An Oxidative Stress-Related Genes Signature for Predicting Survival in Bladder Cancer: Based on TCGA Database and Bioinformatics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35300137", "kind": "science-record", "revision": 8, "content_hash": "sha256:f3e30afcd6e4be8c8c867641204024a70e7b26b0f7792e95bcba86ff0f683cce", "created_at": "2026-11-01T18:14:05Z", "updated_at": "2026-11-01T23:46:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "An Oxidative Stress-Related Genes Signature for Predicting Survival in Bladder Cancer: Based on TCGA Database and Bioinformatics.", "doi": "10.2147/IJGM.S348945", "pmid": "35300137", "authors": ["Min Zhang", "Gang Du", "Zhengtian Li", "Dehui Li", "Weichao Li", "Hening Li", "Xingxin Gao", "Zhanhong Tang"], "journal": "International journal of general medicine", "year": 2022, "abstract": "Background: Oxidative stress (OS) responses have been linked to oncogenesis and tumor progression and have recently been regarded as a potential strategy for tumor therapy. However, OS-related therapeutic targets have not been identified to date in the bladder cancer (BC).\nMethods: The mRNA expression and clinical data of BC were downloaded from the public database. Prognostic risk score signature was constructed using LASSO Cox regression analysis. External validation was performed in GSE15307 cohort. ESTIMATE, CIBERSORT, and ssGSEA algorithm were used to analyze immune cell infiltration and immune microenvironment. Next, functional enrichment analysis was performed to elucidate the mechanism underlying the signature. Additionally, we performed a nomogram to forecast the survival rate of individual BC patients.\nResults: An OS-related genes (OSRGs) signature was constructed. Overall survival was lower in the high-risk group than in the low-risk group, according to survival analyses. The area under the curve (AUC) of ROC curves further validated the prognostic signature's strong prediction performance in these two cohorts. The risk score was verified as an independent risk factor for BC by independent prognostic analysis. Moreover, as compared to TNM stage alone, a nomogram that integrated the risk score with TNM stage showed a much superior predictive value. Immune infiltration and tumor microenvironment studies indicated that immune cells and functions may play a significant role in carcinogenesis and development. The levels of expression of prognostic genes were shown to be substantially linked with drug sensitivity.\nConclusion: We developed a novel OSRGs signature for predicting overall survival and impacting the immune status in patients with BC. New nomogram can help clinicians predict the survival rate of BC patients. These findings shed new light on the potential usage of OSRGs signature in BC patients.", "claims": [{"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the corresponding author upon request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35300137", "locator": "sections[26] \"Data Sharing Statement\" paragraph 2", "quote": "The data used to support the findings of this study are available from the corresponding author upon request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The results demonstrated that the area under the ROC curve (AUC) for 5 years OS was 0.714 and 0.778, respectively (Figure 11C and D), which showed the nomogram plot was accuracy and convenient.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35300137", "locator": "sections[18] \"Performance and Verification of Nomogram\" paragraph 2", "quote": "The results demonstrated that the area under the ROC curve (AUC) for 5 years OS was 0.714 and 0.778, respectively (Figure 11C and D), which showed the nomogram plot was accuracy and convenient."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The results demonstrated that the area under the ROC curve (AUC) for 5 years OS was 0.714 and 0.778, respectively (Figure 11C and D), which showed the nomogram plot was accuracy and convenient.", "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:35300137"], "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_unresolved", "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:GSE15307"], "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:GSE15307"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_unresolved"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35300137", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S348945", "sha256": "sha256:08245528d1d6c8f41c6f15086165f1145f8520139a69f4c341c34f93422fe570", "bytes": 46613, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/IJGM.S348945", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8922338", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15307", "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=GSE15307", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15307", "version": null, "note": "The data used to support the findings of this study are available from the corresponding author upon request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data used to support the findings of this study are available from the corresponding author upon request.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-11-01T23:38:31Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE15307 not downloaded", "asset:data:geo:GSE15307 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Cox regression"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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