Abstract

   Speckle type BTB/POZ protein (SPOP) may have cancer promoting or
   inhibiting effects. At present, the role of SPOP in hepatocellular
   carcinoma (HCC) has rarely been studied. In this study, to investigate
   the effects of SPOP in HCC and elucidate the underlying molecular
   mechanisms of its relationship with genes, differentially expressed
   genes (DEGs) were classified through RNA sequencing. The gene ontology
   analysis and Kyoto Encyclopedia of Genes and Genomes functional pathway
   analysis were used to further predict the function of DEGs after the
   overexpression of SPOP. The biological function of SPOP-regulated
   alternative splicing events in cells is comprehensively assessed. The
   Cancer Genome Atlas database and Gene Expression Omnibus dataset were
   performed to evaluate the correlation between SPOP and HCC progression.
   Due to SPOP overexpression, 56 DEGs in the HCC related pathway were
   further identified. The results showed that SPOP overexpression
   facilitated the cell proliferation and changed the gene expression
   profiles of human normal hepatocytes. SPOP-regulated alternative
   splicing events were involved in pathways associated with cellular
   processes, metabolism, environmental information procession, organismal
   systems, and so on. In conclusion, SPOP may potentially exhibit
   tumor-promoting effects, necessitating further investigations to unveil
   its molecular mechanisms comprehensively.

   Keywords: speckle type BTB/POZ protein, gene expression profiles,
   alternative splicing events, human hepatocytes, hepatocellular
   carcinoma

1. Introduction

   Hepatocellular carcinoma (HCC) stands as the most prevalent form of
   primary liver cancer and ranks as the fourth leading cause of
   cancer-related deaths globally [[41]1]. Up to now, the exact molecular
   mechanism in the progression of HCC is not completely clear. Clinical
   data and laboratory research results show that external environmental
   factors such as virus infection, alcoholism, and nutritional disorders
   may associate with the occurrence, development, deterioration, and
   metastasis of HCC [[42]2–[43]4]. At the same time, the genetic
   background of the individual also plays an important role during HCC
   carcinogenesis [[44]5].

   The occurrence of cancer may be related to the abnormal function of
   signal transduction pathways during cell division and proliferation
   [[45]6]. Abnormal signal transduction may lead to abnormal gene
   regulation, leading to uncontrolled cell proliferation [[46]7,[47]8].
   The ubiquitination level of various signal factors in cellular signal
   transduction pathways and networks is closely related to their content
   and activity. Ubiquitination constitutes a significant
   post-translational modification of eukaryotic proteins, enabling an
   important pathway for the non-lysosomal degradation of proteins
   [[48]9,[49]10]. The dynamic balance of protein ubiquitination and
   deubiquitination plays an important role in maintaining the stability
   of individual genomes and regulating the process of tumorigenesis
   [[50]11].

   Speckle type BTB/POZ protein (SPOP) derives its name primarily from its
   nuclear distribution and the presence of a POZ domain [[51]12]. The
   SPOP gene is located at human 17q21.33, with a coding region of
   1,125 bp, encoding 374 amino acids, with an approximate molecular
   weight of 42 kDa [[52]13]. The protein of SPOP contains an N-terminal
   MATH region, a BTB/POZ region, a 3-box region, and a C-terminal NLS
   region [[53]14]. Notably, the MATH region predominantly governs
   substrate recognition and binding, while the POZ region chiefly
   interacts with the E3 ligase complex [[54]15]. Being an aptamer of the
   E3 ubiquitin ligase complex, SPOP is a very crucial element in the
   ubiquitination of various substrate proteins [[55]16]. In various
   cancer cell types and under differing expression conditions SPOP may
   play different roles and mechanisms. For example, SPOP can inhibit the
   growth rate of cancer cells in lung cancer, colorectal cancer, prostate
   cancer, and so on. However, SPOP might also play a role in promoting
   cancer. It was reported that SPOP could promote the
   epithelial–mesenchymal transition of kidney, epithelial–mesenchymal
   transition in renal cancer epithelial cells, and promote the
   development of renal cancer [[56]17]. The role of SPOP in HCC is rarely
   studied, only Huang’s team reported that SPOP can inhibit the
   proliferation of HCC cells and suppress the metastasis and
   mesenchymalization of HCC cells by downregulating the mRNA
   transcription level of Zeb2 [[57]18].

