ArticleFrontiers in oncology2021
The Identification of Stemness-Related Genes in the Risk of Head and Neck Squamous Cell Carcinoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Early detection of oral squamous cell carcinoma using five tumor-associated autoantibodies and a Naive Bayes-based machine learning model.Scientific reports · 2026Article
- Construction and validation of risk prediction model for glioblastoma associated with cancer stem cells and disulfidptosis.Translational cancer research · 2026Article
- A novel TAp73-inhibitory compound counteracts stemness features of glioblastoma stem cells.Molecular oncology · 2025Article
- Analysis of ABCC3 in glioma progression: implications for prognosis, immunotherapy, and drug resistance.Discover oncology · 2025Article
- The mechanism of mitochondrial metabolic gene PMAIP1 involved in Alzheimer's disease process based on bioinformatics analysis and experimental validation.Clinics (Sao Paulo, Brazil) · 2024Article
- Article
- Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma.Apoptosis : an international journal on programmed cell death · 2023Article
- Development and Optimization of a Prognostic Model Associated with Stemness Genes in Hepatocellular Carcinoma.BioMed research international · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to identify genes regulating cancer stemness of head and neck squamous cell carcinoma (HNSCC) and evaluate the ability of these genes to predict clinical outcomes. MATERIALS AND
methodsThe stemness index (mRNAsi) was obtained using a one-class logistic regression machine learning algorithm based on sequencing data of HNSCC patients. Stemness-related genes were identified by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis (LASSO). The coefficient of LASSO was applied to construct a diagnostic risk score model. The Cancer Genome Atlas database, the Gene Expression Omnibus database, Oncomine database and the Human Protein Atlas database were used to validate the expression of key genes. Interaction network analysis was performed using String database and DisNor database. The Connectivity Map database was used to screen potential compounds. The expressions of stemness-related genes were validated using quantitative real-time polymerase chain reaction (qRT-PCR).
resultsTTK, KIF14, KIF18A and DLGAP5 were identified. Stemness-related genes were upregulated in HNSCC samples. The risk score model had a significant predictive ability. CDK inhibitor was the top hit of potential compounds.
conclusionStemness-related gene expression profiles may be a potential biomarker for HNSCC.
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