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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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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