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\begin{document}

\title{Squalene epoxidase promotes hepatocellular carcinoma development by
activating STRAP transcription and TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling\selectlanguage{english}}



\author[1]{Wu Yin}%
\author[2]{zhirui zhang}%
\author[3]{Wei Wu}%
\author[4]{Hao Jiao}%
\author[5]{Yuzhong Chen}%
\author[6]{Xiaojun Ji}%
\author[7]{Jing Cao}%
\author[8]{Fangzhou Yin}%
\affil[1]{Nanjing University }%
\affil[2]{The State Key Lab of Pharmaceutical Biotechnology, College of Life Sciences, Nanjing University, Nanjing}%
\affil[3]{Organ Transplantation Center, Southern District, the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital)}%
\affil[4]{Department of Pharmacy, Fuyang People’s Hospital}%
\affil[5]{Department of Surgical Oncology，First Affiliated Hospital of Bengbu Medical College}%
\affil[6]{Department of Innovation, Nanjing Chia Tai Tianqing Pharmaceutical Co., Ltd}%
\affil[7]{Department of Pharmacy, Women's Hospital of Nanjing Medical University/Nanjing Maternity and Child Health Care Hospital}%
\affil[8]{School of Pharmacy, Nanjing University of Chinese Medicine}%


\vspace{-1em}



  
  \date{May 22, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Background and Purpose Squalene epoxidase (SQLE) is a key enzyme
involved in cholesterol biosynthesis, but increasing evidence reveals
that SQLE is abnormally expressed in some types of malignant tumors, and
the underlying mechanism remains poorly understood. Experimental
Approach Bioinformatics analysis and RNA sequencing were applied to
detect to differentially expressed genes in clinical HCC tumors.
AnnexinV/PI, EdU assay, transwell, western blot, qRT-PCR, IHC staining,
RNA sequencing, dual-luciferase reporters and HE staining were evaluated
to investigate the pharmacological effects and possible mechanisms of
SQLE in vitro and in vivo. Key Results We found that SQLE expression is
specifically elevated in HCC tumors, correlating with poor clinical
outcomes. SQLE promoted HCC growth, EMT, and metastasis both in vitro
and in vivo. In contrast, silencing of SQLE expression prevented HCC
development. Both RNA-seq and functional experiments revealed that the
protumorigenic effect of SQLE on HCC is closely related to the
activation of cellular TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling, but interestingly, the
stimulatory effect of SQLE on TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling and HCC development
is also critically dependent on STRAP. SQLE expression is well
correlated with STRAP in HCC, and further, to amplify TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD
signaling, SQLE even transcriptionally increased STRAP expression
mediated by the trans-acting factor AP-2\selectlanguage{greek}α. \selectlanguage{english}Finally, as a chemical
inhibitor of SQLE, NB-598 markedly inhibited HCC cell growth and tumor
development in mouse models. Conclusions and Implications Taken
together, SQLE serves as a novel oncogene in HCC development by
activating TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling, and targeting SQLE could be useful in
drug development and therapy for HCC.%
\end{abstract}%



\sloppy


\selectlanguage{greek}\textbf{Squalene epoxidase promotes hepatocellular carcinoma development
by activating STRAP transcription and TGF-β/SMAD signaling}\selectlanguage{english}

Zhirui Zhang*\textsuperscript{1}, Wei Wu*\textsuperscript{2}, Hao
Jiao\textsuperscript{3}, Yuzhong Chen\textsuperscript{4}, Xiaojun
Ji\textsuperscript{5}, Jing Cao\textsuperscript{6}, Fangzhou
Yin\textsuperscript{7}, and Wu Yin\textsuperscript{1}

\textsuperscript{1} The State Key Lab of Pharmaceutical Biotechnology,
College of Life Sciences, Nanjing University, Nanjing, 210046, China.

\textsuperscript{2} Organ Transplantation Center, Southern District, the
First Affiliated Hospital of University of Science and Technology of
China (Anhui Provincial Hospital), Hefei, 230001, China

\textsuperscript{3} Department of Pharmacy, Fuyang People's Hospital,
Fuyang, 236000, China

\textsuperscript{4} Department of Surgical Oncology,First Affiliated
Hospital of Bengbu Medical College,Bengbu, 233000, China

\textsuperscript{5} Department of Innovation, Nanjing Chia Tai
Tianqing~Pharmaceutical Co., Ltd,Nanjing, 210000, China

\textsuperscript{6} Department of Pharmacy, Women's Hospital of Nanjing
Medical University/Nanjing Maternity and Child Health Care Hospital,
Nanjing, 210004, China

\textsuperscript{7} School of Pharmacy, Nanjing University of Chinese
Medicine, Nanjing, 210046, China

\textsuperscript{*} These authors contributed equally to this work.

\textbf{Correspondence to:}

\textbf{(1) Yin Wu} Ph.D.

State Key lab of Pharmaceutical Biotechnology, College of Life Sciences
in Nanjing University (Xianlin Campus), Nanjing, 210046, China

Email address: wyin@nju.edu.cn

\textbf{(2) Jing Cao}

Department of Pharmacy, Women's Hospital of Nanjing Medical
University/Nanjing Maternity and Child Health Care Hospital, Nanjing,
210004, China

E-mail address: \emph{njfycj@163.com}

\textbf{(3) Yin Fang-zhou} Ph.D.

School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing,
210046, China

E-mail address; yfz2003@163.com

\textbf{Abstract}

\textbf{Background and Purpose}

Squalene epoxidase (SQLE) is a key enzyme involved in cholesterol
biosynthesis, but increasing evidence reveals that SQLE is abnormally
expressed in some types of malignant tumors, and the underlying
mechanism remains poorly understood.

\textbf{Experimental Approach}

Bioinformatics analysis and RNA sequencing were applied to detect to
differentially expressed genes in clinical hepatocellular carcinoma
(HCC) tumors. MTT, colony formation assay, AnnexinV-FITC/PI, EdU assay,
wound healing, transwell, western blot, qRT-PCR, IHC staining,
cytoskeleton F-actin filaments assay, RNA sequencing, dual-luciferase
reporters and HE staining were evaluated to investigate the
pharmacological effects and possible mechanisms of SQLE in vitro and in
vivo.

\textbf{Key Results}

We found that SQLE expression is specifically elevated in hepatocellular
carcinoma (HCC) tumors, correlating with poor clinical outcomes. SQLE
significantly promoted HCC growth, epithelial-mesenchymal transition,
and metastasis both in vitro and in vivo. In contrast, silencing of SQLE
expression prevented HCC development. Both RNA-seq and functional
experiments revealed that the protumorigenic effect of SQLE on HCC is
closely related to the activation of cellular TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling, but
interestingly, the stimulatory effect of SQLE on TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling
and HCC development is also critically dependent on STRAP, a serine and
threonine kinase. SQLE expression is well correlated with STRAP in HCC,
and further, to amplify TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling, SQLE even
transcriptionally increased STRAP gene expression mediated by the
trans-acting factor AP-2\selectlanguage{greek}α. \selectlanguage{english}Finally, as a chemical inhibitor of SQLE,
NB-598 markedly inhibited HCC cell growth and tumor development in mouse
models.

\textbf{Conclusions and Implications}

Taken together, SQLE serves as a novel oncogene in HCC development by
activating TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling, and targeting SQLE could be useful in
drug development and therapy for HCC.

\textbf{Abbreviations}

HCC, hepatocellular carcinoma; SQLE, squalene epoxidase; NAFLD,
nonalcoholic fatty liver disease; AFP, alpha fetoprotein; EdU,
5-ethynyl-2'-deoxyuridine; DEGs, differentially expressed genes; OS,
overall survival; RFS, recurrence-free survival; PFS, progression-free
survival; DSS, disease-specific survival; EMT: epithelial-mesenchymal
transformation; STRAP, serine-threonine kinase receptor-associated
protein

\textbf{Keywords} : squalene epoxidase; hepatocellular carcinoma;
TGF-\selectlanguage{greek}β/\selectlanguage{english}SMADs; STRAP; NB-598

\textbf{What is already known}

a. NB-598 is an inhibitor of SQLE.

b. The knowledge of SQLE in HCC is laconic with an unclarified mechanism

\textbf{What this study adds}

a. SQLE is highly expressed in HCC clinical samples and correlates with
poor prognosis.

b. SQLE promotes HCC development by activating STRAP transcription and
TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling.

c. SQLE transcriptionally upregulates STRAP expression in HCC cells by
trans-acting factor AP-2\selectlanguage{greek}α.

\selectlanguage{english}\textbf{Clinical significance}

SQLE is a promising target for HCC therapy and drug development.

\textbf{Introduction}

Hepatocellular carcinoma (HCC) has developed into the third leading
cause of tumor-related death and has become the sixth most common tumor
in the world (Sung \emph{et al.} , 2021; Zhang \emph{et al.} , 2021).
According to statistics, there were 905,677 new liver cancer patients
worldwide in 2020 and 830,180 new liver cancer deaths(Sung \emph{et al.}
, 2021). Hepatitis viral infection, aflatoxin, alcohol, and lipogenesis
are the main risk factors during the HCC formation process (Erkekoglu
\emph{et al.} , 2017; Chen \emph{et al.} , 2019). Although the diagnosis
and treatment methods of HCC have always been developing, surgical
resection and liver transplantation are still the most effective
treatments for HCC. As a highly aggressive tumor, venous invasion and
intrahepatic and distant metastasis often occur in the process of HCC,
which is the main cause of high recurrence and mortality (Yang \emph{et
al.} , 2019a; Chen \emph{et al.} , 2020). Therefore, it is very
important to explore new therapeutic and diagnostic targets for HCC.

