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Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response.
Li, Shanshan; Zhao, Junjie; Wang, Guangyu; Yao, Qingping; Leng, Zhe; Liu, Qinglei; Jiang, Jun; Wang, Wei.
Afiliación
  • Li S; School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
  • Zhao J; School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
  • Wang G; School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
  • Yao Q; Institute of Mechanobiology & Medical Engineering, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
  • Leng Z; School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
  • Liu Q; School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China.
  • Jiang J; Department of Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan Province, 646000, China.
  • Wang W; School of Perfume & Aroma and Cosmetics, Shanghai Institute of Technology, Shanghai, 201418, China. wangweittg@sit.edu.cn.
Arch Dermatol Res ; 316(6): 262, 2024 May 25.
Article en En | MEDLINE | ID: mdl-38795156
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Cutáneas / Microambiente Tumoral / RNA-Seq / Inmunoterapia / Melanoma Límite: Female / Humans / Male Idioma: En Revista: Arch Dermatol Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Cutáneas / Microambiente Tumoral / RNA-Seq / Inmunoterapia / Melanoma Límite: Female / Humans / Male Idioma: En Revista: Arch Dermatol Res Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Alemania