A risk signature of ubiquitin-specific protease family predict the prognosis and therapy of kidney cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37259026", "kind": "science-record", "revision": 2, "content_hash": "sha256:d9fdc733c49a33770d75065686be8d213001c415617f59fe8a97f12dce3561d7", "created_at": "2026-11-01T19:01:58Z", "updated_at": "2026-11-01T21:30:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A risk signature of ubiquitin-specific protease family predict the prognosis and therapy of kidney cancer patients.", "doi": "10.1186/s12882-023-03215-0", "pmid": "37259026", "authors": ["Renjie Wang", "Yang Liu", "Jingxian Li", "Yubao Zhao", "Rui An", "Zhifang Ma"], "journal": "BMC nephrology", "year": 2023, "abstract": "Ubiquitin-specific proteases (USPs) are closely related to protein fate and cellular processes through various molecular signalling pathways, including DNA damage repair, p53, and transforming growth factor-β (TGF-β) pathways. In recent years, increasing evidence has revealed the pivotal role of ubiquitination in tumorigenesis of KIRC. However, USPs' molecular mechanism and clinical relevance in kidney cancer still need further exploration. Our study first determined prognosis-related ubiquitin-specific proteases (PRUSPs) in KIRC. We found these genes co-expressed with each other and might regulate different substrates. Based on the USPs' expression, the PRUSPs risk signature was constructed to predict the survival probability of KIRC patients. The patients in high-PRUSPs-risk group showed a low survival rate. ROC and calibration curve indicated a discriminate capacity of the signature, and uni-/multi-variate Cox regression analysis revealed that the PRUSPs score is an independent prognostic factor. In different KIRC clinical subgroups and external validation cohorts (including E-MTAB-1980 and TCGA-KIRP cohorts), the PRUSPs risk signature showed strong robustness and practicability. Further analysis found that high-risk group showed activation of immune-related pathways and high PD-1/CTLA4 expression, revealing that high-risk patients might be sensitive to immunotherapy. In summary, we constructed the USPs risk signature to predict kidney cancer prognosis, which provided the theoretical foundation for further clinical or pre-clinical experiments.", "claims": [{"id": "claim-method", "statement": "Basing on the glmnet R-package, the training cohort was used to construct a prognostic risk signature utilizing the Lasso-penalized Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37259026", "locator": "sections[6] \"Construction of PRUSPs risk signature\" paragraph 2", "quote": "Basing on the glmnet R-package, the training cohort was used to construct a prognostic risk signature utilizing the Lasso-penalized Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories, including UCSC xena (https://xena.ucsc.edu/); Arraryexpress (https://www.ebi.ac.uk/arrayexpress); GEO database (https://www.ncbi.nlm.nih.gov/gds); HPA website (http://www.proteinatlas.org/); Bioconductor (https://www.bioconductor.org/); string website (https://string-db.org/); designN (https://design-v2.cancerresearch.my/query).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37259026", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The datasets presented in this study can be found in online repositories, including UCSC xena (https://xena.ucsc.edu/); Arraryexpress (https://www.ebi.ac.uk/arrayexpress); GEO database (https://www.ncbi.nlm.nih.gov/gds); HPA website (http://www.proteinatlas.org/); Bioconductor (https://www.bioconductor.org/); string website (https://string-db.org/); designN (https://design-v2.cancerresearch.my/query)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A PRUSPs risk signature constructed with LASSO-penalized Cox regression from KIRC mRNA expression data showed time-dependent AUCs of 0.6676, 0.7008, and 0.7339 for 3-, 5-, and 7-year survival, respectively, in the training cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37259026", "locator": "sections[6] \"Construction of PRUSPs risk signature\" paragraph 2", "quote": "Basing on the glmnet R-package, the training cohort was used to construct a prognostic risk signature utilizing the Lasso-penalized Cox regression analysis."}, {"asset": "source:pmid:37259026", "locator": "sections[13] \"Construction of PRUSPs risk signature\" paragraph 3", "quote": "The time-dependent AUC showed a discriminative accuracy in 3-/5-/7-years survival (3 years = 0.6676, 5 years = 0.7008, and 7 years = 0.7339)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A PRUSPs risk signature constructed with LASSO-penalized Cox regression from KIRC mRNA expression data showed time-dependent AUCs of 0.6676, 0.7008, and 0.7339 for 3-, 5-, and 7-year survival, respectively, in the training cohort.", "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:37259026"], "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. 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