Inflammation-related signature for prognostic prediction, tumor immune, genomic heterogeneity, and drug choices in prostate cancer: Integrated analysis of bulk and single-cell RNA-sequencing.

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Accumulating evidence suggests that inflammation has an intricate relationship with tumorigenesis, tumor progression and tumor immune microenvironment. However, the overall impact of inflammation-related genes on the clinical prognosis and tumor immunity in PCa remains unclear.\nMethods: Machine learning methods were utilized to construct and validate a signature using The Cancer Genome Atlas (TCGA) for training, while the Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70769 cohorts for independent validation. The efficacy of the signature in predicting outcomes and its clinical utility were assessed through a series of investigations encompassing in vitro experiments, survival analysis, and nomogram development. The association between the signature and precision medicine was explored via tumor immunity, genomic heterogeneity, therapeutic response, and molecular docking analyses, using bulk and single-cell RNA-sequencing data.\nResults: We identified 7 inflammation-related genes with prognostic significance and developed an inflammation-related prognostic signature (IRPS) with 6 genes. Furthermore, we demonstrated that both the IRPS and a nomogram integrating risk score and pathologic T stage exhibited excellent predictive ability for the survival outcomes in PCa patients. Moreover, the IRPS was found to be significantly associated with the tumor immune, genomic heterogeneity, therapeutic response, and drug selection.\nConclusion: IRPS can serve as a reliable predictor for PCa patients. The signature may provide clinicians with valuable information on the efficacy of therapy and help personalize treatment for PCa patients.", "claims": [{"id": "claim-method", "statement": "Prognostic IRGs were further identified by the univariate Cox method.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37920511", "locator": "sections[4] \"Screening for differentially expressed and prognostic IRGs\" paragraph 2", "quote": "Prognostic IRGs were further identified by the univariate Cox method."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data included in article/supplementary material/referenced in article.\nSupplementary content related to this article has been published online at [URL].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37920511", "locator": "sections[27] \"Data availability statement\" paragraphs 1-3", "quote": "Data included in article/supplementary material/referenced in article.\nSupplementary content related to this article has been published online at [URL]."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Univariate Cox regression analysis of TCGA data identified 7 inflammation-related genes associated with biochemical recurrence-free survival.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37920511", "locator": "sections[16] \"Identification of prognostic IRGs in the TCGA cohort\" paragraph 2", "quote": "Among these, 7 genes, namely CD55, CDKN1A, INHBA, KCNJ2, PIK3R5, PTGIR, and TNFSF10, were related to bRFS through a univariate Cox regression analysis (Fig. 2A–C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Univariate Cox regression analysis of TCGA data identified 7 inflammation-related genes associated with biochemical recurrence-free survival.", "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:37920511"], "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": "blocked", "blocked_on": "data:accession_unresolved", "description": "The GEO series accession resolves and its sample accessions are listed. 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