Han Weidong, Bian Xiaonan, Fu Haiyang, Liu Min, Wang Hongliang, Liu Haimei
Department of Clinical Laboratory, Affiliated Nantong Hospital of Shanghai University (The Sixth People's Hospital of Nantong), Nantong, Jiangsu, China.
Department of Neurobiology, Harbin Medical University, Harbin, Heilongjiang, China.
World Neurosurg. 2025 May;197:123908. doi: 10.1016/j.wneu.2025.123908. Epub 2025 Mar 19.
Early diagnosis of ischemic stroke (IS) remains challenging. Given the crucial role of ferroptosis in IS, this study aims to identify key genes associated with ferroptosis in IS, providing insights into its molecular mechanisms and potential biomarkers for early detection.
The single-cell transcriptome dataset GSE247474 from the Gene Expression Omnibus. Ferroptosis scores in astrocytes were calculated using the WP_FERROPTOSIS gene set, and differential analysis was conducted to compare ferroptosis activity between the disease and control groups. Key ferroptosis-related genes were identified using Lasso regression and support vector machine algorithms, and their diagnostic potential was assessed through receiver operating characteristic curve analysis. Additionally, we performed immune infiltration analysis and transcription factor network prediction. Pseudotime analysis was used to explore the differentiation trajectories of astrocytes and T-cell subsets.
Astrocytes in the disease group showed significantly higher ferroptosis scores than those in the control group. Using machine learning algorithms, we identified 3 key ferroptosis-related genes-SLC3A2 (solute carrier family 3 member 2), FDFT1 (farnesyl-diphosphate farnesyltransferase 1), and BACH1 (BTB and CNC homology 1)-and validated their diagnostic value (area under the curve >0.9). Immune infiltration analysis revealed that SLC3A2 and BACH1 expression levels were positively correlated with CD4 follicular T cells and negatively correlated with CD4 memory T cells. FDFT1 showed positive correlations with both mast cells and CD4 memory T cells. Pseudotime analysis demonstrated dynamic changes in key gene expression along the differentiation trajectories of astrocytes and T cells.
SLC3A2, FDFT1, and BACH1 are potential molecular markers for IS diagnosis.
缺血性脑卒中（IS）的早期诊断仍然具有挑战性。鉴于铁死亡在IS中的关键作用，本研究旨在识别与IS中铁死亡相关的关键基因，深入了解其分子机制以及早期检测的潜在生物标志物。
来自基因表达综合数据库的单细胞转录组数据集GSE247474。使用WP_FERROPTOSIS基因集计算星形胶质细胞中的铁死亡评分，并进行差异分析以比较疾病组和对照组之间的铁死亡活性。使用套索回归和支持向量机算法识别关键的铁死亡相关基因，并通过受试者工作特征曲线分析评估其诊断潜力。此外，我们进行了免疫浸润分析和转录因子网络预测。使用伪时间分析来探索星形胶质细胞和T细胞亚群的分化轨迹。
疾病组中的星形胶质细胞显示出比对照组显著更高的铁死亡评分。使用机器学习算法，我们识别出3个关键的铁死亡相关基因——溶质载体家族3成员2（SLC3A2）、法尼基二磷酸法尼基转移酶1（FDFT1）和BTB和CNC同源1（BACH1），并验证了它们的诊断价值（曲线下面积>0.9）。免疫浸润分析显示，SLC3A2和BACH1的表达水平与CD4滤泡T细胞呈正相关，与CD4记忆T细胞呈负相关。FDFT1与肥大细胞和CD4记忆T细胞均呈正相关。伪时间分析表明关键基因表达沿着星形胶质细胞和T细胞的分化轨迹发生动态变化。
SLC3A2、FDFT1和BACH1是IS诊断的潜在分子标志物。
