Journal of Otolaryngology and Ophthalmology of Shandong University ›› 2026, Vol. 40 ›› Issue (2): 49-64.doi: 10.6040/j.issn.1673-3770.0.2025.048

• Original Article • Previous Articles     Next Articles

Construct of a cuproptosis-related lncRNA model to predict prognosis in head and neck squamous cell carcinoma

LIU Jiahui, TIAN Ruxian, LI Yumei, SONG Xicheng   

  1. Department of Otorhinolaryngology & Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University/ Shandong Provincial Key Laboratory of Neuroimmune Interaction and Regulation/ Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases/ Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai 264000, Shandong, China
  • Published:2026-03-26

Abstract: Objective To construct a cuproptosis-related long non-coding ribonucleotide(lncRNA)model and explore its prognostic value in patients with head and neck squamous cell carcinoma(HNSCC), and to preliminarily explore the drug sensitivity of HNSCC patients by drug sensitivity analysis. Methods Transcriptome data, tumor mutation profiles, and clinical information of HNSCC patients were downloaded from The Cancer Genome Atlas(TCGA)database. Pearson correlation analysis was used to identify lncRNAs co-expressed with cuproptosis-related gene. Univariate Cox regression and least absolute shrinkage and selection operator(LASSO)regression were used to screen the prognostic cuproptosis-related lncRNA(CRL)and establish a prognostic model. The risk score of each patient was calculated, and the median was used as the cutoff to divide the patients into low-risk and high-risk groups for survival difference analysis. Kaplan-Meier(KM)curve, receiver operating characteristic(ROC)curve, C-index, and calibration curve were used to evaluate the performance of the prognostic model. Independent prognostic risk factors were identified by univariate and multivariate Cox regression analysis. A predictive nomogram was then constructed based on the risk scores and clinical characteristics. Functional enrichment analysis and immune infiltration analysis were performed to further explore the potential molecular mechanism and biological basis of the prognostic model. Finally, the tumor mutational burden(TMB)and drug sensitivity of patients with different prognosis were analyzed. Results A total of 781 CRLs were identified, and 9 prognostic CRLs were selected and included in the prognostic model. The KM curve showed that the high-risk group was associated with poor prognosis of HNSCC(P<0.05). In the train set, the area under the ROC curves(AUCs)of the CRL prognostic model for 1-year, 3-year, and 5-year survival rates were 0.694, 0.753, and 0.643, respectively. In addition, the C-index of the model was higher than that of the clinical characteristics. Age, tumor stage, and risk score were independent prognostic risk factors(all P<0.05). Functional enrichment analysis showed that the differentially expressed genes between the low-risk and high-risk groups were mainly enriched in immune-related proteins and functions. Immune infiltration analysis showed that the expression levels of 8 immune cells and 7 immune function scores were higher in the low-risk group. KM curve showed that patients with low TMB + low risk have the best prognosis, and patients with high TMB + high risk have the worst prognosis(P<0.05). Drug sensitivity analysis showed that cisplatin, etoposide, gemcitabine, mitomycin C, sorafenib, and vinorelbine were potential drugs for the treatment of high-risk patients, while rapamycin and phenformin were potential drugs for the treatment of low-risk patients. Conclusion The prognostic prediction model of HNSCC patients based on 9 CRLs can well predict the prognosis of HNSCC patients, and evaluated the sensitivity of patients to chemotherapy drugs to provide a reference for the study of HNSCC treatment.

Key words: Cuproptosis, Long non-coding ribonucleotide, Head and neck squamous cell carcinoma, Prognostic model, Drug sensitivity

CLC Number: 

  • R739.6
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