NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing - Zijie Yu; Zihao Xu; Xi Zhang; Wenchuan Shao; Da Zhong; Xinghan Yan; Tingfei Jiang; Yichun Wang; Ninghong Song (Scientific Reports)
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.