NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing
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 in...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-11095-7 |
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| author | Zijie Yu Zihao Xu Xi Zhang Wenchuan Shao Da Zhong Xinghan Yan Tingfei Jiang Yichun Wang Ninghong Song |
| author_facet | Zijie Yu Zihao Xu Xi Zhang Wenchuan Shao Da Zhong Xinghan Yan Tingfei Jiang Yichun Wang Ninghong Song |
| author_sort | Zijie Yu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-05b25c05f2c4401b874fe4cfcfb4b859 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-05b25c05f2c4401b874fe4cfcfb4b8592025-08-20T03:05:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-11095-7NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencingZijie Yu0Zihao Xu1Xi Zhang2Wenchuan Shao3Da Zhong4Xinghan Yan5Tingfei Jiang6Yichun Wang7Ninghong Song8Department of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchDepartment of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchDepartment of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchDepartment of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchDepartment of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchDepartment of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchDepartment of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchDepartment of Urology, The First Affiliated Hospital of Nanjing Medical UniversityDepartment of Urology, The Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer ResearchAbstract 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.https://doi.org/10.1038/s41598-025-11095-7Single-cell RNA sequencingBulk RNA sequencingClear cell renal cell carcinomaImmune environmentPrognostic model |
| spellingShingle | Zijie Yu Zihao Xu Xi Zhang Wenchuan Shao Da Zhong Xinghan Yan Tingfei Jiang Yichun Wang Ninghong Song NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing Scientific Reports Single-cell RNA sequencing Bulk RNA sequencing Clear cell renal cell carcinoma Immune environment Prognostic model |
| title | NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing |
| title_full | NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing |
| title_fullStr | NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing |
| title_full_unstemmed | NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing |
| title_short | NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing |
| title_sort | netosis based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single cell and bulk rna sequencing |
| topic | Single-cell RNA sequencing Bulk RNA sequencing Clear cell renal cell carcinoma Immune environment Prognostic model |
| url | https://doi.org/10.1038/s41598-025-11095-7 |
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