   SPOP exhibits a dual role, both as a tumor promoter and a tumor
   suppressor, although the research about its specific mechanism is still
   limited. As ongoing research advances the mechanism of SPOP function in
   different tumors might become clearer. The main purpose of this study
   was to investigate the downstream signaling pathways and the
   corresponding molecular mechanisms of SPOP in HCC.

2. Materials and methods

2.1. Cell culture and lentivirus transfection

   Human normal hepatocytes WRL68 (ATCC, Manassas, USA) were cultured in
   Dulbecco’s Modified Eagle Medium (cat. no. PM150210, Procell, Wuhan,
   China) with 10% Gibco™ fetal bovine serum (cat. no. 10099141C,
   Invitrogen, AUS) and 1% penicillin–streptomycin (10,000 U/mL) (cat. no.
   15140122, Invitrogen, USA), at 37°C in a 5% CO[2] humidified
   atmosphere. To establish the SPOP overexpression model, lentivirus
   containing the SPOP gene was constructed and packaged by GeneCopoeia
   (Guangzhou, China). SPOP-overexpressing lentivirus was transfected into
   WRL68 cells (the SPOP group), resulting in the formation of the SPOP
   group. In parallel, an empty lentivirus was transfected into WRL68
   cells to serve as the control group (referred to as the vec group). The
   transfection flow was performed according to the reagent manufacturer’s
   instructions using the Lipofectamine™ 2000 Transfection Reagent (cat.
   no. 11668027, Invitrogen, USA). The cells were selected with 1.5 μg/mL
   puromycin for 2 weeks.

2.2. Assessment of SPOP gene overexpression

   Total RNA was extracted from the SPOP group and the vec group cells,
   respectively, using the RNA isolater Total RNA Extraction Reagent (cat.
   no. R401-01, Vazyme, China). Subsequently, the cDNA was synthesized
   according to the reverse transcription instruction of Hiscript III
   Reverse Transcriptase (cat. no. R302-01, Vazyme, China). The real-time
   fluorescent quantitative PCR (qRT-PCR) was conducted using Taq Pro
   Universal SYBR qPCR Master Mix (cat. no. Q712-02, Vazyme, China) in
   LightCycler^® 96SW1.1 real-time PCR system (Roche).
   Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was selected as the
   mRNA internal reference to access the efficiency of the SPOP
   overexpression. The primer sequences are listed in Table 1. The
   expression levels of SPOP in different groups were normalized to GAPDH
   mRNA levels using the 2^−ΔΔCT method.

2.3. Cell counting kit 8 (CCK8) assay

   Cell proliferation was tested using CCK8 (cat. no. C0038, Beyotime
   Biotechnology, China). Cells from different groups were seeded at a
   density of 1 × 10^3 cells in 100 μL culture medium per 96-well plates
   for cell culture for 24, 48, 72, and 96 h. Then, CCK8 reagent was added
   with 10 μL to each well. After incubation at 37°C for 2 h, the
   absorbance at 450 nm (OD) of each well was measured.

2.4. Colony formation assay

   During colony formation assay, cells were seeded at a density of 1 ×
   10^3 cells/well into six-well plates, and cultured for 2 weeks. Two
   weeks later, colonies were fixed in 4% paraformaldehyde for 10 min,
   stained with 0.5% crystal violet for 10 min, and gently rinsed three
   times with PBS. A spot with more than 50 cells was counted as one
   clone.

