Prognostic Role of the Ubiquitin Proteasome System in Clear Cell Renal Cell Carcinoma: A Bioinformatic Perspective.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34093816", "kind": "science-record", "revision": 8, "content_hash": "sha256:18c1c197ccee542a47b8444b48d8758a7cfe2d30fba29927b594cebe707513ce", "created_at": "2026-11-01T15:41:38Z", "updated_at": "2026-11-01T22:46:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognostic Role of the Ubiquitin Proteasome System in Clear Cell Renal Cell Carcinoma: A Bioinformatic Perspective.", "doi": "10.7150/jca.53760", "pmid": "34093816", "authors": ["Hongda Guo", "Yan Li", "Yaxiao Liu", "Lipeng Chen", "Zhengdong Gao", "Lekai Zhang", "Nan Zhou", "Hu Guo", "Benkang Shi"], "journal": "Journal of Cancer", "year": 2021, "abstract": "Background: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urinary system. The ubiquitin proteasome system (UPS) plays an important role in the generation, metabolism and survival of tumor. We are aimed to make a comprehensive exploration of the UPS's role in ccRCC with bioinformatic tools, which may contribute to the understanding of UPS in ccRCC, and give insight for further research. Methods: The UPS-related genes (UPSs) were collected by an integrative approach. The expression and clinical data were downloaded from TCGA database. R soft was used to perform the differentially expressed UPSs analysis, functional enrichment analysis. We also estimated prognostic value of each UPS with the help of GEPIA database. Two predicting models were constructed with the differentially expressed UPSs and prognosis-related genes, respectively. The correlations of risk score with clinical characteristics were also evaluated. Data of GSE29609 cohort were obtained from GEO database to validate the prognostic models. Results: We finally identified 91 differentially expressed UPSs, 48 prognosis related genes among them, and constructed a prognostic model with 18 UPSs successfully, the AUC was 0.760. With the help of GEPIA, we found 391 prognosis-related UPSs, accounting for 57.84% of all UPSs. Another prognostic model was constructed with 28 prognosis-related genes of them, and with a better AUC of 0.825. Additionally, our models can also stratify patients into high and low risk groups accurately in GSE29609 cohort. Similar prognostic values of our models were observed in the validated GSE29609 cohort. Conclusions: UPS is dysregulated in ccRCC. UPS related genes have significant prognostic value in ccRCC. Models constructed with UPSs are effective and applicable. An abnormal ubiquitin proteasome system should play an important role in ccRCC and be worthy of further study.", "claims": [{"id": "claim-method", "statement": "R software (version 4.0.2) and “limma” package were used to make the analysis of differentially expressed UPSs. We set a false discovery rate (FDR) < 0.05 and a | log2 fold change (FC) | > 1 as screening criteria to obtain the differentially expressed UPSs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34093816", "locator": "sections[5] \"Identification of differentially expressed UPSs\" paragraph 2", "quote": "R software (version 4.0.2) and “limma” package were used to make the analysis of differentially expressed UPSs. We set a false discovery rate (FDR) < 0.05 and a | log2 fold change (FC) | > 1 as screening criteria to obtain the differentially expressed UPSs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Availability of data and materialsThe results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34093816", "locator": "sections[22] \"ack\" paragraph 1", "quote": "Availability of data and materialsThe results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:34093816"], "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:GSE29609"], "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:GSE29609"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34093816", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7150/jca.53760", "sha256": "sha256:0d82ee49ebb6de31b8b355a390181f045d6cb3cf6680ba327bf6a7c4c09e25b5", "bytes": 29301, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7150/jca.53760", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8176417", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE29609", "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=GSE29609", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE29609", "version": null, "note": "Availability of data and materialsThe results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Availability of data and materialsThe results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM733579", "GSM733580", "GSM733581", "GSM733582", "GSM733583", "GSM733584", "GSM733585", "GSM733586", "GSM733587", "GSM733588", "GSM733589", "GSM733590", "GSM733591", "GSM733592", "GSM733593", "GSM733594", "GSM733595", "GSM733596", "GSM733597", "GSM733598", "GSM733599", "GSM733600", "GSM733601", "GSM733602", "GSM733603", "GSM733604", "GSM733605", "GSM733606", "GSM733607", "GSM733608", "GSM733609", "GSM733610", "GSM733611", "GSM733612", "GSM733613", "GSM733614", "GSM733615", "GSM733616", "GSM733617"], "sample_count": 39, "group_sizes": null, "group_sizes_basis": null, "platform": "1708", "taxon": "Homo sapiens", "title": "Clear-cell renal cell carcinomas tumors", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE29609 not downloaded", "asset:data:geo:GSE29609 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "R software (version 4.0.2) and “limma” package were used to make the analysis of differentially expressed UPSs. 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