Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

• Published in: BMC medical genomics Vol. 14; no. 1; pp. 96 - 11
• Main Authors: Jia, Guoliang, Song, Zheyu, Xu, Zhonghang, Tao, Youmao, Wu, Yuanyu, Wan, Xiaoyu
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06.04.2021; BioMed Central; BMC
• Subjects: Algorithms; Bioinformatics; Biotechnology; Cancer; Cluster analysis; Computational Biology; Computer programs; Cutaneous melanoma; Datasets; Differentially expressed genes; Gene expression; Gene Expression Profiling; Genetic aspects; Genetic markers; Genetic screening; Genomes; Humans; Medical prognosis; Melanoma; Melanoma, Cutaneous Malignant; Metastases; Metastasis; Oncology, Experimental; Prognosis; Proteins; Regression analysis; Skin cancer; Skin Neoplasms; Survival; Survival analysis; China; Differentially expressed genes; Prognosis; Metastasis; Cutaneous melanoma; Bioinformatics
• ISSN: 1755-8794; 1755-8794
• DOI: 10.1186/s12920-021-00923-0

Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein-protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.