Recently, melanoma patients are growing younger and with highly metastasize and deadly threatening, which places a huge burden to thousands of people worldwide. In spite of numerous advanced therapeutic methods were used to treat melanoma, such as chemotherapy and radiotherapy and immunotherapies, their survival rate still remains low[1, 3]. Besides, the traditional classification is often ineffective and lacks clinical benefits. Therefore, researchers are struggling to explore the new biomarkers for better diagnose and predict prognosis. Huang et al identified eight immune-relate genes biomarker which could predict the prognosis of melanoma. An RNA sequencing-based 12-gene signature was established by applying univariate and multivariate regression models to predict the survival of malignant melanoma patients. Lu et al discovery a five-miRNA signature by analyzing microarray dataset in GEO database, which could be an independent prognostic biomarker in melanoma patients. Recently, the tumor immune microenvironment in melanoma has become a research hotspot and under active investigation. Moreover, the immune cell types which differentially distributed in cancer tissue on diagnosis has attracted great interest in recent years. Therefore, in this study, we systematically analyzed the immune microenvironment and tried to establish a more evaluable and precise signature for advanced melanoma patients.
In spite of various differential expression of genes were analyzed to diagnose tumors. Nevertheless, little research attention looked at the effects of immune cell on the diagnosis of melanoma. Firstly, we used the ssGSEA method to calculate the relative expression of 24 human immune cells. Because of compared to normal tissues, the distribution of immune cell was significantly higher in tumor tissues. The overlapping DEICs were identified and put into machine learning analysis. The high sensitivity and specificity of multiple machine learning algorithms indicated that DECIs was an efficient indicator for diagnosis of melanoma. In addition, we built a diagnostic score model by logistic regression method, which could effectively distinguish the melanomas from the normal controls, replying that the immune system is closely associated with the tumorigenesis of melanoma. Similar results have been reported that infiltration of immune cell can be used to diagnose colon cancer, even all digestive system cancers[32, 33]. In this sense, immune infiltration opened a novel strategy for diagnosing and treating melanoma.
To subsequently investigate the prognostic value of immune infiltration in melanoma, LASSO, RF-FS and SVM-RFE methods jointly applied to select potential immune cells for building the prognostic model. Finally, four types of immune cell including Th2 cells, T helper cells, Macrophages, iDC were used to construct the risk score system by Cox regression method, which also was validated in internal and external datasets. Among these immune cells, some have been proven to be associated with melanoma. For instance, approximate 70% of melanoma metastatic lymph nodes were detected the distribution of immature DCs which may take an immunosuppressive function in melanoma. Under normal immune environment, Th1 cells and Th2 cells are in a relatively balanced state. Th2 bias signifies the imbalance of Th1/Th2. Th2 could strongly inhibit Th1 responses. Th2 bias is one of the mechanisms of tumor immune escape. Studies have also shown that Th2 dominance could mediate a chronic inflammation which could promote melanoma metastasis. Moreover, Falleni at el confirmed that the accumulation of Macrophages was a poor predictor for survival of melanoma patients and could be regarded as a potential therapeutic target. In order to assess the accuracy of prognostic prediction, we also constructed nomogram integrate risk score and clinical information. The calibration curve for the observed 3-year, and 5-year outcomes showed that the nomogram model performed well with the ideal prediction model. What’s more, compared with the tumor stage, the decision curve plots depicted that the nomogram model can acquire more benefit. The multivariate regression analysis also indicated that the risk score of immune cell related biomarker could be regard as an independent prognostic factor in melanoma.
According to the optimal cutoff value of risk score, melanoma patients classified into different risk groups. The Kaplan–Meier revealed that patients in high risk group have a poor prognosis. Thus, in order to explore the underlying mechanism with different subgroups, stratified analyses of clinical characteristics and gene phenotypes were performed. The risk score distribution of clinical features showed that the risk score was only correlated to vital status and tumor status, and had on effect on other clinical features. Presently, checkpoint blockade immunotherapies represent a promising strategy for cancer therapy and acquired extensive investigations[37, 38]. However, the efficacy of immunotherapies is dramatic varied in individual patients and different subtypes of cancer. In our research, the expression of immune checkpoint related genes including CD28, CTLA4, ICOS, PDCD1, TIGIT, CD274, CD226, CD40 and CD40LG were highly expressed in high risk of melanoma patients. Besides, epithelial mesenchymal transition (EMT) recognized the indictor for the invasion and progression of many cancers[39, 40]. The selected EMT related genes also including our research and the results also manifested that most of them are highly expressed in high risk group. Hence, we have enough reasons to believe that our immune cell feature closely correlated with the prognosis of melanoma.
Further investigating the potential biological mechanism in high risk phenotype, GSEA method was applied to analyze the candidate pathways. The results showed that high risk phenotype was positively associated with cancer hallmarks such as allograft rejection, complement, EMT and inflammatory response, which supported the previous findings that EMT and immune related genes are highly expressed in high risk group. The complement system, an essential constituent of innate immunity, affects tumor growth and metastasis by regulating chronic inflammation. Moreover, KEGG pathway analysis showed that complement and coagulation cascades, ECM receptor interaction, natural killer cell mediated cytotoxicity and T cell receptor signaling pathways were enriched in high risk phenotype, which largely consisted with cancer hallmarks analysis. ECM-receptor interaction pathway is important in tumor metastasis. The significance of the ECM-receptor interaction pathway implied the interaction between tumor cell and environment are very dynamic.