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
page: 10

MATERIAL AND METHODS

Microarray data and data preprocessing GES83452 (14) in the NCBI-GEO (https://www.ncbi. nlm.nih.gov/) (15) database were downloaded on April 10, 2020, which included a total of 231 samples, including 159 patients at baseline (44 no NASH, 104 NASH, and 4 undefined) and 79 patients at 1-year follow-up (54 no NASH, 22 NASH, and 3 undefined) based on platforms GPL16686 [HuGene-2_0-st] Affymetrix Human Gene 2.0 ST Array [transcript (gene) version]. Screening significantly differentially expressed RNAs and functional enrichment analyses The mRNA and lncRNA in the GES83452 datasets were reannotated using the HUGO Gene Nomenclature Committee (http://www.genenames.org/) (16) based on information of Transcript ID, RefSeq ID, etc., which contain 4600 lncRNAs and 19195 protein coding genes. The Limma package (version 3.34.0, https://bioconductor. org/packages/release/bioc/html/limma.html) (17) in R was used to identify DERs between the NAFLD and non-NAFLD samples of the baseline and 1-year follow-up time point group. False discovery rate (FDR) < 0.05 and |log2 fold change (FC)| > 0.5 were used as the cutoff criteria to define DERs, and the ggplot2packages in R was used to visualize the volcano plots. The heat map was plotted using the pheat map package (version 1.0.8, https://cran.rproject. org/package=pheat map) (18) in R and was presented by two-way hierarchical clustering heat maps (19) based on Euclidean distance (20). P < .05 was considered statistically significant. The Venn software online (http:// bioinformatics.psb.ugent.be/webtools/Venn/) was used to detect overlapping DERs among the baseline and 1-year follow-up time point groups. Then, GO and KEGG enrichment analyses were performed on intersection mRNAs that commonly contained DERs using the online tool DAVID (version 6.8, https://david.ncifcrf.gov/) (21, 22) P < .05 was considered as significant enrichment. Construction of ceRNA network The miRNAs related to NAFLD included in the Human MicroRNA Disease Database (HMDD) database (http://www.cuilab.cn/hmdd) were downloaded (23). We constructed a ceRNA network based on NAFLD directly related to lncRNAs and miRNAs, as well as the miRNAs with significantly consistent expression. Firstly, we downloaded the connection relationship pairs of lncRNAmiRNA in the DIANA–LncBase (version 2, http://carolina. imis.athena-innovation.gr/diana_tools/web/index.php) (24). The regulatory relationship between significantly DElncRNA and NAFLD-related differentially expressed miRNA (DEmiRNA) was retained, with retention connection score (miRNA target gene score (miTG–score): the target gene score of DEmiRNA; the higher the value, the greater the probability of targeting) higher than 0.6, thereby the lncRNA–miRNA connection network was constructed. Then, the starBase database (version 2.0, http://starbase. sysu.edu.cn/) (25) was used to predict target genes regulated by miRNA linked to lncRNA, and the comprehensive target gene prediction information from five databases (targetScan, picTar, RNA22, PITA and miRanda) was provided in the StarBase database. The target miRNA regulatory target gene relationship pair was selected in at least one of the databases, and the miRNA–mRNA pairs with the opposite significant differential expression direction was retained to construct the miRNA–mRNA connection network. Finally, a ceRNA regulation network composed of lncRNA–miRNA–mRNA was constructed by combining lncRNA–miRNA and miRNA–mRNA, and the ceRNA network was visualizatied by using the cytoscape (version 3.6.1, http://www.cytoscape.org/). The screened target genes in the ceRNA regulatory network were submitted to DAVID 6.8 online tool to perform functional annotation based on GO biological processes and KEGG pathway enrichment analysis, the P value < 0.05 as the significance threshold. Construction of drug–gene regulation network The pharmacogenetics and pharmacogenomics knowledge base (PharmGKB) (https://www.pharmgkb. org/) (26) collected the most complete genotype and phenotype information related to the drug genome and was classified systematically, which contained 27,007 genes related to 3579 drugs and 3410 diseases. In this study, the PharmGKB database was used to search for and obtain the gene-related drug molecules in the regulated ceRNA network; then the gene–drug connection network was constructed, the important gene molecules were screened out, and the KEGG signaling pathway of those genes participated in in-depth analysis.



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