Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma
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Published:2023-09-02
Issue:1
Volume:21
Page:
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ISSN:1479-5876
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Container-title:Journal of Translational Medicine
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language:en
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Short-container-title:J Transl Med
Author:
Qin Hu,Abulaiti Aimitaji,Maimaiti Aierpati,Abulaiti Zulihuma,Fan Guofeng,Aili Yirizhati,Ji Wenyu,Wang Zengliang,Wang Yongxin
Abstract
Abstract
Background
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.
Methods
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.
Results
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.
Conclusion
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.
Funder
Shanghai Cooperation Organization Science and Technology Partnership Program and International Science and Technology Cooperation Program Xinjiang Natural Science Foundation Surface Project
Publisher
Springer Science and Business Media LLC
Subject
General Biochemistry, Genetics and Molecular Biology,General Medicine
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