Analyzing Prognostic Hub Genes in the Microenvironment of Cutaneous Melanoma by Computer Integrated Bioinformatics. (8th March 2022)

• Record Type: Journal Article
• Authors: Li, Guangyao; Zhang, Jingye; Liu, Yourao; Cheng, Xiqing; Sun, Kai; Hong, Wenjuan; Sha, Ke
• Other Names: Chaudhary Gopal Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-03-08
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/4493347
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : Cutaneous melanoma (CM) is attracting increasing attention due to high mortality. In response to this, we synthetically analyze the CM dataset from the TCGA database and explore microenvironment-related genes that effectively predict patient prognosis. Immune/stromal scores of cases are calculated using the ESTIMATE algorithm and are significantly associated with overall patient survival. Then, differentially expressed genes are identified by comparing the immune score and stromal score, also prognostic genes are subsequently screened. Functional analysis shows that these genes are enriched in different activities of immune system. Moreover, 19 prognosis-related hub genes are extracted from the protein-protein interaction network, of which four unreported genes (IL7R, FLT3, C1QC, and HLA-DRB5) are chosen for validation. A significant negative relationship is found between the expression levels of the 4 genes and pathological stages, notably T grade. Furthermore, the K-M plots and TIMER results show that these genes have favorable value for CM prognosis. In conclusion, these results give a novel insight into CM and identify IL7R, FLT3, C1QC, and HLA-DRB5 as crucial roles for the diagnosis and treatment of CM.