Hu Hui, Yu Li, Cheng Yating, Xiong Yao, Qi Daoxi, Li Boyu, Zhang Xiaokang, Zheng Fang
Department of Laboratory Medicine, Shanghai East Hospital, Tongji University School of Medicine, 800 Yuntai Road, Pudong New District, Shanghai, 200123, China.
Center for Gene Diagnosis and Department of Clinical Laboratory Medicine, Zhongnan Hospital of Wuhan University, Donghu Road 169, Wuhan, 430071, China.
Mol Divers. 2024 Sep 3. doi: 10.1007/s11030-024-10947-0.
It has been recognized that oxidative stress (OS) is implicated in the etiology of recurrent pregnancy loss (RPL), yet the biomarkers reflecting oxidative stress in association with RPL remain scarce. The dataset GSE165004 was retrieved from the Gene Expression Omnibus (GEO) database. From the GeneCards database, a compendium of 789 genes related to oxidative stress-related genes (OSRGs) was compiled. By intersecting differentially expressed genes (DEGs) in normal and RPL samples with OSRGs, differentially expressed OSRGs (DE-OSRGs) were identified. In addition, four machine learning algorithms were employed for the selection of diagnostic markers for RPL. The Receiver Operating Characteristic (ROC) curves for these genes were generated and a predictive nomogram for the diagnostic markers was established. The functions and pathways associated with the diagnostic markers were elucidated, and the correlations between immune cells and diagnostic markers were examined. Potential therapeutics targeting the diagnostic markers were proposed based on data from the Comparative Toxicogenomics Database and ClinicalTrials.gov. The candidate biomarker genes from the four models were further validated in RPL tissue samples using RT-PCR and immunohistochemistry. A set of 20 DE-OSRGs was identified, with 4 genes (KRAS, C2orf69, CYP17A1, and UCP3) being recognized by machine learning algorithms as diagnostic markers exhibiting robust diagnostic capabilities. The nomogram constructed demonstrated favorable predictive accuracy. Pathways including ribosome, peroxisome, Parkinson's disease, oxidative phosphorylation, Huntington's disease, and Alzheimer's disease were co-enriched by KRAS, C2orf69, and CYP17A1. Cell chemotaxis terms were commonly enriched by all four diagnostic markers. Significant differences in the abundance of five cell types, namely eosinophils, monocytes, natural killer cells, regulatory T cells, and T follicular helper cells, were observed between normal and RPL samples. A total of 180 drugs were predicted to target the diagnostic markers, including C544151, D014635, and CYP17A1. In the validation cohort of RPL patients, the LASSO model demonstrated superiority over other models. The expression levels of KRAS, C2orf69, and CYP17A1 were significantly reduced in RPL, while UCP3 levels were elevated, indicating their suitability as molecular markers for RPL. Four oxidative stress-related diagnostic markers (KRAS, C2orf69, CYP17A1, and UCP3) have been proposed to diagnose and potentially treat RPL.
人们已经认识到氧化应激（OS）与复发性流产（RPL）的病因有关，但反映氧化应激与RPL相关的生物标志物仍然很少。数据集GSE165004从基因表达综合数据库（GEO）中检索得到。从基因卡片数据库中，汇编了789个与氧化应激相关基因（OSRGs）相关的基因集。通过将正常样本和RPL样本中的差异表达基因（DEGs）与OSRGs进行交叉分析，确定了差异表达的OSRGs（DE-OSRGs）。此外，采用四种机器学习算法来选择RPL的诊断标志物。生成了这些基因的受试者工作特征（ROC）曲线，并建立了诊断标志物的预测列线图。阐明了与诊断标志物相关的功能和通路，并研究了免疫细胞与诊断标志物之间的相关性。基于比较毒理基因组学数据库和ClinicalTrials.gov的数据，提出了针对诊断标志物的潜在治疗方法。使用RT-PCR和免疫组织化学在RPL组织样本中进一步验证了来自四个模型的候选生物标志物基因。确定了一组20个DE-OSRGs，其中4个基因（KRAS、C2orf69、CYP17A1和UCP3）被机器学习算法识别为具有强大诊断能力的诊断标志物。构建的列线图显示出良好的预测准确性。包括核糖体、过氧化物酶体、帕金森病、氧化磷酸化、亨廷顿病和阿尔茨海默病在内的通路被KRAS、C2orf69和CYP17A1共同富集。所有四个诊断标志物都普遍富集了细胞趋化作用相关术语。在正常样本和RPL样本之间，观察到嗜酸性粒细胞、单核细胞、自然杀伤细胞、调节性T细胞和滤泡辅助性T细胞这五种细胞类型的丰度存在显著差异。总共预测有180种药物靶向诊断标志物，包括C544151、D014635和CYP17A1。在RPL患者的验证队列中，LASSO模型显示出优于其他模型的优势。RPL中KRAS、C2orf69和CYP17A1的表达水平显著降低，而UCP3水平升高，表明它们适合作为RPL的分子标志物。已提出四种氧化应激相关的诊断标志物（KRAS、C2orf69、CYP17A1和UCP3）用于诊断和潜在治疗RPL。
