Nucleolar NOL9 regulated by DNA methylation promotes hepatocellular carcinoma growth by activating wnt/beta-catenin pathway

doi:10.21203/rs.3.rs-4657392/v1

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Nucleolar NOL9 regulated by DNA methylation promotes hepatocellular carcinoma growth by activating wnt/beta-catenin pathway

https://doi.org/10.21203/rs.3.rs-4657392/v1

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Hepatocellular carcinoma (HCC) presents significant clinical challenges, necessitating a deeper understanding of its molecular drivers for improved therapeutic strategies. Our study focused on nucleolar protein 9 (NOL9), a 5’-polynucleotide kinase, elucidating its role in HCC pathogenesis and therapeutic response. We found NOL9 to be significantly upregulated in HCC tissues compared to normal counterparts, correlating with larger tumor diameters, advanced pathological grades, and poor patient survival. Furthermore, NOL9 exhibited diagnostic potential, effectively distinguishing HCC samples from normal tissues. High NOL9 expression correlated with poorer prognosis, both in a TCGA-LIHC cohort and our HCC cohort, as well as in patients with poor sorafenib efficacy. Functional assays revealed that NOL9 modulates HCC cell proliferation and apoptosis. Knockdown of NOL9 inhibited cellular proliferation and clonogenic survival while promoting apoptosis, whereas NOL9 overexpression had the opposite effect. In vivo and in vivo studies corroborated these findings, with NOL9 depletion leading to reduced tumor growth. Additionally, NOL9 influenced the HCC cell cycle dynamics, facilitating the G1/S transition and promoting cell proliferation. Mechanistically, NOL9 expression was regulated by DNA methylation and ZNF384. ZNF384 was identified as a key transcriptional regulator of NOL9, enhancing its expression and activating its promoter. DNA methylation analysis revealed an inverse correlation between NOL9 expression and methylation at specific CpG sites, with DNMT1 playing a role in epigenetic regulation. Furthermore, NOL9-mediated cell proliferation was found to be beta-catenin-dependent, with NOL9 activating the wnt/beta-catenin pathway at the transcriptional level, thus promoting HCC cell proliferation. In summary, our study unveils the multifaceted role of NOL9 in HCC pathogenesis, emphasizing its potential as a diagnostic biomarker and therapeutic target. Understanding the intricate molecular mechanisms underlying NOL9 regulation provides insights into HCC biology and opens avenues for the development of targeted therapies to improve patient outcomes.

Biological sciences/Cancer/Tumour biomarkers

Biological sciences/Molecular biology/Epigenetics

HCC

NOL9

DNA methylation

cell proliferation

Hepatocellular carcinoma (HCC) stands as the predominant form of primary liver malignancy, comprising approximately 75–85% of all cases worldwide [1]. Despite strides in medical care, HCC continues to pose a formidable challenge, ranking as the sixth most prevalent cancer globally [2]. Particularly in regions like China, where HCC prevalence is pronounced, patients with advanced-stage disease face a median survival time of approximately nine months, with a discouraging five-year survival rate of around 20% [3, 4]. Consequently, there's a pressing need to delve into the mechanisms underlying HCC development.

Cancer cells, characterized by their unlimited replicative potential and heightened metabolic activity, necessitate enhanced global protein synthesis [5]. Ribosome biogenesis, a complex multistep process essential for protein synthesis, emerges as a pivotal player in this context [6]. It involves the coordinated action of three RNA polymerases, approximately 80 ribosomal proteins, and numerous non-ribosomal trans-acting factors [7, 8]. Recent advancements in cancer research have highlighted ribosome biogenesis as a promising avenue for therapeutic intervention [9]. Ribosome biogenesis, a tightly regulated process essential for cellular growth and proliferation, has emerged as a central player in cancer occurrence and metastasis [10, 11]. Dysregulation of ribosome biogenesis fuels unrestricted cell growth, providing targetable vulnerabilities for cancer therapy [12]. Efforts to target ribosome biogenesis offer a broader approach to cancer treatment, complementing precision oncology strategies. In particular, targeting ribosome biogenesis presents promising opportunities to impede cancer cell proliferation and metastasis [12]. Notably, chemical inhibition of ribosome biogenesis has garnered attention as a potential anti-cancer therapeutic strategy, offering insights into novel small-molecule inhibitors and repurposed compounds with ribosome biogenesis-inhibitory potential.

Our investigation focuses on nucleolar protein 9 (NOL9), a 5’-polynucleotide kinase intricately involved in ribosome biogenesis [13]. NOL9, part of a nucleolus complex with PELP1, TEX10, and LAS1, plays a crucial role in pre-rRNA cleavage and 28S ribosomal RNA (rRNA) maturation, thereby promoting ribosome biogenesis (RiboSis) efficiency [14]. In a recent pan-oncogenic analysis, which looked at the characteristic of somatic genetic variation in ribosome biosynthesis genes, NOL9 was one of the top 10 RiboSis genes more likely to be amplified/deleted [6]. However, despite these insights into its role in ribosome biogenesis and tumorigenesis, its influence on HCC pathogenesis remains ambiguous.

