A novel risk score model incorporating six co-stimulatory molecules for accurate prognosis prediction of laryngeal cancer
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a risk score model using co-stimulatory molecules to predict laryngeal cancer prognosis. The model shows strong predictive power for patient survival outcomes.
Area Of Science
- Oncology
- Immunology
- Bioinformatics
Background
- Laryngeal cancer (LC) is a significant respiratory malignancy with poor prognosis in advanced stages.
- Current treatments for advanced LC have limited efficacy, highlighting the need for improved prognostic tools.
Purpose Of The Study
- To investigate the relationship between co-stimulatory molecules and immune infiltration in laryngeal cancer.
- To develop a robust risk score (RS) model for predicting patient prognosis in laryngeal cancer.
Main Methods
- Utilized RNA sequencing data from TCGA (training) and GSE27020 (validation) datasets.
- Employed univariate Cox regression and LASSO algorithm to identify key co-stimulatory molecules for the RS model.
- Performed Kaplan-Meier survival analysis to assess prognostic significance of identified subtypes and the RS model.
Main Results
- Identified 18 immune co-stimulatory molecules and classified LC samples into three prognostic subtypes.
- Found associations between subtypes, eight immune cell types, and ten differentially expressed immune checkpoint genes.
- Developed and validated an RS model using six co-stimulatory molecules, achieving AUCs of 0.870 (TCGA) and 0.736 (GSE27020) for 1-year survival prediction.
Conclusions
- A validated risk score model incorporating six co-stimulatory molecules effectively predicts laryngeal cancer prognosis.
- The developed RS model offers a promising tool for assessing patient outcomes and guiding treatment strategies.
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