Identification of a Gene-Related Risk Signature in Melanoma Patients Using Bioinformatic Profiling
Autor: | Hang-Cheng Zhou, Peng-Fei Kong, Xin Kong, Ming Li, Bo Meng, Hai-Yan Weng, Wen-Qing Wu, Chuan-Ying Li, Jian Yong Shao, Jing-Jing Chen, Jing Wang, Zong-Ke Chen, Hai-Yun Wang, Di Song |
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Rok vydání: | 2019 |
Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty Microarray Article Subject medicine.medical_treatment 03 medical and health sciences 0302 clinical medicine Internal medicine Gene expression Medicine Gene RC254-282 business.industry Melanoma Neoplasms. Tumors. Oncology. Including cancer and carcinogens Immunotherapy Gene signature medicine.disease 030104 developmental biology Expression data 030220 oncology & carcinogenesis ATP1B1 business Research Article |
Zdroj: | Journal of Oncology Journal of Oncology, Vol 2020 (2020) |
ISSN: | 1687-8450 |
Popis: | Introduction. Gene signature has been used to predict prognosis in melanoma patients. Meanwhile, the efficacy of immunotherapy was correlated with particular genes expression or mutation. In this study, we systematically explored the gene expression pattern in the melanoma-immune microenvironment and its relationship with prognosis. Methods. A cohort of 122 melanoma cases with whole-genome microarray expression data were enrolled from the Gene Expression Omnibus (GEO) database. The findings were validated using The Cancer Genome Atlas (TCGA) database. A principal component analysis (PCA), gene set enrichment analysis (GSEA), and gene oncology (GO) analysis were performed to explore the bioinformatic implications. Results. Different gene expression patterns were identified according to the clinical stage. All eligible gene sets were analyzed, and the 8 genes (GPR87, KIT, SH3GL3, PVRL1, ATP1B1, CDAN1, FAU, and TNFSF14) with the greatest prognostic impact on melanoma. A gene-related risk signature was developed to distinguish patients with a high or low risk of an unfavorable outcome, and this signature was validated using the TCGA database. Furthermore, the prognostic significance of the signature between the classified subgroups was verified as an independent prognostic predictor of melanoma. Additionally, the low-risk melanoma patients presented an enhanced immune phenotype compared to that of the high-risk gene signature patients. Conclusions. The gene pattern differences in melanoma were profiled, and a gene signature that could independently predict melanoma patients with a high risk of poor survival was established, highlighting the relationship between prognosis and the local immune response. |
Databáze: | OpenAIRE |
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