ArticleCancer cell international2022
A novel signature derived from metabolism-related genes GPT and SMS to predict prognosis of laryngeal squamous cell carcinoma.
Article in Cancer cell international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 7 citations in OpenAlex.
- ACOT9, a mitochondrial metabolism-related gene, promotes ROS-associated epithelial remodeling in laryngeal squamous cell carcinoma.Journal of translational medicine · 2026Article
- IMP metabolic mechanisms and IMPDH targeting strategies in tumor metabolic reprogramming and therapy (Review).International journal of molecular medicine · 2026Review
- Resistance-oriented immunometabolic circuits in laryngeal squamous cell carcinoma: glycolysis-lactate signaling, mitochondrial stress, and tumor-myeloid crosstalk.Frontiers in immunology · 2026Review
- SHMT1, an amino acid metabolism-related gene, is an independent prognostic biomarker in laryngeal squamous cell carcinoma.BMC cancer · 2025Article
- Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma.BMC cancer · 2025Article
- A novel fumaric acid metabolism-related prognostic signature associated with prognosis and immune infiltration landscape in laryngeal squamous cell carcinoma.Translational cancer research · 2025Article
- A prognostic model for laryngeal squamous cell carcinoma based on the mitochondrial metabolism-related genes.Translational cancer research · 2025Article
- Metabolic profiles in laryngeal cancer defined two distinct molecular subtypes with divergent prognoses.Frontiers in immunology · 2025Article
- Targeting glutamine metabolism as a therapeutic strategy for cancer.Experimental & molecular medicine · 2023Review
- Article
- HPV-Related Prognostic Signature Predicts Survival in Head and Neck Squamous Cell Carcinoma.Journal of oncology · 2022Article
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5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundA growing body of evidence has suggested the involvement of metabolism in the occurrence and development of tumors. But the link between metabolism and laryngeal squamous cell carcinoma (LSCC) has rarely been reported. This study seeks to understand and explain the role of metabolic biomarkers in predicting the prognosis of LSCC.
methodsWe identified the differentially expressed metabolism-related genes (MRGs) through RNA-seq data of The Cancer Genome Atlas (TCGA) and Gene set enrichment analysis (GSEA). After the screening of protein-protein interaction (PPI), hub MRGs were analyzed by least absolute shrinkage and selection operator (LASSO) and Cox regression analyses to construct a prognostic signature. Kaplan-Meier survival analysis and the receiver operating characteristic (ROC) was applied to verify the effectiveness of the prognostic signature in four cohorts (TCGA cohort, GSE27020 cohort, TCGA-sub1 cohort and TCGA-sub2 cohort). The expressions of the hub MRGs in LSCC cell lines and clinical samples were verified by quantitative reverse transcriptase PCR (qRT-PCR). The immunofluorescence staining of the tissue microarray (TMA) was carried out to further verify the reliability and validity of the prognostic signature. Cox regression analysis was then used to screen for independent prognostic factors of LSCC and a nomogram was constructed based on the results.
resultsAmong the 180 differentially expressed MRGs, 14 prognostic MRGs were identified. A prognostic signature based on two MRGs (GPT and SMS) was then constructed and verified via internal and external validation cohorts. Compared to the adjacent normal tissues, SMS expression was higher while GPT expression was lower in LSCC tissues, indicating poorer outcomes. The prognostic signature was proven as an independent risk factor for LSCC in both internal and external validation cohorts. A nomogram based on these results was developed for clinical application.
conclusionsDifferentially expressed MRGs were found and proven to be related to the prognosis of LSCC. We constructed a novel prognostic signature based on MRGs in LSCC for the first time and verified it via different cohorts from both databases and clinical samples. A nomogram based on this prognostic signature was developed.
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