Identification of inflammation related gene signatures for bladder cancer prognosis prediction.

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The data were initially processed through differential analysis and prognostic correlation analysis, then a Least absolute shrinkage and selection operator (LASSO) regression model was constructed by M-cohort and a nomogram was designed to increase the model readability. The T-cohort was used for internal validation, with the GSE32894 and Imvigor210 cohorts used as external data to verify the model's accuracy. The model's predictive ability was verified for the prognosis of patients of different ages, gender, tumor stage, and tumour grade. The GSE3167, GSE13507 and GeneExpression Profiling Interactive Analysis (GEPIA) datasets and Human Protein Atlas (HPA) database were used to verify the expression of the inflammation-related genes, which were confirmed by real-time Polymerase Chain Reaction (PCR). A comprehensive analysis of the model's inflammation-related genes, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA) enrichment analysis, and immune-related analysis were also performed. Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations. Hierarchical validation results demonstrated that the model's predictive power is reliable for various patient stratifications. The expression of inflammation-related genes was consistent across The Cancer Genome Atlas (TCGA) database, GSE3167 dataset, GSE13507 dataset, Gene Expression Profiling Interactive Analysis (GEPIA) database, and the Human Protein Atlas (HPA) database, and was further validated by real-time Polymerase Chain Reaction (PCR). Pathway enrichment analysis indicated that patients in the high-risk (H-risk) group exhibited a variety of tumors. Meanwhile, patients in the low-risk (L-risk) group may be candidates for immunotherapy, whereas those in the high-risk group are more likely to benefit from chemotherapy. The model of inflammation-related genes can accurately predict bladder cancer patient prognosis, with MEST, FASN, KRT6B, and RGS2 anticipated to become new prognostic bladder cancer markers.", "claims": [{"id": "claim-method", "statement": "The M-cohort was utilized to build the LASSO regression model, which offers advantages over traditional linear regression models due to its parameter selection and shrinkage capabilities.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39572651", "locator": "sections[4] \"The LASSO regression model and nomogram construction\" paragraph 2", "quote": "The M-cohort was utilized to build the LASSO regression model, which offers advantages over traditional linear regression models due to its parameter selection and shrinkage capabilities."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this study are available in public databases. All data are attributed to the source.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39572651", "locator": "sections[26] \"Data availability\" paragraph 2", "quote": "The data used in this study are available in public databases. All data are attributed to the source."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39572651", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations."}, {"asset": "source:pmid:39572651", "locator": "sections[16] \"The model showed excellent predictive performance in internal data\" paragraph 2", "quote": "The area under the ROC curve of the nomogram was 0.714, which was much higher than common clinical indicators such as age, sex, and tumour grade (Fig. 3H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Both internal and external data validations confirmed that the developed model can accurately predict the prognosis across different patient populations.", "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:39572651"], "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. 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