Background

Atherosclerosis is a complex disease with multiple molecular subtypes that are not yet fully understood. Recent studies have suggested that N6-methyladenosine (m6A) alterations may play a role in the pathogenesis of atherosclerosis. However, the relationship between m6A regulators and atherosclerosis remains unclear.

Methods

In this study, we analyzed the expression levels of 25 m6A regulators in a cohort of atherosclerosis (AS) and non-AS patients using the R “limma” package. We also used machine learning models, including random forest (RF), support vector machine (SVM), generalized linear model (GLM), and extreme gradient boosting (XGB), to predict the molecular subtypes of atherosclerosis based on m6A immune cell infiltration.

Results

We found that METTL3, YTHDF2, IGFBP1, and IGF2BP1 were overexpressed in AS patients compared to non-AS patients, while the other significant m6A regulators showed no significant difference. Our machine learning models achieved high accuracy in predicting the molecular subtypes of atherosclerosis based on m6A immune cell infiltration.

Conclusion

Our study suggests that m6A alterations may play a role in the pathogenesis of atherosclerosis, and that machine learning models can be used to predict molecular subtypes of atherosclerosis based on m6A immune cell infiltration. These findings may have important implications for the detection and management of atherosclerosis.

中文翻译：

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基于m6A免疫细胞浸润的动脉粥样硬化分子亚型识别与预测

背景

动脉粥样硬化是一种复杂的疾病，具有多种尚未完全了解的分子亚型。最近的研究表明，N6-甲基腺苷 (m6A) 的改变可能在动脉粥样硬化的发病机制中发挥作用。然而，m6A 调节因子与动脉粥样硬化之间的关系仍不清楚。

方法

在这项研究中，我们使用 R“limma”包分析了动脉粥样硬化 (AS) 和非 AS 患者队列中 25 个 m6A 调节因子的表达水平。我们还使用机器学习模型，包括随机森林 (RF)、支持向量机 (SVM)、广义线性模型 (GLM) 和极限梯度增强 (XGB)，根据 m6A 免疫细胞浸润来预测动脉粥样硬化的分子亚型。

结果

我们发现，与非 AS 患者相比，METTL3、YTHDF2、IGFBP1 和 IGF2BP1 在 AS 患者中过表达，而其他重要的 m6A 调节因子没有表现出显着差异。我们的机器学习模型在基于 m6A 免疫细胞浸润预测动脉粥样硬化的分子亚型方面实现了高精度。

结论

我们的研究表明，m6A 的改变可能在动脉粥样硬化的发病机制中发挥作用，并且机器学习模型可用于根据 m6A 免疫细胞浸润来预测动脉粥样硬化的分子亚型。这些发现可能对动脉粥样硬化的检测和治疗具有重要意义。