Construction of a necroptosis-related lncRNA signature to predict the prognosis and immune microenvironment of head and neck squamous cell carcinoma

Abstract

Background: Previous studies have determined that necroptosis-related genes are potential biomarkers in head and neck squamous cell carcinoma (HNSCC). Herein, we established a novel risk model based on necroptosis-related lncRNAs (nrlncRNAs) to predict the prognosis of HNSCC patients.

Methods: Transcriptome and related information were obtained from TCGA database, and an nrlncRNA signature was established based on univariate Cox analysis and least absolute shrinkage and selection operator Cox regression. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) analysis were used to evaluate the model, and a nomogram for survival prediction was established. Gene set enrichment analysis, immune analysis, drug sensitivity analysis, correlation with N6-methylandenosin (m6A), and tumor stemness analysis were performed. Furthermore, the entire set was divided into two clusters for further discussion.

Results: A novel signature was established with six nrlncRNAs. The areas under the ROC curves (AUCs) for 1-, 3-, and 5-year overall survival (OS) were 0.699, 0.686, and 0.645, respectively. Patients in low-risk group and cluster 2 had a better prognosis, more immune cell infiltration, higher immune function activity, and higher immune scores; however, patients in high-risk group and cluster 1 were more sensitive to chemotherapy. Moreover, the risk score had negative correlation with m6A-related gene expression and tumor stemness.

Conclusion: According to this study, we constructed a novel signature with nrlncRNA pairs to predict the survival of HNSCC patients and guide immunotherapy and chemotherapy. This may possibly promote the development of individualized and precise treatment for HNSCC patients.

Keywords: head and neck squamous cell carcinoma; immunotherapy; long non-coding RNA; necroptosis; prognosis.

FIGURE 1

Flow diagram of study design

FIGURE 2

Expression of necroptosis‐related lncRNAs (nrlncRNAs) in TCGA‐HNSCC dataset and development of a nrlncRNAs prognosis signature. (A) The network of genes and nrlncRNAs; (B) Heatmap and (C) volcano diagram of differentially expressed nrlncRNAs; (D) Forest plot showing the prognostic value of 29 nrlncRNAs; (E) Heatmap of expression profiles of 29 prognostic nrlncRNAs; (F) Sankey diagram of necroptosis genes and nrlncRNAs; (G) Cross‐validation plot for the penalty term; (H) Diagram for LASSO expression coefficients

FIGURE 3

Prognosis value of the 6 nrlncRNAs model. A (train), B (test), C (entire), (K‐M) survival curves of overall survival (OS); D (train), E (test), F (entire), Exhibition of nrlncRNAs model; G (train), H (test), I (entire), Survival time and survival status; J (train), K (test), L (entire), Heatmaps of 6 nrlncRNAs expression

FIGURE 4

Relationship between risk model and clinical characters. (A–M, K–M) survival curves of overall survival (OS) prognostic value in the entire set; (N) Heatmap of correlation between risk model and clinical characters; (O) 1‐, 3‐, and 5‐year ROC curves of the entire sets; (P) 1‐year ROC curves of risk score, and clinical characteristics

FIGURE 5

Development a nomogram. (A) Uni‐Cox and (B) Multi‐Cox analyses of clinical features and risk scores. (C) Nomogram; (D) Calibration curves plot

FIGURE 6

Exploration of tumor immune factors, chemotherapy, m6A‐related genes, and tumor stemness. (A) GSEA analysis; (B) Correlation of immune cells and risk scores; (C) Relationship between risk groups, immune cells, and immune functions; (D) Immune‐related scores in the two groups; (E) Expression of checkpoints in risk groups. (F) Drug sensitivity to four chemotherapy medicine; (G) Expression of m6A‐related genes in risk groups; (H) Correlation of risk score and tumor stemness

FIGURE 7

Clusters analysis. (A) Patients divided into two clusters; (B) Sankey diagram. (C) K‐M survival curves of OS in clusters; (D) The PCA of risk groups and clusters; (E) The t‐SNE of risk groups and clusters; (F) Immune‐related scores in clusters; (G) Heatmap of immune cells in clusters; (H) Different expression of checkpoints in clusters. (I) Drug sensitivity to four chemotherapy medicine in clusters