Metabolic abnormalities are one of the hallmarks of tumor cells(Counihan
\emph{et al.} , 2018). Reports have shown that cholesterol metabolism in
tumor cells is different from that in normal cells. In patients with
breast or prostate carcinoma, serum cholesterol content is positively
correlated with the risk of cancer (Kuzu \emph{et al.} , 2016; Xu
\emph{et al.} , 2020). There are more than 30 different enzymes involved
in the cholesterol synthesis pathway (Sharpe \emph{et al.} , 2013). SQLE
catalyzes the first oxidation step in sterol biosynthesis and produces
two key downstream metabolites, cholesterol ester and nicotinamide
adenine dinucleotide phosphate (NADP+) (Gill \emph{et al.} , 2011; Zhang
\emph{et al.} , 2019). The gene of SQLE is located on human chromosome
8q24.1. Recent studies have found that SQLE is highly expressed in some
types of malignant tumors, including prostate cancer, breast cancer,
lung cancer, and colon cancer (Brown\emph{et al.} , 2016; Stopsack
\emph{et al.} , 2016; Ge \emph{et al.} , 2019; Jun \emph{et al.} ,
2021), suggesting that it could be a drug target for cancer therapy.
Reports have also demonstrated that SQLE expression is upregulated in
hepatocellular carcinoma. Nonalcoholic fatty liver disease (NAFLD)
increases the risk of hepatocellular carcinoma. Notably, SQLE was the
top outlier gene that was highly expressed in NAFLD-HCC patients. By
increasing the production of NADP\textsuperscript{+} and cholesteryl
ester, SQLE epigenetically inactivated PTEN expression and promoted HCC
development as a result of reactive oxygen species generation and
AKT-mTOR activation. Chemical inhibitors of SQLE suppressed HCC
development by reducing cholesterol content and arresting cells at G2
phase (Liu \emph{et al.} , 2018).

Although the oncogenic role of SQLE in HCC has been increasingly
appreciated, the underlying signaling mechanism remains unclear.
TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling regulates a wide array of cellular processes in
tumor development (Nickel \emph{et al.} , 2018). Here, we described a
novel role of SQLE in HCC development; that is, SQLE is a strong
activator of TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling in HCC mediated by STRAP, a
serine/threonine kinase. Moreover, TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD activation by SQLE is an
early event in HCC that occurs upstream of PTEN inactivation.

\textbf{Materials and methods}

\textbf{Human samples collection}

A total of 6 paired human HCC tumor and adjacent nontumor tissues were
collected from patients with biopsy-proven HCC at the First Affiliated
Hospital of University of Science and Technology of China (Anhui
Provincial Hospital). All human HCC tissues obtained written informed
consent from patients or their guardians. The Institutional Review Board
of the University of Science and Technology of China approved the use of
the tumor specimens in this study (2020KY131).

\textbf{Reagents and antibodies}

The Annexin V-FITC/PI Apoptosis Detection Kit and HifairTM II 1st Strand
cDNA Synthesis SuperMix were purchased from Yeasen Biotech (Shanghai,
China). DAPI and BeyoClick EdU Cell Proliferation Kit with Alexa Fluor
594 were obtained from Beyotime Institute of Biotechnology (Wuhan,
China). SB-431542 was acquired from MedChemExpress (Shanghai, China).
The TraKine\textsuperscript{TM}F-actin Staining kit (Green Fluorescence)
was purchased from Abbkine Scientific (Wuhan, China). Alpha fetoprotein
(AFP) ELISA was acquired from NEOBIOSCIENCE (Shenzhen, China). NB-598
was obtained from MedMol (Shanghai, China). The following antibodies
were used: SQLE, STRAP, DDDDK-tag, GAPDH and Tubulin \selectlanguage{greek}α \selectlanguage{english}(Bioworld,
Nanjing, China); PCNA, E-cadherin, N-cadherin, Vimentin, SMAD3, SMAD2
and HA-Tag (Proteintech, Wuhan, China); phospho-SMAD2 (Affinity
Biosciences, OH, USA); phospho-SMAD3 (Abcam, Cambridge, MA, USA); and
TGF-\selectlanguage{greek}β\selectlanguage{english}1 (Zen Bioscience, Chengdu, China).

\textbf{Cell lines and cell culture}

The HEK-293T and hepatocellular carcinoma cell lines SMMC7721, Huh-7,
and H22 were all obtained from Shanghai Cell Bank (Shanghai, China).
Cells were maintained in DMEM (WISENT, Canada) supplemented with 10\%
fetal bovine serum (FBS) (ExCell Bio, Shanghai, China), 100 U/mL
penicillin, and 100 \selectlanguage{greek}μ\selectlanguage{english}g/mL streptomycin. Cells were cultured at 37 \selectlanguage{ngerman}°C
with 5\% CO\textsubscript{2}.

\textbf{Cell transfection}

The cells were transfected with the plasmids using Hieff
Trans\textsuperscript{TM} Liposomal Transfection Reagent (Yeasen
Biotech, Shanghai, China) and transfected with siRNA with Lipofectamine\selectlanguage{english}
2000 Transfection Reagent (Thermo Fisher, Waltham, MA, USA) according to
the manufacturer's instructions. The following siRNAs were used in this
study: SQLE siRNA\#1: GAAACAAGCUUAAUAGGAATT; SQLE siRNA\#2:
CCAAGGAAUUUAAGA

GAAUTT; STRAP siRNA: AGAAAUUGCUUCAGAGAAUTT; SP1 siRNA:
GGAUGGUUCUGGUCAAAUATT; CEBP/\selectlanguage{greek}β \selectlanguage{english}siRNA: AGGUCAAGAGCAAGGCCAATT; AP-2\selectlanguage{greek}α \selectlanguage{english}siRNA:
CGCCAAAAGCAGUGACAAATT, and the control siRNA was UUCUCCGAACGUGUCACGUTT.

\textbf{Western blot analysis}

Cells were harvested and homogenized in RIPA lysis buffer (Beyotime
Institute of Biotechnology), and the protein concentration was detected
by BCA assay (Yeasen Biotech, Shanghai, China). Equal amounts of total
protein were separated by SDS--PAGE and transferred to PVDF membranes.
After blocking with 5\% nonfat milk for 2 h, the membranes were
incubated with specific primary antibodies for at least 4 h, followed by
incubation with horseradish peroxidase-conjugated anti-rabbit or
anti-mouse IgG for 1 h at room temperature. All membranes were imaged
with ECL super (Sparkjade, Shandong, China) and acquired by Tanon 4600SF
(Shanghai, China).

\textbf{RNA isolation and real-time quantitative PCR (} qRT--PCR)

Total RNA was extracted using SparkZol Reagent (Shandong Sparkjade
Biotechnology Co., Ltd., Shandong, China) according to the
manufacturer's instructions. qRT--PCR was performed using the SYBR\selectlanguage{ngerman}®
Green Premix Pro Taq HS qPCR Kit (Accorate Biology, Hunan, China). The
primer sequences are listed in Supplementary Table S1.

\textbf{Immunohistochemistry (IHC) staining}

IHC staining was performed as previously reported (Yang \emph{et al.} ,
2019b). In brief, paraffin sections were deparaffinized with xylene and
rehydrated with graded ethanol solutions (100\%, 95\%, 80\%). Hydrogen
peroxide (3\%) was used to quench endogenous peroxidase activity, and
the sections were incubated for 10\textasciitilde{}15 min. Then, the
sections were microwaved for 20 min in a citric acid solution (pH=6.0)
at 95 °C and blocked with 5\% BSA for 30 min after cooling. Primary
antibodies against SQLE (BS71537, 1:200), PCNA (10205-2-AP, 1:500),
SMAD2 (12570-1-AP, 1:300), and SMAD3 (25494-1-AP, 1:300) were incubated
overnight. After washing three times with PBS, the sections were
incubated with a horseradish-conjugated secondary antibody for 1 h,
followed by DAB substrate kit (Boster, Wuhan, China) display signal. The
images were all captured with an optical microscope (NEXCOPE930,
Nexcope, USA).

\textbf{Cell viability assays}

The cells were plated at a density of 5×10\textsuperscript{3} cells/well
in 96-well plates. After treatments, 15 \selectlanguage{greek}μ\selectlanguage{english}l of 5 mg/ml MTT was added, and
then the plates were incubated further for 2 h at 37 \selectlanguage{ngerman}°C. After
supernatant removal, 150 \selectlanguage{greek}μ\selectlanguage{english}l DMSO was added to each well, and the
absorbance of each sample was read at 490 nm using a spectrophotometer
(Elx800, BioTEk instrument, USA).

\textbf{Colony formation assay}

Cells at a density of 3\selectlanguage{ngerman}×10\textsuperscript{3}/ml were seeded in a
12-well plate and cultured continuously until colonies were formed after
plasmid transfection. Then, the cells were fixed with paraformaldehyde
for at least 10 min and stained with 1\% crystal violet for
10\textasciitilde{}15 min. After washing twice with PBS, cell colonies
were photographed using a camera.

\textbf{Annexin V-FITC/PI apoptosis assay}

The cells (1.5×105 cells/well) were seeded in a 12-well plate and
transfected with siNC or siSQLE for 36 h. Cells were harvested and
analyzed by flow cytometry using an Annexin V-FITC/PI apoptosis
detection kit. Before measurement, cells were incubated in buffer
containing 400 \selectlanguage{greek}μ\selectlanguage{english}l of Annexin-binding buffer, 5 \selectlanguage{greek}μ\selectlanguage{english}l of Annexin V-FITC, and
10 \selectlanguage{greek}μ\selectlanguage{english}l PI for 30 min in the dark. Flow cytometry analysis was conducted
on a BD Accuri C6 flow cytometer (BD Biosciences, San Jose, USA).

\textbf{5-ethynyl-2'-deoxyuridine (EdU) assay}

An EdU incorporation assay was performed using the BeyoClick EdU Cell
Proliferation Kit with Alexa Fluor 594 according to the manufacturer's
instructions. Briefly, cells after treatments were seeded into 24-well
plates at a density of 1.5 \selectlanguage{ngerman}×10\textsuperscript{5} cells/well and then
incubated with 10 \selectlanguage{greek}μ\selectlanguage{english}M EdU for 2 h at 37 \selectlanguage{ngerman}°C. The medium was removed, and
samples were fixed with 4\% paraformaldehyde for 15 min and
permeabilized with 0.3\% Triton X-100 for another 15 min. After washing
with PBS, the cells were reacted with 1× Click reaction cocktail for 30
min in the dark, and the nuclei were stained with Hoechst 33342 for 10
min. Images were captured with an optical microscope.

