ArticleTranslational cancer research2025
A prognostic model for laryngeal squamous cell carcinoma based on the mitochondrial metabolism-related genes.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mitochondrial dysfunction in peri-implantitis: bioinformatics and machine learning analysis with in vivo experiment.Odontology · 2026Article
- ACOT9, a mitochondrial metabolism-related gene, promotes ROS-associated epithelial remodeling in laryngeal squamous cell carcinoma.Journal of translational medicine · 2026Article
- An LSCC-specific R-loop-related model predicts prognosis and neoadjuvant immunotherapy response and identifies EIF5A2-mediated tumor-immune crosstalk.World journal of surgical oncology · 2026Article
- Nomogram outperforms gradient boosting machine for prognostic prediction of laryngeal squamous cell carcinoma: a combined analysis of SEER and single-center data.American journal of cancer research · 2026Article
- Resistance-oriented immunometabolic circuits in laryngeal squamous cell carcinoma: glycolysis-lactate signaling, mitochondrial stress, and tumor-myeloid crosstalk.Frontiers in immunology · 2026Review
- Single-Cell Transcriptomics Identifies Novel Prognostic Signatures in HNSCC Immunotherapy Response.Cancer science · 2025Article
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2 authors.
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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 ( Conclusions: This study highlights the prognostic significance of MMRGs in LSCC and underscores their potential as biomarkers for LSCC therapy.
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