Background
HCC constitutes the fourth most prevalent contributor to carcinoma-related fatalities globally [
1,
2]. Despite extensive studies on HCC in recent years that have led to an increasing understanding of its biology, a large proportion of drivers remain incurable. Multigenerational kinase inhibitors, such as levatinib and sorafenib, as first-line therapies for patients with HCC are currently the most common treatments [
3]. Although some efficacy has been achieved with the use of immunotherapies (e.g. nivolumab), however, their response rates are very low (less than 20%) [
4]. Under this situation, it is urgently requested new prognostic biomarkers or treatment strategies to be implemented to enhance the prognosis of patients with HCC.
Cancer stem cells (CSCs) represent a critical subset of tumor cells, sharing functional characteristics with normal stem cells [
5]. Despite their limited numbers, they possess attributes such as self-renewal, unrestricted proliferation, and multidirectional differentiation, enabling evasion of immune surveillance and playing a crucial role in tumor development [
6]. The presence of CSCs accounts for various clinical phenomena in cancer, including HCC, such as nearly inevitable tumor recurrence post successful chemotherapy or radiotherapy, tumor dormancy, and development of therapeutic resistance [
7]. Hence, precise targeting of CSCs holds potential to enhance the therapeutic effectiveness against cancer. Promising therapeutic strategies are actively being developed, focusing on targeting CSCs, specifically in HCC [
8,
9].
Understanding the molecular mechanisms underlying the behavior of CSCs in specific cancers, such as HCC, is crucial for developing effective therapeutic interventions. Identifying key genes and proteins that regulate CSCs can provide valuable insights into potential targets for tailored therapies. U3 small nucleolar RNA-associated protein 11 (UTP11) is one such protein of interest encoded by the
UTP11 gene [
10,
11]. UTP11, in conjunction with the small subunit (SSU) procesome, plays a vital role as a component of the U3-snoRNA-containing complex, participating in the processing of small subunits of the eukaryotic ribosome [
12]. Existing literature suggests that UTP11 may be a promising gene implicated in HCC pathology and could potentially serve as a predictive prognostic biomarker for HCC patients [
13,
14]. However, biological, mechanistic and clinical studies of UTP11 in HCC are extremely limited. In this study, we have confirmed through analyses of the TCGA database and a series of in vitro and in vivo experiments that UTP11 indeed plays a role in promoting the growth of HCC. Furthermore, our exploration of its potential regulatory mechanism has revealed that UTP11 contributes to the stabilization of CSCs by enhancing the stability of CSC-related genes, including Oct4.
Materials and methods
TCGA database analysis
Download and assort RNAseq data from TCGA database (
https://portal.gdc.cancer.gov) for HCC (TCGA-LIHC) and extract the data in transcripts per million reads (TPM) format for the normal as well as cancer samples. The RNAseq data in TPM format was then log2 transformed. The patient’s clinical information was downloaded from UCSC XENA (
https://xenabrowser.net/datapages/) [
15]. For the mRNA expression of UTP11, ggplot2 package (3.3.6) in R language (4.2.1) visualizes UTP11 mRNA levels from normal and LIHC clinical samples. ROC curves were obtained using the pROC package (1.18.0) and time-dependent ROC curves were analyzed via timeROC package (0.4). The survivals of UTP11 were tested using the survival package (3.3.1) for proportional risk hypothesis testing and fitted survival regressions, and the results were visualized using the survminer package. For GO and KEGG analysis, the pearson correlation test was first performed using cor.test to obtain with significantly different genes, then performed with the clusterProfiler R package [
16‐
18]. The relevance of UTP11 to other genes was analyzed by correlation analysis, and the analysis results were visualized by co-expression heat map using ggplot package. Tumor stem cell characteristics were derived from the transcriptome data of LIHC samples obtained from TCGA [
19]. The association between the expression levels of UTP11 and scores indicating tumor stemness was subjected to statistical analysis using the Spearman test.
