Identification of Immune-Related Genes Associated With Bladder Cancer Based on Immunological Characteristics and Their Correlation With the Prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34899848", "kind": "science-record", "revision": 4, "content_hash": "sha256:9b55435d84813e3f8847e3cd8361b92edb3c53387ca3085312bf4f8961794872", "created_at": "2026-11-01T17:36:36Z", "updated_at": "2026-11-01T22:05:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Immune-Related Genes Associated With Bladder Cancer Based on Immunological Characteristics and Their Correlation With the Prognosis.", "doi": "10.3389/fgene.2021.763590", "pmid": "34899848", "authors": ["Zhen Kang", "Wei Li", "Yan-Hong Yu", "Meng Che", "Mao-Lin Yang", "Jin-Jun Len", "Yue-Rong Wu", "Jun-Feng Yang"], "journal": "Frontiers in genetics", "year": 2021, "abstract": "BACKGROUND: To identify the immune-related genes of bladder cancer (BLCA) based on immunological characteristics and explore their correlation with the prognosis.\nMETHODS: We downloaded the gene and clinical data of BLCA from the Cancer Genome Atlas (TCGA) as the training group, and obtained immune-related genes from the Immport database. We downloaded GSE31684 and GSE39281 from the Gene Expression Omnibus (GEO) as the external validation group. R (version 4.0.5) and Perl were used to analyze all data.\nRESULT: Univariate Cox regression analysis and Lasso regression analysis revealed that 9 prognosis-related immunity genes (PIMGs) of differentially expressed immune genes (DEIGs) were significantly associated with the survival of BLCA patients (p < 0.01), of which 5 genes, including NPR2, PDGFRA, VIM, RBP1, RBP1 and TNC, increased the risk of the prognosis, while the rest, including CD3D, GNLY, LCK, and ZAP70, decreased the risk of the prognosis. Then, we used these genes to establish a prognostic model. We drew receiver operator characteristic (ROC) curves in the training group, and estimated the area under the curve (AUC) of 1-, 3- and 5-year survival for this model, which were 0.688, 0.719, and 0.706, respectively. The accuracy of the prognostic model was verified by the calibration chart. Combining clinical factors, we established a nomogram. The ROC curve in the external validation group showed that the nomogram had a good predictive ability for the survival rate, with a high accuracy, and the AUC values of 1-, 3-, and 5-year survival were 0.744, 0.770, and 0.782, respectively. The calibration chart indicated that the nomogram performed similarly with the ideal model.\nCONCLUSION: We had identified nine genes, including PDGFRA, VIM, RBP1, RBP1, TNC, CD3D, GNLY, LCK, and ZAP70, which played important roles in the occurrence and development of BLCA. The prognostic model based on these genes had good accuracy in predicting the OS of patients and might be promising candidates of therapeutic targets. This study may provide a new insight for the diagnosis, treatment and prognosis of BLCA from the perspective of immunology. However, further experimental studies are necessary to reveal the underlying mechanisms by which these genes mediate the progression of BLCA.", "claims": [{"id": "claim-method", "statement": "We used the LASSO Cox regression model in R package (Dalal et al., 2012) “glmnet” to find genes significantly associated with the prognosis to construct the prognostic model of BLCA (PMB).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34899848", "locator": "sections[6] \"Constructing and Validating the Prognostic Model of the Immune-Related Genes\" paragraph 2", "quote": "We used the LASSO Cox regression model in R package (Dalal et al., 2012) “glmnet” to find genes significantly associated with the prognosis to construct the prognostic model of BLCA (PMB)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34899848", "locator": "sections[15] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A nine-gene immune-related prognostic model was established with LASSO Cox regression using TCGA-BLCA data and validated in GSE31684 and GSE39281, with nomogram AUCs of 0.744, 0.770, and 0.782 for 1-, 3-, and 5-year survival.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34899848", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Then, we used these genes to establish a prognostic model."}, {"asset": "source:pmid:34899848", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We drew receiver operator characteristic (ROC) curves in the training group, and estimated the area under the curve (AUC) of 1-, 3- and 5-year survival for this model, which were 0.688, 0.719, and 0.706, respectively."}, {"asset": "source:pmid:34899848", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The ROC curve in the external validation group showed that the nomogram had a good predictive ability for the survival rate, with a high accuracy, and the AUC values of 1-, 3-, and 5-year survival were 0.744, 0.770, and 0.782, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A nine-gene immune-related prognostic model was established with LASSO Cox regression using TCGA-BLCA data and validated in GSE31684 and GSE39281, with nomogram AUCs of 0.744, 0.770, and 0.782 for 1-, 3-, and 5-year survival.", "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:34899848"], "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:GSE31684"], "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:GSE31684"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34899848", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2021.763590", "sha256": "sha256:7dacdaecfd60b3a326c7e5b4481b384654b0b4ebee190d8036dcf6b0017ef935", "bytes": 38716, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2021.763590", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8664377", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31684", "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=GSE31684", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31684", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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