Zhang Tianxiang, Yuan Chunhui, Chen Mo, Liu Jinjiang, Shao Wei, Cheng Ning
Henan Digital Image and Intelligent Processing of Big Data Engineering Research Center, College of Life Science and Agricultural Engineering, Nanyang Normal University, Nanyang, 473000, China.
Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, 570228, China.
Eur J Med Res. 2025 Jul 24;30(1):665. doi: 10.1186/s40001-025-02940-6.
BACKGROUND: Cardioembolic stroke (CS) and atherosclerosis (AS) are closely related diseases. Ferroptosis, a novel form of programmed cell death, may play a key role in CS and AS. However, the pathophysiological mechanisms underlying their coexistence remain unclear. This study aims to identify the hub genes and pathways involved in developing both diseases. METHODS: CS (GSE58294) and AS (GSE20129) datasets were obtained from the Gene Expression Omnibus database, and a ferroptosis (FR)-related gene dataset was downloaded from the FR database. A study was conducted to examine differentially expressed genes (DEGs) in healthy individuals and patients diagnosed with CS and AS. Gene ontology and Kyoto encyclopedia of genes and genomes analyses were performed to explore the functions of common FR-related DEGs (FRDEGs). Two machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were used to screen for overlapping FRDEGs in CS and AS. To validate the prediction results, blood samples were collected from healthy controls and patients with CS and AS for quantitative real-time PCR. The correlation between biomarkers and clinical features was also evaluated. RESULTS: A total of 69 and 39 FRDEGs were identified in CS and AS, respectively. The hub genes, CIRBP, CREB5, MAPK14, PEBP1, and PTGS2, were identified using multiple methods. The area under the curve was > 0.7 for both models constructed using CS and AS datasets. A strong correlation was observed between neutrophil levels and expression of the hub genes. Additionally, several types of cancer indicated elevated expression of these hub genes compared to normal tissues. CONCLUSIONS: In summary, the diagnostic model based on the FR-related gene PTGS2 demonstrated significant and specific diagnostic value for CS and AS, reflecting the status of blood lymphocytes, monocytes, and neutrophils. A pan-cancer study suggested it could serve as a new clinical prognostic marker and therapeutic target across various cancer types. This model may aid in the diagnosis of CS and AS. The findings offer new insights into the pathogenesis of these diseases.
背景：心源性栓塞性中风（CS）和动脉粥样硬化（AS）是密切相关的疾病。铁死亡是一种新型的程序性细胞死亡形式，可能在CS和AS中起关键作用。然而，它们共存的病理生理机制仍不清楚。本研究旨在确定参与这两种疾病发展的核心基因和通路。 方法：从基因表达综合数据库中获取CS（GSE58294）和AS（GSE20129）数据集，并从铁死亡（FR）数据库下载与FR相关的基因数据集。对健康个体以及被诊断为CS和AS的患者进行研究，以检测差异表达基因（DEG）。进行基因本体论和京都基因与基因组百科全书分析，以探索常见的与FR相关的DEG（FRDEG）的功能。使用两种机器学习算法，即最小绝对收缩和选择算子（LASSO）回归以及支持向量机递归特征消除（SVM-RFE），来筛选CS和AS中重叠的FRDEG。为了验证预测结果，从健康对照以及CS和AS患者中采集血样进行定量实时PCR。还评估了生物标志物与临床特征之间的相关性。 结果：在CS和AS中分别鉴定出69个和39个FRDEG。使用多种方法鉴定出核心基因CIRBP、CREB5、MAPK14、PEBP1和PTGS2。使用CS和AS数据集构建的两个模型的曲线下面积均>0.7。观察到中性粒细胞水平与核心基因表达之间存在强相关性。此外，与正常组织相比，几种癌症类型显示这些核心基因表达升高。 结论：总之，基于与FR相关的基因PTGS2的诊断模型对CS和AS具有显著且特异的诊断价值，反映了血液淋巴细胞、单核细胞和中性粒细胞的状态。一项泛癌研究表明，它可作为各种癌症类型的新的临床预后标志物和治疗靶点。该模型可能有助于CS和AS的诊断。这些发现为这些疾病的发病机制提供了新的见解。
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