for clinical use. Fourth, tumor immune correlation analyses, such as the ESTIMATE, CIBERSORT algorithm, and ssGSEA, showed that the high-risk group had higher immune cell infiltration, lower tumour purity, and worse RFS. Various immune checkpoints were expressed at higher levels in high-risk patients. In summary, this prognostic model is a promising prognostic biomarker for PCa that should improve the prognosis of PCa patients. These data provide new ideas for antitumour immunotherapy and have good potential value for the development of targeted drugs.", "claims": [{"id": "claim-method", "statement": "First, differentially expressed fatty acid metabolism-related genes associated with RFS in prostate cancer patients were determined using univariate analysis, and filtering criteria were P < 0.05. Second, the genes related to fatty acid metabolism associated with RFS were used to construct a prognostic model using LASSO Cox regression analysis (glmnet package) (Tibshirani et al., 2012).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36643625", "locator": "sections[5] \"Prognostic model construction and verification\" paragraph 2", "quote": "First, differentially expressed fatty acid metabolism-related genes associated with RFS in prostate cancer patients were determined using univariate analysis, and filtering criteria were P < 0.05. Second, the genes related to fatty acid metabolism associated with RFS were used to construct a prognostic model using LASSO Cox regression analysis (glmnet package) (Tibshirani et al., 2012)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data is available at NCBI GEO: GSE21032 and Zenodo: Hongbo Wang. (2022). Comprehensive analysis of fatty acid metabolism-related gene signatures for predicting prognosis in patients with prostate cancer. https://doi.org/10.5281/zenodo.7323463.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36643625", "locator": "sections[30] \"Additional Information and Declarations\" paragraph 4", "quote": "The data is available at NCBI GEO: GSE21032 and Zenodo: Hongbo Wang. (2022). Comprehensive analysis of fatty acid metabolism-related gene signatures for predicting prognosis in patients with prostate cancer. https://doi.org/10.5281/zenodo.7323463."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The risk model had AUC values of 0.813, 0.799, and 0.790 at 1 year, 3 years, and 5 years, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36643625", "locator": "sections[17] \"Construction of a prognostic model of prostate cancer based on fatty acid metabolism-related genes\" paragraph 2", "quote": "The ROC curve showed that the area under the curve (AUC) values were 0.813, 0.799, and 0.790 at 1 year, 3 years, and 5 years, respectively"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The risk model had AUC values of 0.813, 0.799, and 0.790 at 1 year, 3 years, and 5 years, respectively.", "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:36643625"], "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:GSE21032"], "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:GSE21032"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36643625", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.14646", "sha256": "sha256:423ef5122fa0976312b69d868ad66a163c56b1cf55a4aa716e480aad5ad8c49b", "bytes": 52648, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.14646", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9838212", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21032", "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=GSE21032", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21032", "version": null, "note": "The data is available at NCBI GEO: GSE21032 and Zenodo: Hongbo Wang. 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