\textbf{Wound healing assays}

Huh7 or SMMC7721 cells (1.5 ×10\textsuperscript{5} cells/well) were
seeded in 12-well plates. After transfection for 24 h, cell wounds were
made using a 100 \selectlanguage{greek}µ\selectlanguage{english}l pipette tip (24 h after transfection), and cells
were further cultured in fresh medium for 36 h. The wound healing of
each sample was observed at 0 and 36 h and captured by an inverted
optical microscope.

\textbf{Transwell migration assay}

After transfection for 24 h, Huh7 or SMMC7721 cells at a density of
2\selectlanguage{ngerman}×10\textsuperscript{4} cells/well were seeded into the upper chamber in
serum-free medium, and the bottom chamber was loaded with 10\%
FBS-containing medium. After incubation at 37 °C for 24 h, the cells in
the upper chamber were removed with cotton swabs. The filters were fixed
with 4\% paraformaldehyde for 10 min and stained with 1\% crystal violet
for 15 min. Images were captured by inverted optical microscopy.

\textbf{Cytoskeleton F-actin filaments Assay}

Huh7 or SMMC7721 cells were plated on sterile cover slips and
transfected with HA-SQLE or siSQLE as well as their control. After 36 h,
cells were fixed with ice-cold 4\% paraformaldehyde for 15-30 min and
then permeabilized with 0.1\% Triton X-100 in PBS for 10 min. After
adding 200 \selectlanguage{greek}μ\selectlanguage{english}l/well (24-well plate) phalloidin (green fluorescence)
staining solution into cells and staining for 0.5 h at room temperature,
the samples were observed using a fluorescence microscope with an
anti-fluorescence quencher.

\textbf{RNA-sequencing and analysis}

For RNA sequencing, total RNA was extracted using SparkZol Reagent at a
concentration of 300\textasciitilde{}500 \selectlanguage{greek}μ\selectlanguage{english}g/\selectlanguage{greek}μ\selectlanguage{english}l according to the
manufacturer's protocol. Shanghai Major-bio Biopharm Biotechnology
(Shanghai, China) performed the transcriptome sequencing and analyses,
and each group had three biological replicates. The data were analyzed
on the Majorbio Cloud Platform (\emph{www.majorbio.com}). Differentially
expressed genes (DEGs) with \emph{P} \textless{} 0.05 were identified.

\textbf{Dual-Luciferase reporter assay}

SMMC7721 cells at a density of 1.2\selectlanguage{ngerman}×10\textsuperscript{4} cells/well were
plated in a 24-well plate 24 h before transfection. pRL-TK (0.08 \selectlanguage{greek}μ\selectlanguage{english}g) and
the indicated constructs (0.8 \selectlanguage{greek}μ\selectlanguage{english}g) were incubated for 36 h using Hieff
TransTM Liposomal Transfection Reagent according to the manufacturer's
instructions, and pGL3-Basic was used as the control vector. For
luciferase assays, SMMC7721 cells were lysed in lysis buffer and
oscillated violently on a vortex for 15 min, and promoter activities
were detected using a Dual Luciferase Reporter Assay System (Promega,
Madison, WI, USA). The data are shown as the ratio of firefly luciferase
to renal cell luciferase activity.

\textbf{Construction of recombinant adenovirus}

The recombinant adenoviruses Ad-shSQLE and Ad-shNC were established in
our lab using the AdEasy system according to the protocol(Luo \emph{et
al.} , 2007). Ad-shSQLE or Ad-shNC was cloned into the shuttle vector
pAdTrack-CMV, generating recombinant adenovirus plasmids using BJ5183
cells. Ten\textasciitilde{}15 of the smallest colonies were picked and
grown in LB medium for 12 h in a 37 orbital shaker. The next day, the
plasmid DNA was extracted by the alkaline cleavage method and identified
by PacI digestion. To generate recombinant adenoviruses in HEK-293A
packaging cells and prepare viral lysates, a large-scale preparation of
high-titer viruses needed at least four rounds of amplification.

\textbf{Establishment of the mouse tumor model and hematoxylin-eosin
staining}

SPF male C57BL/6 mice weighing between 18 and 22 g were purchased from
the Model Animal Research Center of Nanjing University (Nanjing, China).
H22 cells were injected into the abdominal cavity of C57BL/6 mice. After
5\textasciitilde{}7 days of injection, the ascites were extracted from
tumor-bearing mice and diluted to a concentration of
2\selectlanguage{ngerman}×10\textsuperscript{7} cells/ml with normal saline. A volume of 200 \selectlanguage{greek}μ\selectlanguage{english}l
of the cell suspension was subcutaneously injected into the right side
of the axillary or liver of each mouse to establish a subcutaneous or
orthotopic tumor-bearing mouse model. Orthotopic tumor-bearing mice were
randomly divided into two groups and injected with recombinant
adenovirus expressing control or shSQLE via the tail vein, and the mice
continued to be fed for 12 days. In a subcutaneous tumor-bearing mouse
model, when the average tumor size reached 100 mm\textsuperscript{3},
the mice were divided into two groups: the 0.5\% carboxy methyl
cellulose (CMC) group and the NB-598 (an inhibitor of SQLE, 10 mg/kg/d)
group with gastric infusion 8 times. The tumor sizes were measured once
every two days. At the end of the experiment, the serum AFP content in
each mouse was measured. The mice were sacrificed, and tumors from
either subcutaneous or orthotopic models were collected for further
analysis. H\&E staining of tumor tissue was performed as previously
described (Feldman \emph{et al.} , 2014). The images were captured with
optical microscope.

\textbf{Statistical analysis}

Data are expressed as the means \selectlanguage{ngerman}± SEM for triplicates. GraphPad Prism 5
was used for plotting and analysis. Comparisons between two groups were
analyzed using the two-tailed Student's t test. \emph{p} \textless{}0.05
was considered statistically significant.

\textbf{Results}

\textbf{SQLE is specifically upregulated in HCC and predicts poor
prognosis}

To explore the differentially expressed genes in HCC, RNA-seq analysis
was performed on three pairs of clinical HCC and paracancerous tissue
samples, and GO, KEGG, and GSEA analyses were conducted by the Majorbio
Cloud Platform (\emph{www.majorbio.com}). As shown in Fig. 1A, a total
of 30692 genes, including 30153 known and 539 new genes, were detected
in the analysis. In addition, a total of 134,592 expressed transcripts,
including 122,031 known and 12,561 new transcripts, were identified.
Based on the quantitative results of gene expression, the differentially
expressed genes (DEGs) between cancer and paracancerous tissues were
obtained by using variance analysis software DESeq2, and the threshold
value was set as follows: \textbar{} log2FC \textbar{} \textgreater{} =
3 \& \emph{p} value \textless{} 0.01. A total of 562 DEGs were
identified, among which 210 genes were upregulated and 352 genes were
downregulated (Supplementary Table S2). The upregulated genes in HCC
include TRIM55, GPC3, AFP, SQLE, TOP2A and so on. Following GO
enrichment analysis, HCC-related DEGs were significantly enriched in the
spindle checkpoint, DNA replication checkpoint, and chromosome
segregation (Fig. 1B). The KEGG enrichment analysis showed that
HCC-related DEGs were highly relevant to DNA replication, the cell
cycle, oocyte meiosis, mismatch repair, and nitrogen metabolism (Fig.
1C). GSEA further indicated that these DEGs were closely related to the
NF-kappa B, HIF-1, cAMP, JAK-STAT, AMPK, and TGF-\selectlanguage{greek}β \selectlanguage{english}signaling pathways
(Fig. 1D). AFP is a reliable biomarker in HCC development. Here, the
volcano plot revealed that SQLE, an enzyme involved in cholesterol
synthesis, was significantly upregulated in HCC in parallel with AFP
(Fig. 1E). Furthermore, the TIMER (tumor immune estimation resource)
database indicated that SQLE expression was significantly elevated in
many tumors, including HCC (Fig. 1F). Similar results were also obtained
by using GEPIA (Fig. 1G) and Oncomine (Fig. 1H) database analysis.

To further confirm the above results, we collected clinical samples and
examined the expression of SQLE in the tumor and adjacent nontumor
tissues. Consistent with the database analysis presented above, both the
protein and mRNA expression levels of SQLE in HCC tumor tissues were
significantly upregulated compared to those in adjacent nontumor
tissues. PCNA expression serves as a control (Fig. 2A\&2B). The IHC
staining analysis revealed that cytoplasmic SQLE was heavily expressed
in HCC tumor tissue compared to the relatively weak expression in the
nontumor tissues (Fig. 2C). Consistent results were also obtained by
using tissue microarray analysis (Fig. 2D). To examine the clinical
relevance of SQLE, Kaplan--Meier survival analysis revealed that HCC
patients with higher SQLE expression had relatively poorer overall
survival (OS), recurrence-free survival (RFS), progression-free survival
(PFS), and disease-specific survival (DSS) than those with lower SQLE
expression (log-rank test, \emph{p} value was 0.043, 0.081, 0.019, and
0.038, respectively, Fig. 2E). Taken together, SQLE is an important
protein in HCC development.

