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Journal of Translational Medicine
Published in: Journal of Translational Medicine 1/2023

Open Access 01-12-2023 | Glioma | Research

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

Authors: Hu Qin, Aimitaji Abulaiti, Aierpati Maimaiti, Zulihuma Abulaiti, Guofeng Fan, Yirizhati Aili, Wenyu Ji, Zengliang Wang, Yongxin Wang

Published in: Journal of Translational Medicine | Issue 1/2023

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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.
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Metadata
Title
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
Authors
Hu Qin
Aimitaji Abulaiti
Aierpati Maimaiti
Zulihuma Abulaiti
Guofeng Fan
Yirizhati Aili
Wenyu Ji
Zengliang Wang
Yongxin Wang
Publication date
01-12-2023
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2023
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-023-04468-x

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WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

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