Validation of protein levels of UTP11 in HPA database
UTP11 protein levels were verified by immunohistochemical (IHC) assay in normal and hepatocellular carcinoma tissues, and the data were obtained from the HPA database (
https://www.proteinatlas.org/), which is an IHC-based database for protein expression analysis [
20‐
22].
CCLE database analysis
The CCLE database was used to analyze UTP11 expression levels at the cellular levels for GO and KEGG analysis. The CCLE database (
https://sites.broadinstitute.org/ccle/) contains genomic data for more than 1100 cell lines from various types of cancers, including HepG2, Huh7, SK-Hep-1, SNU-182, and a total of 25 HCC cell lines [
23]. Data were mainly obtained by high-throughput sequencing, containing copy number, mRNA expression (Affymetrix), reverse-phase protein array, RNA sequencing and reduced representative bisulfite sequencing. UTP11-related differential genes were identified by pearson analysis, and heat maps were derived by utilizing the pheatmap R package (1.0.12). Differential genes were ID-transformed by org.Hs.eg.db package, GO-KEGG enrichment was carried out by clusterProfiler package [
16‐
18]. Adjustment of P value is performed by BH method.
Cell transfection
Culture the cells in a 6-well plate overnight until they reach around 50% confluence on the day of infection. Replace the culture medium in each well with a 5 µg/ml Polybrene (Santa Cruz Biotechnology, sc-134,220) and medium mixture. Introduce UTP11 shRNA lentiviral particles (Santa Cruz Biotechnology, sc-88,082-V) into the culture medium to infect the cells, gently swirling the plate for even distribution, and then incubate overnight. Approximately 8 to 12 h later, the complete medium was replenished. Subsequently, choose stable shRNA-expressing clones through puromycin selection (2 to 10 µg/ml).
Real-time quantitative PCR (RT-qPCR)
TRIzol reagent (Invitrogen) was applied to extraction of total RNA from HCC cells. 500 ng of RNA was reversely transcribed into cDNA (Takara Biotechnology), and then mRNA expression was detected by SYBR Green method (Takara Biotechnology). The sequences of the primers used in this study were as follows: UTP11 F: TCGGAAGAAGGCTCTTGAAA, R: GCTTCTGCAACCCTCTTCAT; Oct4 F: 5′-CTTGAATCCCGAATGGAAAGGG-3′, R: 5′-GTGTATATCCCAGGGTGATCCTC-3′; Nanog F: 5′-ACCTATGCCTGTGATTTGTGG-3′, R: 5′-AGTGGGTTGTTTGCCTTTGG-3′; Sox2 F: 5′-GCCGAGTGGAAACTTTTGTCG-3’, R: 5’-GGCAGCGTGTACTTATCCTTCT-3’; CD133 F: 5′-AGTCGGAAACTGGCAGATAGC-3′, R: 5′-GGTAGTGTTGTACTGGGCCAAT-3′; GAPDH F: 5’-TGACTTCAACAGCGACCCA-3’, R: 5’-ACCCTGTTGCTGTAGCCAAA-3’. The statistical approaches of RT-qPCR were conducted with the 2−ΔΔCt method.
Western blotting assay
HCC cells were collected, lysed using RIPA buffer, and the concentration of protein was testing using the BCA kit (ThermoFisher Scientific). Total protein (30 µg per sample) was separated within an SDS PAGE gel at 80 V for 2 h and transferred to a PVDF membrane (Millipore) at 4 °C for 2–3 h. Membranes were treated with 5% milk (diluted in PBS-T solution (1×PBS containing 0.1% Tween-20), follwed by incubating with primary antibodies (UTP11, abcam, #ab247068, 1:1000 dilution; Oct4, abcam, ab18976; GAPDH, Santa Cruz, #sc-47,724, 1:1000 dilution) overnight at 4 °C. After washing with PBS-T buffer, the membranes were incubated with secondary antibody (Cell Signaling Technology, #14,708 or #14,709, 1:10,000 dilution) for 2 h at room temperature. Blots were processed using ECL reagent (ThermoFisher Scientific).
