Alteration in glycolytic/cholesterogenic gene expression is associated with bladder cancer prognosis and immune cell infiltration.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34980012", "kind": "science-record", "revision": 5, "content_hash": "sha256:4432d98bd5795fc89a1ed9f277480f6d1e18a02ad953d2a2003b8b100b8d95c3", "created_at": "2026-11-01T17:36:41Z", "updated_at": "2026-11-01T22:48:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Alteration in glycolytic/cholesterogenic gene expression is associated with bladder cancer prognosis and immune cell infiltration.", "doi": "10.1186/s12885-021-09064-0", "pmid": "34980012", "authors": ["Yuying Zhang", "Baoyi Zhu", "Yi Cai", "Sihua Zhu", "Hongjun Zhao", "Xiaoling Ying", "Chonghe Jiang", "Jianwen Zeng"], "journal": "BMC cancer", "year": 2022, "abstract": "BACKGROUND: Oncogenic metabolic reprogramming contributes to tumor growth and immune evasion. The intertumoral metabolic heterogeneity and interaction of distinct metabolic pathways may determine patient outcomes. In this study, we aim to determine the clinical and immunological significance of metabolic subtypes according to the expression levels of genes related to glycolysis and cholesterol-synthesis in bladder cancer (BCa).\nMETHODS: Based on the median expression levels of glycolytic and cholesterogenic genes, patients were stratified into 4 subtypes (mixed, cholesterogenic, glycolytic, and quiescent) in an integrated cohort including TCGA, GSE13507, and IMvigor210. Clinical, genomic, transcriptomic, and tumor microenvironment characteristics were compared between the 4 subtypes.\nRESULTS: The 4 metabolic subtypes exhibited distinct clinical, molecular, and genomic patterns. Compared to quiescent subtype, mixed subtype was more likely to be basal tumors and was significantly associated with poorer prognosis even after controlling for age, gender, histological grade, clinical stage, and molecular phenotypes. Additionally, mixed tumors harbored a higher frequency of RB1 and LRP1B copy number deletion compared to quiescent tumors (25.7% vs. 12.7 and 27.9% vs. 10.2%, respectively, both adjusted P value< 0.05). Furthermore, aberrant PIK3CA expression level was significantly correlated with those of glycolytic and cholesterogenic genes. The quiescent subtype was associated with lower stemness indices and lower signature scores for gene sets involved in genomic instability, including DNA replication, DNA damage repair, mismatch repair, and homologous recombination genes. Moreover, quiescent tumors exhibited lower expression levels of pyruvate dehydrogenase kinases 1-3 (PDK1-3) than the other subtypes. In addition, distinct immune cell infiltration patterns were observed across the 4 metabolic subtypes, with greater infiltration of M0/M2 macrophages observed in glycolytic and mixed subtypes. However, no significant difference in immunotherapy response was observed across the 4 metabolic subtypes.\nCONCLUSION: This study proposed a new metabolic subtyping method for BCa based on genes involved in glycolysis and cholesterol synthesis pathways. Our findings may provide novel insight for the development of personalized subtype-specific treatment strategies targeting metabolic vulnerabilities.", "claims": [{"id": "claim-method", "statement": "R (version 4.0.2) was used for statistical analyses. Kaplan-Meier plots with log-rank test were used to test differences in overall survival using the ‘survival’ and ‘survminer’ tools in the R software package. Cox regression was used to evaluate differences in overall survival after adjusting for potential confounders such as age, gender, histological grade, and cancer stage. The Kruskal-Wallis test, Chi-square test, ANOVA, or Fisher exact test was used for between-group comparisons where appropriate.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34980012", "locator": "sections[9] \"Statistical analysis\" paragraph 2", "quote": "R (version 4.0.2) was used for statistical analyses. Kaplan-Meier plots with log-rank test were used to test differences in overall survival using the ‘survival’ and ‘survminer’ tools in the R software package. Cox regression was used to evaluate differences in overall survival after adjusting for potential confounders such as age, gender, histological grade, and cancer stage. The Kruskal-Wallis test, Chi-square test, ANOVA, or Fisher exact test was used for between-group comparisons where appropriate."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The current study analyzed publicly available datasets which can be found in https://xena.ucsc.edu/welcome-to-ucsc-xena (TCGA); https://www.ncbi.nlm.nih.gov/gds/?term=GSE13507 (GSE13507); http://research-pub.gene.com/IMvigor210CoreBiologies/ (IMvigor210).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34980012", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The current study analyzed publicly available datasets which can be found in https://xena.ucsc.edu/welcome-to-ucsc-xena (TCGA); https://www.ncbi.nlm.nih.gov/gds/?term=GSE13507 (GSE13507); http://research-pub.gene.com/IMvigor210CoreBiologies/ (IMvigor210)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 4 metabolic subtypes exhibited distinct clinical, molecular, and genomic patterns.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34980012", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The 4 metabolic subtypes exhibited distinct clinical, molecular, and genomic patterns."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The 4 metabolic subtypes exhibited distinct clinical, molecular, and genomic patterns.", "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:34980012"], "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:34980012", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-021-09064-0", "sha256": "sha256:bc52e060441e4da14c9d1623f81e2a426f9a4c3c63c0268717a71a82e7324b3a", "bytes": 41078, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-021-09064-0", "version": null, "note": "Full article fetched from PMC; 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