ArticleInternational journal of general medicine2022
A Bioinformatic Analysis of Immune-Related Prognostic Genes in Clear Cell Renal Cell Carcinoma Based on TCGA and GEO Databases.
Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 9 citations in OpenAlex.
- Eosinophils in solid cancers: sentinels, predictors, and therapeutic allies.Journal of experimental & clinical cancer research : CR · 2026Review
- A pan-cancer comparative analysis of the cancer genome atlas transcriptomic TIL-immune signatures.Cancer immunology, immunotherapy : CII · 2025Article
- A Pan-Cancer Comparative Analysis of The Cancer Genome Atlas Transcriptomic TIL-Immune Signatures.Research square · 2025Article
- Inhibiting ADORA1 enhances glioma apoptosis and increases its sensitivity to anti-PD1 therapy.Frontiers in oncology · 2025Article
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- Bioinformatics analysis and identification of genes and pathways involved in patients with Wilms tumor.Translational cancer research · 2022Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC.
methodsThe gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan-Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs.
resultsFour immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included
conclusionA total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC.
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