A novel prognostic model based on cellular senescence-related gene signature for bladder cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36505846", "kind": "science-record", "revision": 2, "content_hash": "sha256:8bcf022d27617175dbee31a1853dda65627874f9ad6f6125eeeee14696bee976", "created_at": "2026-11-01T19:01:00Z", "updated_at": "2026-11-01T21:29:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel prognostic model based on cellular senescence-related gene signature for bladder cancer.", "doi": "10.3389/fonc.2022.937951", "pmid": "36505846", "authors": ["Lianmin Luo", "Fenghua Li", "Binbin Gong", "Ping Xi", "Wenjie Xie"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: Cellular senescence plays crucial role in the progression of tumors. However, the expression patterns and clinical significance of cellular senescence-related genes in bladder cancer (BCa) are still not clearly clarified. This study aimed to establish a prognosis model based on senescence-related genes in BCa.\nMethods: The transcriptional profile data and clinical information of BCa were downloaded from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analyses were performed to develop a prognostic model in the TCGA cohort. The GSE13507 cohort were used for validation. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate underlying mechanisms.\nResults: A six-gene signature (CBX7, EPHA3, STK40, TGFB1I1, SREBF1, MYC) was constructed in the TCGA databases. Patients were classified into high risk and low risk group in terms of the median risk score. Survival analysis revealed that patients in the higher risk group presented significantly worse prognosis. Receiver operating characteristic (ROC) curve analysis verified the moderate predictive power of the risk model based on the six senescence-related genes signature. Further analysis indicated that the clinicopathological features analysis were significantly different between the two risk groups. As expected, the signature presented prognostic significance in the GSE13507 cohort. Functional analysis indicated that immune-related pathways activity, immune cell infiltration and immune-related function were different between two risk groups. In addition, risk score were positively correlated with multiple immunotherapy biomarkers.\nConclusion: Our study revealed that a novel model based on senescence-related genes could serve as a reliable predictor of survival for patients with BCa.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was used to screen senescence-related genes with prognostic values (P < 0.05). The Least Absolute Shrinkage and Selection Operator (LASSO) regression with “glmnet” R package was applied in order to avoid overfitting. Subsequently, the candidate genes selected into multivariate Cox regression analysis in order to determine final prognostic senescence-related genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36505846", "locator": "sections[5] \"Construction and validation of a prognostic senescence-related gene model\" paragraph 2", "quote": "Univariate Cox regression analysis was used to screen senescence-related genes with prognostic values (P < 0.05). The Least Absolute Shrinkage and Selection Operator (LASSO) regression with “glmnet” R package was applied in order to avoid overfitting. Subsequently, the candidate genes selected into multivariate Cox regression analysis in order to determine final prognostic senescence-related genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36505846", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A novel model based on senescence-related genes could serve as a reliable predictor of survival for patients with BCa.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36505846", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study revealed that a novel model based on senescence-related genes could serve as a reliable predictor of survival for patients with BCa."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A novel model based on senescence-related genes could serve as a reliable predictor of survival for patients with BCa.", "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:36505846"], "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:GSE13507"], "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:GSE13507"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36505846", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2022.937951", "sha256": 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