ArticleAging2024
A novel mitochondrial metabolism-related gene signature for predicting the prognosis of oesophageal squamous cell carcinoma.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Article
- Metabolic gene expression-based stratification and prognostic risk predictive model of head and neck squamous cell carcinoma.NPJ systems biology and applications · 2026Article
- Multi-Omics and -Organ Insights into Energy Metabolic Adaptations in Early Sepsis Onset.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Prognostic value of FCER1G expression and M2 macrophage infiltration in esophageal squamous cell carcinoma.Discover oncology · 2025Article
- Development and validation of a nomogram for predicting sleep disturbance in pregnant and postpartum women: A pilot study.Heliyon · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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