2.5. RNA sequencing

   The RNA samples of the SPOP group and the vec group cells were
   sequenced with three replicates per group at Novogene Co., Ltd
   (Beijing, China). RNA was extracted from cells using TRIzol reagent,
   followed by strict quality control of RNA samples using RNA Nano 6000
   Assay Kit of the Bioanalyzer 2100 system. For library preparation, the
   NEBNext^® Ultra™ Directional RNA Library Prep Kit for Illumina^® (NEB,
   USA) was used. The insert size of the resulting library was assessed
   using the Agilent 2100 Bioanalyzer. After library preparation, the
   samples were subjected to sequencing on an Illumina platform, which
   generated 150 bp paired-end reads. The paired-end clean reads were
   aligned to the human reference genome using the software HISAT2
   [[58]19]. The data files for RNA-seq have been deposited in the
   Sequence Read Archive database under PRJNA853057.

2.6. Screening of differentially expressed genes (DEGs)

   After processing and analysis of RNA sequencing data, the alignment
   results were transferred to the program StringTie for transcript
   assembly [[59]20]. The gene expression levels of RNA-seq were expressed
   as fragments per kilobase of transcript sequence per million base pairs
   sequenced (FPKM). To identify DEGs between the SPOP group and the vec
   group, the DESeq algorithm, implemented as a package in the R software,
   was utilized. The resulting p-values were adjusted using the Benjamini
   and Hochberg’s approach for controlling the false discovery rate. The
   thresholds to screen DEGs were |log2Fold change (FC)| > 0 and adjust
   p-value <0.05.

2.7. qRT-PCR validation of selected DEGs

   To verify the validity of the RNA-seq results, qRT-PCR was conducted on
   some randomly selected DEGs, which were identified using
   RNA-sequencing. The primer sequences used for DEGs validation are also
   presented in [60]Table 1. During the validation process, the RNA
   expression levels of the selected genes were standardized with that of
   GAPDH.

Table 1.

   Primer sequences for qRT-PCR
   Gene name Forward primer sequence (5′−3′) Reverse primer sequence
   (5′−3′)
   GAPDH ATGGGTGTGAACCATGAGAAGTA GAGTGGGTGTCGCTGTTGAAGTC
   SPOP GCCCCGTAGCTGAGAGTTG ACTCGCAAACACCATTTCAGT
   CALU AATAGACGCGGATAAAGATGGGT GCCATTGGTTTTCAACATTGTCA
   CCNA1 GAGGTCCCGATGCTTGTCAG GTTAGCAGCCCTAGCACTGTC
   CCNA2 CGCTGGCGGTACTGAAGTC GAGGAACGGTGACATGCTCAT
   CCNC CCTTGCATGGAGGATAGTGAATG AAGGAGGATACAGTAGGCAAAGA
   CDK1 GGATGTGCTTATGCAGGATTCC CATGTACTGACCAGGAGGGATAG
   CDK4 ATGGCTACCTCTCGATATGAGC CATTGGGGACTCTCACACTCT
   CDK6 CCAGATGGCTCTAACCTCAGT AACTTCCACGAAAAAGAGGCTT
   FXR1 CTGCGACAGATTGGTTCTAGG TGTACCATAACCGGAGGTGTAA
   GIGYF1 AACCTGCTCCCGACGATGA AGGCTGAGGTATGTACGTCCC
   KIAA1522 CCAGGACAACGTCTTCTTTCC CAGCCACCCTTGTTCAGTTTC
   KRAS ACAGAGAGTGGAGGATGCTTT ACAGAGAGTGGAGGATGCTTT
   MYBL2 CCGGAGCAGAGGGATAGCA CAGTGCGGTTAGGGAAGTGG
   PAPOLG TGTCTCTGGATAGCAGTTGTCTGG TTCGTCCTACTACGGTAGGAATGG
   PER1 GCCAACCAGGAATACTACCAGC GTGTGTACTCAGACGTGATGTG
   PRKAG2 TGCCCGTTATTGACCCTATCA CAGGCTTTGGCATATCAGACAT
   SOX12 AAGAGGCCGATGAACGCATT TAGTCCGGGTAATCCGCCAT
   TP53 CAGCACATGACGGAGGTTGT TCATCCAAATACTCCACACGC
   XPO1 ATCTGACCCAACTTGTGTAGAGA TGGTCCTACTTGCTCCAACAAT
   [61]Open in a new tab

2.8. Gene function enrichment analysis

   The gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and
   Genomes (KEGG) functional pathway analysis were used to predict the
   function of DEGs between the SPOP group and the vec group.
   Additionally, the DisGeNET databases were also used to screen coronary
   liver disease-related disease targets. The enrichment was regarded as
   be significant when the corrected p-value was less than 0.05.

