恶性黑色素瘤是一种高度恶性且异质性的皮肤癌症。虽然免疫疗法改善了生存率，但肿瘤微环境的抑制效应削弱了其疗效。为了改善生存率和治疗策略，我们需要建立免疫相关的预后模型。本研究基于癌症基因组图谱（TCGA）、基因表达文库（GEO）和测序读档（SRA）数据库的分析，旨在建立一个免疫相关的预后预测模型，并通过风险评分评估肿瘤免疫微环境，以指导免疫治疗。从TCGA数据库获取了皮肤黑色素瘤（SKCM）转录组测序数据和相应的临床信息，分析了差异表达基因，并采用单因素Cox回归、LASSO方法和逐步回归建立预后模型。通过实时逆转录PCR（real-time RT-PCR）和Western印迹验证了预后模型中的差异表达基因。采用Kaplan-Meier方法进行生存分析，并利用时间相关的受试者工作特征曲线以及多因素Cox回归评估模型效果，同时使用2个GEO黑色素瘤数据集对预后模型进行验证。此外，还分析了风险评分与免疫细胞浸润、肿瘤组织中的评估储备和免疫细胞、免疫检查点mRNA表达水平、肿瘤免疫周期或肿瘤免疫微环境通路之间的相关性。最后，我们对风险评分与免疫疗法疗效进行了关联分析。 我们在TCGA-SKCM数据集中鉴定到了4个在基因表达上有差异的基因，这些基因主要与肿瘤免疫微环境有关。同时，我们基于这4个基因建立了一个预后模型。其中，杀伤细胞准白细胞素样受体D1（KLRD1）、白血病抑制因子（LIF）和细胞型视黄酸结合蛋白2（CRABP2）基因的mRNA和蛋白水平在黑色素瘤组织中与正常皮肤明显不同（P<0.01）。预后模型对SKCM患者的预后具有良好的预测能力。高风险评分患者的总生存期明显短于低风险评分患者，并且在训练队列和多个验证队列中都得到了一致的结果（P<0.001）。风险评分与免疫细胞浸润、储备评估和免疫细胞检查点mRNA表达水平、肿瘤免疫周期和肿瘤免疫微环境通路密切相关（P<0.001）。相关性分析显示，基于预后模型的高风险评分患者处于一个抑制性免疫微环境中（P<0.01）。本研究构建的免疫相关SKCM预后模型能够有效预测SKCM患者的预后。考虑到其与肿瘤免疫微环境的密切关联，该模型对于SKCM的临床免疫治疗具有一定的参考价值。

Malignant melanoma is a highly malignant and heterogeneous skin cancer. Although immunotherapy has improved survival rates, the inhibitory effect of tumor microenvironment has weakened its efficacy. To improve survival and treatment strategies, we need to develop immune-related prognostic models. Based on the analysis of the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Sequence Read Archive (SRA) database, this study aims to establish an immune-related prognosis prediction model, and to evaluate the tumor immune microenvironment by risk score to guide immunotherapy.Skin cutaneous melanoma (SKCM) transcriptome sequencing data and corresponding clinical information were obtained from the TCGA database, differentially expressed genes were analyzed, and prognostic models were developed using univariate Cox regression, the LASSO method, and stepwise regression. Differentially expressed genes in prognostic models confirmed by real-time reverse transcription PCR (real-time RT-PCR) and Western blotting. Survival analysis was performed by using the Kaplan-Meier method, and the effect of the model was evaluated by time-dependent receiver operating characteristic curve as well as multivariate Cox regression, and the prognostic model was validated by 2 GEO melanoma datasets. Furthermore, correlations between risk score and immune cell infiltration, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score, immune checkpoint mRNA expression levels, tumor immune cycle, or tumor immune micro-environmental pathways were analyzed. Finally, we performed association analysis for risk score and the efficacy of immunotherapy.We identified 4 genes that were differentially expressed in TCGA-SKCM datasets, which were mainly associated with the tumor immune microenvironment. A prognostic model was also established based on 4 genes. Among 4 genes, the mRNA and protein levels of killer cell lectin like receptor D1 (KLRD1), leukemia inhibitory factor (LIF), and cellular retinoic acid binding protein 2 (CRABP2) genes in melanoma tissues differed significantly from those in normal skin (all P<0.01). The prognostic model was a good predictor of prognosis for patients with SKCM. The patients with high-risk scores had significantly shorter overall survival than those with low-risk scores, and consistent results were achieved in the training cohort and multiple validation cohorts (P<0.001). The risk score was strongly associated with immune cell infiltration, ESTIMATE score, immune checkpoint mRNA expression levels, tumor immune cycle, and tumor immune microenvironmental pathways (P<0.001). The correlation analysis showed that patients with the high-risk scores were in an inhibitory immune microenvironment based on the prognostic model (P<0.01).The immune-related SKCM prognostic model constructed in this study can effectively predict the prognosis of SKCM patients. Considering its close correlation to the tumor immune microenvironment, the model has some reference value for clinical immunotherapy of SKCM.