\textbf{SQLE expression is necessary for HCC cell growth}

To understand the role of SQLE in HCC, we first examined SQLE expression
in several liver cell lines. As shown in Figure 3A and B, when compared
with human normal hepatocytes (LO2 cells), both the gene and protein
expression levels of SQLE were significantly upregulated in an array of
HCC cell lines, including Huh7, SMMC7721, HepG2.2.15, and PLC/PRF/5
cells. Huh7 and SMMC7721 cells were selected for the transient knockdown
of SQLE expression with siRNAs (Fig. 3C). The MTT assay indicated a
considerable decrease in cell viability after SQLE knockdown compared to
the control group (Fig. 3D), and silencing of SQLE expression also
significantly suppressed clone formation in both SMMC7721 and Huh7 cells
(Fig. 3E). The Annexin V-FITC/PI apoptosis assay revealed that the
reduced SQLE expression in either SMMC7721 or Huh7 cells triggered
significant apoptosis occurrence (Fig. 3F). Similarly, the EdU
incorporation assay showed that Huh7 and SMMC7721 cell proliferation was
significantly suppressed in cells after SQLE expression was silenced
(Fig. 3G). In contrast, when SQLE was overexpressed in Huh7 and SMMC7721
cells (Fig. 3H), cell growth was significantly promoted compared with
that of control cells (Fig. 3I). Similar results were also observed in
colony formation experiments (Fig. 3J) and EdU incorporation assays
(Fig. 3K).

\textbf{SQLE promotes HCC cell migration and F-actin assembly}

To examine whether SQLE influences cell migration in HCC, we first
observed a noticeable reduction in wound healing in both Huh7 and
SMMC7721 cells after endogenous SQLE expression was silenced (Fig. 4A);
in contrast, SQLE overexpression significantly accelerated wound healing
(Fig. 4B). The numbers of migrating Huh7 and SMMC7721 cells were
significantly reduced when SQLE expression was silenced in the Transwell
migration assay (Fig. 4C). SQLE overexpression, however, increased the
cell migration capacity (Fig. 4D). Epithelial-mesenchymal transformation
(EMT) is a remarkable hallmark of tumor cell metastasis. In this study,
we observed that the silencing of SQLE led to a significant reduction in
N-cadherin and vimentin expression but an increase in E-cadherin
expression at both the protein and mRNA levels. SQLE overexpression,
however, had the opposite result (Fig. 4E-H). In addition, SQLE
knockdown markedly prevented F-actin formation (Fig. 4I), while forced
SQLE expression facilitated the overgrowth of F-actin (Fig. 4J) in Huh7
and SMMC2771 cells, suggesting a pivotal role of SQLE in cytoskeletal
reorganization that facilitates tumor cell migration.

\selectlanguage{greek}\textbf{SQLE increases HCC cell invasiveness by activating theTGF-β/SMAD
signaling pathway}\selectlanguage{english}

To explore the underlying mechanism of SQLE in HCC, unbiased
transcriptome analysis was performed by RNA sequencing (RNA-seq) on
samples of Huh7 cells with or without SQLE silencing, and the data were
analyzed by the Majorbio Cloud Platform. As a result, a total of 31270
genes, including 30912 known genes and 358 yet-unidentified genes, were
detected. There were 143,463 expressed transcripts, including 130,298
known transcripts and 13,165 new transcripts. The differentially
expressed genes were analyzed by DESeq2 software. The results
demonstrated that there were a total of 800 differentially expressed
genes (NC \emph{vs} siSQLE), of which 294 were upregulated and 506 were
downregulated genes (Fig. 5A and Supplementary Table S3). The KEGG
enrichment analysis showed that TGF-\selectlanguage{greek}β \selectlanguage{english}signaling is the most relevant
biological pathway to SQLE's function in Huh7 cells (Fig. 5B). On the
other hand, the GO enrichment analysis revealed that SQLE is closely
related to cell contraction, movement, wound healing, and matrix
reorganization, all of which are typical characteristics of tumor cell
migration (Fig. 5C).

TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling is critical in HCC development(Li \emph{et al.} ,
2020). In principle, the TGF-\selectlanguage{greek}β \selectlanguage{english}superfamily comprises nearly 30 kinds of
structurally and functionally related proteins, such as BMPs, GDFs,
GDNFs, and activins(Massague \emph{et al.} , 2000). TGF-\selectlanguage{greek}β \selectlanguage{english}exerts its
biological activity through SMAD-dependent or SMAD-independent
pathways(Zhang, 2017). TGF-\selectlanguage{greek}β \selectlanguage{english}binds to and activates its receptors and
then activates downstream SMAD proteins through phosphorylation and
nuclear translocation(Li \emph{et al.} , 2020). To understand the
relationship between SQLE and TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling in HCC, we first
examined the TGF-\selectlanguage{greek}β\selectlanguage{english}1, TGF-\selectlanguage{greek}β\selectlanguage{english}2, and TGF-\selectlanguage{greek}β\selectlanguage{english}3 mRNA levels in Huh7 and SMMC7721
cells after transfection with siRNAs against SQLE. Among them, TGF-\selectlanguage{greek}β\selectlanguage{english}1
mRNA was significantly decreased by SQLE knockdown in cells (Fig. 5D).
Silencing of SQLE also decreased TGF-\selectlanguage{greek}β\selectlanguage{english}1 protein expression, and further,
the phosphorylation of SMAD2 and SMAD3 in both Huh7 and SMMC7721 cells
(Fig. 5E) and SQLE overexpression, however, had the opposite result
(Fig. 5F). Functional experiments revealed that TGF-\selectlanguage{greek}β-\selectlanguage{english}induced wound
healing was significantly retarded in cells after transfection with
siRNAs against SQLE (Fig. 5G). Similar results were also obtained in the
Transwell migration assay (Fig. 5H). TGF-\selectlanguage{greek}β\selectlanguage{english}1 is a well-established
regulator of the process of EMT(Moustakas\emph{et al.} , 2007). Here,
TGF-\selectlanguage{greek}β-\selectlanguage{english}induced EMT, characterized by mRNA increases in N-cadherin,
vimentin, snail, Zeb-1, and Zeb-2, a decrease in E-cadherin mRNA, and an
increase in TGF-\selectlanguage{greek}β, \selectlanguage{english}SMAD2 and SMAD3 phosphorylation, was largely
alleviated in cells after transfection with SQLE siRNAs (Fig. 5I-J).
SB-431542 is an inhibitor of TGF-\selectlanguage{greek}β \selectlanguage{english}receptor-mediated signaling.
SQLE-induced EMT (Fig. 5K) and increases in cell wound-healing activity
and migration were largely attenuated in the presence of SB-431542 (Fig.
5L-M). A previous study revealed that SQLE can promote HCC development
by stimulating DNMT3A expression and its activity, leading to activation
of the PTEN/PI3K/AKT/mTOR signaling cascade. We found that the
upregulation of DNMT3A and PTEN mRNA levels was largely suppressed by
SB-431542 (Fig. 5N); as such, SQLE also regulates PTEN and DNMT3A
expression through TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling.

\textbf{The protumorigenic activity of SQLE in tumor-bearing models of
hepatocellular carcinoma}

To further characterize the protumorigenic role of SQLE in vivo, we
constructed an orthotopic transplantation tumor model of HCC in mice
bearing intraperitoneal H22 tumors. Adenovirus-mediated SQLE shRNA
constructs were injected into mice via the tail vein. After experiments,
mouse livers were removed, and immunoblotting analysis revealed that
the\emph{in vivo} delivery of Ad-shSQLE led to a significant reduction
in SQLE protein expression in mouse livers (Fig. 6A). Under this
condition, the average H22 tumor size in mice challenged with shSQLE was
significantly smaller than that in control mice (Fig. 6B). In fact, the
liver weight of mice injected with shSQLE was also lighter than that of
control mice (Fig. 6C). Furthermore, SQLE silencing in mice correlated
with a reduction in serum AFP content (Fig. 6D). H\&E staining of liver
tissue further confirmed the reduced tumor lesions in mice challenged
with shSQLE (Fig. 6E). Notably, lung metastasis nodules were found in
either control or shSQLE-injected mice, but there were significantly
fewer nodules in SQLE-silenced mice than in control mice (Fig. 6F). The
decreased SQLE expression in tumor tissue by siRNA silencing also
correlated with a reduction in PCNA expression and the phosphorylation
of SMAD2 or SMAD3, as confirmed by IHC staining (Fig. 6G-H).

\textbf{STRAP is critically involved in the oncogenic effect of SQLE on
HCC development}

To understand why SQLE is able to promote HCC development, we noted that
among the downregulated genes in Huh7 cells after transfection with SQLE
siRNA, STRAP was one of the genes that was strongly inhibited by SQLE.
To verify the RNA-seq analysis, silencing of SQLE expression in SMMC7721
cells significantly decreased STRAP mRNA expression, but SQLE
overexpression increased it (Fig. 7A). A previous study indicated that
STRAP is an oncogene (Wang \emph{et al.} , 2019). Here, TCGA analysis
revealed that STRAP was highly expressed in HCC tumor tissue compared
with adjacent nontumor tissues (Fig. 7B), and further, a high expression
level of STRAP in HCC patients correlated with poor prognosis (Fig. 7C).
Notably, GEPIA2 analysis revealed that SQLE was positively correlated
with STRAP expression in HCC patients but not in nontumor tissue (Fig.
7D). Taken together, we speculated that STRAP could be an important gene
involved in the protumorigenic effect of SQLE on HCC.

To test the above speculation, STRAP expression in Huh7 and SMMC7721
cells was silenced by siRNAs (Fig. 7E). As a result, SQLE
overexpression-induced Huh7 and SMMC7721 cell growth was largely
attenuated in cells after STRAP siRNA transfection (Fig. 7F).
Furthermore, the enhanced F-actin expression (Fig. 7G), tumor cell
proliferation (Fig. 7H), migration (Fig. 7I), and wound-healing ability
(Fig. 7J) in SQLE-overexpressing cells were all largely attenuated by
STRAP silencing. Surprisingly, SQLE-induced SMAD2/3 phosphorylation and
TGF\selectlanguage{greek}β\selectlanguage{english}1 expression were almost completely suppressed in HCC cells after
transfection with STRAP siRNAs, suggesting that SQLE-activated
TGF\selectlanguage{greek}β\selectlanguage{english}1/SMAD signaling is critically dependent on STRAP (Fig. 7K).

