Department of Plastic Surgery, the First Affiliated Hospital of Jinan University, Guangdong, Guangzhou, China.
Department of Plastic Surgery, Zhongshan City People's Hospital, Guangdong, Zhongshan, China.
Medicine (Baltimore). 2024 Aug 9;103(32):e38924. doi: 10.1097/MD.0000000000038924.
This research endeavor seeks to explore the microenvironment of melanoma tumors and construct a prognostic model by focusing on genes specific to CD8+ T cells. The single-cell sequencing data of melanoma underwent processing with the Seurat package, subsequent to which cell communication network analysis was conducted using the iTALK package and transcription factor analysis was performed using the SCENIC package. Univariate COX and LASSO regression analyses were utilized to pinpoint genes linked to the prognosis of melanoma patients, culminating in the creation of a prognostic model through multivariate COX analysis. The model was validated using the GSE65904 and GSE35640 datasets. Multi-omics analysis was conducted utilizing the maftools, limma, edgeR, ChAMP, and clusterProfiler packages. The examination of single-cell sequencing data revealed the presence of 8 cell types, with the transcription factors RFXAP, CLOCK, MGA, RBBP, and ZNF836 exhibiting notably high expression levels in CD8+ T cells as determined by the SCENIC package. Utilizing these transcription factors and their associated target genes, a prognostic model was developed through COX and LASSO analyses, incorporating the genes GPR171, FAM174A, and BPI. This study validated the model with independent datasets and conducted additional analysis involving multi-omics and immune infiltration to identify a more favorable prognosis for patients in the low-risk group. The findings provide valuable insights into the tumor microenvironment of melanoma and establish a reliable prognostic model. The integration of multi-omics and immune infiltration analyses enhances our understanding of the pathogenesis of melanoma. The identification of specific genes holds promise as potential biomarkers for individuals with melanoma, serving as important indicators for predicting patient outcomes and determining their response to immunotherapy.
本研究旨在探讨黑色素瘤肿瘤的微环境,并通过聚焦于 CD8+T 细胞特异性基因构建预后模型。对黑色素瘤的单细胞测序数据进行 Seurat 包处理,随后使用 iTALK 包进行细胞通讯网络分析,使用 SCENIC 包进行转录因子分析。利用单变量 COX 和 LASSO 回归分析确定与黑色素瘤患者预后相关的基因,通过多变量 COX 分析构建预后模型。利用 GSE65904 和 GSE35640 数据集对模型进行验证。利用 maftools、limma、edgeR、ChAMP 和 clusterProfiler 包进行多组学分析。单细胞测序数据的检查揭示了 8 种细胞类型的存在,SCENIC 包确定转录因子 RFXAP、CLOCK、MGA、RBBP 和 ZNF836 在 CD8+T 细胞中表现出显著高表达。利用这些转录因子及其相关靶基因,通过 COX 和 LASSO 分析构建了一个包含 GPR171、FAM174A 和 BPI 基因的预后模型。本研究利用独立数据集验证了该模型,并进行了多组学和免疫浸润的额外分析,以确定低风险组患者具有更好的预后。研究结果为黑色素瘤肿瘤微环境提供了有价值的见解,并建立了一个可靠的预后模型。多组学和免疫浸润分析的整合增强了我们对黑色素瘤发病机制的理解。特定基因的鉴定有望成为黑色素瘤患者的潜在生物标志物,作为预测患者结局和确定其对免疫治疗反应的重要指标。