Associations Between Circulating Metabolites and Skin Cancer: A Bidirectional Two-Sample Mendelian Randomization Study

IF 3.5 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2026-10-19 DOI:10.1002/cam4.72313

Jiangfan Yu, Hongyan Gong, Zhuotong Zeng, Rong Xiao

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引用次数: 0

Abstract

Background

Skin cancer is the most common type of cancer worldwide, categorized into melanoma and non-melanoma types. While genetics play a crucial role in the development of skin cancer, recent developments in metabolomics have revealed that circulating metabolites influence genetic risk profiles.

Methods

In this study, we utilized a bidirectional two-sample Mendelian Randomization (MR) approach, analyzing metabolite datasets from the Canadian Longitudinal Study of Aging (CLSA) and skin cancer datasets from the UK Biobank. Replication analyses were performed with the 10th round of the FinnGen project. Instrumental variables (IVs) were chosen according to established MR criteria and assessed through the Wald ratio, IVW, MR-Egger, and weighted median methods. Various sensitivity analyses were conducted utilizing Cochrane's Q, Egger's intercept, MR-PRESSO, and leave-one-out (LOO) techniques.

Results

After meticulous analysis, we identified causal relationships between circulating metabolites and skin cancer risks. Specifically, levels of 1,2-dilinoleoyl-GPE (18:2/18:2), 1-palmitoyl-2-linoleoyl-gpc (16:0/18:2), 1-stearoyl-2-arachidonoyl-gpc (18:0/20:4), 1-stearoyl-2-linoleoyl-gpc (18:0/18:2), and Docosapentaenoate n3 DPA, the ratio of Arachidonate (20:4n6) to oleate to vaccenate (18:1), and Oleoyl-linoleoyl-glycerol (18:1 to 18:2) to linoleoyl-arachidonoyl-glycerol (18:2 to 20:4) were linked to non-melanoma skin cancer, whereas levels of trans-urocanate were causally linked to melanoma of skin, and S-adenosylhomocysteine (SAH) showed a putative association. It should be noted that docosapentaenoate n3 DPA and SAH were each supported by a single SNP and should be interpreted as hypothesis-generating rather than definitive causal evidence. Reverse MR analysis highlighted robust causal relationships with 18 and 10 metabolites and non-melanoma skin cancer and melanoma of skin, respectively.

Conclusion

These findings enhance our understanding of skin cancer etiology and risk factors, providing genetic evidence for hypothesis-generating associations that support the development of targeted therapies and aid in the identification of potential biomarkers.

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循环代谢物与皮肤癌之间的关系：一项双向双样本孟德尔随机研究。

背景：皮肤癌是世界范围内最常见的癌症类型，分为黑色素瘤和非黑色素瘤类型。虽然遗传学在皮肤癌的发展中起着至关重要的作用，但代谢组学的最新发展表明，循环代谢物影响遗传风险谱。方法：在本研究中，我们采用双向双样本孟德尔随机化（MR）方法，分析来自加拿大纵向衰老研究（CLSA）的代谢物数据集和来自英国生物银行的皮肤癌数据集。在第10轮FinnGen项目中进行了复制分析。根据建立的MR标准选择工具变量（IVs），并通过Wald比、IVW、MR- egger和加权中位数方法进行评估。利用Cochrane’s Q、Egger’s intercept、MR-PRESSO和leave-one-out （LOO）技术进行了各种敏感性分析。结果：经过细致的分析，我们确定了循环代谢物与皮肤癌风险之间的因果关系。具体来说，1,2-二烯油基- gpe（18:2/18:2）、1-棕榈酰基-2-亚油基-gpc（16:0/18:2）、1-硬脂酰-2-花生四烯酰基-gpc（18:0/20:4）、1-硬脂酰-2-亚油基-gpc（18:0/18:2）和二十二碳五烯酸n3dpa水平、花生四烯酸与油酸的比例（18:1）和油酰亚油基甘油（18:1至18:2）与亚油基-花生四烯酰基-甘油（18:2至20:4）与非黑色素瘤皮肤癌有关，而反式尿酸水平与皮肤黑色素瘤有因果关系。和s -腺苷型同型半胱氨酸（SAH）显示了假定的关联。应该指出的是，docosapentaenoate n3dpa和SAH均由单个SNP支持，应被解释为假设生成而不是确定的因果证据。反向MR分析分别强调了18种和10种代谢物与非黑色素瘤皮肤癌和皮肤黑色素瘤之间的强大因果关系。结论：这些发现增强了我们对皮肤癌病因和危险因素的理解，为假设产生的关联提供了遗传证据，支持靶向治疗的发展，并有助于识别潜在的生物标志物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.