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May 9, 2026Human Genomics0 citationsOpen Access

Integrating single-cell multi-omics and machine learning to reveal triaptosis heterogeneity in clear cell renal cell carcinoma

HDHaojie DaiGansu Agricultural UniversityRLRenjun LuNanyang Medical CollegeMZMingcong ZhangThe First People’s Hospital of Lianyungang

Key Points

  • The central aim is to clarify the heterogeneity of triaptosis in clear cell renal cell carcinoma using advanced omics techniques.
  • Used single-cell transcriptomics to analyze the triaptosis landscape in ccRCC.
  • Developed a 4-gene prognostic signature based on high-triaptosis epithelial subpopulations' module genes.
  • Utilized spatial transcriptomics to assess triaptosis activity relative to tumor location.
  • High triaptosis activity in monocytes and macrophages correlates with pro-angiogenic signaling and improved prognosis (HR not provided).
  • Epithelial subpopulations with elevated triaptosis activity linked to reduced metabolic activity and broader immune activation effects.
  • The 4-gene prognostic model demonstrated strong performance in predicting outcomes and therapeutic responses.

Abstract

Triaptosis, an emerging form of cell death, remains poorly characterized in terms of its heterogeneity within clear cell renal cell carcinoma (ccRCC). Utilizing single-cell transcriptomics, we delineate a landscape of triaptosis heterogeneity and identify monocytes and macrophages as exhibiting the highest triaptosis activity, which further increases upon terminal differentiation. These high-activity cells also demonstrate enhanced pro-angiogenic signaling toward endothelial cells. Within epithelial cells, subpopulations with the strongest triaptosis activity are located at the late differentiation stage and are closely associated with ccRCC traits. Spatial transcriptomic analysis reveals a decline in triaptosis activity with increasing distance from the tumor epithelial core. The epithelial cluster with the highest triaptosis activity showed reduced metabolic activity. In bulk transcriptome analysis, patients with high epithelial triaptosis activity infiltration exhibited improved prognosis, broader immune activation, and similarly suppressed metabolism. We subsequently developed a robust 4-gene prognostic signature based on module genes derived from high-triaptosis epithelial subpopulations. This model showed strong performance in prognostic stratification, immunotherapy guidance, and chemotherapy response prediction. Finally, we identified SLC25A37 as a core oncogenic gene within the signature and proposed Yohimbic acid among several potential molecularly targeted therapeutics.

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Cite This Study

Dai et al. (2026) studied this question.

synapsesocial.com/papers/69fed10fb9154b0b828784b8https://doi.org/10.1186/s40246-026-00982-3
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