Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37969384", "kind": "science-record", "revision": 3, "content_hash": "sha256:36b78fc15a7f49006c533dc3ae27c5a46a4bee931d5cde600982f020251b233d", "created_at": "2026-11-01T19:02:53Z", "updated_at": "2026-11-01T22:10:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis.", "doi": "10.21037/tcr-23-1183", "pmid": "37969384", "authors": ["Yonggui Xiao", "Chonghao Jiang", "Hubo Li", "Danping Xu", "Jinzheng Liu", "Youlong Huili", "Shiwen Nie", "Xiaohai Guan", "Fenghong Cao"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: Clear cell renal cell carcinoma (ccRCC) is the largest subtype of kidney tumour, with inflammatory responses characterising all stages of the tumour. Establishing the relationship between the genes related to inflammatory responses and ccRCC may help the diagnosis and treatment of patients with ccRCC.\nMethods: First, we obtained the data for this study from a public database. After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC. As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable. Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation. The data used for modelling and internal validation came from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (GSE29609) databases. ccRCC data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Tumour data from the E-MTAB-1980 cohort were used for external validation. The GSE40453 and GSE53757 datasets were used to verify the differential expression of inflammation-related gene model signatures (IRGMS). The immunohistochemistry of IRGMS was queried through the Human Protein Atlas (HPA) database. After the adequate validation of the IRGM, we further explored its application by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification.\nResults: The IRGM can robustly predict the prognosis of samples from patients with ccRCC from different databases. The verification results show that nomogram can accurately predict the survival rate of patients. Pathway enrichment analysis showed that patients in the high-risk (HR) group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggested that patients with higher IRGM scores had more treatment options.\nConclusions: The IRGMS can effectively predict the prognosis of ccRCC. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.", "claims": [{"id": "claim-method", "statement": "Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37969384", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All relevant data for this study are freely available through The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), Human Protein Atlas (HPA) databases and Internet public sources.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37969384", "locator": "sections[3] \"Data screening\" paragraph 2", "quote": "All relevant data for this study are freely available through The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Gene Expression Omnibus (GEO), Human Protein Atlas (HPA) databases and Internet public sources."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used differential analysis, Cox regression analysis, and LASSO regression on ccRCC data from multiple public databases to construct an inflammation-related prognostic model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37969384", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC."}, {"asset": "source:pmid:37969384", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable."}, {"asset": "source:pmid:37969384", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used differential analysis, Cox regression analysis, and LASSO regression on ccRCC data from multiple public databases to construct an inflammation-related prognostic model.", "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:37969384"], "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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