低级别胶质瘤（LGG）是一种高度异质性的疾病，准确预测患者预后存在挑战。线粒体在真核细胞的能量代谢中起着核心作用，可影响细胞死亡机制，对肿瘤发生和进展至关重要。然而，在LGG中，线粒体功能和细胞死亡的相互作用在预后中的意义需要进一步研究。我们采用稳健的计算框架，在全球六个多中心队列的1467例LGG患者中，调查了线粒体功能与18种细胞死亡模式之间的关系。共收集了10种常用的机器学习算法，随后将其组合为101种独特的组合。最终，我们使用展现最佳性能的机器学习模型设计了线粒体相关的程序性细胞死亡指数（mtPCDI）。mtPCDI通过结合18个具有较大影响力的基因的方式，展现了在LGG患者预后预测中的强大预测性能。生物学上，mtPCDI与免疫和代谢标志物显著相关。高mtPCDI组呈现出富集的代谢通路和增强的免疫活性。特别重要的是，我们的mtPCDI即使在调整潜在混杂因素后，仍保持其作为最强预后指标的地位，超过了已建立的临床模型在预测强度上。我们对稳健机器学习框架的利用突显了mtPCDI在为LGG诊断个体提供个性化风险评估和定制代谢和免疫治疗建议方面的显著潜力。尤其值得注意的是，该签名特征具有具有较大影响力的基因，为未来研究线粒体功能中的细胞程序性死亡的角色提供了进一步前景。© 2023. BioMed Central Ltd., part of Springer Nature.

Lower-grade glioma (LGG) is a highly heterogeneous disease that presents challenges in accurately predicting patient prognosis. Mitochondria play a central role in the energy metabolism of eukaryotic cells and can influence cell death mechanisms, which are critical in tumorigenesis and progression. However, the prognostic significance of the interplay between mitochondrial function and cell death in LGG requires further investigation.We employed a robust computational framework to investigate the relationship between mitochondrial function and 18 cell death patterns in a cohort of 1467 LGG patients from six multicenter cohorts worldwide. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations. Ultimately, we devised the mitochondria-associated programmed cell death index (mtPCDI) using machine learning models that exhibited optimal performance.The mtPCDI, generated by combining 18 highly influential genes, demonstrated strong predictive performance for prognosis in LGG patients. Biologically, mtPCDI exhibited a significant correlation with immune and metabolic signatures. The high mtPCDI group exhibited enriched metabolic pathways and a heightened immune activity profile. Of particular importance, our mtPCDI maintains its status as the most potent prognostic indicator even following adjustment for potential confounding factors, surpassing established clinical models in predictive strength.Our utilization of a robust machine learning framework highlights the significant potential of mtPCDI in providing personalized risk assessment and tailored recommendations for metabolic and immunotherapy interventions for individuals diagnosed with LGG. Of particular significance, the signature features highly influential genes that present further prospects for future investigations into the role of PCD within mitochondrial function.© 2023. BioMed Central Ltd., part of Springer Nature.