鉴定和验证与氧化应激相关的复发性妊娠丢失诊断标记：机器学习和分子分析的启示。

IF 3.9 2区化学 Q2 CHEMISTRY, APPLIED

Molecular Diversity Pub Date : 2024-09-03 DOI:10.1007/s11030-024-10947-0

Hui Hu, Li Yu, Yating Cheng, Yao Xiong, Daoxi Qi, Boyu Li, Xiaokang Zhang, Fang Zheng

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引用次数: 0

摘要

人们已经认识到氧化应激（OS）与复发性妊娠失败（RPL）的病因有关，但反映与RPL相关的氧化应激的生物标志物仍然很少。我们从基因表达总库（Gene Expression Omnibus，GEO）数据库中检索到了数据集 GSE165004。从基因卡片（GeneCards）数据库中汇编了789个与氧化应激相关的基因（OSRGs）。通过将正常样本和RPL样本中的差异表达基因（DEGs）与OSRGs交叉，确定了差异表达的OSRGs（DE-OSRGs）。此外，还采用了四种机器学习算法来选择 RPL 的诊断标记物。生成了这些基因的接收者操作特征曲线（ROC），并建立了诊断标记物的预测提名图。阐明了与诊断标志物相关的功能和途径，并研究了免疫细胞与诊断标志物之间的相关性。根据比较毒物基因组学数据库和ClinicalTrials.gov的数据，提出了针对诊断标志物的潜在疗法。利用 RT-PCR 和免疫组化技术在 RPL 组织样本中进一步验证了四个模型中的候选生物标记基因。最终确定了一组 20 个 DE-OSRGs，其中 4 个基因（KRAS、C2orf69、CYP17A1 和 UCP3）被机器学习算法认定为诊断标志物，表现出强大的诊断能力。所构建的提名图显示了良好的预测准确性。KRAS、C2orf69 和 CYP17A1 共同富集了包括核糖体、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各种途径。细胞趋化术语通常被所有四种诊断标记物富集。在正常样本和 RPL 样本之间，嗜酸性粒细胞、单核细胞、自然杀伤细胞、调节性 T 细胞和 T 滤泡辅助细胞这五种细胞类型的丰度存在显著差异。预测共有 180 种药物以诊断标记物为靶标，包括 C544151、D014635 和 CYP17A1。在 RPL 患者的验证队列中，LASSO 模型比其他模型更具优势。在 RPL 中，KRAS、C2orf69 和 CYP17A1 的表达水平明显降低，而 UCP3 水平升高，这表明它们适合作为 RPL 的分子标记。四种与氧化应激相关的诊断标志物（KRAS、C2orf69、CYP17A1 和 UCP3）已被提出用于诊断和治疗 RPL。

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Identification and validation of oxidative stress-related diagnostic markers for recurrent pregnancy loss: insights from machine learning and molecular analysis.

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Identification and validation of oxidative stress-related diagnostic markers for recurrent pregnancy loss: insights from machine learning and molecular analysis.

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.

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来源期刊

Molecular Diversity 化学-化学综合

CiteScore

7.30

自引率

7.90%

发文量

219

审稿时长

2.7 months

期刊介绍： Molecular Diversity is a new publication forum for the rapid publication of refereed papers dedicated to describing the development, application and theory of molecular diversity and combinatorial chemistry in basic and applied research and drug discovery. The journal publishes both short and full papers, perspectives, news and reviews dealing with all aspects of the generation of molecular diversity, application of diversity for screening against alternative targets of all types (biological, biophysical, technological), analysis of results obtained and their application in various scientific disciplines/approaches including: combinatorial chemistry and parallel synthesis; small molecule libraries; microwave synthesis; flow synthesis; fluorous synthesis; diversity oriented synthesis (DOS); nanoreactors; click chemistry; multiplex technologies; fragment- and ligand-based design; structure/function/SAR; computational chemistry and molecular design; chemoinformatics; screening techniques and screening interfaces; analytical and purification methods; robotics, automation and miniaturization; targeted libraries; display libraries; peptides and peptoids; proteins; oligonucleotides; carbohydrates; natural diversity; new methods of library formulation and deconvolution; directed evolution, origin of life and recombination; search techniques, landscapes, random chemistry and more;