机构：[1]Department of Dermatology, The First People’s Hospital of Foshan, Foshan, China[2]Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan Key Laboratory of Vaccine Research and Development on Severe Infectious Diseases, Kunming, China[3]Department of Pathology, The First People’s Hospital of Foshan, Foshan, China[4]Department of Pharmacy, Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China, 深圳市康宁医院深圳医学信息中心中国医学科学院阜外医院深圳医院[5]The Second School of Medicine, Wenzhou Medical University, Wenzhou, China[6]Department of Burn Surgery and Skin Regeneration, The First People’s Hospital of Foshan, Foshan, China[7]Department of Orthopedics, The First Affiliated Hospital of China Medical University, Shenyang, China
Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.</p>
基金：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [82002913, 81772136]; Foundation of Foshan City [FS0AA-KJ218-1301-0034, 2018AB003411]; Special Fund of the Foshan Summit plan [2019C002, 2019D008, 2019A006, 2020A015]; GuangDong Basic and Applied Basic Research Foundation [2021A1515011453]; Yunnan Fundamental Research Projects [202001AT070145]
第一作者机构：[1]Department of Dermatology, The First People’s Hospital of Foshan, Foshan, China
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推荐引用方式(GB/T 7714)：
Zhou Sitong,Han Yuanyuan,Li Jiehua,et al.New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation[J].FRONTIERS IN ONCOLOGY.2021,11:doi:10.3389/fonc.2021.745384.
APA：
Zhou, Sitong,Han, Yuanyuan,Li, Jiehua,Pi, Xiaobing,Lyu, Jin...&Yang, Ronghua.(2021).New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation.FRONTIERS IN ONCOLOGY,11,
MLA：
Zhou, Sitong,et al."New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation".FRONTIERS IN ONCOLOGY 11.(2021)
