Cuproptosis, a copper-dependent programmed cell death process, holds promise for controlling cell death in tumor cells. Autophagy, a fundamental cellular process, has been linked to various aspects of cancer, such as proliferation, migration, and drug resistance. This research is centered on the investigation of autophagy- and cuproptosis-related long noncoding RNAs (lncRNAs) and the establishment of a prognostic model for head and neck squamous cell carcinoma. RNA sequencing data from head and neck squamous cell carcinoma patients in The Cancer Genome Atlas database identified cuproptosis-related lncRNAs via Pearson analysis. Patients were divided into training and testing sets. A prognostic model developed in the training set using univariate-least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression was tested for accuracy. Kaplan-Meier analysis showed high-risk patients had poorer outcomes. Cox regression confirmed the model's risk score as an independent prognostic indicator, with receiver operating characteristic and decision curve analyses validating its predictive accuracy. Thirteen lncRNAs associated with autophagy and cuproptosis were identified through bioinformatics analysis. Lasso regression narrowed this to 3 significant prognostic lncRNAs. Based on median risk scores, patients were classified into high-risk and low-risk groups. Kaplan-Meier survival curves revealed significant differences between these groups (P < .01). Through a set of bioinformatics analyses, we identified 13 autophagy- and cuproptosis-related lncRNAs. By Lasso regression, 3 prognostic-related lncRNAs were further selected. We also investigated these 3 lncRNAs in relation to clinicopathologic features. The principal component analysis visually showed differences between the high-risk and low-risk groups.
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