Identification and Characterization of Genes Associated with Intestinal Ischemia-Reperfusion Injury and Oxidative Stress: A Bioinformatics and Experimental Approach Integrating High-Throughput Sequencing, Machine Learning, and Validation - Xie Y; Yang M; Huang J; Jiang Z (Journal of Inflammation Research)
Yongguo Xie,1,* Mingpu Yang,2,* Juanjuan Huang,3 Zongbin Jiang4 1Department of Anesthesiology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530007, People’s Republic of China; 2General Surgery, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530007, People’s Republic of China; 3Department of Microbiology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People’s Republic of China; 4Department of Pain Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530007, People’s Republic of China*These authors contributed equally to this workCorrespondence: Juanjuan Huang, Department of Microbiology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530021, People’s Republic of China, Email hjjcbw@163.com Zongbin Jiang, Department of Pain Medicine, The Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, 530007, People’s Republic of China, Email jiangzongbin@sr.gxmu.edu.cnPurpose: Intestinal ischemia-reperfusion injury (IIRI) occurs as a result of temporary blood flow interruption, leading to tissue damage upon reperfusion. Oxidative stress plays a critical role in this process, instigating inflammation and cell death. Identifying and characterizing genes associated with the oxidative stress response can offer valuable insights into potential therapeutic targets for managing IIRI.Patients and Methods: The IIRI dataset was sourced from the NCBI Gene Expression Omnibus Database (GEO), while oxidative stress genes were obtained from the Genecards database. Following the acquisition of differentially expressed genes in IIRI, they were cross-linked with oxidative stress genes to yield IIRI oxidative stress related genes (IOSRGs). The least absolute shrinkage and selection operator, as well as the support vector machine with random forest algorithm, were utilized for machine learning. Subsequently, the PPI network was established, and the Degree and MNC algorithms of the Cytohuba plugin were integrated with the genes obtained through the machine learning algorithms to identify hub IOSRGs (HIOSRGs). A mouse IIRI model and ROC curve were employed to verify the accuracy of HIOSRGs. Finally, siRNA was utilized to suppress the expression of HDAC3 in Caco2 cells, and the changes in oxidative stress levels before and after hypoxia-reoxygenation in Caco2 cells were observed.Results: A total of 277 OSRGs and 4 HIOSRGs were obtained. Concurrently, in vivo experimental results of IIRI in C57BL/6 mice, and the establishment of ROC curves, reflected the accuracy and specificity of HIOSRGs. The knockdown of HDAC3 in Caco2 cells resulted in increased oxidative stress levels before and after hypoxia-reoxygenation, underscoring the significant role of HDAC3 in IIRI.Conclusion: This study elucidates the interplay between oxidative stress genes and IIRI, offering novel insights into the potential pathogenesis of IIRI and medical interventions for IIRI.Keywords: IIRI, bioinformatics, potential targets, early diagnosis