Identication of Potential Biomarkers and Pathways in Neonatal Hypoxic-Ischemic Brain Injury: Based on Bioinformatics Technology Shangbin Li The First Hospital of Hebei Medical University Shuangshuang Li Chengdu University of Traditional Chinese Medicine Qian Zhao The First Hospital of Hebei Medical University Jiayu Huang The First Hospital of Hebei Medical University Jinfeng Meng The First Hospital of Hebei Medical University Weichen Yan The First Hospital of Hebei Medical University Jie Wang The First Hospital of Hebei Medical University Changjun Ren ( [email protected] ) The First Hospital of Hebei Medical University Ling Hao The First Hospital of Hebei Medical University Research Article Keywords: neonatal hypoxic-ischemic brain injury, inammatory response, immune response, differentially expressed genes, bioinformatics analysis Posted Date: August 5th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-777916/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/22 Abstract Background Neonatal hypoxic-ischemic brain damage (HIBD) is one of the most common serious diseases in newborns, with a high mortality and disability rate. This study aims to use the bioinformatics analysis to identify potential hematologic/immune systems tissue-specic genes and related signaling pathways neonatal HIBD. Methods Microarray datasets in HIBD were downloaded from the Gene Expression Omnibus database, and DEGs were identied by R software.Enrichment analyses were performed and protein–protein interaction networks were constructed to understand the functions and enriched pathways of DEGs and to identify central genes and key modules. Results In the cerebral cortex tissue with HIBD, 2598 DEGs were identied, including 2362 up-regulated and 236 down-regulated DEGs. In the blood with HIBD, 1442 DEGs were identied, including 540 up-regulated and 902 down-regulated DEGs. The results of biological processes and KEGG enrichment were very similar in DEGs of the two kinds of tissues, and both involved inammation, immunity and apoptosis. The common DEGs of the two kinds of tissues also showed similar results in biological processes and KEGG enrichment.and four hematologic/immune system tissues specically expressed potential biomarker genes were conrmed through a variety of methods, which were veried by GEO datasets and published experimental research. Conclusion The DEGs of HIBD including the potential peripheral biomarkers TYROBP, ITGAM, EGR1 and HMOX1, which may play a role in the pathogenesis of HIBD through inammation and immune-mediated signaling pathways. 1. Introduction Hypoxic ischemic brain injury (HIBD), also known as hypoxic-ischemic encephalopathy (HIE), is one of the most common serious diseases in newborns. In developed countries, HIBD occurs in 1–8 cases per 1,000 live births, and 10–26 cases per 1,000 in low and middle income countries[1–2]. In the long term, HIBD can cause cerebral palsy, epilepsy, visual impairment and motor cognitive impairment[3]. At present, neonatal HIBD is mainly based on the clinical manifestations of the nervous system, imaging and electrophysiological examinations to diagnose and predict prognosis[4]. These methods of diagnosis and prognostic judgment require a long time and cause delay in diagnosis. So that delay in diagnosis oftentimes leads to that many HIBD babies miss the optimal treatment time, which may cause different Page 2/22 levels of neurological sequelae.With the rapid development of biomedicine, there have been many reports on biomarkers related to neonatal HIBD[5]. However, there is no blood-based marker that is stable enough to diagnose HIBD or predict prognosis[6]. Currently, there is still a lack of effective drugs to treat HIBD, and prevention is the key to this disease. In recent years, mild hypothermia neuroprotective therapy has become the standard for clinical treatment of HIBD, and other adjuvant therapies such as erythropoietin and neuroprotective agents are rapidly transitioning from basic science to clinical application[7–8]. However, pharmacological treatments were solely focused on neurons. Gradually, glial cells started to be considered as alternative targets for neuroprotection[9–10]. More and more evidences show that hypoxia-ischemia can induce inammation in the brain parenchyma and peripheral immune system, which plays a vital role in mediating secondary neuronal death[11– 12].The brain is considered a place of immune privilege because the blood-brain barrier restricts blood- borne cells and proteins from entering the central nervous system (CNS)[13].In the central nervous system, microglia, astrocytes, and oligodendrocytes play the roles of inammatory cells and immune cells. There are currently studies that the sequelae