PDF(Pubmed)
Abstract：
Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis. Materials and Methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbour (KNN) with 5-fold cross-validation. Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN), and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post-traumatic brain repair, neuroinflammation, and inflammation-linked neuronal injuries. These key genes represent a promising tool for further studies in the diagnosis and early detection of open neural tube defects. Conclusion: These key biomarkers help in the diagnosis and early detection of open neural tube defects, thus evaluating the progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD\'s. Thus, among other prenatal treatment options present until now, these biomarkers help in the early detection of open neural tube defects, which provides success in both treatment and prevention of these defects in the advanced stage.
摘要：
背景：开放性脊柱裂（脊髓膜膨出）是脊髓完全闭合失败的结果，是第二常见和严重的出生缺陷。开放性神经管缺陷是多因素的,由于疾病的复杂性，发病机制的确切分子机制尚不清楚，在全球范围内，产前治疗选择仍然有限。机器学习工具等人工智能技术已越来越多地用于精确诊断。目的：本研究的主要目的是使用机器学习方法鉴定开放性神经管缺陷的关键基因，该方法提供有关脊髓膜膨出的其他信息，以获得更准确的诊断。材料和方法：我们的研究报告了具有开放性神经管缺陷的羊水样本的多个数据集（GSE4182和GSE101141）的差异基因表达分析。使用主成分分析(PCA)检测数据集中的样本异常值。我们报告了差异基因表达分析与递归特征消除（RFE）的组合，一种机器学习方法，可以获得开放性神经管缺陷的4个关键基因。选择的特征使用五个二元分类器对患病和健康样本进行了验证：Logistic回归(LR)，决策树分类器(DT)，支持向量机(SVM)随机森林分类器(RF)，和具有5倍交叉验证的K-最近邻(KNN)。结果：生长相关蛋白43（GAP43），胶质纤维酸性蛋白(GFAP),重复(RPTN)，和CD44是研究中鉴定的重要基因。已知这些基因参与轴突生长,中枢神经系统的星形胶质细胞分化，脑外伤后修复,神经炎症，和炎症相关的神经元损伤。这些关键基因代表了进一步研究开放性神经管缺陷的诊断和早期检测的有希望的工具。结论：这些关键生物标志物有助于开放性神经管缺陷的诊断和早期发现。从而评估疾病状况的进展和严重性。这项研究加强了以前证实这些生物标志物与开放NTD相关的文献来源。因此，到目前为止，在其他产前治疗方案中，这些生物标志物有助于早期发现开放性神经管缺陷，这提供了成功的治疗和预防这些缺陷在晚期阶段。
