Identification of IGF2BP2 and long non-coding RNA TUG1 for the prognosis and tumour microenvironment in head and neck squamous cell carcinoma
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a risk model using m6A-related long non-coding RNAs (lncRNAs) to predict head and neck squamous cell carcinoma (HNSCC) patient survival and response to immunotherapy.
Area Of Science
- Oncology
- Molecular Biology
- Genomics
Background
- Head and neck squamous cell carcinoma (HNSCC) remains a significant health challenge with complex tumor microenvironment interactions.
- Long non-coding RNAs (lncRNAs) play crucial roles in cancer development and progression.
- N6-methyladenosine (m6A) modification is a key epitranscriptomic regulatory mechanism influencing gene expression in cancer.
Purpose Of The Study
- To investigate the prognostic and immunotherapeutic roles of m6A-related lncRNAs in HNSCC.
- To develop a predictive risk model for HNSCC patient outcomes.
- To explore the relationship between m6A-related lncRNAs and the tumor microenvironment.
Main Methods
- Analysis of 497 HNSCC samples from The Cancer Genome Atlas (TCGA) to identify m6A-related lncRNAs.
- Utilized correlation models, tripartite regression, Kaplan-Meier analysis, and nomograms for prognostic assessment.
- Performed tumor mutation burden, immune cell infiltration, and RT-qPCR analyses to validate findings.
Main Results
- A risk model based on m6A-related lncRNAs effectively predicted poorer survival outcomes in HNSCC patients.
- The model demonstrated good predictive accuracy for 1-, 3-, and 5-year survival (AUCs 0.70, 0.68, 0.64).
- Seven key m6A-related lncRNAs were associated with immune checkpoint molecules (CTLA4, PD-1), and TUG1 knockdown repressed IGF2BP2 expression.
Conclusions
- The developed m6A-related lncRNA risk model shows potential clinical utility for predicting prognosis and immunotherapeutic responses in HNSCC.
- This model can aid in guiding personalized treatment decisions for HNSCC patients.
- Identifying candidate compounds for immunotherapy highlights the model's relevance in clinical practice.
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