Immune scores were significantly related to CSCC prognosis
The flow chart of our study procedure is presented in Fig. 1. In this study, 210 CSCC patients with complete mRNA expression data and the corresponding clinical information were included for subsequent analysis. According to the ESTIMATE algorithm, immune scores ranged from − 1199.72 and 3426.59, stromal scores ranged from − 2309.11 to 813.34, and ESTIMATE scores ranged from − 3241.44 to 4017.39 (Fig. 2A, B and C). To explore the influence of immune/stromal scores on prognosis, we divided 210 CSCC patients into high- and low-score groups and constructed K-M curves. We found that the immune scores were significantly positively correlated with overall survival (OS) (P = 0.005) (Fig. 2D). On the contrary, stromal scores were not associated with OS (P = 0.364) (Fig. 2E). Then, we investigated the correlation between immune/stromal scores and clinical characteristics. The results showed that the distributions of immune/stromal scores did not vary with stage (Fig. 3A and B) and age (Fig. 3C and D).
Differentially expressed genes with immune score
As stromal scores were not correlated with overall survival in this study, we only explored the difference of gene expression profiles between high- and low immune score groups to construct the prognostic model. Finally, according to the inclusion criteria, 641 DEGs were identified and exhibited by volcano plot (Fig. 4A). Compared with the low immune scores group, 504 genes were upregulated and 137 genes were downregulated in the high immune score group (Supplementary Table 1). Among these DEGs, 72 DEGs were significantly positively correlated with OS (P < 0.01) ( Supplementary Table 2). An additional file shows this in more detail [see Additional file 1]. The heatmap showed the expression profiles of the 72 genes of prognostic value of cases in low and high immune scores groups (Fig. 4B).
Functional enrichment analysis
The potential functions of the 641 DEGs were evaluated by GO analysis and KEGG pathway. The top 5 GO terms identified included immunoglobulin complex, antigen binding, adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains, lymphocyte mediated immunity and complement activation classical pathway (Fig. 5A). The KEGG pathway analysis revealed that the DEGs were enriched in allograft rejection, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, cell adhesion molecules (CAMs) and staphylococcus aureus infection (Fig. 5B).
Construction of a riskscore model and evaluation of its predictive ability
Seventy-two genes of prognostic value were included in LASSO analysis, in which the contributions of 72 genes were weighted by their relative coefficients (Fig. 6A and B). In consequence, two genes were selected for constructing the riskscore model, and the final riskscore formula was as follows: Risk score = [-0.0115 × expression level of FOXP3] + [-0.0201 × expression level of ZAP70]. Then the riskscore of each sample was calculated according to the formula and the samples were devided into high- or low-risk groups based on the median riskscore. The distribution of the riskscore was indicated in Fig. 7A. As indicated in the K-M curve plot (Fig. 7B), there was a significant difference between high- and low-risk groups in patients’ OS. Patients in the low-risk group had significantly longer OS than those in the high-risk group. The area under the ROC curve (AUC) of the prognostic model for OS was 0.766, 0.717 and 0.701 for the first, third, and fifth years, respectively (Fig. 7C), demonstrating that the riskscore model had good performance.
Correlation of the riskscore with immune infiltration
By using the CIBERSORT algorithm, we acquired the relative fractions of 22 immune cell types. 185 cases of CSCC in the TCGA dataset were enrolled for subsequent analysis after the filter criteria with P value < 0.05, with 97 cases in low-risk group and 88 cases in high-risk group. As shown in the bar plot, the T cells CD8 and the macrophage M0 were the most significant enrichment of immune cells among 22 immune cell types (Fig. 8A). As shown in the heatmap, the fractions of immune cells in CSCC varied significantly between the low- and high-risk groups (Fig. 8B). The vioplot showed the fraction of T cells CD8, the macrophage M1 and resting Mast cells was significantly higher in low-risk group compared with high-risk group. In contrast, the fraction of resting CD4 memory T cells, the macrophage M0 and activated Dendritic cells were significantly higher in high-risk group patients compared with low-risk group (Fig. 8C). As the expresion T cells CD4 naive in all samples were estimated as zero, we eliminated this cell type in the analysis of the correlation between different tumor-infiltrating immune cells (TIICs). The corHeatmap indicated a weak to moderate correlation in the proportions of 21 types of TIICs (Fig. 8D). These results indicated that the different immune cell infiltration in patients with CSCC may be a potential prognostic biomarker and targets for immunotherapy.
A nomogram based on the riskscore model
To establish a clinically applicable method to evaluate the prognosis of CSCC patients, a nomogram was generated for OS prediction using the three prognostic factors including riskscore, age and pathologic stage (Fig. 9A). The concordance index (C-index) was 0.746. The calibration plots for the possibility of 3- and 5-year survival showed an optimal agreement between the prediction by the novel nomogram (Fig. 9B-C). The AUC was 0.805, 0.723 and 0.748 for the 1, 3 and 5-year survival times, respectively (Fig. 10A). However, the AUC was 0.3, 0.375 and 0.358 for the 1, 3 and 5-year survival times (Fig. 10B) when immunescore was included as a single prognostic factor to predict patients’ OS, suggesting that the nomogram has a much better predictive accuracy than the immunescore model and our method to construct a prognostic model is effctive. In brief, the nomogram has a good predictive accuracy and aid clinical management.