Identification of a Nuclear Mitochondrial-Related Multi-Genes Signature to Predict the Prognosis of Bladder Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34692528", "kind": "science-record", "revision": 3, "content_hash": "sha256:a659f6c2008890118c50d9e3dab39ac875f6e524ba7e39f6fbabb0a7f7f38aaf", "created_at": "2026-11-01T17:36:21Z", "updated_at": "2026-11-01T21:26:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a Nuclear Mitochondrial-Related Multi-Genes Signature to Predict the Prognosis of Bladder Cancer.", "doi": "10.3389/fonc.2021.746029", "pmid": "34692528", "authors": ["Xuewen Jiang", "Yangyang Xia", "Hui Meng", "Yaxiao Liu", "Jianfeng Cui", "Huangwei Huang", "Gang Yin", "Benkang Shi"], "journal": "Frontiers in oncology", "year": 2021, "abstract": "INTRODUCTION: Bladder cancer (BC) is one of the most prevalent urinary cancers, and its management is still a problem causing recurrence and progression, elevating mortality.\nMATERIALS AND METHODS: We aimed at the nuclear mitochondria-related genes (MTRGs), collected from the MITOMAP: A Human Mitochondrial Genome Database. Meanwhile, the expression profiles and clinical information of BC were downloaded from the Cancer Genome Atlas (TCGA) as a training group. The univariate, multivariate, and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to construct a nuclear mitochondrial-related multi-genes signature and the prognostic nomogram.\nRESULTS: A total of 17 nuclear MTRGs were identified to be correlated with the overall survival (OS) of BC patients, and a nuclear MTRGs signature based on 16 genes expression was further determined by the LASSO Cox regression analysis. Based on a nuclear MTRGs scoring system, BC patients from the TCGA cohort were divided into high- and low- nuclear MTRGs score groups. Patients with a high nuclear MTRGs score exhibited a significantly poorer outcome (median OS: 92.90 vs 20.20 months, p<0.0001). The nuclear MTRGs signature was further verified in three independent datasets, namely, GSE13507, GSE31684, and GSE32548, from the Gene Expression Omnibus (GEO). The BC patients with a high nuclear MTRGs score had significantly worse survival (median OS in GSE13507: 31.52 vs 98.00 months, p<0.05; GSE31684: 32.85 months vs unreached, p<0.05; GSE32548: unreached vs unreached, p<0.05). Furthermore, muscle-invasive bladder cancer (MIBC) patients had a significantly higher nuclear MTRGs score (p<0.05) than non-muscle-invasive bladder cancer (NMIBC) patients. The integrated signature outperformed each involved MTRG. In addition, a nuclear MTRGs-based nomogram was constructed as a novel prediction prognosis model, whose AUC values for OS at 1, 3, 5 years were 0.76, 0.75, and 0.75, respectively, showing the prognostic nomogram had good and stable predicting ability. Enrichment analyses of the hallmark gene set and KEGG pathway revealed that the E2F targets, G2M checkpoint pathways, and cell cycle had influences on the survival of BC patients. Furthermore, the analysis of tumor microenvironment indicated more CD8+ T cells and higher immune score in patients with high nuclear MTRGs score, which might confer sensitivity to immune checkpoint inhibitors.\nCONCLUSIONS: Not only could the signature and prognostic nomogram predict the prognosis of BC, but it also had potential therapeutic guidance.", "claims": [{"id": "claim-method", "statement": "The TCGA bladder cancer dataset was analyzed to determine whether the nuclear MTRGs alterations correlated with survival of BC patients via univariate Cox proportional hazards regression analysis, which was conducted in R studio (v. 3.4.3, https://rstudio.com/). Then, least absolute shrinkage and selection operator (LASSO) Cox regression with 10-fold cross-validation was conducted by using the “glmnet” package in R studio (21). Meanwhile, multivariate Cox regression analysis was further utilized to identify prognostic nuclear MTRGs and to construct a nuclear MTRGs signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34692528", "locator": "sections[5] \"Survival Analysis and Construction of a Prognostic Nuclear MTRGs Signature\" paragraph 2", "quote": "The TCGA bladder cancer dataset was analyzed to determine whether the nuclear MTRGs alterations correlated with survival of BC patients via univariate Cox proportional hazards regression analysis, which was conducted in R studio (v. 3.4.3, https://rstudio.com/). Then, least absolute shrinkage and selection operator (LASSO) Cox regression with 10-fold cross-validation was conducted by using the “glmnet” package in R studio (21). Meanwhile, multivariate Cox regression analysis was further utilized to identify prognostic