of the nervous system are related to the activation of astrocytes[14].The inammatory response of the brain can lead to the activation of astrocytes and the activation and aggregation of microglia to provide immune defense for nerve cells, which can lead to rapid changes in their gene expression[15].The research team of Dr. Ting Qi and Professor Jian Yang found that the blood has genetic targets that can be used to identify brain-related properties (such as IQ) and diseases (such as schizophrenia)[16].This discovery provides a new research idea for brain disease research. Therefore, this study aims to apply the public gene expression comprehensive database (GEO) for bioinformatics analysis to determine the differentially expressed genes (DEGs) in neonatal cerebral cortex tissue and blood.Our research study focused on nding the potential hematologic/immune system tissue specicity gene and related signal pathways that are closely related to HIBD and revealing the underlying molecular mechanisms. 2. Materials And Methods 2.1. Microarray Data The expression prole datasets of HIBD were searched from the publicly available Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo). The microarray datasets generated from the cerebral cortical in rats with HIBD and from the whole blood in human with HIBD and controls were used in the study. Four gene expression proles include GSE37777, GSE18356, GSE121178 and GSE112137 were obtained.The details of the datasets are shown in Table 1. 2.2 Identication of differentially expressed genesDEGs We analyzed the DEGs of healthy and HIBD samples in the cerebral cortex tissue and blood by using the limma software package, especially the raw data of datasets GSE121178 and GSE37777 were extracted Page 3/22 with the affy package and individually normalized with the robust multi-array average (RMA) package, and batch effects were eliminated between experiments by applying the ComBat function in the SVA package. The DEGs cut-off thresholds of both are set as follows: (1) p<0.05; (2) log 2 (FoldChange, FC)> 0.585 or log 2 (FoldChange, FC) <-0.585. 2.3 GO Enrichment and KEGG Pathway Analysis Gene ontology (GO) includes three types of data, namely, cellular components (CC), molecular functions (MF), and biological processes (BP)[17]. GO is often used to annotate genes according to a dened set of structured words. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is used to match the information of gene pathways[18]. We used the DAVID 6.8 (https://david.ncifcrf.gov) online tool[19] to perform GO biological process (GO-BP) analysis on DEGs in HIBD, and use KOBAS 3.0 (http://kobas.cbi.pku.edu.cn/) online tool for KEGG pathway analysis[20]. P<0.05 was selected as the threshold to identify the signicant enrichment of GEGs in GO terms and signal pathways. 2.4 PPI network construction, central genes and key module Analysis The tool for searching gene/protein interactions (STRING: http://string-db.org/) is an online biological database designed to evaluate the protein–protein interaction(PPI) network. Intersecting the DEGs of cerebral cortex tissue and whole blood to obtain a common DEGs. According to the information in the STRING database (PPI score> 0.4), we selected the protein related to the common DEGs (species: Homo), and used the Cytoscape software (http://cytoscape.org/ ) build a PPI network. Then we used ve methods(degree, Maximum Neighborhood Component (MNC), Radiality centrality, Stress centrality, Closeness centrality) to sequence and evaluate central genes . In addition, we used the MCODE algorithm to obtain key modules. 2.5 Validation of central genes and determination of tissue-specic biomarker genes We identied the central genes by using the Genecards database[21]and further determine the genes potentially related to the disease. The central genes was located for further determine the central genes expressed in the hematologic/immune system by using the Biogps database[22].The results obtained from the two databases were integrated to obtain HIBD hematologic/immune system tissue specic biomarker genes. We applied the GSE121137 data set for external verication of tissue-specic genes.Box-plot analysis and receiver operating characteristic(ROC) curve were performed to display tissue-specic genes for external verication of the diagnostic potential of HIBD by using GraphPad Prism8.0 soft. 2.6 Prediction of transcription factors and miRNAs of key central genes The CHEA3 database[23] are used to predict the relationship between key central genes and transcription factors (TF); The mirDIP database[24] was identied to predict the relationship between key central genes Page 4/22 and miRNA.Network analysis was performed by running Cytoscape application,and