Cell proliferation assay
To measure the cell viability of HCC cells, the Cell Counting Kit-8 (CCK-8) reagent (Beyotime Biotechnology, #C0037) was applied. Briefly, HCC cells (2000 cells /well) were seeded in a 96-well plate, and after adding CCK-8 solution (10 µl per well), incubation was continued for 1 h in a cell incubator, and absorbance was measured at 450 nm.
For EdU (5-ethynyl-2’ -deoxyuridine) assay, HCC cells were seeded in confocal Petri dishes. To be tested, incubate cells with medium containing 10µM EdU for 3 h at 37 °C. After washing with 1×PBS for three times, the HCC cells were followed by fixing with 4% paraformaldehyde (PFA) and blocking with permeability solution (1×PBS containing with 0.3% Triton X-100). Then cells were stained with reaction solution (Beyotime Biotechnology, #C0075S).
Flow cytometry analysis
To detect the cell death status, cells were collected and washed with 1×PBS. After resuspending with 500 µl of PBS, the HCC cells were further incubated with 5 µM 7-AAD (invitrogen) at 37˚C for 30 min, followed by flow cytometric analysis.
HCC cells were cultured at a density of 1000 cells/well. The serum-free medium utilized for cultivating spheres consisted of DMEM/F12 medium supplemented with epidermal growth factor (EGF, 20 ng/ml), fibroblast growth factor (FGF, 20 ng/ml), 1% GlutaMax, 1% nonessential amino acids, 2% B27 supplement.
Mice experiment
For the xenograft model, the 6-8-week-old male nude mice were subcutaneously injected with 1 × 106 control or UTP11 knockdown HCC-LM3 cells on the back of the mice (5 mice per group). The sizes of the subcutaneous tumors were measured 1–2 times per week and the mice were euthanized after four weeks. For the orthotopic model, the 6–8 weeks male nude mice were inoculated with 2 × 105 control or UTP11 knockdown HCC-LM3 cells (5 mice per group) in the left lobe of the mouse liver. All mice were raised under pathogen-free conditions. Tumor size was measured 1–2 times per week using ultrasound, and the volume based on ultrasound measurement was calculated as V = (length × width2)/2. Mice were euthanized when they showed weight loss of more than 20% or decreased activity. Animal experiments were approved by the animal ethics of the Second Affiliated Hospital of Shandong First Medical University (approval number 2023-002).
Immunofluorescence assay
Tissue samples were fixed with 4% PFA overnight at 4 °C and then treated with 30% sucrose solution until the tissue settled. Then tissue samples were embedded with OCT and cut into 10 μm sections. The slides were treated with acetone at -20 °C for 5 min and blocked with permeability solution (1×PBS containing with 0.3% Triton X-100), followed with blocking in 5% BSA at room temperature for 1 h. The slides were treated with primary antibody (Ki67, abcam, #ab16667, 1:250 dilution; PCNA, abcam, #ab92552, 1:200 dilution) overnight at 4 °C and the next day incubated with secondary antibody (Invitrogen, #A-31,572, 1:500 dilution; Invitrogen, #A-11,008, 1:500 dilution). Nuclei were stained by DAPI reagent (1:1000 dilution). The slides were observed and imaged using a confocal microscope.
Statistical analysis
All statistical analyses were performed using R language 4.2.1 and GraphPad Prism 7.0 software. Two-tailed t-test was used for two groups analysis. Survival curves were performed using the Kaplan-Meier method. P < 0.05 was considered to be statistically significant.
Discussion
The survival rate of HCC is dramatically low attributed to the difficulties of early diagnosis, rapidly progressive disease and deficiency of controlled drugs [
25,
29]. The presence of aberrant heterogeneity greatly limits the progress of early HCC studies and the detection of living cancers [
25]. Hence, the identification of a prevalent molecular regulator in HCC that can distinguish clearly between tumor tissues and paraneoplastic tissues to obtain an early diagnosis. Several reports have suggested that UTP11 may be a promising gene involved in HCC pathology and may be a prognostic predictive biomarker for HCC patients [
13,
14]. Our data indicated that UTP11 levels were much higher in HCC samples compared to normal tissues; ROC curves revealed that UTP11 was able to discriminate considerably between liver cancer tissues and paraneoplastic samples, which are consistent with the previously reported literatures. Further, UTP11 expression in HCC patient samples was linked to nfavorable survival outcomes. However, previous reports on the function of UTP11 in HCC are very limited, where our results demonstrated the capability of UTP11 to promote the growth and proliferation of HCC in vitro and in vivo.
