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. 2024 Feb 28;49(2):159-174.
doi: 10.11817/j.issn.1672-7347.2024.230401.

Multi - omics analysis for ferroptosis - related genes as prognostic factors in cutaneous melanoma

[Article in English, Chinese]
Meng Wu  1 , Ke Li  2 , Yangying Liao  2 , Lan Li  2 , Xiao Xiao  2 , Yongjian Chen  2 , Junweichen Guo  2 , Feng Hu  2 , Jing Qu  3 , Zheng Wang  4 , Hao Feng  5
Affiliations

Multi - omics analysis for ferroptosis - related genes as prognostic factors in cutaneous melanoma

[Article in English, Chinese]
Meng Wu et al. Zhong Nan Da Xue Xue Bao Yi Xue Ban. .

Abstract
in English, Chinese, Chinese

Objectives: Melanoma is highly malignant and heterogeneous. It is essential to develop a specific prognostic model for improving the patients' survival and treatment strategies. Recent studies have shown that ferroptosis results from the overproduction of lipid peroxidation and is an iron-dependent form of programmed cell death. Despite this, ferroptosis-related genes (FRGs) and their clinical significances remain unknown in malignant melanoma. This study aims to assess the role of FRGs in melanoma, with the goal of developing a novel prognostic model that provides new insights into personalized treatment and improvement of therapeutic outcomes for melanoma.

Methods: We systematically characterized the genetic alterations and mRNA expression of 73 FRGs in The Cancer Genome Atlas (TCGA)-skin cutaneous melanoma (SKCM) dataset in this study. The results were validated with real-time RT-PCR and Western blotting. Subsequently, a multi-gene feature model was constructed using the TCGA-SKCM cohort. Melanoma patients were classified into a high-risk group and a low-risk group based on the feature model. As a final step, correlations between ferroptosis-related signatures and immune features, immunotherapy efficacy, or drug response were analyzed.

Results: By analyzing melanoma samples from TCGA-SKCM dataset, FRGs exhibited a high frequency of genetic mutations and copy number variations (CNVs), significantly impacting gene expression. Additionally, compared with normal skin tissue, 30 genes with significantly differential expression were identified in melanoma tissues. A prognostic model related to FRGs, constructed using the LASSO Cox regression method, identified 13 FRGs associated with overall survival prognosis in patients and was validated with external datasets. Finally, functional enrichment and immune response analysis further indicated significant differences in immune cell infiltration, mutation burden, and hypoxia status between the high-risk group and the low-risk group, and the model was effective in predicting responses to immunotherapy and drug sensitivity.

Conclusions: This study develops a strong ferroptosis-related prognostic signature model which could put forward new insights into target therapy and immunotherapy for patients with melanoma.

目的: 黑色素瘤具有高度恶性和异质性。开发特定的黑色素瘤预后预测模型对提高患者的生存率和选择治疗策略至关重要。最近，铁死亡已被证明是一种由过度脂质过氧化诱导的铁依赖性程序性细胞死亡形式。然而，铁死亡相关基因(ferroptosis-related genes，FRGs)与黑色素瘤预后的相关性仍不清晰。本研究评估FRGs在黑色素瘤中的作用，开发一种新的预后模型，旨在为黑色素瘤的个性化治疗及疗效改善提供新思路。方法: 首先通过系统地表征癌症基因组图谱(The Cancer Genome Atlas，TCGA)-皮肤黑色素瘤(skin cutaneous melanoma，SKCM)中73个FRGs的遗传改变和mRNA表达。同时通过反转录聚合酶链反应和蛋白质印迹法验证筛选的特定靶基因。随后使用TCGA-SKCM队列构建多基因特征模型。根据特征模型将黑色素瘤患者分为高风险和低风险组，对铁死亡相关的特征模型与免疫特征、免疫治疗的疗效或药物反应进行相关分析。结果: 通过分析TCGA-SKCM数据集中的黑色素瘤样本，发现FRGs在基因变异和拷贝数变异方面表现出高频率，这些变化显著影响了基因的表达。此外，与正常皮肤组织相比，在黑色素瘤组织中发现了30个显著差异表达的基因。随后使用LASSO Cox回归方法构建的FRGs相关预后模型成功识别了13个与患者总体生存预后相关的FRGs，并通过外部数据集验证了该模型的有效性。最后，功能富集和免疫响应结果分析进一步表明高风险和低风险组之间存在免疫细胞浸润、突变负担和低氧状态的显著差异，且该模型能有效预测免疫治疗响应和药物敏感性。结论: 本研究建立了一种强预后预测模型，可为黑色素瘤患者的靶向治疗和免疫治疗提供新的方向。.

目的: 黑色素瘤具有高度恶性和异质性。开发特定的黑色素瘤预后预测模型对提高患者的生存率和选择治疗策略至关重要。最近，铁死亡已被证明是一种由过度脂质过氧化诱导的铁依赖性程序性细胞死亡形式。然而，铁死亡相关基因(ferroptosis-related genes，FRGs)与黑色素瘤预后的相关性仍不清晰。本研究评估FRGs在黑色素瘤中的作用，开发一种新的预后模型，旨在为黑色素瘤的个性化治疗及疗效改善提供新思路。

方法: 首先通过系统地表征癌症基因组图谱(The Cancer Genome Atlas，TCGA)-皮肤黑色素瘤(skin cutaneous melanoma，SKCM)中73个FRGs的遗传改变和mRNA表达。同时通过反转录聚合酶链反应和蛋白质印迹法验证筛选的特定靶基因。随后使用TCGA-SKCM队列构建多基因特征模型。根据特征模型将黑色素瘤患者分为高风险和低风险组，对铁死亡相关的特征模型与免疫特征、免疫治疗的疗效或药物反应进行相关分析。

