Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32117435", "kind": "science-record", "revision": 7, "content_hash": "sha256:b982e540a27c09d3c525ac01d8811377b7abd15494e48348fa281106192abe96", "created_at": "2026-11-01T15:40:57Z", "updated_at": "2026-11-01T22:01:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of an Individualized Prognostic Signature of Bladder Cancer Based on Seven Immune Related Genes.", "doi": "10.3389/fgene.2020.00012", "pmid": "32117435", "authors": ["Huaide Qiu", "Xiaorong Hu", "Chuan He", "Binbin Yu", "Yongqiang Li", "Jianan Li"], "journal": "Frontiers in genetics", "year": 2020, "abstract": "BACKGROUND: There has been no report of prognostic signature based on immune-related genes (IRGs). This study aimed to develop an IRG-based prognostic signature that could stratify patients with bladder cancer (BLCA).\nMETHODS: RNA-seq data along with clinical information on BLCA were retrieved from the Cancer Genome Atlas (TCGA) and gene expression omnibus (GEO). Based on TCGA dataset, differentially expressed IRGs were identified via Wilcoxon test. Among these genes, prognostic IRGs were identified using univariate Cox regression analysis. Subsequently, we split TCGA dataset into the training (n = 284) and test datasets (n = 119). Based on the training dataset, we built a least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model with multiple prognostic IRGs. It was validated in the training dataset, test dataset, and external dataset GSE13507 (n = 165). Additionally, we accessed the six types of tumor-infiltrating immune cells from Tumor Immune Estimation Resource (TIMER) website and analyzed the difference between risk groups. Further, we constructed and validated a nomogram to tailor treatment for patients with BLCA.\nRESULTS: A set of 47 prognostic IRGs was identified. LASSO regression and identified seven BLCA-specific prognostic IRGs, i.e., RBP7, PDGFRA, AHNAK, OAS1, RAC3, EDNRA, and SH3BP2. We developed an IRG-based prognostic signature that stratify BLCA patients into two subgroups with statistically different survival outcomes [hazard ratio (HR) = 10, 95% confidence interval (CI) = 5.6-19, P < 0.001]. The ROC curve analysis showed acceptable discrimination with AUCs of 0.711, 0.754, and 0.772 at 1-, 3-, and 5-year follow-up respectively. The predictive performance was validated in the train set, test set, and external dataset GSE13507. Besides, the increased infiltration of CD4+ T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells in the high-risk group (as defined by the signature) indicated chronic inflammation may reduce the survival chances of BLCA patients. The nomogram demonstrated to be clinically-relevant and effective with accurate prediction and positive net benefit.\nCONCLUSION: The present immune-related signature can effectively classify BLCA patients into high-risk and low-risk groups in terms of survival rate, which may help select high-risk BLCA patients for more intensive treatment.", "claims": [{"id": "claim-method", "statement": "In the R software, differentially expressed genes (DEGs) between BLCA and normal tissues were identified using Wilcoxon test after within-array replicate probes were replaced with their average via limma package (Ritchie et al., 2015; Yue et al., 2019).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32117435", "locator": "sections[4] \"Data Preprocessing and Differential Analysis\" paragraph 2", "quote": "In the R software, differentially expressed genes (DEGs) between BLCA and normal tissues were identified using Wilcoxon test after within-array replicate probes were replaced with their average via limma package (Ritchie et al., 2015; Yue et al., 2019)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Expression profile can be accessed in The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), while immune related gene list can be retrieved from the Immunology Database and Analysis Portal (IMMPORT) website (https://www.immport.org). Abundance of tumor-infiltrating immune cells were retrieved from the Tumor Immune Estimation Resource (TIMER) website (https://cistrome.shinyapps.io/timer/). The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32117435", "locator": "sections[19] \"Data Availability Statement\" paragraph 2", "quote": "Expression profile can be accessed in The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), while immune related gene list can be retrieved from the Immunology Database and Analysis Portal (IMMPORT) website (https://www.immport.org). Abundance of tumor-infiltrating immune cells were retrieved from the Tumor Immune Estimation Resource (TIMER) website (https://cistrome.shinyapps.io/timer/). The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "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:32117435"], "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": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32117435", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2020.00012", "sha256": "sha256:7654521443bbb2e1a94ec28ce595c9427716e7b41e3a01d666c12e7f41ee895a", "bytes": 44494, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2020.00012", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7013035", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13507", "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=GSE13507", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13507", "version": null, "note": "Expression profile can be accessed in The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), while immune related gene list can be retrieved from the Immunology Database and Analysis Portal (IMMPORT) website (https://www.immport.org). Abundance of tumor-infiltrating immune cells were retrieved from the Tumor Immune Estimation Resource (TIMER) website (https://cistrome.shinyapps.io/timer/). The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Expression profile can be accessed in The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), while immune related gene list can be retrieved from the Immunology Database and Analysis Portal (IMMPORT) website (https://www.immport.org). Abundance of tumor-infiltrating immune cells were retrieved from the Tumor Immune Estimation Resource (TIMER) website (https://cistrome.shinyapps.io/timer/). 