皮肤黑色素瘤（SKCM）是最具侵袭性和致命性的皮肤肿瘤之一，其发病率在全球范围内不断增加。然而，对于晚期SKCM，我们仍然缺乏准确有效的方法来预测其预后，也缺乏新的理论来指导SKCM患者治疗方案的规划。乳酰化（LAC）是一种新型的组蛋白翻译后修饰，已被证明可以通过多种方式促进肿瘤生长并抑制肿瘤微环境（TME）的抗肿瘤反应。我们希望这项研究能够为SKCM患者的治疗选择，以及SKCM发病和发展的分子机制研究提供新的思路。在RNA测序集（TCGA、GTEx）水平上，我们利用差异表达分析， LASSO回归分析、多因素Cox回归分析筛选预后相关基因并计算相应的LAC评分。采用CIBERSORT算法计算肿瘤组织中TME细胞的含量，并根据其结果计算TME评分。最后，建立LAC-TME分类器，并根据两个评分进行进一步分析，包括预后模型的构建、临床病理特征分析以及肿瘤突变负荷（TMB）与免疫治疗的相关性分析。本研究基于单细胞RNA测序数据，分析了SKCM组织中的细胞组成，并探讨了LAC评分在细胞间通讯中的作用。为了验证模型中关键基因CLPB的功能，最终进行了细胞实验。我们总共筛选了6个预后相关基因（NDUFA10、NDUFA13、CLPB、RRM2B、HPDL、NARS2）和7个预后良好的TME细胞。根据Kaplan-Meier生存分析，我们发现LAClow/TMEhigh组的总生存期（OS）最高，LAChigh/TMElow组的OS最低（p值 < 0.05）。在进一步分析免疫浸润、肿瘤微环境（TME）、功能富集、肿瘤突变负荷和免疫治疗时，我们发现免疫治疗更适合LAClow/TMEhigh组。此外，细胞分析显示，在 CLPB 敲低后，A375 和 A2058 细胞系中黑色素瘤细胞的增殖、迁移和侵袭潜力均显着降低。使用 LAC 评分和 TME 评分相结合的预后模型能够预测 SKCM 患者的预后更加一致，LAC-TME 分类器能够显着区分 SKCM 患者多种临床病理特征的预后。 LAC-TME 分类器在 SKCM 患者免疫治疗方案的开发中发挥着重要作用。© 2023。作者获得 Springer-Verlag GmbH 德国（Springer Nature 旗下公司）的独家许可。

The incidence of skin cutaneous melanoma (SKCM), one of the most aggressive and lethal skin tumors, is increasing worldwide. However, for advanced SKCM, we still lack an accurate and valid way to predict its prognosis, as well as novel theories to guide the planning of treatment options for SKCM patients. Lactylation (LAC), a novel post-translational modification of histones, has been shown to promote tumor growth and inhibit the antitumor response of the tumor microenvironment (TME) in a variety of ways. We hope that this study will provide new ideas for treatment options for SKCM patients, as well as research on the molecular mechanisms of SKCM pathogenesis and development.At the level of the RNA sequencing set (TCGA, GTEx), we used differential expression analysis, LASSO regression analysis, and multifactor Cox regression analysis to screen for prognosis-related genes and calculate the corresponding LAC scores. The content of TME cells in the tumor tissue was calculated using the CIBERSORT algorithm, and the TME score was calculated based on its results. Finally, the LAC-TME classifier was established and further analyzed based on the two scores, including the construction of a prognostic model, analysis of clinicopathological characteristics, and correlation analysis of tumor mutation burden (TMB) and immunotherapy. Based on single-cell RNA sequencing data, this study analyzed the cellular composition in SKCM tissues and explored the role of LAC scores in intercellular communication. To validate the functionality of the pivotal gene CLPB in the model, cellular experiments were ultimately executed.We screened a total of six prognosis-related genes (NDUFA10, NDUFA13, CLPB, RRM2B, HPDL, NARS2) and 7 TME cells with good prognosis. According to Kaplan-Meier survival analysis, we found that the LAClow/TMEhigh group had the highest overall survival (OS) and the LAChigh/TMElow group had the lowest OS (p value < 0.05). In further analysis of immune infiltration, tumor microenvironment (TME), functional enrichment, tumor mutational load and immunotherapy, we found that immunotherapy was more appropriate in the LAClow/TMEhigh group. Moreover, the cellular assays exhibited substantial reductions in proliferation, migration, and invasive potentials of melanoma cells in both A375 and A2058 cell lines upon CLPB knockdown.The prognostic model using the combined LAC score and TME score was able to predict the prognosis of SKCM patients more consistently, and the LAC-TME classifier was able to significantly differentiate the prognosis of SKCM patients across multiple clinicopathological features. The LAC-TME classifier has an important role in the development of immunotherapy regimens for SKCM patients.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.