Identification of immune-related lncRNA panel for predicting immune checkpoint blockade and prognosis in head and neck squamous cell carcinoma

Abstract

Purpose: 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 (lncRNAs) impact every step of cancer immunity. Therefore, reliable immune-lncRNAs able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management.

Methods: In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Atlas 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, and chemotherapeutic and immunotherapeutic efficacy between two groups.

Results: In our study, we identified 256 Deir-lncRNAs in HNSCC. A total of 18 Deir-lncRNA pairs (consisting of 35 Deir-lncRNAs) were used to construct a risk model significantly associated with survival of HNSCC. Cox proportional hazard regression analysis confirmed that our risk model could be served as an independent prognostic indicator. Besides, HNSCC patients with low-risk score significantly enriched of CD8⁺ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity.

Conclusion: 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.

Keywords: drug sensitivity; head and neck squamous cell carcinoma; immune-related long non-coding RNAs; immunity cell infiltration; immunotherapy.

FIGURE 1

Volcano (1A) and heatmap (1B) plots of DEir‐lncRNAs of HNSCC

FIGURE 2

Risk scores of each HNSCC patient based on risk model

FIGURE 3

Time‐dependent receiver operating characteristic (ROC) analysis at 1–6 years to identify the maximum of the AUC (A) and to identify the optimal cut‐off point to group HNSCC case (B)

FIGURE 4

The Kaplan–Meier plot of HNSCC grouped by risk model

FIGURE 5

Univariate and multivariate Cox proportional hazard regression of risk model and other clinical features

FIGURE 6

Estimation of tumor‐infiltrating cells by the risk model

FIGURE 7

The gene expression of PD1, CTLA4, and PD‐L1 in high‐risk and low‐risk groups

FIGURE 8

The association between the response to ICIs and risk model in HNSCC

FIGURE 9

The associations between risk model and the efficacy of common chemotherapeutics (including cisplatin, docetaxel, gemcitabine, and paclitaxel) in HNSCC