Wang Xing-Jie, Huang Lei, Hou Min, Guo Jie, Li Xi-Ming
Clinical Laboratory, Chest Hospital, Tianjin University, Tianjin, 300222, China.
Heart Center, Tianjin Third Central Hospital, Tianjin, 300170, China.
Heliyon. 2024 Dec 27;11(1):e41534. doi: 10.1016/j.heliyon.2024.e41534. eCollection 2025 Jan 15.
The aim of this paper is to discover differentially expressed genes related to ferroptosis (DEFRGs) in patients with ST-segment elevation myocardial infarction (STEMI) and to construct a reliable prognostic signature that incorporates key DEFRGs and easily accessible clinical factors.
We did a systematic review of Gene Expression Omnibus datasets and picked datasets SE49925, GSE60993, and GSE61144 for analysis. We applied GEO2R to find DEFRGs and overlapped them among the picked datasets. We performed functional enrichment analysis to explore their biological functions. We built an optimal model with least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression. We tested the clinical value of the signature with survival analysis, ROC curve, decision curve analysis and a prognostic nomogram. We also confirmed the model externally with plasma samples from our center's patients.
A prognostic signature combining three overexpressed DEFRGs (, , ) and two clinical variables (serum creatinine level, Gensini score) was established. The signature effectively classified patients into low- and high-risk groups. Survival analysis, ROC curve analysis, and DCA showed its robust predictive performance and clinical utility of the signature within two years after the onset of the disease. The external validation cohort confirmed the significant difference in major adverse cardiovascular events (MACEs) between the low- and high-risk groups.
This study revealed DEFRGs in patients with STEMI and developed a prognostic signature that integrates gene expression levels and clinical factors for stratifying patients and predicting the risk of MACEs.
本文旨在发现ST段抬高型心肌梗死（STEMI）患者中与铁死亡相关的差异表达基因（DEFRGs），并构建一个可靠的预后特征模型，该模型纳入关键的DEFRGs和易于获取的临床因素。
我们对基因表达综合数据库（Gene Expression Omnibus）中的数据集进行了系统回顾，并挑选了SE49925、GSE60993和GSE61144数据集进行分析。我们应用GEO2R来寻找DEFRGs，并在挑选的数据集之间对它们进行重叠分析。我们进行了功能富集分析以探索其生物学功能。我们使用最小绝对收缩和选择算子（LASSO）惩罚的Cox比例风险回归构建了一个最优模型。我们通过生存分析、ROC曲线、决策曲线分析和预后列线图来测试该特征模型的临床价值。我们还使用来自本中心患者的血浆样本对该模型进行了外部验证。
建立了一个结合三个过表达的DEFRGs（ ， ， ）和两个临床变量（血清肌酐水平、Gensini评分）的预后特征模型。该特征模型有效地将患者分为低风险和高风险组。生存分析、ROC曲线分析和决策曲线分析显示了该特征模型在疾病发作后两年内强大的预测性能和临床实用性。外部验证队列证实了低风险和高风险组之间主要不良心血管事件（MACEs）的显著差异。
本研究揭示了STEMI患者中的DEFRGs，并开发了一个预后特征模型，该模型整合了基因表达水平和临床因素，用于对患者进行分层并预测MACEs的风险。
