ArticleScientific reports2025
NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Neutrophils in cancer.Nature reviews. Cancer · 2026Review
- Deciphering the Heterogeneous Microenvironment of Head and Neck Squamous Cell Carcinoma Through an Integrated Immune Inflammation Framework.International journal of genomics · 2026Article
- Tumor-associated neutrophils in renal cell carcinoma.Frontiers in immunology · 2026Review
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Authors and funding
9 authors.
Funding
Abstract
Further research is needed to investigate the association between netosis and clear cell renal cell carcinoma (ccRCC). We developed a prognostic framework for netosis using univariate, Lasso, and multivariate Cox regression analyses. The CIBERSORT algorithm was employed to compute immune infiltration metrics for The Cancer Genome Atlas (TCGA) dataset. These scores, combined with Cox regression analysis and patient survival data, contribute to the establishment of a prognostic model for the tumor microenvironment (TME). A combined prognostic model incorporating netosis and TME was then developed, stratifying patients based on median results. Further evaluation of the variations in the pathways within the model was conducted using Fast Gene Set Enrichment Analysis (FGSEA) and Weighted Correlation Network Analysis (WGCNA). Additionally, single-cell data integration allowed us to examine netosis-related genes in the context of cell communication and tumor development using the CellChat and Monocle packages. Netosis and TME scores exhibited a high degree of predictive power for patient survival, as illustrated by Kaplan-Meier (KM) curves. Gene set enrichment analysis (GSEA) revealed significant disparities in pathways associated with tumor occurrence between netosis and TME scores. A combined prognostic model incorporating both netosis and TME scores showed excellent performance in the validation set and TCGA data. FGSEA and WGCNA revealed significant differences in pathways associated with traditional tumor development and occurrence within distinct groups of the combined model. Furthermore, single-cell data analysis revealed substantial variations in intercellular communication levels among groups of netosis model genes with high and low expression. Pseudotime analysis highlighted increased expression of EREG, LYZ, S100A8, and S100A9. The combined netosis and TME prognostic model demonstrated high accuracy and efficacy, underscoring its potential value in guiding the treatment and prognosis of future ccRCC patients.
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