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

Guoliang Jia,Youmao Tao,Zhonghang Xu,Yuanyu Wu,Zheyu Song,Xiaoyu Wan

doi:10.1186/s12920-021-00923-0

Guoliang Jia, Youmao Tao + Show 4 more

Open Access

https://doi.org/10.1186/s12920-021-00923-0

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Abstract

BackgroundBioinformatics 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.MethodsWe 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.ResultsOf 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.ConclusionThe 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.

Highlights

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
Differentially expressed genes (DEGs) screening On the basis of the clinical information, SKCM tumor samples in the The Cancer Genome Atlas (TCGA) training dataset were divided into metastatic (n = 358) and nonmetastatic (n = 102) groups
After screening the 11 feature DEGs using Logit regression (LR), we only focused on their protein–protein interaction (PPI) network, which contained 107 pairs of interaction connections related to the 11 feature DEGs

Summary

Introduction

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. Skin cutaneous melanoma (SKCM) is a common skin malignancy with poor prognosis due to aggressiveness and metastasis [1]. Previous studies have focused on molecular markers related to SKCM metastasis and prognosis. Da Forno et al [5] showed that high expression of Wnt-5a indicates an increase in SKCM aggressiveness, distant metastases, and poor prognosis. Melanoma inhibitory protein is considered a diagnostic marker of SKCM metastasis and poor prognosis [6]. Ci et al [7] showed that CDCA8 overexpression promotes the malignant progression of SKCM and leads to poor prognosis. The mechanism underlying metastasis of a nevus into SKCM is still unclear, and further research is urgently needed

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