A seven-gene prognosis model to predict biochemical recurrence for prostate cancer based on the TCGA database.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37255653", "kind": "science-record", "revision": 3, "content_hash": "sha256:6afec688a901559154dbe501686ead0218eb00f23911bbded16f2cfa360b44f1", "created_at": "2026-11-01T19:01:58Z", "updated_at": "2026-11-01T22:09:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A seven-gene prognosis model to predict biochemical recurrence for prostate cancer based on the TCGA database.", "doi": "10.3389/fsurg.2022.923473", "pmid": "37255653", "authors": ["Yijun He", "Jinxiong Zhang", "Zhihao Chen", "Kening Sun", "Xin Wu", "Jianhong Wu", "Lu Sheng"], "journal": "Frontiers in surgery", "year": 2022, "abstract": "Background: The incidence rate of prostate cancer is increasing rapidly. This study aims to explore the gene-associated mechanism of prostate cancer biochemical recurrence (BCR) after radical prostatectomy and to construct a biochemical recurrence of prostate cancer prognostic model.\nMethods: The DEseq2 R package was used for the differential expression of mRNA. The ClusterProfiler R package was used to analyze the functional enrichment of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore related mechanisms. The Survival, Survminer, and My.stepwise R packages were used to construct the prognostic model to predict the biochemical recurrence-free probability. The RMS R package was used to draw the nomogram. For evaluating the prognostic model, the timeROC R package was used to draw the time-dependent ROC curve (receiver operating characteristic curve).\nResult: To investigate the association between mRNA and prostate cancer, we performed differential expression analysis on the TCGA (The Cancer Genome Atlas) database. Seven protein-coding genes (VWA5B2, ARC, SOX11, MGAM, FOXN4, PRAME, and MMP26) were picked as independent prognostic genes by regression analysis. Based on their Cox coefficient, a risk score formula was proposed. According to the risk scores, patients were divided into high- and low-risk groups based on the median score. Kaplan-Meier plot curves showed that the low-risk group had a better biochemical recurrence-free probability compared to the high-risk group. The 1-year, 3-year, and 5-year AUCs (areas under the ROC curve) of the model were 77%, 81%, and 86%, respectively. In addition, we built a nomogram based on the result of multivariate Cox regression analysis. Furthermore, we select the GSE46602 dataset as our external validation. The 1-year, 3-year, and 5-year AUCs of BCR-free probability were 83%, 82%, and 80%, respectively. Finally, the levels of seven genes showed a difference between PRAD tissues and adjacent non-tumorous tissues.\nConclusions: This study shows that establishing a biochemical recurrence prediction prognostic model comprising seven protein-coding genes is an effective and precise method for predicting the progression of prostate cancer.", "claims": [{"id": "claim-method", "statement": "Using Lasso and multivariate Cox regression analyses, seven protein-coding genes were identified as significant BCR indicators of prostate cancer.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37255653", "locator": "sections[1] \"Introduction\" paragraph 3", "quote": "Using Lasso and multivariate Cox regression analyses, seven protein-coding genes were identified as significant BCR indicators of prostate cancer."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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