皮肤黑色素瘤（SKCM）是皮肤癌的一种形式，是最可怕和致命的恶性肿瘤之一。探索基于肿瘤微环境（TME）的预后指标将有助于提高 SKCM 患者免疫治疗的疗效。这项研究分析了 SKCM scRNA-seq 数据，对非恶性细胞进行聚类，可用于将 TME 探索为九种免疫/基质细胞类型，包括 B 细胞、CD4 T 细胞、CD8 T 细胞、树突状细胞、内皮细胞、成纤维细胞、巨噬细胞、神经元和自然杀伤 (NK) 细胞。利用癌症基因组图谱 (TCGA) 的数据，我们采用 SKCM 表达谱来识别差异表达的免疫相关基因 (DEIAG)，然后将其纳入加权基因共表达网络分析 (WGCNA) 以研究 TME 相关的中枢基因。基于关键基因发现候选小分子药物。用于构建 TIMAS 的肿瘤免疫微环境相关基因 (TIMAG) 已被鉴定和验证。最后，分析了TIAMS亚组的特征以及TIMAS预测免疫治疗结果的能力。我们确定了五个 TIMAG（CD86、CD80、SEMA4D、C1QA 和 IRF1）并使用它们构建 TIMAS。此外，还确定了五种潜在的 SKCM 药物。结果显示，TIMAS-low患者与免疫相关信号通路、MUC16突变频率高、T细胞浸润高、M1巨噬细胞相关，更利于免疫治疗。总的来说，通过综合分析 scRNA-seq 和批量 RNA-seq 数据构建的 TIMAS 是预测 ICI 治疗结果和改善 SKCM 患者个体化治疗的有前途的标记。© 2024。作者，获得 Springer-Verlag 独家许可GmbH 德国，隶属于施普林格自然集团。

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.