A novel mitochondrial metabolism-related gene signature for predicting the prognosis of oesophageal squamous cell carcinoma

Oesophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers worldwide. Due to the important role of mitochondrial metabolism in cancer progression, a clinical prognostic model based on mitochondrial metabolism and clinical features was constructed in this study to predict the progno...

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Bibliographic Details
Published in:Aging (Albany, NY.) NY.), 2024-06, Vol.16 (11), p.9649-9679
Main Authors: Lin, Wenhao, Ye, Changchun, Sun, Liangzhang, Chen, Zilu, Qu, Chao, Zhu, Minxia, Li, Jianzhong, Kong, Ranran, Xu, Zhengshui
Format: Article
Language:English
Subjects:
Biomarkers, Tumor - genetics
Biomarkers, Tumor - metabolism
Esophageal Neoplasms - genetics
Esophageal Neoplasms - metabolism
Esophageal Neoplasms - mortality
Esophageal Neoplasms - pathology
Esophageal Squamous Cell Carcinoma - genetics
Esophageal Squamous Cell Carcinoma - metabolism
Esophageal Squamous Cell Carcinoma - mortality
Esophageal Squamous Cell Carcinoma - pathology
Female
Gene Expression Profiling
Gene Expression Regulation, Neoplastic
Humans
Male
Middle Aged
Mitochondria - genetics
Mitochondria - metabolism
Nomograms
Prognosis
Research Paper
Transcriptome
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Summary:Oesophageal squamous cell carcinoma (ESCC) is one of the most lethal cancers worldwide. Due to the important role of mitochondrial metabolism in cancer progression, a clinical prognostic model based on mitochondrial metabolism and clinical features was constructed in this study to predict the prognosis of ESCC. Firstly, the mitochondrial metabolism scores (MMs) were calculated based on 152 mitochondrial metabolism-related genes (MMRGs) by single sample gene set enrichment analysis (ssGSEA). Subsequently, univariate Cox regression and LASSO algorithm were used to identify prognosis-associated MMRG and risk-stratify patients. Functional enrichment, interaction network and immune-related analyses were performed to explore the features differences in patients at different risks. Finally, a prognostic nomogram incorporating clinical factors was constructed to assess the prognosis of ESCC. Our results found there were differences in clinical features between the MMs-high group and the MMs-low group in the TCGA-ESCC dataset (
ISSN:1945-4589
1945-4589
DOI:10.18632/aging.205892