In our study, we aim to elucidate the impact of NOL9 on HCC cell proliferation and hepatocarcinogenesis, shedding light on the epigenetic mechanisms driving its aberrant expression. Notably, our research underscores the epigenetic activation of oncogenic NOL9, mediated through DNA methylation of CpG islands within its promoter region.

2.1 Patient Selection and Specimen Collection

A cohort of thirty-one HCC patients, who underwent interventional surgical procedures at the Third Affiliated Hospital of Sun Yat-sen University between 2017 and 2019, contributed tissue samples for this study. All participating patients received postoperative pathological validation to confirm HCC diagnosis. The study’s design and execution received the endorsement of the Ethics Committee at the Third Affiliated Hospital of Sun Yat-sen University (II2023-284-01).

2.2 Data Acquisition and Analytical Procedures

TCGA Database Analysis: We procured RNA-sequencing data (in FPKM format) alongside clinical records for 50 normal samples and 374 HCC patients from the Liver Hepatocellular Carcinoma (LIHC) project housed within The Cancer Genome Atlas (TCGA) database. ROC Curve Generation: Utilizing the R package “ROCR”, we constructed Receiver Operating Characteristic (ROC) curves. Differential Expression Analysis: The “limma” package in R facilitated the comparative analysis of gene expression data between groups demarcated by median NOL9 expression levels. Criteria for differentially expressed genes (DEGs) were set at |logFoldChange| > 0.5 and an adjusted p-value (adj.P.Val) < 0.05.

Functional Enrichment Analysis: We employed the “clusterProfiler” package in R to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on DEGs significantly associated with NOL9 expression. The top 30 terms, filtered by an adj.P.Val < 0.05 and counts > 2, were cataloged based on gene count.

Correlation Analysis: The interplay between NOL9 DNA methylation and its transcript expression in the TCGA-LIHC dataset was explored. Kaplan-Meier (KM) survival analyses, executed using the Survival and Survminer software packages, gauged the potential influence of NOL9 DNA methylation/expression on HCC patient prognosis.

Gene Expression Profiling Interactive Analysis (GEIPA): The mRNA expression interrelation between NOL9 and ZNF384 was scrutinized via the GEIPA database (http://gepia.cancer-pku.cn/).

Transcription Factor Binding Site Prediction: The JASPAR software facilitated predictions regarding transcription factor binding sites.

2.3 ssGSEA Enrichment Scores of Ribosome Biogenesis Gene Sets

To perform ssGSEA analysis, we first downloaded the gene expression data from the GEO database, including GSE83148 with 127 hepatitis B (HB) cases, GSE17548 with 10 hepatitis B virus (HBV)-related HCC cases, and GSE14520 with 160 HBV-related HCC cases. Subsequently, we computed the ssGSEA enrichment scores using the “GSEABase” and “limma” packages in R software.

For each sample, we normalized the gene expression data before calculating the ssGSEA enrichment scores using predefined gene sets (GOPB_ribosome_biogenesis and GOBP_regulation_of_ribosome_biogenesis) obtained from the MSigDB database. The ssGSEA enrichment scores were calculated based on a comparison of the gene expression levels of the gene set members with those of the background gene set.

To compare the differences between HBV and HBV-related HCC, we employed the Wilcoxon rank-sum test to assess the statistical significance of the ssGSEA enrichment scores between the two groups. A similar analysis was conducted using the gene expression data from the TCGA-LIHC dataset, comparing cancer tissues and adjacent non-tumor tissues in HCC.

All statistical analyses were performed using R software, and a P-value of < 0.05 was considered statistically significant.

2.4 Cell Culture and Proliferation Assays

Human HCC cell lines, namely HepG2, Huh7, and HEK293T, were sourced from the Hepatology Laboratory of the Third Affiliated Hospital of Sun Yat-sen University. Cells were cultured in DMEM supplemented with 10% FBS and 1% Penicillin/Streptomycin.

CCK-8 Assay: Cells (1,000/well) were seeded into 96-well plates. Post cell attachment (designated as 0h), the medium was replaced with CCK-8 containing media. After a 1-hour incubation at 37°C, absorbance was measured at 450nm using a microplate reader.

EdU Assay: Cells (2 × 10^4/mL) were seeded into 48-well plates and subjected to the EdU Cell Proliferation Kit as per the manufacturer's instructions. Post Hochest staining for 3 minutes, cellular imaging was conducted using fluorescence microscopy.

Colony Formation Assay: Cells (1,000/well) were seeded into 6-well plates. Following a culture period of 14–20 days, cells underwent fixation and staining. Colonies comprising more than 50 cells were enumerated.

