Immune-related gene risk score predicting the effect of immunotherapy and prognosis in bladder cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36267410", "kind": "science-record", "revision": 4, "content_hash": "sha256:c6f48bead71046dc6352f94acb45091fd4207a92845f5eb5c7a321d7c225336b", "created_at": "2026-11-01T18:15:39Z", "updated_at": "2026-11-01T22:07:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune-related gene risk score predicting the effect of immunotherapy and prognosis in bladder cancer patients.", "doi": "10.3389/fgene.2022.1011390", "pmid": "36267410", "authors": ["Yuantao Zou", "Gangjun Yuan", "Xingliang Tan", "Sihao Luo", "Cong Yang", "Yi Tang", "Yanjun Wang", "Kai Yao"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Immune checkpoint inhibitor therapy has changed the treatment model of metastatic bladder cancer. However, only approximately 20% of patients benefit from this therapy, and robust biomarkers to predict the effect of immunotherapy are still lacking. In this study, we aimed to investigate whether immune-related genes could be indicators for the prognosis of bladder cancer patients and the effect of immunotherapy. Methods: Based on bladder cancer dataset from the Cancer Genome Atlas (TCGA) and GSE48075, 22 immune microenvironment-related cells were identified by CIBERSORT. After performing a series of bioinformatic and machine learning approaches, we identified distinct tumor microenvironment clusters and three bladder cancer specific immune-related genes (EGFR, OAS1 and MST1R). Then, we constructed immune-related gene risk score (IRGRS) by using the Cox regression method and validated it with the IMvigor210 dataset. Results: IRGRS-high patients had a worse overall survival than IRGRS-low patients, which was consistent with the result in the IMvigor210 dataset. Comprehensive analysis shows that patients with high IRGRS scores are mainly enriched in basal/squamous type (Ba/Sq), and tumor metabolism-related pathways are more Active, with higher TP53 and RB1 gene mutation rates, lower CD4+/CD8+ T cell infiltration, higher M0 macrophage infiltration, and lower immunotherapy efficacy. In contrast, Patients with low IRGRS scores are mainly enriched in the luminal papillary type (LumP), which is associated with the activation of IL-17 and TNF signaling pathways, higher mutation rates of FGFR3 and CDKN1A genes, higher CD4+/CD8+ T cell infiltration content, and The level of M0 macrophage infiltration was relatively low, and the immunotherapy was more probably effective. Conclusion: Our study constructed an IRGRS for bladder cancer and clarified the immune and molecular characteristics of IRGRS-defined subgroups of bladder cancer to investigate the association between IRGRS and its potential implications for prognosis and immunotherapy.", "claims": [{"id": "claim-method", "statement": "Then, we constructed immune-related gene risk score (IRGRS) by using the Cox regression method and validated it with the IMvigor210 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36267410", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Then, we constructed immune-related gene risk score (IRGRS) by using the Cox regression method and validated it with the IMvigor210 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All the data used in this study are from public datasets (TCGA, GEO, IMvigor210) and can be accessed without restriction.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36267410", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "All the data used in this study are from public datasets (TCGA, GEO, IMvigor210) and can be accessed without restriction."}], "state": "complete"}, {"id": "claim-target-result", "statement": "IRGRS-high patients had a worse overall survival than IRGRS-low patients, which was consistent with the result in the IMvigor210 dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36267410", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "IRGRS-high patients had a worse overall survival than IRGRS-low patients, which was consistent with the result in the IMvigor210 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "IRGRS-high patients had a worse overall survival than IRGRS-low patients, which was consistent with the result in the IMvigor210 dataset.", "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:36267410"], "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:GSE48075"], "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:GSE48075"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36267410", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.1011390", "sha256": "sha256:384d6ed9e0aa144c3caa5087abedc53b386ff9046d916d15a4b84b5a012dce7b", "bytes": 55275, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.1011390", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9577248", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE48075", "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=GSE48075", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE48075", "version": null, "note": "All the data used in this study are from public datasets (TCGA, GEO, IMvigor210) and can be accessed without restriction.