\selectlanguage{greek}\textbf{SQLE promotes STRAP gene transcription through AP-2α}

\selectlanguage{english}To clarify why STRAP gene expression is regulated by SQLE, we first used
actinomycin D (Act D) to halt global gene transcription and then
observed whether SQLE affects the mRNA stability of STRAP. As a result,
the half-life of STRAP mRNA was not affected in SQLE-silenced cells
compared with control cells (Fig. 8A), suggesting that SQLE may increase
STRAP expression at the transcriptional level.

To confirm the effects of SQLE on STRAP gene transcription and the
potential cis-elements involved in this process, we constructed the
full-length human STRAP promoter (-1029 to +213) and its truncated
variants (-738 to +213, -498 to +213, -148 to +213, -28 to +213 bp). The
results revealed that SQLE was able to increase the promoter activity of
STRAP, and cis-elements between -148 and -28 are essential for the
activation effects of SQLE (Fig. 8B). Bioinformatics analysis (JASPAR
databases) indicated that SP1, c/EBP\selectlanguage{greek}β, \selectlanguage{english}and AP-2\selectlanguage{greek}α \selectlanguage{english}are putative
transcription factors that can bind with cis-elements within the -148 to
-28 region (Fig. 8C upper). To identify the potential transcription
factors, we continued to construct plasmids with mutations in SP1, c/EBP
\selectlanguage{greek}β, \selectlanguage{english}and AP-2\selectlanguage{greek}α \selectlanguage{english}potential binding sites (Fig. 8C below). The results of the
luciferase reporter assay showed that the effects of SQLE on STRAP
promoter transactivation were largely attenuated when the AP-2\selectlanguage{greek}α \selectlanguage{english}binding
site but not the SP1 or c/EBP \selectlanguage{greek}β \selectlanguage{english}binding site was mutated (Fig. 8D). To
further verify the results, SP1, c/EBP \selectlanguage{greek}β \selectlanguage{english}or AP-2\selectlanguage{greek}α \selectlanguage{english}expression was
silenced in SMMC7721 cells. As shown in Fig. 8E-F, SQLE-induced STRAP
promoter activation was significantly alleviated in cells with reduced
expression of AP-2\selectlanguage{greek}α \selectlanguage{english}but not SP1 or c/EBP \selectlanguage{greek}β. \selectlanguage{english}Accordingly, SQLE
overexpression-induced STRAP gene expression was also largely suppressed
in cells transfected with AP-2\selectlanguage{greek}α \selectlanguage{english}siRNA but not SP1 or c/EBP \selectlanguage{greek}β \selectlanguage{english}(Fig. 8G).
As such, AP-2\selectlanguage{greek}α \selectlanguage{english}is the key trans-acting factor involved in STRAP gene
expression by SQLE.

\textbf{The SQLE inhibitor NB-598 suppresses HCC growth both in vitro
and in vivo.}

In view of the important role of SQLE in HCC development, we tried to
examine whether SQLE could be a target for HCC therapy. NB-598, as a
terbinafine derivative obtained by modification of aromatic groups, is a
specific inhibitor of SQLE that can suppress triglyceride biosynthesis
(Hiyoshi \emph{et al.} , 2003) and effectively decrease serum
cholesterol and triacylglycerol levels (Horie \emph{et al.} , 1993). We
set out to determine whether NB-598 can be repurposed for the prevention
or treatment of HCC. Huh7 and SMMC7721 cells were treated with different
doses of NB-598 (0.005, 0.01, 0.05, 0.1, 0.5, 1, 2 \selectlanguage{greek}μ\selectlanguage{english}M), and the results
demonstrated that NB-598 significantly inhibited the viability of Huh7
and SMMC7721 cells, as examined by MTT (Fig. 9A) and colony formation
assays (Fig. 9B). Consistently, NB-598 reduced EdU incorporation in Huh7
and SMMC7721 cells (Fig. 9C) and dose-dependently triggered cell
apoptosis, as examined by Annexin V-FITC/PI staining assay (Fig. 9D).
The western blot assay indicated that NB-598 inhibited TGF-\selectlanguage{greek}β\selectlanguage{english}1 expression
and SMAD2/3 phosphorylation in a dose-dependent manner, in line with the
results obtained by SQLE knockdown (Fig. 9E). In addition to the\emph{in
vitro} results, the \emph{in vivo} experiments revealed that NB-598
significantly suppressed the tumor growth of subcutaneous H22 xenografts
(Fig. ). Furthermore, the H22 tumor weight in NB-598-treated mice was
significantly lighter than that in control mice (Fig. 9G-I). NB-598
treatment also decreased the serum content of AFP in tumor-bearing mice
(Fig. 9J). H\&E staining of tumor tissues further confirmed the
antitumorigenesis effect of NB-598 (Fig. 9K). Moreover, NB-598 inhibited
SQLE and PCNA protein expression in tumor tissue (Fig. 9L, M).

\textbf{Discussion}

In the present study, both bioinformatics and RNA sequencing analysis of
HCC samples revealed that SQLE is highly expressed in HCC and is
associated with poor prognosis. Mechanistically, SQLE promoted HCC
development by activating the TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling pathway, and STRAP is
an essential protein involved in this process. To potentiate the
activation of TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling, SQLE even increased STRAP
transcription by promoting AP-2\selectlanguage{greek}α \selectlanguage{english}binding with the promoter. A chemical
inhibitor of SQLE significantly suppressed HCC development both \emph{in
vitro} and \emph{in vivo} . All of these data suggest that SQLE is a
promising target for HCC therapy and drug development.

Increasing evidence reveals that TGF-\selectlanguage{greek}β \selectlanguage{english}signaling is a promising target
in cancer therapy (Colak \emph{et al.} , 2017). In the late stage of
cancer development, the activation of TGF-\selectlanguage{greek}β \selectlanguage{english}signaling promotes
tumorigenesis, metastasis, and chemoresistance (Hu \emph{et al.} ,
2018). Among TGF-\selectlanguage{greek}β \selectlanguage{english}isoforms, TGF-\selectlanguage{greek}β\selectlanguage{english}1 plays a crucial role in EMT and
fibrogenesis by activating SMAD2/3 phosphorylation (Chen\emph{et al.} ,
2018). In this study, we identified a novel role of SQLE in regulating
TGF-\selectlanguage{greek}β \selectlanguage{english}signaling in HCC. The evidence is as follows: SQLE promoted
N-cadherin, vimentin, snail, zeb1, and zeb2 expression but decreased
E-cadherin expression. SQLE overexpression increased cytoskeletal
rearrangement by enhancing F-actin expression. TGF-\selectlanguage{greek}β\selectlanguage{english}1 mRNA, but not
TGF-\selectlanguage{greek}β\selectlanguage{english}2 or TGF-\selectlanguage{greek}β\selectlanguage{english}3 mRNA expression, was upregulated by SQLE; as a result,
SQLE triggered SMAD2/3 phosphorylation. All of these events could be
reversed by SQLE knockdown in HCC. In parallel, SB431542, a selective
inhibitor of TGF-\selectlanguage{greek}β \selectlanguage{english}signaling, abrogated the promoting effect of SQLE on
EMT, cell migration, and cytoskeletal rearrangement. Thus, by activating
TGF-\selectlanguage{greek}β \selectlanguage{english}signaling, SQLE plays a key role in liver cancer metastasis. A
previous study indicated that SQLE has a pro-oncogenic role in
NAFLD-induced HCC by activating the PTEN/PI3K/AKT/mTOR signaling
cascade(Liu \emph{et al.} , 2018). More specifically, SQLE mediated
oxidative stress and then stimulated DNMT3A expression and activity,
decreasing PTEN via promoter methylation. In fact, the SMAD3-PTEN
signaling axis determines the cellular responses to TGF-\selectlanguage{greek}β \selectlanguage{english}signaling in a
previous study(Eritja\emph{et al.} , 2017), and DNMTs are regulated by
TGF-\selectlanguage{greek}β \selectlanguage{english}signaling. The stimulatory effects of TGF-\selectlanguage{greek}β \selectlanguage{english}on SOCS3 promoter
methylation were mediated by DNMT3A in a SMAD-dependent manner
(Dees\emph{et al.} , 2020). Consistent with these results, we found here
that the regulation of PTEN and DNMT3A by SQLE was abolished in the
presence of SB-431542. Taken together, in addition to serving as an
enzyme in cholesterol synthesis, SQLE emerges as an important molecule
involved in TGF-\selectlanguage{greek}β \selectlanguage{english}signaling that acts upstream of DNMT3A, PTEN, and PI3K
activation.

To understand the mechanism of TGF-\selectlanguage{greek}β \selectlanguage{english}signaling activation by SQLE, we
identified that STRAP is critically involved by RNA-seq analysis and
many other functional experiments. As a serine-threonine kinase
receptor-associated protein, STRAP has been reported as an oncogene in a
variety of cancers, including neuroblastoma(Bownes \emph{et al.} , 2021)
and pancreatic ductal adenocarcinoma(Hu\emph{et al.} , 2020). STRAP is
one of the WD40 superfamily proteins that contains seven WD40-repeat
domains. In general, proteins containing WD domains have regulatory
effects on signal transduction, gene transcription, RNA synthesis and
processing, programmed cell death, and vesicle transport (Datta \emph{et
al.} , 1998; Halder \emph{et al.} , 2006). A previous study revealed
that STRAP can activate the Wnt/\selectlanguage{greek}β-\selectlanguage{english}catenin signaling (Wang \emph{et al.}
, 2019) and MEK/ERK signaling (Halder \emph{et al.} , 2006) pathways to
stimulate cell growth. Notably, the regulatory effect of STRAP on TGF-\selectlanguage{greek}β
\selectlanguage{english}signaling is rather complicated, mainly because TGF-\selectlanguage{greek}β \selectlanguage{english}signaling has dual
roles in regulating cancer cell destiny (Syed, 2016). STRAP was found to
negatively regulate the TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD7 signaling pathway by stabilizing the
binding between the TGF-\selectlanguage{greek}β \selectlanguage{english}receptor and SMAD7 (Halder \emph{et al.} ,
2006); it can also prevent the binding between SMAD2/3 and the TGF-\selectlanguage{greek}β
\selectlanguage{english}receptor, thus inhibiting TGF-\selectlanguage{greek}β \selectlanguage{english}signaling(Datta \emph{et al.} , 1998).
However, another study also found that STRAP inhibited classic
TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD7 but promoted TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD3 signaling, and as a result, STRAP
overexpression alleviated TGF-\selectlanguage{greek}β-\selectlanguage{english}induced cell growth inhibition and
induced tumorigenicity in lung adenocarcinoma and colonic
adenocarcinoma(Halder \emph{et al.} , 2006). In our study, we found that
SQLE upregulated the mRNA expression of STRAP, and there was a positive
correlation between SQLE and STRAP in HCC. Further experiments
demonstrated that SQLE enhanced STRAP transcription through AP-2\selectlanguage{greek}α.
\selectlanguage{english}Meanwhile, the effect of SQLE on the TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling pathway was
dependent on STRAP. From the above results, we presume that in
SQLE-mediated activation of TGF-\selectlanguage{greek}β \selectlanguage{english}signaling and HCC development, STRAP
was actively upregulated by SQLE to probably influence the protein
interactions between SMADs and TGF-\selectlanguage{greek}β \selectlanguage{english}receptors, which favors SMAD2/3
phosphorylation. The specific mechanism remains to be further explored.

