Open AccessIdentification and validation of a ferroptosis‐related gene signature for predicting survival in skin cutaneous melanoma
Author(s) -
Ping Shuai,
Wang Siyuan,
Zhao Yingsong,
He Jinbing,
Li Guanglei,
Li Dinglin,
Wei Zhuo,
Chen Jianghai
Publication year - 2022
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.4706
Subject(s) - signature (topology) , identification (biology) , melanoma , gene signature , gene , computational biology , medicine , oncology , biology , cancer research , genetics , gene expression , botany , mathematics , geometry
Purpose Ferroptosis plays a crucial role in the initiation and progression of melanoma. This study developed a robust signature with ferroptosis‐related genes (FRGs) and assessed the ability of this signature to predict OS in patients with skin cutaneous melanoma (SKCM). Methods RNA‐sequencing data and clinical information of melanoma patients were extracted from TCGA, GEO, and GTEx. Univariate, multivariate, and LASSO regression analyses were conducted to identify the gene signature. A 10 FRG signature was an independent and strong predictor of survival. The predictive performance was assessed using ROC curve. The functions of this gene signature were assessed by GO and KEGG analysis. The statuses of low‐risk and high‐risk groups according to the gene signature were compared by GSEA. In addition, we investigated the possible relationship of FRGs with immunotherapy efficacy. Results A prognostic signature with 10 FRGs (CYBB, IFNG, FBXW7, ARNTL, PROM2, GPX2, JDP2, SLC7A5, TUBE1, and HAMP) was identified by Cox regression analysis. This signature had a higher prediction efficiency than clinicopathological features (AUC = 0.70). The enrichment analyses of DEGs indicated that ferroptosis‐related immune pathways were largely enriched. Furthermore, GSEA showed that ferroptosis was associated with immunosuppression in the high‐risk group. Finally, immune checkpoints such as PDCD‐1 (PD‐1), CTLA4, CD274 (PD‐L1), and LAG3 were also differential expression in two risk groups. Conclusions The 10 FRGs signature were a strong predictor of OS in SKCM and could be used to predict therapeutic targets for melanoma.