Unveiling NRlncRNAs as prognostic biomarkers and therapeutic targets for head-and-neck squamous cell carcinoma through machine learning

Head-and-neck squamous cell carcinoma (HNSCC) patients often exhibit insensitivity to immunotherapy, leading to treatment failure. Identifying potential biomarkers that can predict prognosis and improve the efficacy of treatment is crucial. In this study, we aimed to identify necroptosis-related long noncoding RNAs (NRlncRNAs) as potential therapeutic targets to improve the prognosis of HNSCC patients. By exploring the Genotype-Tissue Expression Project (GTEx) and the Cancer Genome Atlas (TCGA) databases, we identified NRlncRNAs and developed a risk model comprising 17 NRlncRNAs to predict the prognosis of HNSCC patients and to classify patients into two clusters based on their expression levels. We conducted various analyses, such as the Kaplan-Meier analysis, GSEA and IC50 prediction, to evaluate the differences in sensitivity to immunotherapy between the two clusters. Our findings suggest that NRlncRNAs have potential as therapeutic targets for improving the prognosis of HNSCC patients, and that individualized treatment approaches based on NRlncRNA expression levels can improve the sensitivity of immunotherapy and overall treatment outcomes. This study highlights new perspectives within clinical cancer informatics and provides insight into potential therapeutic strategies for HNSCC patients.