The SQLE amino acid sequence homology in mammals is approximately
83-93\%, and in fungi, it is 65-95\%; however, the sequence homology of
SQLE between fungi and mammals is only 30.2-35\%, which provides a
theoretical basis for the specificity and broad spectrum of antifungal
drugs(Lee \emph{et al.} , 2004; Motavaze \emph{et al.} , 2008). At
present, the main reported SQLE inhibitors are antifungal compounds,
including terbinafine and naphthalene, and lipid-lowering compounds,
including propionamide, natural compounds, and their
derivatives(Sanossian \emph{et al.} , 2008; Nowosielski \emph{et al.} ,
2011). Based on the chemical modification of terbinafine, a series of
compounds that exert inhibitory effects on mammalian SQLE were produced,
and the NB-598 compound was obtained by modification of the aromatic
group of terbinafine in 1990. NB-598 is a strong competitive inhibitor
of mammalian SQLE, effectively inhibiting rat and dog liver microsome
SQLE enzymes (Horie \emph{et al.} , 1990; Hidaka \emph{et al.} , 1991).
In the present study, NB-598 effectively inhibited the proliferation of
Huh7 and SMMC7721 cells, induced cell apoptosis by inhibiting the
TGF-\selectlanguage{greek}β/\selectlanguage{english}Smad signaling pathway, and suppressed the occurrence of HCC in
\emph{vivo} . These results suggest that NB-598 has the potential to be
repurposed for HCC treatment.

\textbf{Data Availability}

All data supporting the paper are presented in the paper and/or the
Supplementary Materials. The original datasets are also available from
the corresponding author upon request.

\textbf{Acknowledgments}

This project was sponsored by the Natural Science Fund of China
(81673462, 81873002, 82073916, and 91540119). Key development project of
Jiangsu Province (BE2017712). A Project Funded by the Priority Academic
Program Development of Jiangsu Higher Education Institutions (PAPD). The
Open Projects of the Discipline of Chinese Medicine of Nanjing
University of Chinese Medicine Supported by the Subject of Academic
priority discipline of Jiangsu Higher Education Institutions
(ZYX03KF036). Natural Science Foundation of JiangSu (BK20210055). Anhui
Provincial Natural Science Foundation (2008085J35).

\textbf{Author contributions}

Z.R.Z., J.C., F.Z.Y. and W.Y. designed the overall study. W.W., H.J. and
X.J.J. analyzed the data, Z.R. Z and W.Y. wrote the paper, W.W., H.J.
and Y.Z. C conducted the clinical experiment, Z.R. Z conducted the
experiments. All authors have read and approved the article.

\textbf{Conflicts of interest}

The authors declare that they have no conflicts of interest.

\textbf{Declaration of transparency and scientific rigour}

This Declaration acknowledges that this paper adheres to the principles
for transparent reporting and scientific rigour of preclinical research
as stated in the BJP guidelines for Design and Analysis, Immunoblotting
and Immunochemistry, and Animal Experimentation, and as recommended by
funding agencies, publishers and other organizations engaged with
supporting research.

\textbf{Figure Legends}

\textbf{Fig 1} . \textbf{RNA-Seq analysis of differentially expressed
genes (DEGs) inclinical HCC and paracancerous tissues.} (A) Heatmap
showing 562 genes differentially expressed in clinical HCC and
paracancerous tissues (T: clinical HCC tissue; NT: clinical HCC adjacent
tissue). (B-D) GO, KEGG and GSEA enrichment analysis of DEGs. (E)
Volcano plot of DEGs in NT vs. T. Upregulated genes are shown in red
circles, downregulated genes are shown in green circles, and genes with
no significance are shown in gray circles. (F) SQLE expression in
multiple cancers in the TIMER database
(\emph{https://cistrome.shinyapps.io/timer/}). (G) The mRNA level of
SQLE expression among adjacent nontumor and HCC tissues in the GEPIA2
database (\emph{http://gepia2.cancer-pku.cn/\#index}). (H) The mRNA
level of SQLE expression among adjacent nontumor and HCC tissues in the
Oncomine database.\textsuperscript{*}\emph{P}
\textless{}0.05,\textsuperscript{***}\emph{P} \textless{}0.001.

\textbf{Fig 2} .\textbf{SQLEis upregulated in HCC tumor tissues with
unfavorable prognosis.}(A) Western blotting analyses of the SQLE and
PCNA protein expression levels in six pairs of clinical HCC and adjacent
nontumor tissue samples; GAPDH was used as the internal loading control.
T, tumor tissue; NT, nontumor tissue. (B) RT--PCR analyses of SQLE in
six pairs of clinical HCC and adjacent nontumor tissue samples. (C)
Representative images of IHC staining for SQLE in clinical HCC and
adjacent nontumor tissue samples. Magnification: 100\selectlanguage{ngerman}× and 50×. (D)
Representative images of IHC staining for SQLE in a clinical sample
tissue microarray. Magnification: 200× and 20×. (E) The Kaplan--Meier
plot for the survival of HCC patients according to SQLE expression
levels (\emph{http://kmplot.com/analysis/}).

\textbf{Fig 3} . \textbf{SQLE expression is necessary for the survival
of HCC cells.} (A, B) The protein and mRNA levels of SQLE expression in
several HCC cell lines detected by western blot and RT--PCR. (C) Western
blot analysis of SQLE expression in Huh7 and SMMC7721 cells transfected
with siRNAs against SQLE (siSQLE). (D) MTT assay detected the viability
of HCC cells with or without SQLE knockdown. (E) The effect of SQLE on
clone formation in Huh7 and SMMC7721 cells. (F) The cells transfected
with siNC or siSQLE were analyzed using an Annexin V-FITC/PI assay. (G)
Representative images of the EdU experiment of lower SQLE expression
levels. Scale bars: 200 \selectlanguage{greek}μ\selectlanguage{english}m. (H) Western blot analysis confirming the
effect of HA-SQLE on overexpressing SQLE. (I) The cell viability in
cells with or without SQLE overexpression was detected by MTT assay. (J)
The effect of SQLE overexpression on clone formation. (K) Representative
images of the EdU experiment of higher SQLE expression levels and
coincident with that of FCM. Scale bars: 200 \selectlanguage{greek}μ\selectlanguage{english}m.

\textbf{Fig 4} .\textbf{SQLE is positively correlated with the migration
of HCC cells.}(A-D) Wound healing assays and migration assays were
conducted to determine the effect of SQLE on the migratory abilities of
Huh7 and SMMC7721 cells. (E-H) Western blot and qRT--PCR showed the
changes in EMT markers after SQLE knockdown or overexpression. (I-J)
Huh7 and SMMC7721 cells were transfected with siNC and siSQLE or
pcDNA3.1-HA and HA-SQLE plasmids, followed by immunofluorescence assay
to observe F-actin (green). Scale bars: 200 \selectlanguage{greek}μ\selectlanguage{english}m.

\textbf{Fig 5} . \selectlanguage{greek}\textbf{SQLE promotes metastasis by activating the
TGF-β/SMAD signaling pathway in HCC.}\selectlanguage{english} (A) Heatmap shows differentially
expressed genes in Huh7 cells with or without SQLE knockdown. (B, C)
KEGG and GO enrichment analysis of DEGs in Huh7 cells (NC \emph{vs} .
siSQLE\#2). (D) qRT--PCR analysis of TGF\selectlanguage{greek}β\selectlanguage{english}1, TGF\selectlanguage{greek}β\selectlanguage{english}2, and TGF\selectlanguage{greek}β\selectlanguage{english}3 in
SQLE-knockdown Huh7 and SMMC7721 cells. GAPDH was used as a control, and
data are expressed as the mean \selectlanguage{ngerman}± SEM (n = 3). (E, F) The protein
expression of SMAD2/3 and p-SMAD2/3 in Huh7 and SMMC7721 cells
transfected with siSQLE\#2 or HA-SQLE plasmid was determined by western
blot analysis. Tubulin was used as the loading control. (G, H) Huh7 and
SMMC7721 cells were treated with siSQLE\#2 for 36 h in the presence of
TGF-\selectlanguage{greek}β\selectlanguage{english}1 (5 ng/ml) for 12 h. Wound healing assays and migration assays
were used to evaluate cell metastasis viability. (I) qRT--PCR showed the
changes in EMT markers after treatment with TGF-\selectlanguage{greek}β\selectlanguage{english}1 and
siSQLE\#2.\textsuperscript{**}\emph{P} \textless{} 0.01 compared with
the control group; \textsuperscript{\#\#}\emph{P} \textless{} 0.01
compared with the TGF-\selectlanguage{greek}β\selectlanguage{english}1-treated group. (J) SQLE-siRNA reversed the
upregulation effect of TGF-\selectlanguage{greek}β\selectlanguage{english}1 on TGF/SMAD signaling pathway proteins.
Tubulin was used as the loading control. (K-M) Huh7 and SMMC7721 cells
were treated with HA-SQLE for 36 h with or without SB-431542 (5 \selectlanguage{greek}μ\selectlanguage{english}M) for
24 h. qRT--PCR (K) showed the changes in EMT markers, and wound healing
assays (L) and migration assays (M) evaluated cell metastasis viability.
(N) qRT--PCR showed the changes in the PTEN and DNMT3A genes after
SB-431542 treatment. \textsuperscript{*}\emph{P}\textless{} 0.05,
\textsuperscript{**}\emph{P} \textless{} 0.01 compared with HA-SQLE
group.

