Department of Orthopaedics, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Integrated Traditional Chinese and Western Medicine, Liyuan Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan, China.
Cancer Med. 2022 Sep;11(18):3529-3541. doi: 10.1002/cam4.4706. Epub 2022 Apr 4.
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).
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.
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.
The 10 FRGs signature were a strong predictor of OS in SKCM and could be used to predict therapeutic targets for melanoma.
铁死亡在黑色素瘤的发生和发展中起着关键作用。本研究构建了一个稳健的与铁死亡相关基因（FRGs）的特征，并评估了该特征预测皮肤黑色素瘤（SKCM）患者总生存期（OS）的能力。
从 TCGA、GEO 和 GTEx 中提取黑色素瘤患者的 RNA-seq 数据和临床信息。进行单变量、多变量和 LASSO 回归分析以识别基因特征。一个由 10 个 FRG 组成的特征是生存的独立且强有力的预测因子。使用 ROC 曲线评估预测性能。通过 GO 和 KEGG 分析评估该基因特征的功能。根据基因特征比较低风险和高风险组的状态。此外，我们还研究了 FRGs 与免疫治疗疗效的可能关系。
通过 Cox 回归分析鉴定了一个由 10 个 FRGs（CYBB、IFNG、FBXW7、ARNTL、PROM2、GPX2、JDP2、SLC7A5、TUBE1 和 HAMP）组成的预后特征。该特征比临床病理特征具有更高的预测效率（AUC=0.70）。差异表达基因的富集分析表明，铁死亡相关的免疫途径得到了很大的富集。此外，GSEA 表明铁死亡与高危组的免疫抑制有关。最后，免疫检查点如 PDCD-1（PD-1）、CTLA4、CD274（PD-L1）和 LAG3 在两个风险组中也有差异表达。
这 10 个 FRGs 特征是 SKCM OS 的强有力预测因子，可用于预测黑色素瘤的治疗靶点。
