IDENTIFICATION OF KEY TARGET GENES AND PATHWAY ANALYSIS IN NONALCOHOLIC FATTY LIVER DISEASE VIA INTEGRATED BIOINFORMATICS ANALYSIS
Chen X.1, Zhang L.2, Wang Y.1, Li R.1, Yang M.1, Gao L.3*
*Corresponding Author: Lei Gao, MD, College of Basic Medicine, Changchun University of Chinese Medicine, 1035 Boshuo, Road, Jingyue District, Changchun City, Jilin Province, 130117, China; Tel:+ 86-431-8604 5309, Email: gaolei790708@163.com
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INTRODUCTION

Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease with a prevalence rate of 25% worldwide (1). Several lifestyle-related factors are associated with incident fatty liver such as alcohol intake, lower physical activity, smoking, and shift work. Poor lifestyle choices are often the main cause of fatty liver, these include smoking, drinking, lack of physical activity, and shift work, etc. In addition, high triglycerides, type 2 diabetes mellitus, obesity, and hypertension are associated with incident fatty liver. Therefore, lifestyle modification is strongly recommended to prevent fatty liver (2, 3). It is difficult to detect this problem in the earlier stages of the disease, and may thus further develop into advanced liver diseases, such as cirrhosis and hepatocellular carcinoma, bringing forth clinical challenges to the treatment of NAFLD (4). In the literature, the severity of NAFLD in patients with type 2 diabetes and obesity will be significantly affected, increasing the degree of deterioration of liver fibrosis and the possibility of further development of endstage liver disease (5-7). Likewise, studies have shown that when NAFLD patients suffer from cardiovascular diseases and dyslipidemia, these factors have a negative impact on the natural progression of NAFLD (8-10). Nearly 40% of patients with NAFLD die of complications, as previously reported (1). However, the detailed mechanisms under which NAFLD develops remain largely unknown. Diet adjustment and weight loss can improve NAFLD, but it is difficult to maintain. Moreover, the theory of insulin resistance has been widely accepted clinically. Insulin sensitizers have a certain therapeutic effect, but they can cause adverse reactions such as increased body weight and its therapeutic target is too limited. Therefore, this study aimed at finding new molecular targets to provide a theoretical basis for new and effective treatment methods of NAFLD. Long non-coding RNA (lncRNA) is the main component of the human transcriptome. Long non-coding RNA plays an important role in regulating cell migration, proliferation, invasion, and metastasis. It can also be used as a diagnostic marker or therapeutic target for malignant tumors and other diseases. Competitive endogenous RNA (ceRNA) is a transcript with the same microRNA (miRNA) response element, which binds to miRNA to compete and regulate its target gene, thereby affecting the biological behavior of the disease. Studies have confirmed that the mutual regulation between lncRNA and miRNA and their downstream target genes plays an important role in the occurrence and development of diseases (11). The inflammatory component of nonalcoholic steatohepatitis (NASH) is more difficult to capture with ultrasound-assisted techniques. Although more and more technologies are applied in clinical practice, such as quantitative and contrast-enhanced ultrasound, there are still many technical barriers to be broken; and not all technologies have been successful in clinical and research practice (12). Due to the limitations of liver biopsy, searching for non-invasive and reliable diagnostic biomarkers for NAFLD is a priority for current research. Bioinformatics has been widely used to explore biomarkers of different diseases, but NAFLD-related biomarkers need to be further explored to help the early diagnosis and prognosis evaluation of NAFLD (13). In this study, human samples from the Gene Expression Omnibus (GEO) database were used to identify key genes related to NAFLD and non-NASH samples during the baseline and 1-year follow-up time point, and to explore the underlying mechanism of NAFLD and develop new NAFLD diagnostic biomarkers. Then, the lncRNA–miRNA–mRNA network related to NAFLD was constructed by mapping the differentially expressed RNAs (DERs) into a global triple network via starBase and miRcode databases. This was done to identify which RNAs can be used as sensitive and specific markers for NAFLD. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the potential regulatory functions of RNAs. Finally, the PharmGKB database was used to search and obtain gene-related drug molecules in the ceRNA regulatory network and then build a gene–drug connection network to screen out important gene molecules and KEGG signaling pathways involved in genes.



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