\textbf{Fig 6} . \textbf{Knockdown of SQLE inhibits tumor formation in
C57BL/6 mice.} (A) Immunoblotting of SQLE expression in liver tissues
from shNC and shSQLE mice. (B) Gross morphological images of livers from
orthotopic transplantation tumor models in C57BL/6 mice (n=6). The red
circles are nodules of tumor. (C) Liver weights of shNC and shSQLE
groups. (D) The AFP content in the orbital blood of mice injected with
shNC or shSQLE adenovirus. (E-F) Representative images of H\&E staining
of liver and lung tissues in the shNC and shSQLE groups. Scale bar, 2000
\selectlanguage{greek}μ\selectlanguage{english}m and 200 \selectlanguage{greek}μ\selectlanguage{english}m. (G-H) Representative IHC staining images of SQLE, PCNA,
SMAD2 and SMAD3 expression in liver tissues in the shNC and shSQLE
groups. Scale bar, 2000 \selectlanguage{greek}μ\selectlanguage{english}m and 200 \selectlanguage{greek}μ\selectlanguage{english}m. *\emph{P} \textless{}0.05,
***\emph{P} \textless{}0.001.

\textbf{Fig 7} . \textbf{STRAP is critically involved in the oncogenic
effect of SQLE on HCC development.} (A) SMMC7721 cells were transfected
with siSQLE\#2 or HA-SQLE, and the mRNA levels of STRAP were determined
by RT--PCR. (B) STRAP expression levels in HCC (n=369) and nontumor
tissues (n=160) in the GEPIA2 database. (C) The overall survival curves
of HCC patients with high (n=111) and low (n=253) expression of STRAP
using Kaplan--Meier Plotter. (D) Correlations among SQLE and STRAP in
HCC tumor and nontumor tissues were denoted with Kendall, Spearman and
Pearson's correlation coefficients. (E) Western blot verified the
interference effect of siSTRAP. (F) The viability of Huh7 and SMMC7721
cells overexpressing SQLE with or without transferred siSTRAP was
detected by MTT assay (***\emph{P} \textless{}0.001). (G)
Immunofluorescence assay displayed F-actin (green) formation in HCC
cells cotransfected with HA-SQLE and siSTRAP. Scale bars: 200 \selectlanguage{greek}μ\selectlanguage{english}m. (H)
The effect of siSTRAP overexpression on clone formation in Huh7 and
SMMC7721 cells. (I-J) Migration assays and wound-healing assays were
performed to observe the migratory abilities of HCC cells cotransfected
with HA-SQLE and siSTRAP. (K) Knockdown of STRAP reversed the
upregulation effect of HA-SQLE on TGF-\selectlanguage{greek}β/\selectlanguage{english}SMAD signaling pathway proteins.
Tubulin was used as the loading control.

\textbf{Fig 8} . \selectlanguage{greek}\textbf{SQLE promotes STRAP gene transcription through
AP-2α.} \selectlanguage{english}(A) SQLE did not affect the STRAP mRNA half-life in SMMC7721
cells. SMMC7721 cells were transfected with siNC or siSQLE\#2, incubated
for 24 hours and treated with ActD (4 \selectlanguage{greek}μ\selectlanguage{english}g/ml) for 2, 4 and 6 h. Gene
expressions were measured with RT--PCR. (B) Serial deletion mutant
analysis of the STRAP promoter. SMMC7721 cells were transfected with the
indicated plasmids (0.8 \selectlanguage{greek}μ\selectlanguage{english}g) and pRL plasmid (0.08 \selectlanguage{greek}μ\selectlanguage{english}g) and incubated with
or without siSQLE\#2/HA-SQLE for 36 h. The transcriptional activity of
truncated STRAP was measured according to the protocols. (C) The
transcription factor binding sites of the human STRAP gene were
predicted by PROMO HOME PAGE (upper), and variants of the STRAP promoter
with point mutations in the indicated sites were constructed (below).
(D) AP-2\selectlanguage{greek}α \selectlanguage{english}binding sites played an important role during the
transcriptional process. STRAP promoter constructs with mutations in
specific SP1, C/EBP beta and AP-2\selectlanguage{greek}α \selectlanguage{english}sites were transfected into SMMC7721
cells incubated with siSQLE\#2/HA-SQLE or vector, and luciferase
activity was detected according to the protocols. (E) Effects of SP1,
C/EBP beta and AP-2\selectlanguage{greek}α \selectlanguage{english}siRNA on STRAP luciferase reporting activity in
SMMC7721 cells. (F) The interference efficiency of SP1, C/EBP beta and
AP-2\selectlanguage{greek}α \selectlanguage{english}siRNA in SMMC7721 cells. (G) Effects of SP1, C/EBP beta and AP-2\selectlanguage{greek}α
\selectlanguage{english}siRNA on STRAP mRNA expression in SMMC7721 cells incubated with HA-SQLE.
Data are shown as means \selectlanguage{ngerman}± SEM. *\emph{P}\textless{} 0.05, **\emph{P}
\textless{} 0.01, ***\emph{P} \textless{} 0.001, ns indicates
nonsignificant.

\textbf{Fig 9} . The \textbf{SQLE inhibitor NB-598 suppresses HCC growth
in vitro and in vivo.} (A, B) NB-598 treatment inhibited cell viability
and colony formation in Huh7 and SMMC7721 cells. (C) Representative
images of the EdU experiment of NB-598 treatment in HCC cells. Scale
bars: 200 \selectlanguage{greek}μ\selectlanguage{english}m. (D) The Annexin V-FITC/PI assay detected that NB-598
induced apoptosis in Huh7 and SMMC7721 cells. (E) Western blotting
showed the suppression efficiency of the TGF/SMAD signaling pathway
after treatment with NB-598. (F) Representative images of tumor size in
C57BL/6 mice injected subcutaneously with H22 cells treated with either
CMC or NB-598 (n =5, for each experimental group). (G-I) The mouse
weight, tumor growth curves and weight of xenografts in the control and
NB-598 groups are shown. The data shown are the mean \selectlanguage{ngerman}± SEM. (J) The
content of AFP in the orbital blood of mice treated with CMC or NB-598.
The data shown are the mean ± SEM. (K) H\&E staining of tumor tissues in
the control and NB-598 groups. Scale bar, 2000 \selectlanguage{greek}μ\selectlanguage{english}m and 200 \selectlanguage{greek}μ\selectlanguage{english}m. (L, M)
Representative IHC staining of SQLE and PCNA expression in the control
and NB-598 groups is shown. Scale bar, 2000 \selectlanguage{greek}μ\selectlanguage{english}m and 200 \selectlanguage{greek}μ\selectlanguage{english}m. *\emph{P}
\textless{}0.05, **\emph{P} \textless{}0.01, ***\emph{P}
\textless{}0.001.

\textbf{References}

Bownes LV, Williams AP, Marayati R, Quinn CH, Hutchins SC, Stewart
JE\emph{, et al.} (2021). Serine-Threonine Kinase Receptor-Associated
Protein (STRAP) Knockout Decreases the Malignant Phenotype in
Neuroblastoma Cell Lines.\emph{Cancers} \textbf{13} (13).

Brown DN, Caffa I, Cirmena G, Piras D, Garuti A, Gallo M\emph{, et al.}
(2016). Squalene epoxidase is a bona fide oncogene by amplification with
clinical relevance in breast cancer.\emph{Scientific reports}
\textbf{6:} 19435.

Chen J, Ding C, Chen Y, Hu W, Lu Y, Wu W\emph{, et al.} (2020). ACSL4
promotes hepatocellular carcinoma progression via c-Myc stability
mediated by ERK/FBW7/c-Myc axis.\emph{Oncogenesis} \textbf{9}
(4)\textbf{:} 42.

Chen L, Yang T, Lu DW, Zhao H, Feng YL, Chen H\emph{, et al.} (2018).
Central role of dysregulation of TGF-beta/Smad in CKD progression and
potential targets of its treatment.\emph{Biomedicine \& pharmacotherapy
= Biomedecine \& pharmacotherapie}\textbf{101:} 670-681.

Chen Y, Tian Z (2019). HBV-Induced Immune Imbalance in the Development
of HCC. \emph{Frontiers in immunology} \textbf{10:} 2048.

Colak S, Ten Dijke P (2017). Targeting TGF-beta Signaling in Cancer.
\emph{Trends in cancer}\textbf{3} (1)\textbf{:} 56-71.

Counihan JL, Grossman EA, Nomura DK (2018). Cancer Metabolism: Current
Understanding and Therapies.\emph{Chemical reviews} \textbf{118}
(14)\textbf{:} 6893-6923.

Datta PK, Chytil A, Gorska AE, Moses HL (1998). Identification of STRAP,
a novel WD domain protein in transforming growth factor-beta signaling.
\emph{The Journal of biological chemistry} \textbf{273} (52)\textbf{:}
34671-34674.

Dees C, Potter S, Zhang Y, Bergmann C, Zhou X, Luber M\emph{, et al.}
(2020). TGF-beta-induced epigenetic deregulation of SOCS3 facilitates
STAT3 signaling to promote fibrosis.\emph{The Journal of clinical
investigation} \textbf{130} (5)\textbf{:}2347-2363.

