ArticleInternational journal of general medicine2021
Prognostic Value of Metabolism-Related Genes and Immune Infiltration in Clear Cell Renal Cell Carcinoma.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Decoding the Sphingolipid Landscape of Clear Cell Renal Cell Carcinoma: A Single-Cell-Guided Prognostic Model Built With 101 Machine Learning.Human mutation · 2026Article
- KIF14 plays a role in the regulation of the cell cycle and has implications for prognosis in clear cell renal cell carcinoma.BMC urology · 2025Article
- Identifying ADME-related gene signature for immune landscape and prognosis in KIRC by single-cell and spatial transcriptome analysis.Scientific reports · 2025Article
- Migrasome-Related Prognostic Genes in Gastric Cancer: A Transcriptomic and Immunotherapeutic Analysis.OncoTargets and therapy · 2025Article
- Highly expressed of BID indicates poor prognosis and mediates different tumor microenvironment characteristics in clear cell renal cell carcinoma.Discover oncology · 2024Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is one of the most prevalent cancers. Thus, it is warranted to detect the status of metabolism-related genes (MRGs) and infiltrating immune cells in ccRCC progression for the prognosis of ccRCC. This research was designed to establish and verify the prognostic signature of ccRCC using MRGs. In addition, we investigated the potential link between the relative proportion of tumor infiltrated immune cells (TIICs) and ccRCC prognosis.
methodsSequencing data of metabolism-related gene sets in ccRCC cases were obtained from The Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus Database (GEO). The R Programming Language software packages were applied for differential analysis of MRGs. First, a univariate Cox regression model was applied to determine the MRGs linked with overall survival (OS). Then, the multivariate Cox regression model was applied to establish the prognostic signature. Finally, the CIBERSORT algorithm was used to determine the proportion of TIICs.
resultsOverall, 286 differentially expressed MRGs were identified in the TCGA dataset. Univariate and multivariate Cox regression models were applied to develop a prognostic signature with six MRGs. The predictive capability of the prognostic signature was further verified by TCGA and GEO database. In addition,
conclusionHerein, a prognostic signature was developed using MRGs for ccRCC prognosis. The proportion of 22 TIICs in ccRCC and the association between TIICs and clinical outcomes were also determined. The identified genes and cells could guide future targeted therapy and immunotherapy.
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