ArticleTranslational cancer research2023
Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis.
Article in Translational cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed, 5 citations in OpenAlex.
- Bioinformatics screening of prognostic immune-related genes in renal clear cell carcinoma.Journal of applied genetics · 2025Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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. Methods: 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. Results: 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. Conclusions: 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.
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