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

Abstract

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 (POLD1, PON2, SMS, and THEM5) accurately predicted patient outcomes, with the high-risk group exhibiting poorer survival. Additionally, high expression of POLD1 and THEM5 while low expression of PON2 and SMS 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.

Keywords: Laryngeal squamous cell carcinoma (LSCC); immune microenvironment; mitochondrial metabolism-related gene (MMRG); mutation; prognosis.

Figure 1

Identification and functional analysis of MMRGs in LSCC. (A) Volcano plot of DEGs in normal and tumor samples within LSCC patients. (B) Identification of overlapping genes between DEGs and MMRGs. (C-F) GO and KEGG enrichment analyses based on the overlapping genes: (C) BP terms of GO, (D) MF terms of GO, (E) CC terms of GO, (F) KEGG pathways. FC, fold change; DEGs, differentially expressed genes; MMRGs, mitochondrial metabolism-related genes; BP, biological process; MF, molecular function; CC, cellular components; KEGG, Kyoto Encyclopedia of Genes and Genomes; LSCC, laryngeal squamous cell carcinoma; GO, Gene Ontology.

Figure 2

Construction of the prognostic model. (A) Univariate Cox regression analysis based on the differentially expressed MMRGs. (B) Correlation coefficient change curve and cross-validation curve. (C) Multivariate Cox regression analysis identified four hub MMRG-related prognostic genes. CI, confidence interval; MMRGs, mitochondrial metabolism-related genes.

Figure 3

Assessment of the prognostic model. (A) Kaplan-Meier curve of LSCC patients in the low- and high-risk group. (B) ROC curve at 1-, 3-, and 5-year. HR, hazard ratio; CI, confidence interval; AUC, area under the curve; LSCC, laryngeal squamous cell carcinoma; ROC, receiver operating characteristic.

Figure 4

Validation of four MMRGs as the potential prognostic biomarkers. (A) Expression of four MMRGs in the low- and high-risk groups. (B) Kaplan-Meier curve of LSCC patients in the low expression and high expression of THEM5, SMS, PON2, or POLD1. HR, hazard ratio; CI, confidence interval; LSCC, laryngeal squamous cell carcinoma; MMRGs, mitochondrial metabolism-related genes.

Figure 5

GSEA enrichment analysis. Pathways related to (A) POLD1, (B) PON2, (C) SMS, and (D) THEM5. GSEA, gene set enrichment analysis.

Figure 6

The interacting genes of four prognostic genes. (A) Venn diagram was utilized to intersect interacting genes of POLD1, PON2, SMS, and THEM5. (B) PPI network between four prognostic genes and the common interacting genes. PPI, protein-protein interaction.

Figure 7

Tumor immune microenvironment analysis. (A) Cell infiltration analysis in the low- and high-risk groups. (B) Expression of immune checkpoint regulators in the low- and high-risk groups. (C) Correlation among immune cells, immune checkpoint regulators, and hub prognostic genes. *, P<0.05; **, P<0.01; ***, P<0.005; ****, P<0.001. NKT, natural killer T cells.

Figure 8

Mutation analysis. (A) Somatic mutations in the low-risk group. (B) Somatic mutations in the high-risk group. TMB, tumor mutational burden.

Figure 9

Correlation of four hub prognostic gene expression levels with drug sensitivity. GDSC, Genomics of Drug Sensitivity in Cancer; mRNA, messenger RNA; FDR, false discovery rate.