Establishment of a Novel Combined Nomogram for Predicting the Risk of Progression Related to Castration Resistance in Patients With Prostate Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35620461", "kind": "science-record", "revision": 4, "content_hash": "sha256:24900f60ae7d1761d9c2b3ac514b8b53ce4bc85bdf2e3db86c05d9a68c5acae9", "created_at": "2026-11-01T18:14:32Z", "updated_at": "2026-11-01T22:06:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment of a Novel Combined Nomogram for Predicting the Risk of Progression Related to Castration Resistance in Patients With Prostate Cancer.", "doi": "10.3389/fgene.2022.823716", "pmid": "35620461", "authors": ["Shuqiang Li", "Lei Shi", "Fan Li", "Bing Yao", "Liansheng Chang", "Hongyan Lu", "Dongkui Song"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: The emergence of castration resistance is fatal for patients with prostate cancer (PCa); however, there is still a lack of effective means to detect the early progression. In this study, a novel combined nomogram was established to predict the risk of progression related to castration resistance. Methods: The castration-resistant prostate cancer (CRPC)-related differentially expressed genes (DEGs) were identified by R packages \"limma\" and \"WGCNA\" in GSE35988-GPL6480 and GSE70768-GPL10558, respectively. Relationships between DEGs and progression-free interval (PFI) were analyzed using the Kaplan-Meier method in TCGA PCa patients. A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan-Meier curve. Finally, the univariate and multivariate Cox regression analyses were used to establish a combined nomogram. The prognostic value of the nomogram was validated by concordance index (C-index), calibration plots, ROC curve, and decision curve analysis (DCA). Results: 15 CRPC-related DEGs were identified finally, of which 13 genes were significantly associated with PFI and used as the candidate genes for modeling. A two-gene (KIFC2 and BCAS1) signature was built to predict the risk of progression. The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively. Patients with high-risk scores were significantly associated with poorer PFI (p < 0.0001). A novel combined nomogram was successfully established for individualized prediction integrating with T stage, Gleason score, and risk score. While the 1-year, 3-year, and 5-year AUC were 0.76, 0.761, and 0.762, respectively, the good prognostic value of the nomogram was also validated by the C-index (0.734), calibration plots, and DCA. Conclusion: The combined nomogram can be used to predict the individualized risk of progression related to castration resistance for PCa patients and has been preliminarily verified to have good predictive ability.", "claims": [{"id": "claim-method", "statement": "A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35620461", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A multigene signature was built by lasso-penalized Cox regression analysis, and assessed by the receiver operator characteristic (ROC) curve and Kaplan–Meier curve."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35988, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70768, https://xenabrowser.net/datapages/?cohort=TCGA%20Prostate%20Cancer%20(PRAD)& removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35620461", "locator": "sections[20] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35988, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70768, https://xenabrowser.net/datapages/?cohort=TCGA%20Prostate%20Cancer%20(PRAD)& removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35620461", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The ROC curve indicated that 5-year area under curve (AUC) in the training, testing, and whole TCGA dataset was 0.722, 0.739, and 0.731, 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:35620461"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["Publicly available datasets were analyzed in this study. 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