2.9. Alternative splicing events (ASEs) analysis

   The ASEs between the SPOP group and the vec group were defined and
   quantified using the rMATS software. In this study, according to the
   splice junction reads, five types of ASEs were identified, including
   alternative 3′ splice site (A3SS), alternative 5′ splice site (A5SS),
   mutually exclusive exons (MXE), retained intron (RI), and skipped exon
   (SE). To evaluate the significance of SPOP-regulated ASEs, a Student’s
   t-test was performed to evaluate the significance of the ratio
   alteration of ASEs. The thresholds to screen DEGs were adjust to a
   p-value <0.05.

2.10. Public data source bioinformatics analysis

   HCC expression profile data were obtained from The Cancer Genome Atlas
   (TCGA) database and Gene Expression Omnibus (GEO) database. The TCGA
   database encompassed 374 HCC patients vs 50 normal control individuals
   and 50 pairs of HCC tissues vs matched adjacent non-tumor tissues. The
   relevant clinical data, including, age, sex, grade, invasion depth,
   lymph node metastasis, and distant metastasis were also obtained from
   TCGA database. Additionally, we retrieved raw-intensity expression
   files of [62]GSE14520, [63]GSE25097, and [64]GSE45436 from GEO
   database. The SPOP expression levels in the paraneoplastic and HCC
   tissues of every patient were analyzed. The effect of SPOP expression
   levels on the survival of HCC patients was estimated through the
   Kaplan–Meier (KM) plotter ([65]http://kmplot.com/analysis/), an online
   survival analysis tool.

2.11. Statistical analysis

   All the above analyses were performed by using R software version
   4.0.3. Differential gene expression analysis was performed using the
   limma package in R. To investigate the association between DEGs and
   survival outcomes, KM survival curves were generated using the
   ggsurvplot function from the survminer package. All experiments were
   repeated three times, and the data were expressed as mean ± standard
   deviation. T-test and paired t-test were used to analyze the
   differences between the two groups by independent samples. Pearson
   correlation test provided the basis for correlation analysis. The
   relationship between SPOP expression and clinical characters was
   performed using the Wilcoxon test. Statically significance was defined
   as p < 0.05 (*), p < 0.01 (**), and p < 0.001 (***).

3. Results

3.1. SPOP overexpression facilitated the proliferation of WRL68 human normal
hepatocytes

   To investigate the influence of SPOP on the proliferation of human
   normal hepatocytes (WRL68), the SPOP overexpression model in WRL68
   cells was constructed by transfection with SPOP-overexpressing
   lentivirus (the SPOP group). Simultaneously, the empty lentivirus was
   transfected into WRL68 cells as a control group (the vec group).
   qRT-PCR was operated to verify the transfection efficiency, which
   successfully induced the increased expression of SPOP in WRL68 cells
   ([66]Figure 1a). The proliferation ability of the SPOP group was
   significantly higher than that of the vec group at each period of 48,
   72, and 96 h ([67]Figure 1b). The colony formation assay was also
   conducted to access cell proliferation. It turned out that there was a
   considerable increase of cell proliferation in the SPOP group compared
   with the vec group ([68]Figure 1c, p < 0.05). These findings strongly
   suggest the SPOP overexpression and the proliferation ability of WRL68
   cells.

Figure 1.

   [69]Figure 1
   [70]Open in a new tab

   Overexpression of SPOP could facilitate the proliferation of human
   normal hepatocytes (WRL68). (a) Transfection efficiency of SPOP in
   WRL68 cells was measured by qRT-PCR. (b) Proliferative abilities of
   WRL68 cells in the SPOP group and the vec group were detected by CCK8
   assay. (c) Colony formation assay detected the proliferations of WRL68
   cells in the SPOP group and the vec group. *p < 0.05, **p < 0.01.

