Clinical significance and integrative analysis of the cuproptosis-associated genes in head and neck squamous cell carcinoma

Abstract

Head and neck squamous cell carcinoma (HNSC) is a kind of malignant tumor originating from the oropharynx, larynx, nasopharynx and oral cavity. The incidence of HNSC is increasing and it is the sixth malignant tumor in the world at present. "Cuprotosis" is a novel cuper-dependent cell death mode that is closely related to mitochondrial respiration. Tumorigenesis is closely related to the dysregulation of cell death. However, the relationship between cuprotosis and HNSC remains unclear. Here, we investigated the association between 10 cuprotosis-associated genes (CAGs) and HNSC using multi-omics public data. We found that CAGs had abnormal expression and significant genetic changes in HNSC, especially CDKN2A with 54% mutation rate. Expression of CAGs significantly correlates with the prognosis of HNSC patients. Moreover, the CAGs expression is correlated with the immune checkpoints expression and immune cells infiltration. These CAGs expression was associated with multiple drugs sensitivity of cancer cells, such as cisplatin and docetaxel. These findings indicate that CAGs are likely to serve an essential role in the diagnosis, prognosis, immunotherapy and drug therapy prediction of HNSC.

Keywords: HNSC; cell death; cuprotosis; cuprotosis-associated genes.

Figure 1

Expression and genetic alteration of CAGs in HNSC. (A) The CAGs expression in HNSC tissues and normal tissues. (B) The mutation frequency of CAGs in HNSC. The horizontal coordinates indicate the different sample groups and the vertical coordinates indicate the ratio of CAGs mutations. (C) Correlation of CAGs in HNSC. ^*stands for significance levels, ^*for p < 0.05.

Figure 2

Expression of CAGs in HNSC. CAGs expression data from The Human Protein Atlas database in tumor with or normal tissue were measured by immunohistochemical staining. Scale bar, 20 μm.

Figure 3

Prognostic of CAGs in HNSC. (A) The factors of the elected characteristic are shown as lambda parameters. (B) The partial likelihood deviance was plotted versus log (λ) using the LASSO Cox regression model. (C) Riskscore, survival time and survival status in the selected data set. (D, E) The KM survival curve distribution of the risk model in the data set, among which different groups were tested by log rank (D). The ROC curve and AUC of the risk model at different times (E), where the higher the AUC value, the stronger the predictive ability of the model.

Figure 4

Predictive nomogram of CAGs in HNSC. (A, B) Univariate (A) and multivariate (B) COX analysis were used to determine the P-value, HR, and confidence intervals of 10 CAGs expression and clinical characteristics. (C) The 1-, 3-, and 5-year overall survival rates of HNSC patients were predicted by Nomogram. (D) The diagonal dashed lines represent the ideal nomogram.

Figure 5

Correlation between CAGs expression and various immune cells infiltration of HNSC. (A–D) Four algorithms including EPIC (A), MCP-counter (B), quanTIseq (C) and TIMER (D) to evaluate the correlation between CAGs and immune cell infiltration. The ordinate represents CAGs, the abscissa represents different immune cells. ^*stands for significance levels, ^*for p < 0.05.

Figure 6

Expression distribution of immune checkpoint of HNSC samples with differential CAGs expression. ^*stands for significance levels, ^*for p < 0.05.

Figure 7

TIDE algorithm predicted the immune checkpoint inhibitor response of HNSC samples with differentially expressing CAGs. The upper panel represents the statistical tables of immune responses of samples. Lower panel indicates the distribution of immune response scores. ^*stands for significance levels, ^*for p < 0.05.

Figure 8

Correlation between CAGs expression and drug sensitivity. (A, B) Spearman correlation analysis of cisplatin (A) docetaxel (B) IC50 score and CAG expression.