An Immune-associated lncRNA Signature Predicts the Survival of Patients With Head and Neck Squamous Cell Carcinoma

BackgroundThe malignant progression and treatment resistance of head and neck squamous cell carcinoma are closely related to the tumor immune microenvironment. Long non-coding RNA (lncRNA) plays a regulatory role in this process and may be exploited as new signatures for head and neck squamous cell carcinoma(HNSCC) diagnosis, prognosis, and treatment.MethodsHNSCC transcriptome data was abstracted from the Cancer Genome Atlas (TCGA) data resource, and uncovered immune-linked lncRNA through co-expression analysis. Besides, univariate along with Lasso penalty regression were employed to determine immune-linked lncRNA pairs with different expressions. We then compared area under the curve, calculated the Akaike information criterion (AIC) value of the receiver operating characteristic curve for 5 years, determined cutoff points, and established an optimal predictive model for identifying high- and low-risk HNSCC patients. Then, we evaluated these patients with high- and low-risk HNSCC in terms of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutic efficacy, and immunosuppressed biomarkers.ResultsThis study included 545 samples. By co-expression analysis of known immune-linked genes and lncRNAs, a total of 809 immune-related lncRNAs were collected. 77 differentially expressed immune-related lncRNAs were identified (logFC>2,FDR<0.01). The identified differentially-expressed immune-linked lncRNAs were used to develop differential immune-linked lncRNA pairs. Univariate and modified Lasso regression analysis identified 40 differentially expressed immune linked lncRNAs pairs, 17 of which were incorporated in the Cox proportional hazard model by a stepwise approach. The signature could well predict the survival of patients, and the area under the receiver operating characteristic (ROC) of 17 lncRNA pairs predicted 1, 3, and 5-year survival rates (AUC) were all greater than 0.74. Kaplan-Meier analysis found that patients at low risk had longer survival than those in the high-risk group (p<.001). In addition, T stage, survival status, N stage, and clinical stage, were remarkably linked to the risk. The high- and low risk groups were correlated with tumor invading immune cells like macrophages, CD8+ T-cells, monocytes, along with CD4+ T-cells. ICI-related biomarker correlation analysis showed high risk scores were positively linked to high CDK8 expression (p<0.001) and negatively correlated with BTLA , LAG3 and PDCD1 (p<0.001). High-risk scores were correlated with lower IC50 for chemotherapeutics like Docetaxel (p<0.01), indicating that this model can predict chemotherapeutic efficacy.ConclusionsOur results offer promising prospects for identifying innovative molecular targets of immunotherapy and to improve therapeutic approaches for head and neck squamous cell carcinoma patients.