3.2. SPOP overexpression changed the gene expression profiles of WRL68 human
normal hepatocytes

   To characterize the function of SPOP in human normal hepatocytes,
   RNA-seq of SPOP overexpressed WRL68 cells (the SPOP group) and control
   WRL68 cells (the vec group) was performed (PRJNA853057). The gene
   expression levels of RNA-seq were quantified in FPKM. The distribution
   of gene expression levels in each sample of the SPOP group and the vec
   group is shown as the boxplots ([71]Figure 2a). Despite having only
   three biological replicates in each group, there was a significant
   correlation between the SPOP group and the vec group ([72]Figure 2b).
   Genes with |log2 (Fold Change)| > 0 and p [adj] <0.05 were assigned as
   DEGs, and there were 3,838 DEGs induced by SPOP overexpressing,
   including 1,522 upregulated genes and 2,316 downregulated genes
   ([73]Figure 2c). The list of DEGs is presented in Table S1. To verify
   DEGs identified through from RNA-seq analysis, the gene expression
   levels of 18 genes were also measured by qRT-PCR. Between the two
   techniques, the Pearson’s correlation coefficient of fold change in
   gene expression levels was 0.8576 (p < 0.001) ([74]Figure 2d).

Figure 2.

   [75]Figure 2
   [76]Open in a new tab

   Overexpression of SPOP could change the gene expression profiles of
   WRL68. (a) Boxplot of sample gene expression distribution. (b)
   Correlation between SPOP overexpression (SPOP rep1/2/3) and control
   (vec rep1/2/3) samples. (c) DEGs between the SPOP group and the vec
   group. (d) Correlation of log2 fold-change between RNA-Seq and RT-qPCR
   for significantly DEGs.

3.3. Overview of affected biological functions in WRL68 human normal
hepatocytes with SPOP overexpressing

   To explore the potential biological functions of SPOP-related genes, we
   conducted GO, KEGG, and DisGeNET analyses using significant DEGs.

   For the upregulated genes, the enriched GO biological processes were
   mostly related to the regulation of mRNA metabolic process, mRNA
   stability, and translation ([77]Figure 3a). The complete list is given
   in Table S2. Conversely, the GO biological processes enriched by the
   downregulated genes were mostly related to protein targeting and the
   response to type I interferon and virus, and so on ([78]Figure 3b,
   Table S3). SPOP overexpression led to the identification of 11 KEGG
   pathways with p [adj] <0.05, including oxidative phosphorylation, drug
   metabolism, pyrimidine metabolism, and so on ([79]Figure 3c, Table S4).
   Since less than 20 enriched KEGG pathways reached significant according
   to adjusted p-values, the top 20 most enriched KEGG pathways of all
   DEGs are listed in [80]Figure 3c based on p-values. With SPOP
   overexpressing, 25 DisGeNET with p [adj] <0.05 were identified,
   including neoplasm invasiveness, undifferentiated carcinoma, hepatic
   methionine adenosyltransferase deficiency, steatohepatitis, and so on
   ([81]Figure 3d, Table S5).

Figure 3.

   [82]Figure 3
   [83]Open in a new tab

   Potential biological functions of SPOP-related genes in WRL68 cells.
   (a) Top 30 most enriched GO terms of the upregulated DEGs. (b) Top 30
   most enriched GO terms of the downregulated DEGs. (c) Top 20 most
   enriched KEGG pathways of all DEGs. (d) Top 20 most enriched DisGeNET
   lists of all DEGs.

3.4. Analysis of potential SPOP‑regulated ASEs and enrichment biological
function analysis

   To identify the regulatory effect of SPOP on alternative splicing,
   SPOP-affected ASEs were analyzed by using transcriptional sequencing
   data from WRL68 SPOP cells in the SPOP group and the vec group. This
   analysis identified a total of 158 RASEs that showed significant
   differences between the two groups. Of the RASE types, there were 16
   A3SS events, 11 A5SS events, 20 MXE events, 11 RI events, and 100 SE
   events ([84]Figure 4a). The complete list of RASEs is given in Table
   S6. When SPOP was overexpressed, the proportion of SE events was
   increased. The GO enrichment results revealed that SPOP-regulated ASEs
   were mainly enriched in modulation by virus of host morphology or
   physiology, cell division, sarcomere organization, and so on
   ([85]Figure 4b). Furthermore, KEGG pathway enrichment showed that
   SPOP-regulated ASEs were involved in pathways associated with cellular
   processes, metabolism, environmental information procession, organismal
   systems, and more ([86]Figure 4c).

