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Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients

BACKGROUND: Gliomas are the most common intracranial nervous system tumours that are highly malignant and aggressive, and mitochondria are an important marker of metabolic reprogramming of tumour cells, the prognosis of which cannot be accurately predicted by current histopathology. Therefore, Ident...

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Autores principales: Wu, Ji, Zhou, Jiabin, Chai, Yibo, Qin, Chengjian, Cai, Yuankun, Xu, Dongyuan, Lei, Yu, Mei, Zhimin, Li, Muhua, Shen, Lei, Fang, Guoxing, Yang, Zhaojian, Cai, Songshan, Xiong, Nanxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113561/
https://www.ncbi.nlm.nih.gov/pubmed/37091853
http://dx.doi.org/10.3389/fendo.2023.1172182
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author Wu, Ji
Zhou, Jiabin
Chai, Yibo
Qin, Chengjian
Cai, Yuankun
Xu, Dongyuan
Lei, Yu
Mei, Zhimin
Li, Muhua
Shen, Lei
Fang, Guoxing
Yang, Zhaojian
Cai, Songshan
Xiong, Nanxiang
author_facet Wu, Ji
Zhou, Jiabin
Chai, Yibo
Qin, Chengjian
Cai, Yuankun
Xu, Dongyuan
Lei, Yu
Mei, Zhimin
Li, Muhua
Shen, Lei
Fang, Guoxing
Yang, Zhaojian
Cai, Songshan
Xiong, Nanxiang
author_sort Wu, Ji
collection PubMed
description BACKGROUND: Gliomas are the most common intracranial nervous system tumours that are highly malignant and aggressive, and mitochondria are an important marker of metabolic reprogramming of tumour cells, the prognosis of which cannot be accurately predicted by current histopathology. Therefore, Identify a mitochondrial gene with immune-related features that could be used to predict the prognosis of glioma patients. METHODS: Gliomas data were downloaded from the TCGA database and mitochondrial-associated genes were obtained from the MITOCARTA 3.0 dataset. The CGGA, kamoun and gravendeel databases were used as external datasets. LASSO(Least absolute shrinkage and selection operator) regression was applied to identify prognostic features, and area and nomograms under the ROC(Receiver Operating Characteristic) curve were used to assess the robustness of the model. Single sample genomic enrichment analysis (ssGSEA) was employed to explore the relationship between model genes and immune infiltration, and drug sensitivity was used to identify targeting drugs. Cellular studies were then performed to demonstrate drug killing against tumours. RESULTS: COX assembly mitochondrial protein homolog (CMC1), Cytochrome c oxidase protein 20 homolog (COX20) and Cytochrome b-c1 complex subunit 7 (UQCRB) were identified as prognostic key genes in glioma, with UQCRB, CMC1 progressively increasing and COX20 progressively decreasing with decreasing risk scores. ROC curve analysis of the TCGA training set model yielded AUC (Area Under The Curve) values >0.8 for 1-, 2- and 3-year survival, and the model was associated with both CD8+ T cells and immune checkpoints. Finally, using cellMiner database and molecular docking, it was confirmed that UQCRB binds covalently to Amonafide via lysine at position 78 and threonine at position 82, while cellular assays showed that Amonafide inhibits glioma migration and invasion. CONCLUSION: Our three mitochondrial genomic composition-related features accurately predict Survival in glioma patients, and we also provide glioma chemotherapeutic agents that may be mitochondria-related targets.
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spelling pubmed-101135612023-04-20 Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients Wu, Ji Zhou, Jiabin Chai, Yibo Qin, Chengjian Cai, Yuankun Xu, Dongyuan Lei, Yu Mei, Zhimin Li, Muhua Shen, Lei Fang, Guoxing Yang, Zhaojian Cai, Songshan Xiong, Nanxiang Front Endocrinol (Lausanne) Endocrinology BACKGROUND: Gliomas are the most common intracranial nervous system tumours that are highly malignant and aggressive, and mitochondria are an important marker of metabolic reprogramming of tumour cells, the prognosis of which cannot be accurately predicted by current histopathology. Therefore, Identify a mitochondrial gene with immune-related features that could be used to predict the prognosis of glioma patients. METHODS: Gliomas data were downloaded from the TCGA database and mitochondrial-associated genes were obtained from the MITOCARTA 3.0 dataset. The CGGA, kamoun and gravendeel databases were used as external datasets. LASSO(Least absolute shrinkage and selection operator) regression was applied to identify prognostic features, and area and nomograms under the ROC(Receiver Operating Characteristic) curve were used to assess the robustness of the model. Single sample genomic enrichment analysis (ssGSEA) was employed to explore the relationship between model genes and immune infiltration, and drug sensitivity was used to identify targeting drugs. Cellular studies were then performed to demonstrate drug killing against tumours. RESULTS: COX assembly mitochondrial protein homolog (CMC1), Cytochrome c oxidase protein 20 homolog (COX20) and Cytochrome b-c1 complex subunit 7 (UQCRB) were identified as prognostic key genes in glioma, with UQCRB, CMC1 progressively increasing and COX20 progressively decreasing with decreasing risk scores. ROC curve analysis of the TCGA training set model yielded AUC (Area Under The Curve) values >0.8 for 1-, 2- and 3-year survival, and the model was associated with both CD8+ T cells and immune checkpoints. Finally, using cellMiner database and molecular docking, it was confirmed that UQCRB binds covalently to Amonafide via lysine at position 78 and threonine at position 82, while cellular assays showed that Amonafide inhibits glioma migration and invasion. CONCLUSION: Our three mitochondrial genomic composition-related features accurately predict Survival in glioma patients, and we also provide glioma chemotherapeutic agents that may be mitochondria-related targets. Frontiers Media S.A. 2023-04-05 /pmc/articles/PMC10113561/ /pubmed/37091853 http://dx.doi.org/10.3389/fendo.2023.1172182 Text en Copyright © 2023 Wu, Zhou, Chai, Qin, Cai, Xu, Lei, Mei, Li, Shen, Fang, Yang, Cai and Xiong https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Wu, Ji
Zhou, Jiabin
Chai, Yibo
Qin, Chengjian
Cai, Yuankun
Xu, Dongyuan
Lei, Yu
Mei, Zhimin
Li, Muhua
Shen, Lei
Fang, Guoxing
Yang, Zhaojian
Cai, Songshan
Xiong, Nanxiang
Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients
title Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients
title_full Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients
title_fullStr Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients
title_full_unstemmed Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients
title_short Novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients
title_sort novel prognostic features and personalized treatment strategies for mitochondria-related genes in glioma patients
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10113561/
https://www.ncbi.nlm.nih.gov/pubmed/37091853
http://dx.doi.org/10.3389/fendo.2023.1172182
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