结果: 通过分析TCGA-SKCM数据集中的黑色素瘤样本，发现FRGs在基因变异和拷贝数变异方面表现出高频率，这些变化显著影响了基因的表达。此外，与正常皮肤组织相比，在黑色素瘤组织中发现了30个显著差异表达的基因。随后使用LASSO Cox回归方法构建的FRGs相关预后模型成功识别了13个与患者总体生存预后相关的FRGs，并通过外部数据集验证了该模型的有效性。最后，功能富集和免疫响应结果分析进一步表明高风险和低风险组之间存在免疫细胞浸润、突变负担和低氧状态的显著差异，且该模型能有效预测免疫治疗响应和药物敏感性。

结论: 本研究建立了一种强预后预测模型，可为黑色素瘤患者的靶向治疗和免疫治疗提供新的方向。

Keywords: cutaneous melanoma; ferroptosis; immunotherapy; risk score; targeted therapy; tumor microenvironment.

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Conflict of interest statement

The authors declare no potential conflicts of interest.

Figures

Figure 1
Figure 1. Gene alteration of ferroptosis-related genes in TCGA-SKCM cohort
A: Landscape of mutation profiles in 466 melanoma patients from TCGA-SKCM cohort. Every waterfall plot represents mutation information of each FRG. The above barplot shows mutation burden. Corresponding colors have annotations at the bottom which means different mutation types. The right numbers represent mutation frequency individually. B: Bar graphs show the amplification (red) and deletion (blue) frequency of somatic CNV of FRGs in the TCGA-SKCM cohort. The height of each bar represents the alteration frequency. C: The mRNA expression level [log2(FPKM)] of ferroptosis-related genes among CNV groups (CNV gain/CNV none/CNV loss) in SKCM. Wilcoxon test was used to assess the difference. The boxes indicate as the median (P 25, P 75), with the whiskers extending from the hinge to the smallest or largest value within 1.5× interquartile range from the box boundaries. TCGA: The Cancer Genome Atlas; SKCM: Skin cutaneous melanoma; CNV: Copy number variation; FPKM: Fragments Per Kilobase of exon model per Million mapped fragments.
Figure 2
Figure 2. PPI and differential analysis of ferroptosis-related genes in TCGA-SKCM cohort
A: PPI network shows the interactions of the ferroptosis-related genes. B: Box plots show the expression distribution of ferroptosis-related genes between normal tissue (gray) and melanoma (red) tissues. The upper and lower ends of the boxes represent the interquartile range of values. The lines in the boxes represent median value. PPI: Protein-protein interaction; TCGA: The Cancer Genome Atlas; SKCM: Skin cutaneous melanoma; TPM: Transcripts Per Kilobase of exon model per Million mapped reads.
Figure 3
Figure 3. Construction of risk signature in the TCGA cohort
A: Univariate Cox regression analysis result of OS rate are showed for each ferroptosis-related genes, and 13 genes with P<0.05. B: Heatmap shows a positive (red) and negative (blue) correlation among ferroptosis-related genes in TCGA-SKCM. The black circle represents P<0.05, as determined by the Spearman correlation analysis. C: LASSO Cox regression analysis results of the 13 OS-related genes are showed. D: Cross-validation for tuning the parameter selection are showed in the LASSO Cox regression analysis. E: OS of patients in the high- and low-risk groups is drawn with Kaplan-Meier curves. F: Survival status for each patient is presented (low-risk population: on the left side of the dotted line; high-risk population: on the right side of the dotted line). G: Survival time decreased distribution in melanema with the inereasing of patients the risk score. H: ROC curves demonstrated the predictive efficiency of the ferroptosis-related prognostic signature model. I: Heatmap (blue: low expression; red: high expression) shows the connections between ferroptosis-related genes and the risk groups. TCGA: The Cancer Genome Atlas; SKCM: Skin cutaneous melanoma; LASSO: Least absolute shrinkage and selection operator; OS: Overall survival; ROC: Receiver operator characteristic; AUC: Area under the curve.
Figure 4
Figure 4. Validation of the expression of the ferroptosis-related genes at mRNA and protein levels in tissue samples of melanoma
A: Real-time RT-PCR and Western blotting results show that the mRNA and protein levels of key ferroptosis-promoted genes (ACSL4, ALOX5, and ZEB1) are down-regulated in melanoma tissues compared with normal tissues (n=5). B: Real-time RT-PCR and Western blotting results show that the mRNA and protein levels of key anti-ferroptosis genes (CHAC1, ABCC1, ACACA, and POR) are up-regulated in melanoma tissues compared with normal tissues (n=5). Data are presented as mean±standard error, *P<0.05, **P<0.01, ***P<0.001.
Figure 5
Figure 5. Univariate and multivariate Cox regression analyses for the risk score
A: Univariate analysis for the TCGA cohort; B: Multivariate analysis for the TCGA cohort; C: ROC curve of measuring the predictive value. TCGA: The Cancer Genome Atlas; ROC: Receiver operator characteristic.
Figure 6
Figure 6. Validation of the ferroptosis-related prognostic signature model in the GEO cohort
A-D: Kaplan-Meier curves for comparison of the OS probability between the low- and high-risk groups in GSE19234, GSE22155, GSE54467, and GSE65904 cohorts. GEO: Gene Expression Omnibus; OS: Overall survival.
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