Figure 4.

   [87]Figure 4
   [88]Open in a new tab

   Enrichment biological function of SPOP RASEs in WRL68 cells. (a) Number
   of significant RASEs. (b) GO functional enrichment of different RASEs.
   (c) KEGG pathway enrichment analysis results: A3SS events, 11 A5SS
   events, 20 MXE events, 11 RI events, and 100 SE events.

3.5. Bioinformatics analysis of SPOP in HCC

   To explore the correlation between SPOP expression levels and the
   procession of HCC, the bioinformatics analysis of SPOP was carried out.
   According to the data from TCGA database, the significant upregulations
   of SPOP expression were observed in HCC tissues compared to normal
   liver tissues ([89]Figure 5a). The expression of SPOP was also
   significantly higher in HCC tissues compared to paraneoplastic tissues
   ([90]Figure 5b). The result of relationship between SPOP expression and
   clinicopathological parameters showed that SPOP expression was
   positively correlated with tumor stage ([91]Figure 5c). According to
   the results of the KM mapper analysis, it was evident that the survival
   rate of SPOP high expression group was remarkably lower than that of
   low expression group ([92]Figure 5d).

Figure 5.

   [93]Figure 5
   [94]Open in a new tab

   Results of bioinformatics analysis of SPOP in HCC. (a) Differential
   expression of SPOP in HCC tissues and normal liver tissues. (b)
   Differential expression of SPOP in paraneoplastic and HCC tissues. (c)
   Correlation of SPOP expression with tumor stage. (d) Analysis of
   overall survival of SPOP expression in HCC patients.

3.6. DEGs due to SPOP overexpression in the HCC-related pathway in WRL68
cells

   The result of bioinformatics analysis showed that of SPOP might play a
   role in procession of HCC. Due to SPOP overexpression, a total of 56
   DEGs were implicated in the HCC pathway, among which 33 were
   downregulated and 23 were upregulated. In [95]Figure 6, genes colored
   in red represent upregulated expression in WRL68 cells with SPOP
   overexpression compared to cells in the control group, while genes
   colored in green indicate downregulated expression. From the status of
   the normal liver to dysplastic nodules, nine were downregulated and
   only four were upregulated. From the status of dysplastic nodules to
   early HCC, nine were downregulated and only four were upregulated. In
   the process of HCC metastasis, approximately nine genes were
   downregulated and four were upregulated. The proteins encoded by DEGs
   were located in different parts of the cells. About four down- and two
   upregulated genes were located outside, and three down- and five
   upregulated genes were located on the cell membrane. Moreover, nine
   down- and eight upregulated genes were located in the nucleus.

Figure 6.

   [96]Figure 6
   [97]Open in a new tab

   HCC pathway modified by SPOP overexpression in WRL68 (the figure was
   drawn according to the information from KEGG PATHWAY database. The
   molecules in red were upregulated with SPOP overexpression and the
   molecules in green were downregulated with SPOP overexpression).