RNA splicing is an important biological process in the processing and maturation of tRNA, rRNA, especially mRNA, and is one of the critical molecular diversity mechanisms for generating proteins [
10,
11]. UTP11 is a small nucleolar RNA-associated protein, and we showed that UTP11 is involved in the RNA splicing process by exploring the results of GO-KEGG analysis of TCGA database. The RNA splicing process is very complex, there are plenty of factors involved in it. SART3 (also known as hPrp24) is a central spliceosomal component, a unique spliceosomal protein that selectively binding to U6 and is involved in U6 biogenesis, function in splicing, and recycling [
30‐
32]. PRPF8, as the core of a scaffolding protein stabilizing spliceosome, whose rearranged conformation benefits the three-dimensional encapsulation of the catalysing active of U2/U6 RNA, is one of the largest and most conserved protein, directly involved in splicing fidelity [
33,
34]. MBNL proteins serve as suppressors or activators of RNA splicing in a variety of transcripts [
35]. RSRC1, previously known as SRrp53, is localized to the nuclear speck where it binds to the U2 small ribonucleoprotein (snRNP) cofactor (U2AF35), which has roles in both constitutive and selective precursor mRNA splicing [
36]. In this study, UTP11 was positively correlated with RNA splicing-related proteins MBNL1, SART3, RSRC1 and PRPF8, which further demonstrated that UTP11 may be involved in the RNA splicing process.
CSCs represent one of a small population of carcinoma cells capable of influencing self-renewal, differentiation and tumorigenesis [
37]. The markers of HCC stem cell subsets have been identified, including EpCAM, CD24, CD133, CD44, Oct4, and Nanog [
38‐
40]. CSCs are critical contributors to the recurrence, metastasis and chemoresistance of HCC [
41]. Therefore, increasing studies are searching for treatments that target CSCs in an attempt to control tumor recurrence and metastasis [
9,
42]. Our study showed that UTP11 may mediate tumor stem cells in HCC by stabilizing the mRNA of Oct4. These results provide insight into the potential of UTP11 as a therapeutic candidate for targeting HCC tumor stem cells. However, more experiments are needed to verify this speculation.
Certain modifications of RNA, such as pseudouridine (Ψ), N6,2’-O-dimethyladenosine (m6Am) and N6-methyladenosine (m6A) have proved to modulate the stability of mRNA and thus affect different cellular and biological processes [
43]. Our data suggested that UTP11 expression was associated with proteasome regulation, mRNA stability regulation, and regulation of the mRNA catabolic process. However, whether UTP11 can mediate the mRNA stability of OCT4 by regulating the RNA modifications of Oct4 is unclear. This study also did not delve deeply into the specific mechanisms through which UTP11 regulates stem cells to promote hepatocellular carcinoma growth. In future research, we aim to thoroughly investigate and confirm the role of UTP11 in promoting hepatocellular carcinoma by regulating stem cells at both the cellular and animal levels, enhancing our understanding of this crucial mechanism.
In conclusion, UTP11 knockdown suppressed the tumor growth of HCC and extended the mice survival time. Mechanically, UTP11 might promote the regulation of the mRNA stability of Oct4. UTP11 is potentially a diagnostic molecule and a therapeutic candidate for treatment of HCC. By targeting UTP11, it may be possible to inhibit HCC growth and extend the survival time of patients. In addition, our findings offer novel insights into the molecular mechanisms that drive the progression of HCC, with a particular emphasis on the regulatory role of UTP11 in Oct4 expression.
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