A novel focal adhesion-related risk model predicts prognosis of bladder cancer -- a bioinformatic study based on TCGA and GEO database.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36357874", "kind": "science-record", "revision": 4, "content_hash": "sha256:62feae489b092e5a90716bc7aa677ab78d2834e181be427b0753998af8181c33", "created_at": "2026-11-01T18:15:48Z", "updated_at": "2026-11-01T22:08:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel focal adhesion-related risk model predicts prognosis of bladder cancer -- a bioinformatic study based on TCGA and GEO database.", "doi": "10.1186/s12885-022-10264-5", "pmid": "36357874", "authors": ["Jiyuan Hu", "Linhui Wang", "Luanfeng Li", "Yutao Wang", "Jianbin Bi"], "journal": "BMC cancer", "year": 2022, "abstract": "BACKGROUND: Bladder cancer (BLCA) is the ninth most common cancer globally, as well as the fourth most common cancer in men, with an incidence of 7%. However, few effective prognostic biomarkers or models of BLCA are available at present.\nMETHODS: The prognostic genes of BLCA were screened from one cohort of The Cancer Genome Atlas (TCGA) database through univariate Cox regression analysis and functionally annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The intersecting genes of the BLCA gene set and focal adhesion-related gene were obtained and subjected to the least absolute shrinkage and selection operator regression (LASSO) to construct a prognostic model. Gene set enrichment analysis (GSEA) of high- and low-risk patients was performed to explore further the biological process related to focal adhesion genes. Univariate and multivariate Cox analysis, receiver operating characteristic (ROC) curve analysis, and Kaplan-Meier survival analysis (KM) were used to evaluate the prognostic model. DNA methylation analysis was presented to explore the relationship between prognosis and gene methylation. Furthermore, immune cell infiltration was assessed by CIBERSORT, ESTIMATE, and TIMER. The model was verified in an external GSE32894 cohort of the Gene Expression Omnibus (GEO) database, and the Prognoscan database presented further validation of genes. The HPA database validated the related protein level, and functional experiments verified significant risk factors in the model.\nRESULTS: VCL, COL6A1, RAC3, PDGFD, JUN, LAMA2, and ITGB6 were used to construct a prognostic model in the TCGA-BLCA cohort and validated in the GSE32894 cohort. The 7-gene model successfully stratified the patients into both cohorts' high- and low-risk groups. The higher risk score was associated with a worse prognosis.\nCONCLUSIONS: The 7-gene prognostic model can classify BLCA patients into high- and low-risk groups based on the risk score and predict the overall survival, which may aid clinical decision-making.", "claims": [{"id": "claim-method", "statement": "The above DEGs were subjected to Lasso regression, and 7 genes with the smallest deviance were included in the prognostic model (Figs. 3a, b).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36357874", "locator": "sections[21] \"Establishment of a prognostic model based on the risk score\" paragraph 2", "quote": "The above DEGs were subjected to Lasso regression, and 7 genes with the smallest deviance were included in the prognostic model (Figs. 3a, b)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The study’s GEO dataset (GSE32894) can be downloaded from https://www.ncbi.nlm.nih.gov/gds/. 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TCGA-BLCA gene matrix (HTSeq-FPKM) and clinical data can be obtained from https://xenabrowser.net and http://www.cbioportal.org."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 7-gene risk model can successfully stratify BLCA patients into prognostic groups.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36357874", "locator": "sections[27] \"Verification of the prognostic model\" paragraph 2", "quote": "Taken together, the 7-gene risk model can successfully stratify BLCA patients into prognostic groups."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The 7-gene risk model can successfully stratify BLCA patients into prognostic groups.", "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:36357874"], "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:36357874", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-022-10264-5", "sha256": "sha256:720eec6878d26a83113328133e36646ce6611ea13f6bff06bcfac6bc73b5fd3c", "bytes": 58157, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-022-10264-5", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9647995", "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 study’s GEO dataset (GSE32894) can be downloaded from https://www.ncbi.nlm.nih.gov/gds/. 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Sequences of siRNA for transfection. Additional file 14: Supplementary Table 6.", "hods Cell culture and small interfering RNA (siRNA) transfection The human BLCA cell line T24 w", "with 10% fetal bovine serum. The sequence of siRNA targeting COL6A1 and LAMA2 purchased from JT"], "order_basis": "paper_text_order:2210", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["inese Academy of Sciences cell bank. 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