Characterization of the Lipid Metabolism in Bladder Cancer to Guide Clinical Therapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36131793", "kind": "science-record", "revision": 3, "content_hash": "sha256:375d2609c47d77a0ac422801244cb25d0ae6e32d548d7a67510a67f741c401b0", "created_at": "2026-11-01T18:15:24Z", "updated_at": "2026-11-01T21:28:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of the Lipid Metabolism in Bladder Cancer to Guide Clinical Therapy.", "doi": "10.1155/2022/7679652", "pmid": "36131793", "authors": ["Yuan-Yuan Yang", "Sen-Yuan Hong", "Yang Xun", "Chen-Qian Liu", "Jian-Xuan Sun", "Jin-Zhou Xu", "Meng-Yao Xu", "Ye An", "Deng He", "Qi-Dong Xia", "Shao-Gang Wang"], "journal": "Journal of oncology", "year": 2022, "abstract": "Background: Bladder cancer is one of the most common malignancies of the urinary system with an unfavorable prognosis. More and more studies have suggested that lipid metabolism could influence the progression and treatment of tumors. However, there are few studies exploring the relationship between lipid metabolism and bladder cancer. This study aimed to explore the roles that lipid metabolism-related genes play in patients with bladder cancer.\nMethods: TCGA_BLCA cohort and GSE13507 cohort were included in this study, and transcriptional and somatic mutation profiles of 309 lipid metabolism-related genes were analyzed to discover the critical lipid metabolism-related genes in the incurrence and progression of bladder cancer. Furthermore, the TCGA_BLCA cohort was randomly divided into training set and validation set, and the GSE13507 cohort was served as an external independent validation set. We performed the LASSO regression and multivariate Cox regression in training set to develop a prognostic signature and further verified this signature in TCGA_BLCA validation set and GSE13507 external validation set. Finally, we systematically investigated the association between this signature and tumor microenvironment, drug response, and potential functions and then verified the differential expression status of signature genes in the protein level by immunohistochemistry.\nResults: A novel 6-lipidmetabolism-related gene signature was identified and validated, and this risk score model could predict the prognosis of patients with bladder cancer. In addition, the prognostic model was tightly related to immune cell infiltration and tumor mutation burden. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) showed that mTOR signaling pathway, G2M checkpoint, fatty acid metabolism, and hypoxia were enriched in patients in the high-risk score groups. Furthermore, 3 therapies specific for bladder cancer patients in different risk scores were identified.\nConclusion: s. In conclusion, we investigated the lipid metabolism-related genes in bladder cancer through comprehensive bioinformatic analysis. A novel 6-gene signature associated with lipid metabolism for predicting the outcomes of patients with bladder cancer was conducted and validated. Furthermore, the risk score model could be utilized to indicate the choice of therapy in bladder cancer.", "claims": [{"id": "claim-method", "statement": "Then, we separately conducted LASSO regression to screen appropriate variables and multivariate cox regression to establish the prognostic signature in the training set, and a formula of risk score based on these lipid metabolism-related genes was established:", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36131793", "locator": "sections[5] \"2.3. Identification of the Prognostic Signature\" paragraph 2", "quote": "Then, we separately conducted LASSO regression to screen appropriate variables and multivariate cox regression to establish the prognostic signature in the training set, and a formula of risk score based on these lipid metabolism-related genes was established:"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The source data of this study were derived from the public repositories, as indicated in the section of “Methods” of the manuscript. All data that support the findings of this study are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36131793", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The source data of this study were derived from the public repositories, as indicated in the section of “Methods” of the manuscript. All data that support the findings of this study are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Furthermore, we conducted meta-analysis based on the three cohorts to demonstrate that the risk score was in good validity (HR = 1.49, 95% CI = 1.15–1.94, p = 0.01) (Figure 3(r)).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Furthermore, we conducted meta-analysis based on the three cohorts to demonstrate that the risk score was in good validity (HR = 1.49, 95% CI = 1.15–1.94, p = 0.01) (Figure 3(r))."}, "evidence": [{"asset": "source:pmid:36131793", "locator": "sections[12] \"3.2. Construction and Verification of the Prognostic Index\" paragraph 2", "quote": "Finally, six genes (CYP1B1, ACOT13, NUDT19, SCD, IL4I1, and DECR1) were used for the construction of prognostic risk score model (Figures 3(a) and 3(b))."}, {"asset": "source:pmid:36131793", "locator": "sections[12] \"3.2. Construction and Verification of the Prognostic Index\" paragraph 2", "quote": "Furthermore, we conducted meta-analysis based on the three cohorts to demonstrate that the risk score was in good validity (HR = 1.49, 95% CI = 1.15–1.94, p = 0.01) (Figure 3(r))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Furthermore, we conducted meta-analysis based on the three cohorts to demonstrate that the risk score was in good validity (HR = 1.49, 95% CI = 1.15–1.94, p = 0.01) (Figure 3(r)).", "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:36131793"], "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:36131793", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/7679652", "sha256": "sha256:1418bcdfbd95a189a34ff9684e15622492e5f8fc512402265937f6295a2130f7", "bytes": 38433, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/7679652", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9484922", "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": "The source data of this study were derived from the public repositories, as indicated in the section of “Methods” of the manuscript. 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