A prognostic model for laryngeal squamous cell carcinoma based on the mitochondrial metabolism-related genes.

BACKGROUND

Mitochondrial metabolism-related genes (MMRGs) have emerged as potential therapeutic targets in cancer. This study aimed to construct a prognosis model based on MMRGs for patients with laryngeal squamous cell carcinoma (LSCC).

METHODS

Differentially expressed MMRGs in LSCC were identified from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Their functions were characterized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A prognostic model was established using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses, and its performance was evaluated using Kaplan-Meier and receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was performed to elucidate the biological pathways associated with the hub prognostic MMRGs. Genetic perturbation similarity analysis (GPSA) was used to determine the regulatory network of hub genes. Additionally, the correlation of the hub MMRGs with the immune microenvironment and drug sensitivity was investigated.

RESULTS

We identified 308 differentially expressed MMRGs, enriched in various metabolic processes and pathways. The prognostic model comprising four hub MMRGs (, , , and ) accurately predicted patient outcomes, with the high-risk group exhibiting poorer survival. Additionally, high expression of and while low expression of and indicated better prognosis for LSCC patients. GSEA revealed pathways correlated with each prognostic MMRG, such as PI3K-AKT-mTOR signaling pathways, while GPSA identified key regulatory genes interacting with four hub MMRGs. Furthermore, differences in the tumor immune microenvironment and somatic mutation profiles were observed between high- and low-risk groups. Finally, the correlation of four hub MMRGs with 30 drug sensitivity was revealed.

CONCLUSIONS

This study highlights the prognostic significance of MMRGs in LSCC and underscores their potential as biomarkers for LSCC therapy.