3.1. The expression of CNIH4 in HNSC
Firstly, we analyzed the CNIH4 expression in HNSC. Results showed that the expression of CNIH4 in HNSC was significantly higher than that in head and neck normal tissues (Fig. 1A1). We also found that higher grade HNSC expressed significantly higher CNIH4 than lower grade HNSC (Fig. 1A2). Samples from male patients had significantly higher CNIH4 than those from female patients (Fig. 1A3). The pTNM IV had significantly higher CNIH4 than pTNM I, but the other comparison had no significant differences (Fig. 1A4). There was no significant difference among different pT stagings or different pN stagings (Fig. 1A5-6). Compared to the white, black patients had a slightly higher expression of CNIH4 (Fig.A7). Compared to the patients who smoke, patients who do not smoke expressed a slightly lower CNIH4 (Fig.A8). Besides, we also compared paired cancer-noncancer samples from the same patient, paired t-test showed that CNIH4 was overexpressed in cancer (Fig. 1B). Clinical information of the high (50–100%, red) and low (0–50%, blue) CNIH4 groups were listed in Table 1. To further confirm CNIH4 protein was overexpressed in HNSC compared to paired normal tissue, we compared protein staining results. Results showed that, compared to the normal oral mucosa, and HNSC tissues had a higher staining intensity of CNIH4 (Fig. 2). Thus, CNIH4 was a diagnostic marker of HNSC. In addition, to investigate the reason for overexpression of CNIH4 in HNSC, we compared the copy number of CNIH4 in HNSC and normal tissues in 27 data sets. The overall statistical analysis revealed that the copy number of CNIH4 in HNSC was significantly higher than that in normal tissues. Specifically, the copy number of CNIH4 in HNSC was significantly higher than normal tissues in 20 of the 27 data sets (Fig. 3). Therefore, we suggested that the overexpression of CNIH4 in HNSC resulted from the higher gene copy number of CNIH4 in HNSC.
Table 1
Distribution of HNSC patients with different clinicopathological variables in CNIH4 high and low groups.
Term | AIMP1 high | AIMP1 low | P-value |
Alive | 122 | 162 | |
Dead | 129 | 89 | 0 |
Mean (SD) | 61.9 (11) | 60.2 (12.7) | |
Median [MIN,MAX] | 61 [26,87] | 60 [19,90] | 0.103 |
FEMALE | 61 | 73 | |
MALE | 190 | 178 | 0.19 |
ASIAN | 3 | 7 | |
BLACK | 31 | 16 | |
WHITE | 206 | 222 | 0.074 |
T1 | 16 | 18 | |
T2 | 65 | 79 | |
T3 | 65 | 68 | |
T4 | 16 | 9 | |
T4a | 78 | 74 | |
T4b | 2 | 1 | |
TX | 9 | 2 | 0.21 |
N0 | 121 | 120 | |
N1 | 38 | 43 | |
N2 | 9 | 10 | |
N2a | 10 | 8 | |
N2b | 35 | 41 | |
N2c | 21 | 20 | |
N3 | 6 | 1 | |
NX | 11 | 8 | 0. 644 |
M0 | 234 | 243 | |
M1 | 3 | 2 | |
MX | 14 | 6 | 0. 168 |
I | 8 | 17 | |
II | 39 | 42 | |
III | 42 | 48 | |
IVA | 151 | 139 | |
IVB | 10 | 3 | |
IVC | 1 | 2 | 0.381 |
G1 | 19 | 43 | |
G2 | 146 | 154 | |
G3 | 73 | 46 | |
GX | 11 | 5 | |
G4 | | 2 | 0 |
Metastasis | 11 | 8 | |
Primary | 6 | 3 | |
Recurrence | 24 | 15 | 0.904 |
Non-smoking | 44 | 67 | |
Smoking | 203 | 178 | 0.015 |
Non-radiation | 29 | 33 | |
Radiation | 66 | 55 | 0.401 |
Neoadjuvant | 7 | 3 | |
No neoadjuvant | 244 | 248 | 0.338 |
Chemotherapy | 83 | 76 | |
3.2. Survival Prognostic analysis of CNIH4 gene in HNSC.
To evaluate the prognostic power of CNIH4 for HNSC patients, we analyzed the association of CNIH4 expression and the overall survival of HNSC patients. We conducted a Kaplan-Meier survival analysis and log-rank test to compare the survival of the high (50–100%, red) and low (0–50%, blue) CNIH4 groups. The HR for the high CNIH4 group was 1.5 (95%IC = 1.152–1.983) and the median time for high and low CNIH4 groups were 3 and 5.2 respectively (Fig. 4A). Therefore, we suggested that CNIH4 was a potential impact factor for the overall survival of HNSC. To apply the CNIH4 expression to the clinical prognosis of HNSC, we constructed a survival nomogram. Variables CNIH4 expression, age, gender, grade, and race were analyzed. Univariate Cox regression analysis results showed that, among these five variables, only CNIH4 expression and age were significantly associated with survival (Fig. 4B). Multivariate Cox regression showed that CNIH4 expression and age were independent factors for HNSC patient survival (Fig. 4C). Therefore, based on the Cox regression results, a nomogram including variables CNIH4 expression and age was constructed for the prediction of 1-, 2-, 3-, 5- year survival for HNSC patients. The C-index of the nomogram was 0.602 (Fig. 4D). The prediction results of the nomogram calibration curves were consistent with all patients' observation results (Fig. 4E). These analyses demonstrated the clinical value of CNIH4 for HNSC prognosis.