2.5 Immunohistochemistry Analysis

Paraffin-embedded tissue sections were deparaffinized in an oven. Endogenous peroxidase activity was quenched using 3% hydrogen peroxide. Antigen retrieval was achieved using EDTA repair solution under high-pressure and high-temperature conditions. Tissue sections were then demarcated using a histochemical pen, approximately 0.5cm from the tissue radius. The primary antibody targeting NOL9 (1:200, # abs139241, absin, China) was applied in accordance with the manufacturer's guidelines. Following primary antibody incubation and subsequent washing, sections were treated with appropriate secondary antibodies. Microscopic imaging was conducted post-staining. Scoring Methodology: Ten distinct fields of view were randomly chosen for evaluation. Immunohistochemical staining intensity was categorized into four tiers: no staining (scored as 1), weak staining (scored as 2), moderate staining (scored as 3), and strong staining (scored as 4). The proportion of positively stained cells was graded as follows: <25% (scored as 1), 25%-50% (scored as 2), 50%-75% (scored as 3), and > 75% (scored as 4). The final score for each sample was computed as the product of staining intensity and the positive rate.

2.6 Western Blot Analysis

HCC tissues and cells were lysed using RIPA lysis buffer. Proteins were separated via SDS-PAGE and subsequently transferred onto polyvinylidene fluoride (PVDF) membranes. Post-blocking, membranes were probed overnight at 4°C with primary antibodies: NOL9 (1:1000, # ab140597, Abcam, UK), GAPDH (1:3000, # ab8245, Abcam, UK), beta-tubulin (1:5000, # A12289-50, ABclonal, China), Rb (1:1000, # 9309S, CST, USA), P-Rb (1:1000, # 8516T, CST, USA), CDK6 (1:1000, # PA5-27978, ThermoFisher, USA), Cyclin D1 (1:1000, # 2978T, CST, USA), and beta-catenin (1:1000, # 9562S, CST, USA). Secondary antibodies, either goat anti-rabbit IgG-HRP (1:5000, # E030120-01, SaiguoTech, Guangzhou, China) or goat anti-mouse IgG-HRP (1:5000, # E030110-01, SaiguoTech, Guangzhou, China), were applied for 1 hour at room temperature. Protein bands were visualized using the Excellent Chemiluminescent Substrate detection kit and quantified via grayscale analysis with ImageJ (version 1.8.0).

2.7 Quantitative Real-time PCR (qRT-PCR)

Total RNA was isolated from 1 × 10^6 cells using the RNA Quick Purification kit. Subsequently, 1µg of RNA was reverse transcribed into cDNA employing the Fast Reverse Transcription Kit (5 × Mix with gDNA Remover). qRT-PCR was executed using the Takara TB Green Premix Ex Taq II, adhering to a 3-step cycling protocol and manufacturer's guidelines. Primer sequences are provided in Supplementary Table 2.

2.8 Cell Transfection

Cells were seeded in 6-well plates and, upon reaching 60% confluence, were transfected with either target gene-specific siRNA or control siRNA (Ribobio, Guangzhou, China) using Lipofectamine 3000 (Invitrogen, MA, USA). Post 24-hour incubation, qRT-PCR verified target gene knockdown. At the 48-hour mark, Western blotting assessed knockdown efficiency. Only cultures exhibiting an 80% or greater reduction in target gene mRNA and protein expression, relative to controls, proceeded to subsequent experiments.

2.9 Lentivirus Package and Stable Cell Lines

For the creation of stable NOL9-overexpressing or knockdown cell lines, 293T cells were employed to package recombinant lentiviruses harboring the NOL9 gene or specific shRNAs against NOL9 (shNOL9-#1, shNOL9-#2). A lentiviral vector with a non-specific sequence served as a control. Target cells were incubated in a serum-rich medium supplemented with viral particles and polybrene. Puromycin (InvivoGen, France) was used to select stably transfected cells.

2.10 Methylation Specific PCR (MSP)

Genomic DNA was isolated from 5 × 10^6 cells utilizing the Promega Wizard Genomic DNA Purification Kit, adhering to the manufacturer's guidelines. Subsequently, bisulfite conversion of the DNA was executed using the EpiTect Bisulfite Kits (Qiagen). Quantitative real-time PCR assessed the methylation status of the NOL9 promoter regions. Primer sequences are delineated in Supplementary Table 2.

2.11 Flow Cytometry for Cell Cycle and Apoptosis Analysis

For cell cycle assessment, post 24-hour serum starvation, cells were collected, fixed in 70% ethanol overnight at 4°C, and stained with propidium iodide (PI) prior to flow cytometry. Apoptosis was evaluated by staining cells with PI and Annexin V-APC as per the manufacturer's protocol (KeyGEN, China).

2.12 Luciferase Reporter Assays

Cells were plated in 12-well dishes and, post 24 hours, subjected to plasmid transfection as indicated. 36 hours post-transfection, cellular lysates were analyzed for luciferase activity using the Dual-Luciferase Reporter Assay System (Promega, # E1910) following the manufacturer’s instructions

2.13 Chromatin Immunoprecipitation (CHIP)

Cells underwent cross-linking with 1% formaldehyde for 10 minutes, terminated with glycine. Lysates were sonicated to yield chromatin fragments, which were subsequently immunoprecipitated using the ZNF384 antibody (# ab176689, Abcam, UK). IgG served as a negative control. ChIP primer sequences can be found in Supplementary Table 2.

