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CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
基于 scRNA-seq 和 bulk RNA-seq 建立皮肤黑色素瘤肿瘤免疫微环境相关特征并预测免疫治疗反应
英文原题：Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response.
查看英文原题
Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response.
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中文摘要
皮肤黑色素瘤（SKCM）是一种皮肤癌，属于最凶险和致命的恶性肿瘤之列。探索基于肿瘤微环境（TME）的预后指标有助于提高SKCM患者免疫治疗的疗效。
本研究分析了SKCM scRNA-seq数据，对可用于探索TME的非恶性细胞进行聚类，分为九种免疫/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表达谱分析来鉴定差异表达的免疫相关基因（DEIAGs），随后将其纳入加权基因共表达网络分析（WGCNA）以探究TME相关枢纽基因。基于关键基因发现候选小分子药物。鉴定并验证了用于构建TIMAS的肿瘤免疫微环境相关基因（TIMAGs）。
最后，分析了TIAMS亚组的特征以及TIMAS预测免疫治疗结局的能力。我们鉴定了五个TIMAGs（CD86、CD80、SEMA4D、C1QA和IRF1），并用它们构建了TIMAS。
此外，还鉴定了五种潜在的SKCM药物。结果表明，TIMAS-low患者与免疫相关信号通路、高MUC16突变频率、高T细胞浸润和M1巨噬细胞相关，且更有利于免疫治疗。
总之，通过综合分析scRNA-seq和bulk RNA-seq数据构建的TIMAS是一种有前景的标志物，可用于预测ICI治疗结局并改善SKCM患者的个体化治疗。
展开英文摘要原文
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.
论文信息
- 作者
- Li S、Zhao J、Wang G、Yao Q、Leng Z、Liu Q、Jiang J、Wang W
- 第一作者单位
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.Italy
- 通讯作者单位
- School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China. wangweittg@sit.edu.cn.Italy
- 期刊
- Archives of dermatological research2024 May 25
- 原文标识
- PubMed 38795156 · DOI 10.1007/s00403-024-03080-3