Eritja N, Felip I, Dosil MA, Vigezzi L, Mirantes C, Yeramian A\emph{, et
al.} (2017). A Smad3-PTEN regulatory loop controls proliferation and
apoptotic responses to TGF-beta in mouse endometrium. \emph{Cell death
and differentiation}\textbf{24} (8)\textbf{:} 1443-1458.

Erkekoglu P, Oral D, Chao MW, Kocer-Gumusel B (2017). Hepatocellular
Carcinoma and Possible Chemical and Biological Causes: A Review.
\emph{Journal of environmental pathology, toxicology and oncology :
official organ of the International Society for Environmental Toxicology
and Cancer}\textbf{36} (2)\textbf{:} 171-190.

Feldman AT, Wolfe D (2014). Tissue processing and hematoxylin and eosin
staining. \emph{Methods in molecular biology} \textbf{1180:} 31-43.

Ge H, Zhao Y, Shi X, Tan Z, Chi X, He M\emph{, et al.} (2019). Squalene
epoxidase promotes the proliferation and metastasis of lung squamous
cell carcinoma cells though extracellular signal-regulated kinase
signaling. \emph{Thoracic cancer}\textbf{10} (3)\textbf{:} 428-436.

Gill S, Stevenson J, Kristiana I, Brown AJ (2011). Cholesterol-dependent
degradation of squalene monooxygenase, a control point in cholesterol
synthesis beyond HMG-CoA reductase. \emph{Cell metabolism} \textbf{13}
(3)\textbf{:} 260-273.

Halder SK, Anumanthan G, Maddula R, Mann J, Chytil A, Gonzalez AL\emph{,
et al.} (2006). Oncogenic function of a novel WD-domain protein, STRAP,
in human carcinogenesis.\emph{Cancer research} \textbf{66}
(12)\textbf{:} 6156-6166.

Hidaka Y, Hotta H, Nagata Y, Iwasawa Y, Horie M, Kamei T (1991). Effect
of a novel squalene epoxidase inhibitor, NB-598, on the regulation of
cholesterol metabolism in Hep G2 cells. \emph{The Journal of biological
chemistry}\textbf{266} (20)\textbf{:} 13171-13177.

Hiyoshi H, Yanagimachi M, Ito M, Yasuda N, Okada T, Ikuta H\emph{, et
al.} (2003). Squalene synthase inhibitors suppress triglyceride
biosynthesis through the farnesol pathway in rat hepatocytes.
\emph{Journal of lipid research}\textbf{44} (1)\textbf{:} 128-135.

Horie M, Hayashi M, Satoh T, Hotta H, Nagata Y, Ishida F\emph{, et al.}
(1993). An inhibitor of squalene epoxidase, NB-598, suppresses the
secretion of cholesterol and triacylglycerol and simultaneously reduces
apolipoprotein B in HepG2 cells. \emph{Biochimica et biophysica acta}
\textbf{1168} (1)\textbf{:}45-51.

Horie M, Tsuchiya Y, Hayashi M, Iida Y, Iwasawa Y, Nagata Y\emph{, et
al.} (1990). NB-598: a potent competitive inhibitor of squalene
epoxidase. \emph{The Journal of biological chemistry} \textbf{265}
(30)\textbf{:} 18075-18078.

Hu HH, Chen DQ, Wang YN, Feng YL, Cao G, Vaziri ND\emph{, et al.}
(2018). New insights into TGF-beta/Smad signaling in tissue fibrosis.
\emph{Chemico-biological interactions}\textbf{292:} 76-83.

Hu S, Chen X, Xu X, Zheng C, Huang W, Zhou Y\emph{, et al.} (2020).
STRAP as a New Therapeutic Target for Poor Prognosis of Pancreatic
Ductal Adenocarcinoma Patients Mainly Caused by TP53 Mutation.
\emph{Frontiers in oncology} \textbf{10:} 594224.

Jun SY, Brown AJ, Chua NK, Yoon JY, Lee JJ, Yang JO\emph{, et al.}
(2021). Reduction of Squalene Epoxidase by Cholesterol Accumulation
Accelerates Colorectal Cancer Progression and Metastasis.
\emph{Gastroenterology} \textbf{160} (4)\textbf{:}1194-1207 e1128.

Kuzu OF, Noory MA, Robertson GP (2016). The Role of Cholesterol in
Cancer. \emph{Cancer research}\textbf{76} (8)\textbf{:} 2063-2070.

Lee HK, Zheng YF, Xiao XY, Bai M, Sakakibara J, Ono T\emph{, et al.}
(2004). Photoaffinity labeling identifies the substrate-binding site of
mammalian squalene epoxidase.\emph{Biochemical and biophysical research
communications}\textbf{315} (1)\textbf{:} 1-9.

Li B, Chen Y, Wang F, Guo J, Fu W, Li M\emph{, et al.} (2020). Bmi1
drives hepatocarcinogenesis by repressing the TGFbeta2/SMAD signalling
axis. \emph{Oncogene}\textbf{39} (5)\textbf{:} 1063-1079.

Liu D, Wong CC, Fu L, Chen H, Zhao L, Li C\emph{, et al.} (2018).
Squalene epoxidase drives NAFLD-induced hepatocellular carcinoma and is
a pharmaceutical target. \emph{Science translational medicine}
\textbf{10} (437).

Luo J, Deng ZL, Luo X, Tang N, Song WX, Chen J\emph{, et al.} (2007). A
protocol for rapid generation of recombinant adenoviruses using the
AdEasy system. \emph{Nature protocols} \textbf{2} (5)\textbf{:}
1236-1247.

Massague J, Wotton D (2000). Transcriptional control by the
TGF-beta/Smad signaling system. \emph{The EMBO journal} \textbf{19}
(8)\textbf{:} 1745-1754.

Motavaze K, Namvar Z, Emami M, Noorbakhsh F, Rezaie S (2008). Molecular
characterization of a Squalene epoxidase gene in dermatophyte pathogen
Trichophyton tonsurans.\emph{Iranian biomedical journal} \textbf{12}
(1)\textbf{:} 55-58.

Moustakas A, Heldin CH (2007). Signaling networks guiding
epithelial-mesenchymal transitions during embryogenesis and cancer
progression. \emph{Cancer science}\textbf{98} (10)\textbf{:} 1512-1520.

Nickel J, Ten Dijke P, Mueller TD (2018). TGF-beta family co-receptor
function and signaling. \emph{Acta biochimica et biophysica Sinica}
\textbf{50} (1)\textbf{:} 12-36.

Nowosielski M, Hoffmann M, Wyrwicz LS, Stepniak P, Plewczynski DM,
Lazniewski M\emph{, et al.} (2011). Detailed mechanism of squalene
epoxidase inhibition by terbinafine.\emph{Journal of chemical
information and modeling}\textbf{51} (2)\textbf{:} 455-462.

Sanossian N, Ovbiagele B (2008). Drug insight: translating evidence on
statin therapy into clinical benefits.\emph{Nature clinical practice.
Neurology} \textbf{4} (1)\textbf{:}43-49.

Sharpe LJ, Brown AJ (2013). Controlling cholesterol synthesis beyond
3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR). \emph{The Journal of
biological chemistry}\textbf{288} (26)\textbf{:} 18707-18715.

Stopsack KH, Gerke TA, Sinnott JA, Penney KL, Tyekucheva S, Sesso
HD\emph{, et al.} (2016). Cholesterol Metabolism and Prostate Cancer
Lethality. \emph{Cancer research}\textbf{76} (16)\textbf{:} 4785-4790.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal
A\emph{, et al.} (2021). Global cancer statistics 2020: GLOBOCAN
estimates of incidence and mortality worldwide for 36 cancers in 185
countries. \emph{CA: a cancer journal for clinicians} .

Syed V (2016). TGF-beta Signaling in Cancer. \emph{Journal of cellular
biochemistry}\textbf{117} (6)\textbf{:} 1279-1287.

Wang W, Li S, Liu P, Sideras K, van de Werken HJG, van der Heide
M\emph{, et al.} (2019). Oncogenic STRAP Supports Hepatocellular
Carcinoma Growth by Enhancing Wnt/beta-Catenin Signaling.
\emph{Molecular cancer research : MCR}\textbf{17} (2)\textbf{:} 521-531.

Xu H, Zhou S, Tang Q, Xia H, Bi F (2020). Cholesterol metabolism: New
functions and therapeutic approaches in cancer. \emph{Biochimica et
biophysica acta. Reviews on cancer}\textbf{1874} (1)\textbf{:} 188394.

Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A, Roberts LR (2019a). A
global view of hepatocellular carcinoma: trends, risk, prevention and
management. \emph{Nature reviews. Gastroenterology \& hepatology}
\textbf{16} (10)\textbf{:}589-604.

Yang K, Chen Y, Zhou J, Ma L, Shan Y, Cheng X\emph{, et al.} (2019b).
Ursolic acid promotes apoptosis and mediates transcriptional suppression
of CT45A2 gene expression in non-small-cell lung carcinoma harbouring
EGFR T790M mutations.\emph{British journal of pharmacology} \textbf{176}
(24)\textbf{:}4609-4624.

Zhang C, Zhang H, Zhang M, Lin C, Wang H, Yao J\emph{, et al.} (2019).
OSBPL2 deficiency upregulate SQLE expression increasing intracellular
cholesterol and cholesteryl ester by AMPK/SP1 and SREBF2 signalling
pathway. \emph{Experimental cell research} \textbf{383} (2)\textbf{:}
111512.

Zhang Q, Zhang Y, Sun S, Wang K, Qian J, Cui Z\emph{, et al.} (2021).
ACOX2 is a prognostic marker and impedes the progression of
hepatocellular carcinoma via PPARalpha pathway.\emph{Cell death \&
disease} \textbf{12} (1)\textbf{:} 15.

Zhang YE (2017). Non-Smad Signaling Pathways of the TGF-beta Family.
\emph{Cold Spring Harbor perspectives in biology} \textbf{9} (2).\selectlanguage{english}
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