2.14 TOP/FOP-Flash Wnt Reporting Analysis

HCC cells were cultured in 12-well cell culture dishes for TOP/FOP-flash Wnt reporter assays. The cell transfection density was about 50%. We used the TOP/FOP-flash plasmid (Beyotime, Shanghai, China) to estimate the transcriptional activity of beta-catenin/TCF in cells. First, OE-NOL9/si-NOL9, TOP/FOP-flash and renin luciferase reporter plasmid (pRL-TK) were co-transfected into HCC cells. Forty-eight hours after transfection, we treated the cells using a dual luciferase assay kit (Promega, # E1910). Next, we used a microplate reader to estimate luciferase activity.

2.15 In vivo Tumorigenesis Assay

The tumorigenesis experiment utilized BALB/c nude mice aged 4 to 6 weeks. Each mouse was injected with 5 × 10^6 cells subcutaneously on the right hind side. Successful injections were achieved in three mice per group. Tumor size was determined using the formula: volume (cm^3) = (length x width^2)/2. Following the experiment, the mice were euthanized in a humane manner, and the tumors were surgically removed and weighed. The Animal Experimentation Ethics Committee at The Third Affiliated Hospital of Sun Yat-sen University approved the use of mice, and the Laboratory Animal Center staff provided appropriate care for all animals.

2.16 RNA-sequencing Analysis

Total RNA was extracted from both control HCC cells and NOL9-knockdown HCC cell lines using Trizol, with three biological replicates performed. The qualified RNA was then sent to Guangzhou Kidio Biotechnology Co., Ltd. (Guangzhou, China) for RNA sequencing (RNA-seq) on the Illumina sequencing platform. Subsequent data analysis was completed on Kidio’s cloud platform, OmicShare.

2.17 Statistical Analysis

Data sourced from TCGA and GEO database were integrated and processed using R software (R version 4.1.0) and GraphPad Prism (version 9.0). In vitro experiments were conducted a minimum of three times, with results presented as mean ± standard deviation (SD). Differences between two groups were evaluated using Student’s t-test, while the association between clinical attributes and NOL9 expression was determined using the Chi-squared test. For comparisons involving three or more groups, one-way or two-way ANOVA was employed for parametric data, and Kruskal-Wallis tests for nonparametric data. Pearson’s correlation assessed gene correlations. Hazard ratios (HR) were estimated with a 95% confidence interval (CI). A two-tailed P-value ≤ 0.05 was deemed statistically significant.

3.1 NOL9 is Upregulated in HCC and Correlates with Patient Survival

Ribosome biogenesis is a critical component of protein synthesis, and its dysregulation may be closely associated with HCC development and progression. Figure 1A and 1B reveal that ribosome biogenesis scores escalate from HB to HCC samples, with an even more pronounced elevation observed in cancerous tissues compared to the adjacent non-cancerous tissues. Investigating ribosome biogenesis could potentially offer new targets and strategies for HCC therapy. Prognostic analysis revealed that in HCC, patients with higher ribosome biogenesis scores exhibited lower survival rates compared to those with lower scores (Fig. 1C). Our prior research identified a marked elevation in NOL9 transcriptional levels in HCC compared to normal tissues [15]. In the current study, we first analyzed the clinical significance of NOL9 in that TCGA-LIHC cohort. Clinical data indicated that the high NOL9 expression cohort exhibited larger tumor diameters and advanced pathological grades (Moderate differentiation) (Supplementary Fig. 1A). ROC curve was employed to evaluate NOL9’s diagnostic potential in HCC. The analysis revealed that NOL9 effectively differentiated HCC samples from normal ones (AUC = 0.817) (Supplementary Fig. 1B). To analyze the impact of many factors on survival, we included age, tumor TNM-staging, gender, stage and other clinical pathological factors as well as the gene NOL9 expression for univariate Cox analysis, among which the p-value of T, N, M, Stage, and NOL9 is less than 0.05. Then, these factors were included in the risk model for multivariate Cox analysis. The results showed that the p value of T stage, M stage, and NOL9 were all less than 0.1 (Supplementary Fig. 1C). Based on the above results, a nomogram was constructed using NOL9 and T stage and M stage to analyze the possible 1, 3, and 5-year survival rates of HCC patients (Supplementary Fig. 1D), and a calibration curve of the nomogram was constructed to observe predictions The deviation between survival rate and actual survival rate (Supplementary Fig. 1E).

To validate this, we examined NOL9 expression in our own set of carcinoma and adjacent tissue samples. Both mRNA and protein levels of NOL9 were notably elevated in seven paired fresh HCC tissues (Supplementary Fig. 1F). Further extending our analysis to a larger set of paraffin-embedded tissues reaffirmed NOL9’s overexpression in HCC (Fig. 1D). Kaplan-Meier curve found that high NOL9 expression was correlated to poor prognosis both in the TCGA-LIHC cohort and our HCC cohort (Fig. 1E). We also analyzed samples from 13 patients exhibiting poor sorafenib efficacy. These samples displayed a significant surge in NOL9 protein levels compared to their good response counterparts (P = 0.025) (Fig. 1F).

