ArticleBMC cancer2022
A promising Prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 21 citations in OpenAlex.
- An Integrated Immunometabolic Signature Predicts Prognosis and Immunotherapy Response in ccRCC and IdentifiesCancers · 2026Article
- CDC20 Regulates the Progression of Clear Cell Renal Cell Carcinoma via the Wnt/β-Catenin Signaling Pathway.Genetics research · 2026Article
- Article
- A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma.European journal of medical research · 2024Article
- Establishing a prognostic model of chromatin modulators and identifying potential drug candidates in renal clear cell patients.BMC bioinformatics · 2023Article
- Adjuvant Therapy for Renal Cell Carcinoma: End Points, Outcomes, and Risk Assessments.JCO precision oncology · 2023Review
- The emerging role of long non-coding RNAs in renal cell carcinoma progression and clinical therapyFrontiers in pharmacology · 2023Review
- Identifying tumor antigens and immune subtypes of renal cell carcinoma for immunotherapy development.Frontiers in immunology · 2022Article
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10 authors at 1 institution in 1 country.
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Abstract
backgroundRenal cell carcinoma (RCC) is a third most common tumor of the urinary system. Nowadays, Immunotherapy is a hot topic in the treatment of solid tumors, especially for those tumors with pre-activated immune state.
methodsIn this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas (TCGA) database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. Then we established a prognostic risk model consisting of the genes most related to prognosis from four signatures to value prognosis of the RCC samples via Kaplan-Meier (KM) survival analysis. An independent data from International Cancer Genome Consortium (ICGC) database were used to test the predictive stability of the model. Furthermore, we performed landscape analysis to assess the difference of gene mutant in the RCC samples from TCGA. Finally, we explored the correlation between the selected genes and the level of tumor immune infiltration via Tumor Immune Estimation Resource (TIMER) platform.
resultsWe used four genetic signatures to construct prognostic risk models respectively and found that each of the models could divide the RCC samples into high- and low-risk groups with significantly different prognosis, especially in advanced RCC. A comprehensive prognostic risk model was constructed by 8 candidate genes from four signatures (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) dividing the advanced RCC samples from TCGA database into high-risk and low-risk groups with a significant difference in cancer-specific survival (CSS). The stability of the model was verified by independent data from ICGC database. And the classification efficiency of the model was stable for the samples from different subgroups. Landscape analysis showed that mutation ratios of some genes were different between two risk groups. In addition, the expression levels of the selected genes were significantly correlated with the infiltration degree of immune cells in the advanced RCC.
conclusionsSum up, eight immune-related genes were screened in our study to construct prognostic risk model with great predictive value for the prognosis of advanced RCC, and the genes were associated with infiltrating immune cells in tumors which have potential to conduct personalized treatment for advanced RCC.
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