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

Jun 6, 2024Aging

A new gene pattern related to cell energy use that may predict outcomes in esophageal squamous cell cancer

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗

Abstract

A prognostic model using 6 mitochondrial metabolism-related genes was developed to predict esophageal squamous cell carcinoma (ESCC) outcomes.

Differences in clinical features were observed between high and low mitochondrial metabolism score groups in the TCGA-ESCC dataset (p < 0.05).
Six mitochondrial metabolism-related genes (COX10, ACADVL, IDH3B, AKR1A1, LIAS, and NDUFB8) were identified as significant for risk stratification.
The prognostic nomogram achieved areas under the receiver operating characteristic curve of 0.948, 0.927, and 0.848 at 1, 2, and 3 years, respectively.
COX10 was found to potentially inhibit the malignant progression of ESCC.

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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 (<0.05). Afterwards, we identified 6 MMRGs (COX10, ACADVL, IDH3B, AKR1A1, LIAS, and NDUFB8) signature that could accurately distinguish high-risk and low-risk ESCC patients. A predictive nomogram that combined the 6 MMRGs with sex and N stage to predict the prognosis of ESCC was constructed, and the areas under the receiver operating characteristic (ROC) curve at 1, 2 and 3 years were 0.948, 0.927 and 0.848, respectively. Finally, we found that COX10, one of 6 MMRGs, could inhibit the malignant progression of ESCC. In summary, we constructed a clinical prognosis model based on 6 MMRGs and clinical features which can accurately predict the prognosis of ESCC patients. P in vitro

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

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