Identification of Immune-related lncRNA Panel for Predicting Immune Checkpoint Blockade and Prognosis in Head and Neck Squamous Cell Carcinoma

Abstract

Immunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first-line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non-coding RNAs impact every step of cancer immunity. Therefore, reliable immune-lncRNA able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management. In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Altas and identified differentially expressed immune-related lncRNAs (DEir-lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir-lncRNAs to construct optimal risk model. Low-risk and high-risk groups were classified based on the optimal cut-off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan-Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, chemotherapeutic and immunotherapeutic efficacy between two groups. Our results constructed a risk model consisted of 18 DEir-lncRNA pairs showing significantly association with survival of patients with HNSCC. Besides, HNSCC patients with low risk score significantly enriched of CD8+ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity. In summary, our risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.