ABSTRACT: The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored. Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p < 0.001) and metastatic SKCM (p < 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens. Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.
