RPPH1 drives ovarian cancer cells proliferation via CDK9-c-Myc axis

Abstract

Background

Dysregulation of the proto-oncogene c-Myc is a critical driver of ovarian cancer progression, yet the mechanisms governing its expression remain incompletely characterized.

Methods

A systematic bioinformatic analysis was used to screen long non-coding RNA involved in the regulation of c-Myc transcriptional activity in ovarian cancer without MYC amplification. The functions of RPPH1 were investigated using loss-of-function and gain-of-function strategies in vitro and in vivo. The therapeutic potential of targeting this axis was evaluated using the CDK9 inhibitor Atuveciclib, alone and in combined with cisplatin, in ovarian cancer xenograft models.

Results

RPPH1 expression correlated with c-Myc activity was upregulated in ovarian cancer. High RPPH1 levels predicted worse prognosis. Mechanistically, RPPH1 directly bound CDK9, enhancing its stability and promoting phosphorylation of c-Myc at Ser62. This modification prolongs c-Myc’s half-life and amplifies its transcriptional activity. Atuveciclib abrogated RPPH1-driven c-Myc stabilization and activity, while combination therapy with cisplatin synergistically suppressed RPPH1-high tumour growth in xenograft models.

Conclusions

We delineate a non-canonical RPPH1-CDK9-c-Myc axis governing oncoprotein stabilization in ovarian cancer, providing mechanistic insights into lncRNA-mediated post-translational regulation. CDK9 represents a therapeutic target for c-Myc-driven tumours, particularly RPPH1-overexpressing, cisplatin-resistant cases without MYC amplification. The combination of cisplatin and CDK9 inhibitor offers a promising translational strategy.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s13048-026-02225-4.

Introduction

Ovarian cancer (OC) represents one of the most prevalent and lethal malignancies within the female gynaecological system. Epithelial ovarian cancer (EOC) constitutes the predominant histological subtype, accounting for approximately 90% of all cases [1]. The insidious nature of this disease, characterized by nonspecific symptoms during early stages, frequently results in delayed diagnosis, with the majority of patients presenting at advanced stages of disease progression. Current therapeutic protocols primarily consist of cytoreductive surgery followed by platinum-based adjuvant chemotherapy. However, diagnosis at an advanced stage and the development of chemoresistance substantially compromises therapeutic outcomes, contributing to a dismal 5-year survival rate below 30% [2].

Molecular pathogenesis studies have identified the MYC oncoprotein family (c-Myc) as critical mediators of ovarian carcinogenesis. c-Myc protein overexpression occurs in approximately 70% of EOC cases and correlates significantly with advanced tumour stage, chemoresistance, and reduced overall survival [3]. Genomic amplification is the primary driver of c-Myc dysregulation in approximately 40% EOC patients [4], this indicates that post-transcriptional mechanisms may substantially contribute to the c-Myc protein upregulation. Though evidences highlight the importance of post-translational modifications in stabilizing this labile oncoprotein, particularly through phosphorylation-mediated regulation of protein turnover [5, 6], the underlying mechanisms remain incompletely understood.

Long noncoding RNAs (lncRNAs; >200 nucleotides) have emerged as crucial regulators in cancer biology. LncRNA dysregulation is frequently observed in ovarian cancer and contributes to malignant phenotypical changes [7, 8]. Notable examples include: upregulation of LncBRM [9] or HOXD-AS1 [10, 11] promotes ovarian cancer cells proliferation and invasion; HULC [12], SNHG1 [13] and H19 [14] inhibit cell apoptosis; UCA1 [15] and Linc00312 [16] are associated with chemoresistance. Mechanistically, lncRNAs exert multi-modal functional impacts through chromatin remodelling, transcriptional regulation, and modulation of mRNA and protein stability. For example, human colorectal cancer-specific CCAT1-L lncRNA interacts with CTCF to maintain enhancer-promoter looping at the MYC locus, thereby positively regulating MYC transcription [17]. Similarly, lncRNA GLCC1 stabilizes c-Myc by impeding ubiquitination via HSP90 chaperon binding, consequently potentiating c-Myc signalling, and reprogramming glycolytic metabolism [18]. Therapeutic targeting of noncoding RNAs represents a promising approach for cancer treatment [19].

Our study identifies RPPH1 as an OC-specific and highly expressed lncRNA exhibiting potent oncogenic activity through novel c-Myc regulatory mechanisms. Integrated multi-omics analysis revealed significant functional concordance between RPPH1-associated genes and c-Myc signalling networks. Mechanistically, RPPH1 promotes proliferation dependent on c-Myc. Further, RPPH1 interacts with CDK9, enhancing its stability and subsequent phosphorylation of c-Myc at Ser62. This post-translational modification stabilizes c-Myc protein, amplifying its transcriptional activation of proliferative targets. The RPPH1-CDK9-c-Myc axis demonstrates essentiality in maintaining OC cell proliferation and tumorigenicity, presenting a novel therapeutic target.

