Dataset Information

CSF-Based Analysis for Identification of Potential Serum Biomarkers of Neural Tube Defects.

ABSTRACT: The protein composition of cerebrospinal fluid (CSF) in neural tube defects (NTDs) remains unknown. We investigated the protein composition of CSF from 9 infants with NTDs using isobaric tags for relative and absolute quantitation (iTRAQ). We identified 568 proteins in the CSF of infants with spina bifida, which is the most common type of NTD. Among these, 18 proteins were associated with neural tube closure in the CSF during human embryonic neurulation and 5 were involved in NTDs. Based on these results, an animal model was further utilized to investigate early serum biomarkers for NTDs. We found that the myristoylated alanine-rich C-kinase substrate, Kunitz-type protease inhibitor 2, and apolipoprotein B-100 protein levels were decreased in both embryos and the sera of pregnant Sprague-Dawley rats carrying embryos with NTDs. CSF proteins may be useful in the discovery of potential serum biomarkers for NTDs.

SUBMITTER: Yan X

PROVIDER: S-EPMC5567566 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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CSF-Based Analysis for Identification of Potential Serum Biomarkers of Neural Tube Defects.

Yan Xinyu X Mai Lixin L Lin Changchun C He Wenji W Yin Gengsheng G Yu Jiakang J Huang Lian L Pan Sanqiang S

Neuroscience bulletin 20170710 4

The protein composition of cerebrospinal fluid (CSF) in neural tube defects (NTDs) remains unknown. We investigated the protein composition of CSF from 9 infants with NTDs using isobaric tags for relative and absolute quantitation (iTRAQ). We identified 568 proteins in the CSF of infants with spina bifida, which is the most common type of NTD. Among these, 18 proteins were associated with neural tube closure in the CSF during human embryonic neurulation and 5 were involved in NTDs. Based on thes ...[more]

PMID: 28695418

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Project description:BackgroundNeural tube defects (NTDs) are one of the most common types of birth defects. Oral folic acid (FA) prophylaxis is currently available, but the pathogenesis of NTDs is not fully understood. We conducted this study to examine the role of the immune landscape of NTDs and identify novel diagnostic and therapeutic biomarkers.MethodsWe downloaded the GSE33111 data set of 12 NTD embryos and 12 healthy embryos in the same period of fetal development from the Gene Expression Omnibus (GEO) database. We compared the healthy embryos and NTD embryos to identify the differentially expressed genes (DEGs). We also performed a functional enrichment analysis of the DEGs using the clusterProfiler package. We extracted the top 10 ranked genes as hub immune-related biomarkers. We then used receiver operating characteristic (ROC) curves to determine the expression levels of the hub immune-related genes and the accuracy of the diagnosis of NTDs. Finally, we analyzed the immune landscape of the NTD embryos and healthy embryos via a single-sample gene set enrichment analysis (ssGSEA).ResultsA total of 611 DEGs were identified by the differential analysis, including 95 immune genes. The functional enrichment analysis indicated that Epstein-Barr virus infection, antigen processing and presentation, inflammatory bowel disease, and type I diabetes mellitus were associated with NTDs. The results of the expression level analysis showed that the hub immune-related genes were more highly expressed in the NTD embryos than the healthy embryos. Additionally, the ROC curve analysis also indicated that the expression levels of the 10 hub immune-related genes were highly accurate in the diagnosis of NTDs [area under the curve (AUC) range, 0.708-0.812]. The immune infiltration analysis demonstrated that 20 of the 28 immune cell types were more highly infiltrated in the NTD embryos than the healthy embryos.ConclusionsImmune-related genes are important regulators of the occurrence and development of NTDs.

Project description: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.

Dataset Information

CSF-Based Analysis for Identification of Potential Serum Biomarkers of Neural Tube Defects.

Publications

CSF-Based Analysis for Identification of Potential Serum Biomarkers of Neural Tube Defects.

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