4. Discussion

   The development of HCC is a complex process influenced by both
   environmental and genetic factors [[98]21,[99]22]. It is well known
   that the ubiquitin proteasome pathway could play an important role in
   the progression of cirrhosis and HCC [[100]23]. Ubiquitination serves
   as a crucial post-transcriptional modification of eukaryotic proteins
   and represents an important pathway for non-lysosomal degradation of
   proteins [[101]24]. SPOP, as an important protein molecule involved in
   the regulation of the occurrence and development of various tumors, has
   limited reports regarding its molecular mechanism in liver cancer
   metastasis [[102]25]. For instance, Huang’s team reported that SPOP
   could inhibit hepatoma cell migration, including the suppression of
   ZEB2 expression and the related program of epithelial–mesenchymal
   transition [[103]18]. The results from Ji’s group showed that SPOP
   expression was downregulated in HCC and also related to tumor size,
   differentiation, and metastasis [[104]26]. However, according to the
   TCGA data, SPOP was upregulated markedly in HCC tissues compared to
   normal liver tissues. The expression of SPOP was also significantly
   higher in HCC tissues compared to paraneoplastic tissues. SPOP
   expression was upregulated in HHC patients, which is significantly
   related to lower survival rate and poor prognosis. Collectively, all of
   these indicated that SPOP has a tumor-promoting effect in HCC,
   prompting us to explore its impact further. Despite limited knowledge
   about the transcriptional levels of SPOP in human normal hepatocytes,
   our bioinformatics analysis which involved SPOP overexpression in WRL68
   showed that SPOP regulates the expression profiles and ASEs in human
   hepatocytes.

   SPOP, a crucial ubiquitinylation ligase, also serves as a bridging
   component of Cul3 (a member of the E3 family of ubiquitin ligases)
   which binds to substrate proteins. SPOP is involved in the regulation
   of a variety of cellular activities, influencing two major categories
   of fundamental cellular activities including genome modification and
   cellular signaling. In these processes, SPOP mediates ubiquitination
   modifications of many nuclear proteins, leading to protein degradation
   and thus regulating a variety of cellular functions. In this study, we
   aimed to uncover the mechanism through which SPOP is involved in human
   hepatocytes. To achieve this, we established a cell model with SPOP
   overexpressed. A total of 3,838 DEGs were reliably detected by
   transcriptome profile, including 1,522 upregulated genes and 2,316
   downregulated genes. The enrichment of gene function indicated that the
   upregulated genes were mostly related to the regulation of mRNA
   metabolic process, mRNA stability, and translation, while the
   downregulated genes were mostly related to response to type I
   interferon, response to virus, and protein targeting. Moreover, RASEs
   were identified with SPOP overexpression.

   In this study, a total of 56 DEGs were identified in the pathway of HCC
   with SPOP overexpression. During the early stage of HCC development,
   the reduced expression of transforming growth factor-beta (TGF-β)
   emerges as a crucial factor [[105]27,[106]28]. It has been reported
   that TGF-β plays a vital role in cell proliferation and differentiation
   [[107]29–[108]31]. In response to TGF-β-mediated dimerization between
   TGF-β receptor I (TGF-βRI) and TGF-βRII, receptor-regulated SMADs
   (SMAD2/3) are phosphorylated, interact with SMAD4, and translocate to
   the nucleus with some DNA-binding partners and transcriptional
   co-activators or co-repressors to form a complex controlling gene
   transcription [[109]32]. TGF-β/SMAD3 signaling pathway plays a critical
   role in inhibiting tumor formation by suppressing cell growth and
   promoting apoptosis, both in normal cells and during the early stages
   of tumorigenesis. Jiao et al. reveals that SPOP expression is repressed
   by TGF-β/SMAD signaling axis in PCa CSCs [[110]29]. Our data
   demonstrated that overexpressed SPOP in human normal hepatocytes WRL68
   decreased the mRNA level of TGF-β in a novel TGF-β/SMAD3 signaling
   pathway. Downregulated TGF-β may play a role in promoting cell
   proliferation and tumor formation in WRL68. In this study, SPOP
   expression may negatively regulate SMAD3-mediated TGF-β signaling
   pathway in human normal hepatocytes. Moreover, during the early HCC,
   the expression levels of certain genes involved in the PI3K-Akt
   signaling pathway, p53 signaling pathway, and Wnt signaling were also
   regulated with SPOP overexpression. These gene alterations may indeed
   affect various aspects of cellular function, including cell
   proliferation, cell cycle progression, telomeric DNA repeats,
   maintenance of telomere ends, DNA damage, genomic instability, and so
   on.

