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
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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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