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"1.1783", "1.0856", "1.2790", "0.0001"], ["NR3C2", "1.1619", "1.0673", "1.2648", "0.0005"], ["GLP2R", "1.1511", "1.0672", "1.2416", "0.0003"], ["AGTR1", "1.1437", "1.0661", "1.2270", "0.0002"], ["FGF10", "1.1685", "1.0575", "1.2911", "0.0022"], ["SEMA3E", "1.2198", "1.0529", "1.4131", "0.0081"], ["NGF", "1.1376", "1.0521", "1.2300", "0.0012"], ["EDNRA", "1.0914", "1.0496", "1.1349", "0.0000"], ["ADIPOQ", "1.0894", "1.0438", "1.1369", "0.0001"], ["SEMA3A", "1.1546", "1.0428", "1.2785", "0.0057"], ["PDGFD", "1.0752", "1.0370", "1.1149", "0.0001"], ["NFATC1", "1.1038", "1.0299", "1.1830", "0.0052"], ["AKT3", "1.0724", "1.0284", "1.1183", "0.0011"], ["IL17RD", "1.0712", "1.0245", "1.1201", "0.0025"], ["PDGFRA", "1.0444", "1.0213", "1.0680", "0.0001"], ["ILK", "1.0637", "1.0204", "1.1088", "0.0036"], ["NFATC4", "1.0614", "1.0195", "1.1050", "0.0037"], ["NRP2", "1.0482", "1.0189", "1.0784", "0.0011"], ["KCNH2", "1.0335", "1.0188", "1.0484", "0.0000"], ["IL34", "1.0409", "1.0156", "1.0667", 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"1.0004", "0.0093"], ["TAP1", "0.9950", "0.9915", "0.9986", "0.0061"], ["OAS1", "0.9855", "0.9769", "0.9942", "0.0011"], ["SH3BP2", "0.8812", "0.8154", "0.9523", "0.0014"]], "row_count": 48, "column_count": 5, "numeric_cells": 194, "header_rows": 1, "locator": "section[12]/table-wrap[1]#T1"}}], "gaps": ["asset:data:geo:GSE13507 not downloaded", "asset:data:geo:GSE13507 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "In the R software, differentially expressed genes (DEGs) between BLCA and normal tissues were identified using Wilcoxon test after within-array replicate probes were replaced with their average via limma package (Ritchie et al., 2015; Yue et al., 2019)."}}, {"id": "method-2", "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"}}, {"id": "method-3", "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": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-4", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE13507"], "resolved_accessions": ["GSE13507"], "statements": ["Expression profile can be accessed in The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/), while immune related gene list can be retrieved from the Immunology Database and Analysis Portal (IMMPORT) website (https://www.immport.org). Abundance of tumor-infiltrating immune cells were retrieved from the Tumor Immune Estimation Resource (TIMER) website (https://cistrome.shinyapps.io/timer/). The data sets used and/or analyzed during the current study are available from the corresponding author on reasonable request."], "basis": "The paper names GSE13507 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": [], "assets": ["source:pmid:32117435", "data:geo:GSE13507", "asset:table:pmid:32117435:1"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "group_comparison_wilcoxon", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE13507"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:18:32Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 36040, "completion_tokens": 2532, "cached_tokens": 784}}, "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:32117435", "type": "paper", "label": "pmid:32117435"}, {"id": "source:pmid:32117435", "type": "manuscript", "label": "PMID 32117435 full text"}, {"id": "data:geo:GSE13507", "type": "dataset", "label": "GEO GSE13507"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:32117435", "type": "paper", "label": "pmid:32117435"}, {"id": "source:pmid:32117435", "type": "manuscript", "label": "PMID 32117435 full text"}, {"id": "data:geo:GSE13507", "type": "dataset", "label": "GEO GSE13507"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-32117435", "type": "extraction", "used": ["source:pmid:32117435"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE13507", "type": "accession_resolution", "used": ["data:geo:GSE13507"], "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:32117435", "parent_hash": "sha256:f08f85d61905397fab929ea344456d563194e0dee67b915137cc74bac032d08c", "fork_of": null, "generation": 6}, "gaps": ["asset:data:geo:GSE13507 licence not read", "asset:data:geo:GSE13507 not downloaded", "execution.not_run", "method.group_comparison_wilcoxon", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:group_comparison_wilcoxon"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["atients with bladder cancer (BLCA). METHODS: RNA-seq data along with clinical information on BLCA"], "order_basis": "paper_text_order:309", "id": "assay:1"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In the R software, differentially expressed genes (DEGs) between BLCA and normal tissues were identified using Wilcoxon test after within-array replicate probes were replaced with their average via limma package (Ritchie et al., 2015; Yue et al., 2019)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}