ArticleFrontiers in oncology2022
A Novel Prognostic Signature Associated With the Tumor Microenvironment in Kidney Renal Clear Cell Carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 13 citations in OpenAlex.
- Discovery of Anoikis-correlated Biomarkers for Ovarian Cancer Through Integrated Transcriptome and Single-cell RNA Sequencing Analyses.Current topics in medicinal chemistry · 2026Article
- Multi-omics data integration for enhanced cancer subtyping via interactive multi-kernel learning.Briefings in bioinformatics · 2025Article
- Machine learning-derived prognostic signature integrating programmed cell death and mitochondrial function in renal clear cell carcinoma: identification of PIF1 as a novel target.Cancer immunology, immunotherapy : CII · 2025Article
- Potential prognostic and immunologic significances of ADAM8 in clear cell renal cell carcinoma.Medicine · 2025Article
- Alpha-2-Macroglobulin and Signature Genes: Predictive Biomarkers for Prognosis and Immunotherapy in Clear Cell Renal Cell Carcinoma.Journal of Cancer · 2025Article
- Comprehensive bioinformatics analysis unveils THEMIS2 as a carcinogenic indicator related to immune infiltration and prognosis of thyroid cancer.Scientific reports · 2024Article
- STAC3 as a poor prognostic biomarker in renal clear cell carcinoma: relationship with immune infiltration.American journal of cancer research · 2024Article
- Construction and evaluation of a prognostic model for metabolism-related genes in kidney renal clear cell carcinoma using TCGA database.American journal of clinical and experimental urology · 2024Article
- NPeerJ · 2023Article
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Authors and funding
9 authors at 1 institution in 1 country.
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Abstract
Background: The tumor microenvironment (TME) is a complex and evolving environment, and the tumor immune microenvironment in kidney renal clear cell carcinoma (KIRC) has a strong suppressive profile. This study investigates the potential prognostic role and value of genes of the tumor microenvironment in KIRC. Methods: The transcriptome sequencing data of 530 cases and 39 cases of KIRC and the corresponding clinical prognosis information were downloaded from TCGA data and GEO data, respectively, and TME-related gene expression profiles were extracted. A prognostic signature was constructed and evaluated using univariate Cox regression analysis and LASSO regression analysis. Gene set enrichment analysis (GSEA) was used to obtain the biological process of gene enrichment in patients with high and low-risk groups. Results: A prognostic signature consisting of eight TME-related genes (LRFN1, CSF1, UCN, TUBB2B, SERPINF1, ADAM8, ABCB4, CCL22) was constructed. Kaplan-Meier survival analysis yielded significantly lower survival times for patients in the high-risk group than in the low-risk group, and the AUC values for the ROC curves of this prognostic signature were essentially greater than 0.7, and univariate and multifactorial Cox regression analyses indicated that the risk score was independent risk factors for KIRC prognosis. GSEA analysis showed that immune-related biological processes were enriched in the high-risk group and that risk values were strongly associated with multiple immune cell scores and immune checkpoint-related genes (PDCD1, CTLA4). Conclusions: The prognostic signature can accurately predict the prognosis of KIRC patients, which may provide new ideas for future precision immunotherapy of KIRC.
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