To further verified the clinical implications of NOL9 in our cohort, patients were categorized based on median staining scores into high-expression (12 samples) and low-expression (19 samples) NOL9 groups. Clinical data indicated that the high NOL9 expression cohort also exhibited larger tumor diameters and advanced pathological grades (Table 1), consistent with the results from TCGA.

3.2 NOL9 Modulates HCC Cell Proliferation and Apoptosis

To elucidate NOL9’s functional significance in HCC, we introduced two distinct shRNAs into relatively NOL9-highly expressed cell line, Huh7 (Supplementary Fig. 2A and B; Given that the IHC specimen was hepatitis B-related HCC, Huh7 was chosen instead of HCCLM3.). The efficacy of this knockdown was confirmed via western blotting and qRT-PCR (Fig. 2A and Supplementary Fig. 2C). Given their robust knockdown efficiency, both sh-NOL9#1 and sh-NOL9#2 was employed in subsequent experiments. Conversely, in the NOL9-low expressing cell line HepG2, NOL9 were overexpressed, verified by western blotting and qRT-PCR (Fig. 2A and Supplementary Fig. 2D). NOL9 suppression led to a marked reduction in cellular proliferation and clonogenic survival (Fig. 2B-D), while a concomitant increase in apoptosis (Fig. 2E). Nevertheless, NOL9 overexpression augmented cellular proliferation and clonogenic survival (Fig. 2B-D), while significantly reduced apoptosis (Fig. 2E). To investigate the effect of NOL9 on HCC proliferation in vivo, nude mice were injected with Huh7 cells that had undergone NOL9 depletion or were transduced with sh-ctrl. Compared to the control xenografts showing tumor progression, the xenografts containing NOL9-depleted HCC cells displayed a significant reduction in tumor growth (Fig. 2F). The protein expression of NOL9 on these xenografts were confirmed via western blotting (Fig. 2G). Collectively, these findings underscore NOL9’s dual role: its inhibition curtails cell growth, whereas its overexpression fosters it.

3.3 Effect of NOL9 on the HCC Cell Cycle

KEGG and GO analyses of NOL9-associated genes from the TCGA-LIHC dataset revealed NOL9's pivotal role in cell cycle regulation, potentially influencing processes such as chromosome segregation, nuclear division, DNA replication, and cell cycle checkpoints (Fig. 3A and B). We further explored how modulating NOL9 levels might impact HCC cell cycle distribution. Overexpressing NOL9 induced a notable shift in cell cycle dynamics, characterized by an 18 percent reduction in G1 phase cell population. In contrast, NOL9 downregulation led to an 8 percent increasement in G1 phase cell count (Fig. 3C-E). These observations suggest that NOL9 bolsters HCC cell proliferation and colony formation by facilitating the G1/S cell cycle transition and curtailing apoptosis. Western blotting revealed that NOL9 downregulation led to the reduction of the protein expression of Cyclin D1 and the protein phosphorylation of Rb, while had slight influence on the protein expression of CDK6 (Fig. 3F).

3.4 DNA Methylation and ZNF384 Regulate the Expression of NOL9

We then explore the mechanism underlying the high expression of NOL9 in HBV-related HCC tissues and especially in sorafenib-resistant HCC tissues. It is well-known that transcription factors are essential for the expression of gene. Leveraging the JASPAR database, we conducted a bioinformatics analysis of the NOL9 promoter region to predict potential transcription factor binding sites. Following the transfection of si-RNAs targeting the top four transcription factors into Huh7 cells, both western blotting and RT-qPCR analyses revealed that ZNF384 knockdown most significantly impacted NOL9 expression (Fig. 4A and Supplementary Fig. 3A), suggesting ZNF384’s role in upregulating NOL9 in HCC cells. Additionally, a positive correlation was observed between NOL9 and ZNF384 mRNA levels in HCC (Fig. 4B).

Bioinformatics analysis predicted potential ZNF384 binding sites within the NOL9 promoter region. Given the continuous adenine sequence at site 1, we focused on verifying binding site 2 (TCTCAAAAAAAT) (Fig. 4C). Dual luciferase reporter assays and CHIP-qPCR confirmed ZNF384’s interaction with the NOL9 promoter’s binding site 2. Specifically, co-transfection of the wild-type NOL9 promoter region and ZNF384 plasmid into HEK293T cells upregulated NOL9, an effect diminished when the binding site 2 was mutated (Fig. 4D). CHIP-qPCR further identified ZNF384 enrichment at the NOL9 promoter region (Fig. 4E and Supplementary Fig. 3B). In NOL9-overexpressing cells, ZNF384 knockdown could reduce the expression of NOL9 (Fig. 4F and Supplementary Fig. 3C). Rescue experiments corroborated that ZNF384 knockdown could also counteract NOL9’s gain-of-function, as cells’ proliferation ability were reduced (Supplementary Fig. 3D).