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All the data used in this study are from public datasets (TCGA, GEO, IMvigor210) and can be accessed without restriction.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1167333", "GSM1167334", "GSM1167335", "GSM1167336", "GSM1167337", "GSM1167338", "GSM1167339", "GSM1167340", "GSM1167341", "GSM1167342", "GSM1167343", "GSM1167344", "GSM1167345", "GSM1167346", "GSM1167347", "GSM1167348", "GSM1167349", "GSM1167350", "GSM1167351", "GSM1167352", "GSM1167353", "GSM1167354", "GSM1167355", "GSM1167356", "GSM1167357", "GSM1167358", "GSM1167359", "GSM1167360", "GSM1167361", "GSM1167362", "GSM1167363", "GSM1167364", "GSM1167365", "GSM1167366", "GSM1167367", "GSM1167368", "GSM1167369", "GSM1167370", "GSM1167371", "GSM1167372", "GSM1167373", "GSM1167374", "GSM1167375", "GSM1167376", "GSM1167377", "GSM1167378", "GSM1167379", "GSM1167380", "GSM1167381", "GSM1167382", "GSM1167383", "GSM1167384", "GSM1167385", "GSM1167386", "GSM1167387", "GSM1167388", "GSM1167389", "GSM1167390", "GSM1167391", "GSM1167392", "GSM1167393", "GSM1167394", "GSM1167395", "GSM1167396", "GSM1167397", "GSM1167398", "GSM1167399", "GSM1167400", "GSM1167401", "GSM1167402", "GSM1167403", "GSM1167404", "GSM1167405", "GSM1167406", "GSM1167407", "GSM1167408", "GSM1167409", "GSM1167410", "GSM1167411", "GSM1167412", "GSM1167413", "GSM1167414", "GSM1167415", "GSM1167416", "GSM1167417", "GSM1167418", "GSM1167419", "GSM1167420", "GSM1167421", "GSM1167422", "GSM1167423", "GSM1167424", "GSM1167425", "GSM1167426", "GSM1167427", "GSM1167428", "GSM1167429", "GSM1167430", "GSM1167431", "GSM1167432", "GSM1167433", "GSM1167434", "GSM1167435", "GSM1167436", "GSM1167437", "GSM1167438", "GSM1167439", "GSM1167440", "GSM1167441", "GSM1167442", "GSM1167443", "GSM1167444", "GSM1167445", "GSM1167446", "GSM1167447", "GSM1167448", "GSM1167449", "GSM1167450", "GSM1167451", "GSM1167452", "GSM1167453", "GSM1167454", "GSM1167455", "GSM1167456", "GSM1167457", "GSM1167458", "GSM1167459", "GSM1167460", "GSM1167461", "GSM1167462", "GSM1167463", "GSM1167464", "GSM1167465", "GSM1167466", "GSM1167467", "GSM1167468", "GSM1167469", "GSM1167470", "GSM1167471", "GSM1167472", "GSM1167473", "GSM1167474"], "sample_count": 142, "group_sizes": null, "group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Identification of subsets of urothelial carcinoma", "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:GSE48075 not downloaded", "asset:data:geo:GSE48075 licence not read"]}, "analysis": {"state": "partial", "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": 1.0, "band": "verbatim", "quote": "Then, we constructed immune-related gene risk score (IRGRS) by using the Cox regression method and validated it with the IMvigor210 dataset."}}, {"id": "method-2", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "ANOVA"}}, {"id": "method-3", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE48075"], "resolved_accessions": ["GSE48075"], "statements": ["All the data used in this study are from public datasets (TCGA, GEO, IMvigor210) and can be accessed without restriction."], "basis": "The paper names GSE48075 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:36267410", "data:geo:GSE48075"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE48075"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:12:01Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15100, "completion_tokens": 2032, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36267410", "type": "paper", "label": "pmid:36267410"}, {"id": "source:pmid:36267410", "type": "manuscript", "label": "PMID 36267410 full text"}, {"id": "data:geo:GSE48075", "type": "dataset", "label": "GEO GSE48075"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36267410", "type": "paper", "label": "pmid:36267410"}, {"id": "source:pmid:36267410", "type": "manuscript", "label": "PMID 36267410 full text"}, {"id": "data:geo:GSE48075", "type": "dataset", "label": "GEO GSE48075"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36267410", "type": "extraction", "used": ["source:pmid:36267410"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE48075", "type": "accession_resolution", "used": ["data:geo:GSE48075"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36267410", "parent_hash": "sha256:6f454d488786900ee00ffd975bceb43bcf3ae2b722a09196f4577d9286e40c24", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE48075 licence not read", "asset:data:geo:GSE48075 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, we constructed immune-related gene risk score (IRGRS) by using the Cox regression method and validated it with the IMvigor210 dataset."], "order_basis": "analysis_only", "id": "assay:1"}]}