3.3. CNIH4 associated genes enrichment analysis.
To explore the potential mechanisms involved in the effect of the CNIH4 gene on HNSC, we identified differentially expressed genes (DEGs) between CNIH4 high (75–100%) and low (0–25%) groups. We set cutoff values of 1.5 and 0.01 for fold change and p-value respectively. Results showed that 2012 and 421 genes were identified as DEGs positively and negatively associated with CNIH4 in HNSC respectively (Fig. 5AB). These genes were further enriched in GO terminologies and KEGG pathways. Results of KEGG enrichment showed that genes positively associated with CNIH4 were most enriched in “Cell cycle”, while genes negatively associated with CNIH4 were most enriched in “Cytokine − cytokine receptor interaction”. In terms of GO enrichment, genes positively associated with CNIH4 were highly enriched in “organelle fission”, “nuclear division”, “mitotic nuclear division”, “chromosome segregation”, and “DNA replication”, while genes negatively associated with CNIH4 were highly enriched in “epidermis development”, “skin development”, “epidermal cell differentiation”, “keratinization”, and “keratinocyte differentiation” (Fig. 5C). Other less enriched terms included immune-associated terms, such as “T cell activity”. Because these genes were enriched in multiple terms that were associated with cancer stemness and immune cells, these results inferred that CNIH4 might play a role in cancer stemness and regulation of immunity in HNSC.
3.4. CNIH4 was associated with stemness of HNSC
To further explore the potential role of CNIH4 in HNSC, we investigated a single-cell data set of HNSC. We analyzed the correlation of CNIH4 expression and cancer functional state scores of 2150 single HNSC cells and identified 12 functional states that significantly correlated with CNIH4 (p < 0.05). Results showed that CNIH4 was positively correlated with stemness, cell cycle, DNA repair, invasion, and proliferation with coefficients of 0.26, 0.23, 0.13, 0.06, and 0.05 respectively. On the other hand, CNIH4 was negatively correlated with angiogenesis, quiescence, metastasis, hypoxia, inflammation, DNAdamage, and differentiation with coefficients of -0.16, -0.16, -0.10, -0.10, -0.08, -0.07, and − 0.06 (Fig. 6A). Detailed data were shown in Fig. 6B. As the correlations of stemness and cell cycle were striking, we further demonstrate the association of CNIH4 and cancer stemness. We applied OCLR to compared the stemness of CNIH4 high (75–100%) and low (0–25%) groups. Results showed that the CNIH4 high group had significantly higher stemness than that of the low groups, indicating that CNIH4 might upregulate stemness of HNSC. These results were consistent with the enrichment results that CNIH4 was associated with “cell cycle” and “DNA replicate”.
3.5. CNIH4 was associated with immunity regulation of HNSC
To study the potential role of CNIH4 in the immunity of HNSC, we first analyzed the distribution of CNIH4 in different cell fractions in HNSC. Single-cell seq data sets HNSC_GSE103322 and HNSC_GSE139324 were analyzed. Results showed that HNSC malignant cells expressed much higher CNIH4 than immune cells (Fig. 7). Therefore, we suggested that the expression level of CNIH4 in HNSC samples was mainly dependent on the expression of CNIH4 in tumor cells. Furthermore, we calculated the immune cell infiltration score of TCGA data using the xCell algorithms. We compared immune cell infiltration levels between CNIH4 high (75–100%) and low (0–25%) groups. In detail, compared to the CNIH4 low group, the CNIH4 high group was only significantly higher in the levels of Common lymphoid progenitor and T cell CD4 + Th2. However, CNIH4 high group was significantly lower at levels of T cell CD4 + central memory, Endothelial cell, Myeloid dendritic cell activated, Myeloid dendritic cell, Plasmacytoid dendritic cell, T cell CD8+, T cell CD8 + central memory, B cell plasma, B cell, B cell memory, T cell CD4 + naïve, Class − switched memory B cell, Monocyte, Neutrophil, Mast cell, and T cell CD4 + effector memory. These results suggested that CNIH4 was negatively associated with infiltration levels of most immune cells and might negatively regulate immune and stroma in HNSC. Apart from that, we analyzed the correlation of CNIH4 and four immune checkpoints, including CTLA4, LAG3, PDCD1, and TIGIT. Results showed that CNIH4 expression was negatively correlated with CTLA4, LAG3, PDCD1, and TIGIT expression (Fig. 8-B). To further explore the value of CNIH4 for clinical immune therapy, we compared ICB respond of CNIH4 low (0–25%) and high (75–100%) groups. Potential ICB response was predicted using the TIDE algorithm. Results showed that the CNIH4 low group had a significantly higher TIDE score than the CNIH4 high group (Fig. 8 bottom). The calculation predicted that only 28 out of 98 (22.22%) HNSC patients responded to ICB treatment in the low CNIH4 group, while 84 out of 126 (66.67%) respond to ICB treatment in the high CNIH4 group (Fig. 8 top).