RiboSis gene expression was thought to be negatively correlated with the patient drug response [6]. Our previous histochemical staining of clinical specimens also found that NOL9 was upregulated in tumor tissues of HCC patients with sorafenib resistance. Therefore, we aspire to further verify whether methylation in the promoter region is a potential mechanism for NOL9 upregulation in sorafenib resistant HCC. Sorafenib-resistant HepG2 cell line (Hep.G2-SR) was established through a six-months-long time treatment of sorafenib, and we found that NOL9 was also over-expressed in it, with ZNF384 knockdown could increase Hep.G2-SR’s response to sorafenib (Fig. 4G and 4H). Collectively, these findings underscore ZNF384’s role in enhancing NOL9 expression and activating its promoter.

Transcription factors are pivotal in regulating the expression of DNA-methylated genes. Therefore, we further assessed methylation patterns across various NOL9 gene sites based on the TCGA-LIHC database. With the use of the UCSC Genome Bioinformatics Site, we identified a high enrichment of CpG islands within the promoter region of NOL9 (Supplementary Fig. 3E), indicating a potential relationship between NOL9 expression. A subsequent correlation analysis revealed a remarkable inverse relationship between NOL9 transcriptional expression and cg09513309 methylation (Fig. 4I and Supplementary Fig. 3F). Previous KM survival analysis indicated that higher NOL9 expression correlated with poorer overall survival (OS) and progression-free interval (PFI) in two different HCC cohorts, whereas increased methylation at the NOL9 cg09513309 site was associated with improved prognosis (OS: P = 0.001; PFI: P = 0.018) (Supplementary Fig. 3G).

Our prior analysis suggested that NOL9 promoter methylation inversely correlated with its mRNA levels in HCC, implying methylation-mediated regulation of its mRNA expression. Thus, we supposed that the methylation level of NOL9 promoter was decreased in HCC, and especially in sorafenib-resistant HCC. Utilizing MSP, we observed that the reduced NOL9 methylation appeared in both Hep.G2-SR and decitabine-treated cells, with decitabine exerting a more pronounced effect (Fig. 4J). In order to further explore the mechanism of methylated changes in the promoter region of NOL9, we transfect the si-RNAs of DNA methyltransferases (DNMTs) in Hep.G2-SR. Methylation PCR revealed that DNMT1 knockdown decreased the ratio of methylated NOL9 remarkably, and knockdown of DNMT1 increased the proportion of unmethylated NOL9 (Fig. 4K). Nevertheless, overexpression of DNMT1 could suppress NOL9 expression in Hep.G2-SR, and knockdown of DNMT1 resulted in elevation of NOL9 expression in Hep.G2 (Fig. 4L). ChIP-qPCR assays revealed that the ZNF384 enrichment at the NOL9 promoter region was decreased after DNMT1 overexpression, and knockdown of DNMT1 increased ZNF384 enrichment (Fig. 4M). These results suggest that DNMT1 was involved in epigenetic regulation process in NOL9 promoter, which is essential for HCC cell resistance to sorafenib.

3.5 NOL9-mediated Cell Proliferation is Beta-catenin Dependent

In order to further study the molecular mechanism of NOL9 regulating the proliferation of liver cancer cells, we performed RNA sequencing on NOL9 knockout liver cancer cells. KEGG enrichment analysis results showed that after NOL9 knockout, the wnt/beta-catenin signaling pathway may be involved in the growth process of liver cancer cells (Fig. 5A). This finding prompted us to explore the effect of NOL9 on the wnt/beta-catenin pathway, and we evaluated the transcriptional activity of beta-catenin/TCF in liver cancer cells after NOL9 overexpression or knockdown. Dual-luciferase assay results indicate that NOL9 can increase the activity of the wnt/beta-catenin signaling pathway in liver cancer cells (Fig. 5B). We also analyzed the protein levels of key genes (beta-catenin) in the wnt/beta-catenin pathway. WB detection showed that knockdown of NOL9 inhibited the protein expression of beta-catenin in liver cancer cells, while overexpression had the opposite effect (Fig. 5C). Overexpression of CTNNB1 in NOL9-downregulated stable strains can promote cell proliferation (Fig. 5D). All these results indicate that NOL9 can activate the wnt/beta-catenin pathway of liver cancer cells at the transcriptional level and promote the proliferation of liver cancer cells through the wnt/beta-catenin pathway.

HCC represents a significant global health burden, characterized by a complex interplay of genetic and environmental factors contributing to its pathogenesis. Despite advances in understanding its molecular underpinnings, effective therapeutic strategies remain elusive, necessitating a deeper exploration of the intricate regulatory networks driving HCC progression. Among the emerging candidates in HCC research is NOL9, a 5’-polynucleotide kinase primarily localized in the nucleus [14]. While its canonical role involves rRNA processing, recent studies have implicated NOL9 in various cellular processes, including tumorigenesis [16].