nuclear MTRGs and to construct a nuclear MTRGs signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34692528", "locator": "sections[22] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A nuclear mitochondrial-related 16-gene expression signature stratified bladder cancer patients by overall survival and was validated in three independent cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34692528", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 17 nuclear MTRGs were identified to be correlated with the overall survival (OS) of BC patients, and a nuclear MTRGs signature based on 16 genes expression was further determined by the LASSO Cox regression analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A nuclear mitochondrial-related 16-gene expression signature stratified bladder cancer patients by overall survival and was validated in three independent cohorts.", "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:34692528"], "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:34692528", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2021.746029", "sha256": "sha256:8741fa47032bf8157c80a0e69e9a097b870e0d32244d260b2ffb71b99b252417", "bytes": 39220, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2021.746029", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8528313", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "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": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/ Supplementary Material . 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Then, least absolute shrinkage and selection operator (LASSO) Cox regression with 10-fold cross-validation was conducted by using the “glmnet” package in R studio (21). Meanwhile, multivariate Cox regression analysis was further utilized to identify prognostic nuclear MTRGs and to construct a nuclear MTRGs signature."}}, {"id": "method-2", "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"}}], "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": "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": ["GSE13507", "GSE31684", "GSE32548"], "resolved_accessions": ["GSE13507", "GSE31684", "GSE32548"], "statements": ["The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE13507, GSE31684, GSE32548 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:34692528", "data:geo:GSE13507"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "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": "2026-11-01T17:04:56Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11436, "completion_tokens": 2211, "cached_tokens": 264}}, "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:34692528", "type": "paper", "label": "pmid:34692528"}, {"id": "source:pmid:34692528", "type": "manuscript", "label": "PMID 34692528 full text"}, {"id": "data:geo:GSE13507", "type": "dataset", "label": "GEO GSE13507"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34692528", "type": "paper", "label": "pmid:34692528"}, {"id": "source:pmid:34692528", "type": "manuscript", "label": "PMID 34692528 full text"}, {"id": "data:geo:GSE13507", "type": "dataset", "label": "GEO GSE13507"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34692528", "type": "extraction", "used": ["source:pmid:34692528"], "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:34692528", "parent_hash": "sha256:61df8c73621ba8a736f7dab6fe8e9d4923df2ea36c98c4a57f2ac507c367f090", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE13507 licence not read", "asset:data:geo:GSE13507 not downloaded", "asset:data:geo:GSE31684 licence not read", "asset:data:geo:GSE31684 not downloaded", "asset:data:geo:GSE32548 licence not read", "asset:data:geo:GSE32548 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "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": ["nd Analysis of Genomic Alterations in BC The RNA-sequencing data of BC (404 samples) and normal people ("], "order_basis": "paper_text_order:2786", "id": "assay:1"}, {"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": ["The TCGA bladder cancer dataset was analyzed to determine whether the nuclear MTRGs alterations correlated with survival of BC patients via univariate Cox proportional hazards regression analysis, which was conducted in R studio (v. 3.4.3, https://rstudio.com/). Then, least absolute shrinkage and selection operator (LASSO) Cox regression with 10-fold cross-validation was conducted by using the “glmnet” package in R studio (21). Meanwhile, multivariate Cox regression analysis was further utilized to identify prognostic nuclear MTRGs and to construct a nuclear MTRGs signature."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}