Li Changchang, Wu Nanhui, Lin Xiaoqiong, Zhou Qiaochu, Xu Mingyuan
Department of Dermatology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine, Wenzhou, China.
Department of Dermatopathology, Shanghai Skin Disease Hospital, Tongji University School of Medicine, Shanghai, China.
Front Pharmacol. 2024 May 28;15:1389550. doi: 10.3389/fphar.2024.1389550. eCollection 2024.
The mortality rate associated with cutaneous melanoma (SKCM) remains alarmingly high, highlighting the urgent need for a deeper understanding of its molecular underpinnings. In our study, we leveraged bulk transcriptome sequencing data from the SKCM cohort available in public databases such as TCGA and GEO. We utilized distinct datasets for training and validation purposes and also incorporated mutation and clinical data from TCGA, along with single-cell sequencing data from GEO. Through dimensionality reduction, we annotated cell subtypes within the single-cell data and analyzed the expression of tumor-related pathways across these subtypes. We identified differentially expressed genes (DEGs) in the training set, which were further refined using the Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm, employing tenfold cross-validation. This enabled the construction of a prognostic model, whose diagnostic efficacy we subsequently validated. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the DEGs, and performed immunological profiling on two risk groups to elucidate the relationship between model genes and the immune responses relevant to SKCM diagnosis, treatment, and prognosis. We also knocked down the GMR6 expression level in the melanoma cells and verified its effect on cancer through multiple experiments. The results indicate that the GMR6 gene plays a role in promoting the proliferation, invasion, and migration of cancer cells in human melanoma. Our findings offer novel insights and a theoretical framework that could enhance prognosis, treatment, and drug development strategies for SKCM, potentially leading to more precise therapeutic interventions.
皮肤黑色素瘤（SKCM）的死亡率仍然高得惊人，这凸显了深入了解其分子基础的迫切需求。在我们的研究中，我们利用了公共数据库（如TCGA和GEO）中SKCM队列的批量转录组测序数据。我们使用不同的数据集进行训练和验证，还纳入了来自TCGA的突变和临床数据，以及来自GEO的单细胞测序数据。通过降维，我们注释了单细胞数据中的细胞亚型，并分析了这些亚型中肿瘤相关通路的表达。我们在训练集中鉴定了差异表达基因（DEG），并使用最小绝对收缩和选择算子（LASSO）机器学习算法，采用十折交叉验证对其进行进一步优化。这使得能够构建一个预后模型，随后我们验证了该模型的诊断效力。我们对DEG进行了基因本体（GO）和京都基因与基因组百科全书（KEGG）分析，并对两个风险组进行了免疫分析，以阐明模型基因与SKCM诊断、治疗和预后相关的免疫反应之间的关系。我们还敲低了黑色素瘤细胞中GMR6的表达水平，并通过多个实验验证了其对癌症的影响。结果表明，GMR6基因在促进人类黑色素瘤癌细胞的增殖、侵袭和迁移中发挥作用。我们的研究结果提供了新的见解和理论框架，可增强SKCM的预后、治疗和药物开发策略，可能导致更精确的治疗干预。