NOL9's multifaceted involvement in cancer biology is underscored by its aberrant expression patterns in malignancies such as triple-negative breast cancer and acute myeloid leukemia [16, 17]. Previously, we found that elevated NOL9 expression has been linked to tumor progression and poor prognosis in HCC, highlighting its potential as a prognostic marker and therapeutic target [15]. However, the underlying mechanisms driving NOL9 dysregulation and its functional significance in HCC pathogenesis remain incompletely understood. Building upon this backdrop, this study delves into the functional significance of NOL9 in HCC progression and its interplay with epigenetic regulators and signaling pathways.

At first, our study commences by harnessing the datasets of TCGA-LIHC and our institution’s HCC database, encompassing a cohort of 402 patients. It is demonstrated that NOL9 overexpression correlates with poor prognosis in HCC patients, highlighting its potential as a prognostic biomarker. Through comprehensive clinical analyses and nomogram construction, we establish NOL9 as a key determinant of HCC patient survival, offering valuable insights for risk stratification and treatment decision-making. Mounting evidence underscores the dysregulation of key components in ribosome biosynthesis, including ribosome proteins [18], rRNA processing enzymes [19], and assembly factors [20], as a precursor to heightened malignant potential. Indeed, dysregulated ribosome biogenesis has emerged as a hallmark of therapeutic resistance in various cancer models [21]. Our study unveils NOL9’s pivotal role in driving proliferation, suppressing apoptosis, and fostering resistance to sorafenib in vivo and in vitro.

Ribosome biosynthesis, a complex and energy-intensive process, intricately intertwines with the cell cycle to ensure coordinated cellular growth and proliferation [22]. By KEGG and GO, NOL9 is correlated with cell cycle regulation, potentially influencing processes such as chromosome segregation, nuclear division, DNA replication, and cell cycle checkpoints. Further flow cytometry verifies that NOL9 facilitates the G1/S cell cycle transition. During the G1 phase, key regulators of ribosome biogenesis, including transcription factors like c-Myc and RNA polymerases, are activated to promote the transcription of rRNA genes and ribosomal protein genes [23, 24]. As cells progress into the S phase, the rRNA genes are transcribed by RNA polymerase I, yielding precursor rRNA transcripts that undergo processing and modification to generate mature 18S, 5.8S, and 28S rRNAs, which assemble with ribosomal proteins to form the small and large subunits of the ribosome [9]. NOL9, as key participate in pre-rRNA cleavage and 28S rRNA maturation, is essential for cell cycle. And we presume that its dysregulation might lead to genomic instability, impaired proliferation, and ultimately, disease states such as cancer.

Epigenetic dysregulation, particularly DNA methylation, has emerged as a crucial mechanism in cancer development, influencing gene expression patterns and cellular phenotypes [25]. The dynamic interplay between DNA methylation and transcription factor binding modulates gene expression, shaping the molecular landscape of tumors [26]. In HCC, aberrant DNA methylation patterns contribute to tumor heterogeneity and therapeutic resistance, underscoring its clinical significance [27]. Moreover, our mechanistic studies elucidate the intricate regulatory mechanisms governing NOL9 expression in HCC. We identify DNA methylation, particularly mediated by DNMT1, as a critical epigenetic regulator of NOL9 expression. The interaction between NOL9 and ZNF384 further underscores the complexity of NOL9 regulation, providing a potential therapeutic avenue for targeting NOL9 dysregulation in HCC.

Furthermore, the wnt/beta-catenin signaling pathway has garnered considerable attention in HCC due to its pivotal role in cell proliferation, differentiation, and survival [28]. Dysregulated wnt/beta-catenin signaling is a hallmark feature of HCC, driving tumor progression and therapeutic resistance [29, 30]. Our findings elucidate a novel link between NOL9 and the wnt/beta-catenin signaling pathway, further underscoring its oncogenic potential and highlighting potential therapeutic strategies targeting this axis in HCC.

However, while our study provides valuable insights into the role of NOL9 in HCC, several questions remain unanswered. Future investigations should explore the broader regulatory networks and downstream effectors of NOL9, unraveling its full spectrum of functions in HCC pathogenesis. Additionally, the therapeutic implications of targeting NOL9 and its associated pathways warrant further exploration, with potential applications in overcoming therapeutic resistance and improving patient outcomes in HCC.

In summary, our findings not only corroborate previous observations regarding NOL9's oncogenic role but also unveil novel insights into its regulation and downstream effects in HCC. Additionally, our exploration of ribosome biogenesis as a target in cancer underscores the broader implications of our findings, offering insights into novel avenues for improving therapeutic strategies against HCC and other cancers.

In short, our study demonstrates that NOL9, upregulated in HCC tissues, drives tumor progression and influences sorafenib resistance. Mechanistically, NOL9's expression is regulated by DNA methylation and ZNF384, with NOL9 promoting HCC cell proliferation via the β-catenin-dependent pathway. These findings underscore NOL9's potential as a diagnostic biomarker and therapeutic target for improving HCC management.