   During the metastasis stage, the cellular-mesenchymal epithelial
   transition factor (c-Met) might play the key role [[111]33]. The
   protein of c-Met is implicated in the central carbon metabolism in
   various cancer types, including renal cell carcinoma, melanoma, HCC,
   gastric cancer, non-small cell lung cancer, and so on [[112]34]. The
   overexpression of SPOP was found to trigger a cascade phosphorylation
   reaction of c-Met, which in turn amplified the signaling process,
   ultimately leading to the activation of various downstream pathways
   such as PI3K/Akt and RAS/MAPK. These activated signaling pathways
   collectively regulated a range of biological processes, including cell
   growth, survival, motility, and proliferation. The abnormal c-Met
   signaling pathway has been reported in a variety of tumor studies.
   Normally, the c-Met signaling pathway is only fully activated during
   wound healing and tissue regeneration, but c-Met signaling pathway in
   tumor could be frequently activated by cancer cells, leading to tumor
   formation, invasive growth, and metastasis. The c-Met signaling pathway
   has been reported that it might be abnormally regulated in many types
   of solid tumors (lung, gastric, liver, colorectal, etc.) and could play
   a crucial role in the development of colorectal cancer, the invasion
   and metastasis of HCC, and the formation, growth and metastasis of oral
   squamous cancer [[113]35].

   RASEs are key biological processes that extend gene expression patterns
   and generate protein diversity [[114]36]. Growing evidence indicates
   that protein heterodimers resulted by variable splicing are closely
   linked in the formation of many tumors, including HCC, and are valuable
   as next-generation diagnostic markers due to their specificity of
   expression. In this study, a total of 158 RASEs were identified with
   SPOP overexpressed. The enrichment analysis suggested that RASEs would
   modify the function via calcium, FoxO, HIF-1, Hippo, PI3K-Akt, Rap1,
   TGF-beta, and Wnt signaling pathways, which play key roles during
   environmental information processing. Moreover, the changes of
   transcripts might be related to viral carcinogenesis, non-alcoholics
   fatty liver disease, cancer development, and so on.

5. Conclusions

   In conclusion, RNA sequencing analysis offered a novel idea into the
   molecular mechanisms of SPOP in HCC. We explored the regulatory
   mechanism of SPOP overexpression in human normal hepatocytes. The
   downstream signaling pathways and the corresponding molecular
   mechanisms of SPOP manifest the potential of its tumor-promoting effect
   in HCC, and thus SPOP may serve as a new opinion for underlying
   tumor-promoting effect in HCC. Further studies are needed to better
   reveal the molecular mechanism of SPOP, and the effect of SPOP in the
   progression of HCC will become clear.

Supplementary Material

   Supplementary Table S1
   Supplementary Table S2
   Supplementary Table S3
   Supplementary Table S4
   Supplementary Table S5
   Supplementary Table S6
   Supplementary Table S7
   Supplementary Table S8
   Supplementary Table S9

Footnotes

   Funding information: This research was funded by Anhui Provincial
   Natural Science Foundation (No. 2108085MH284), the Key Project of
   Natural Science Foundation of Anhui Provincial Department of Education
   (2023AH052002), Key Research and Development Program of Anhui Province
   (2022e07020034), National Innovation and Entrepreneurship Training
   Program for college students (202110367057), Science Research Project
   of Bengbu Medical College (No. 2020byzd080), and National College
   Students’ innovation and entrepreneurship training program (No.
   202110367054).

   Author contributions: Conceptualization: J.D. and Y.W.; methodology:
   X.D.; software: Y.C.; validation: W.X., Q.W., and F.S.; formal
   analysis: Y.Z.; investigation: Y.G.; resources: X.D.; data curation:
   Y.C.; writing – original draft preparation: J.D.; writing – review and
   editing: Y.W.; visualization: S.L.; supervision: Y.G. and Y.W.; project
   administration: Y.W. All authors have read and agreed to the published
   version of the manuscript.

   Conflict of interest: Authors state no conflict of interest.

   Data availability statement: The datasets generated during and/or
   analyzed during the current study are available from the corresponding
   author on reasonable request.

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   Yu Gao, Email: gaoyu@bbmc.edu.cn.

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References