Author Contributions

Xiyao Chen: conception and design; experimental operations; bioinformatic analysis; data analysis and interpretation; drafting of the manuscript.

Xin Song: experimental operations; drafting of the manuscript.

Boxiang Zhang: experimental operations; bioinformatic analysis.

Xingrong Zheng: sample collection; data collection.

Lina Wu: sample collection; bioinformatic analysis; data collection.

Qinghai Lian: experimental operations; bioinformatic analysis; data analysis and interpretation.

Jia Chen: experimental operations; data collection.

Qiumin Luo: sample collection; data collection.

Wenxiong Xu: sample collection.

Tinglin Qian: experimental operations.

Liang Peng: review and revision of the manuscript; financial support; supervision and guidance.

Chan Xie: conception and design; review and revision of the manuscript; financial support; administrative, technical, and material support; supervision and guidance.

All authors made significant contributions to the manuscript and have given their approval for its publication.

Funding

Natural Science Foundation of China (82070611), Guangdong Basic and Applied Basic Research Foundation (2021A1515220029), Guangzhou Science and Technology Program Key Projects (2023B01J1007), Sun Yat-Sen University Clinical Research 5010 Program (2018009, 2020007), Guangzhou Science and Technology Program (202102010204), Five-Year Plan of Third Affiliated Hospital of Sun Yat-Sen University (K00006, P02421).

Data availability

All data supporting the findings of this study are available from the corresponding author upon reasonable request. Correspondence and requests for materials should be addressed to Chan Xie.

Ethics approval and consent to participate

All experimental protocols were approved by the Ethics Committee at the Third Affiliated Hospital of Sun Yat-sen University (ethics number: II2023-284-01).

Competing interest

Conflict of interest statement: All the authors declare that no conflict of interest exists concerning this paper.

Author details

¹Department of Infectious Diseases, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China

²Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

³Department of Cell-Gene Therapy Translational Medicine Research Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

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Table.1 Relationship between NOL9 protein expression and clinical characteristics

Clinical characteristics	Groups		P value
Clinical characteristics	High expression group (n = 12)	Low expression group (n = 19)	P value
Age	54 ± 8.64	58 ± 12.10	0.329
Gender			0.676
Men	10	14
Women	2	5
HBV			0.387
Negative	1	0
Positive	11	19
Liver cirrhosis			1
Yes	9	14
No	3	5
Serum AFP			0.716
< 20 ug/ml	7	9
≥ 20 ug/ml	5	10
TBIL	10.97 ± 2.47	22.13 ± 12.85	*<0.001*
Recurrence			0.056
Yes	7	4
No	5	15
Pathological differentiation			*0.003*
Minor differentiation	2	14
Moderate differentiation	10	2
Tumor size (mm)	88.67 ± 32.03	47.89 ± 20.47	*<0.001*
Tumor number			0.447
Multiple (≥ 2)	5	5
single	7	14
Metastasis			0.384
Yes	4	3
No	8	16
BCLC stage			0.395
A stage	6	14
B stage	2	2
C stage	4	3

Table.2 Relationship between NOL9 protein expression and clinical characteristics in TCGA-LIHC

Clinical characteristics	Groups		P value
Clinical characteristics	High expression group (n = 185)	Low expression group (n = 186)	P value
Tumor size			*0.039*
T1 & T2	130	148
T3 & T4	55	38
Pathological differentiation			*0.004*
G1 & G2	103	130
G3 & G4	81	55

There is no duality of interest

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Nucleolar NOL9 regulated by DNA methylation promotes hepatocellular carcinoma growth by activating wnt/beta-catenin pathway

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Patient Selection and Specimen Collection

2.2 Data Acquisition and Analytical Procedures

2.3 ssGSEA Enrichment Scores of Ribosome Biogenesis Gene Sets

2.4 Cell Culture and Proliferation Assays

2.5 Immunohistochemistry Analysis

2.6 Western Blot Analysis

2.7 Quantitative Real-time PCR (qRT-PCR)

2.8 Cell Transfection

2.9 Lentivirus Package and Stable Cell Lines

2.10 Methylation Specific PCR (MSP)

2.11 Flow Cytometry for Cell Cycle and Apoptosis Analysis

2.12 Luciferase Reporter Assays

2.13 Chromatin Immunoprecipitation (CHIP)

2.14 TOP/FOP-Flash Wnt Reporting Analysis

2.15 In vivo Tumorigenesis Assay

2.16 RNA-sequencing Analysis

2.17 Statistical Analysis

3. Results

3.1 NOL9 is Upregulated in HCC and Correlates with Patient Survival

3.2 NOL9 Modulates HCC Cell Proliferation and Apoptosis

3.3 Effect of NOL9 on the HCC Cell Cycle

3.4 DNA Methylation and ZNF384 Regulate the Expression of NOL9

3.5 NOL9-mediated Cell Proliferation is Beta-catenin Dependent

4. Discussion

5. Conclusion

Declarations

References

Tables

Additional Declarations

Supplementary Files

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