Materials and methods

Bioinformatic analysis and identification of target lncRNA

RNA sequencing data for gynaecologic malignancies and breast cancer were obtained from The Cancer Genome Atlas (TCGA) database through the GDC Data Portal database. The TCGA projects included in this study were TCGA-OV (ovarian serous cystadenocarcinoma), TCGA-UCEC (uterine corpus endometrial carcinoma), TCGA-CESC (cervical squamous cell carcinoma and endocervical adenocarcinoma), and TCGA-BRCA (breast invasive carcinoma).

The expression matrix was used to extract lncRNA expression profiles, including RPPH1. Expression values were normalized using log2(FPKM + 1). The relative abundance of dysregulated lncRNAs in each tumour type was compared, and the proportion of differentially expressed lncRNAs was calculated. For ovarian cancer, differentially expressed lncRNAs were ranked according to fold change and adjusted p value, and RPPH1 was identified as one of the significantly upregulated candidates.

Cell lines and culture

Human ovarian cancer cell lines (SKOV3, CAOV3, A2780, and ES2) were obtained from the American Type Culture Collection (ATCC). 293T cells were maintained in-house. All cells were cultured in DMEM medium (Gibco, Carlsbad, CA, USA) supplemented with 10% FBS (Gibco, Carlsbad, CA, USA) at 37℃ in a 5% CO₂.

Plasmid construction, RNAi and transfection

Full length of RPPH1, CDK9 and MYC coding sequences were cloned into the pCDH-MSCV-MCS-EF1-copGFP-T2A-Puro (mock vector control). Three RPPH1-targeting siRNAs (Ribobio, Guangzhou, China) and CDK9 or MYC [20] targeting short-hairpin RNAs (shRNA) in pLKO.1-EGFP-Puro were used for knockdown. Lentiviral particles were produced in 293T cells using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA), with supernatants harvested after 48 h for ovarian cancer cell transduction. Stable lines were selected with puromycin. Sequences are in Supplementary Table S1.

RNA isolation and qRT-PCR

Total RNA from tissues or cells were extracted by TRIzol reagent (Takara, Dalian, China), quantified (NanoDrop2000), and reverse-transcribed (PrimeScript RT Reagent Kit, Takara). Quantitative real-time PCR (qRT-PCR) used TB green (Takara) with 2^−ΔΔCt analysis. Primers are in Supplementary Table S2.

Generation of knockout cell lines

Two 20-nt single-guide RNAs (sgRNAs) targeting regions immediately upstream of a 5’-NGG-3’ protospacer adjacent motif (PAM) recognized by Streptococcus pyogenes Cas9 (SpCas9) using the CRISPR design tool CRISPOR (GACAGGACGCACTCAGCTCG, GTTCCAAGCTCCGGCAAAGG). Oligonucleotides were annealed and cloned into the pU6-2XMCS-sgRNA-CBH-Cas9-EGFP-Puro vector (#P44666, MiaoLingBio) following BbsI and Bspq1 digestion respectively. The construct was transfected into CAOV3 cells using Lipofectamine 2000 (Thermo Fisher Scientific).

At 48 h post-transfection, cells were seeded into 96-well plates at limiting dilution to obtain single-cell-derived clones. After 10–14 days, wells containing single colonies were identified under a microscope and subsequently expanded into 24-well plates for further analysis. When clones reached 80% confluence, half of the cells were harvested for genomic DNA extraction using gDNA EXTRACTION KIT (#D0061, Beyotime). The target genomic region was amplified by PCR (primers listed in Supplementary Table S2). Clones lacking the expected PCR product were identified as knockout candidates and further validated by RT‑qPCR.

RNA-seq data analysis

Principal component analysis (PCA) was first performed based on the gene expression matrix to evaluate the overall variation and sample clustering between groups. Differential expression analysis was performed using the DESeq2 package. Genes meeting the criteria of p value < 0.05 and fold change > 1.5 were defined as differentially expressed genes (DEGs).

Genes from DEG clusters were separately subjected to functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Hallmark gene set enrichment. The significantly enriched terms were visualized using bar plots, and the enrichment results were presented according to gene ratio and −log10(p value).

Subcellular fractionation

The Nuclear and cytoplasmic RNA in ovarian cancer cells was isolated using the PARIS™ Kit (Life Technologies, Austin, Texas, USA) according to the manufacturer protocols. Distribution was analysed by qRT-PCR and western blotting.

Tissue microarray and In Situ Hybridization (ISH)

An ovarian cancer tissue array (Outdo Biotech, Shanghai, China) was analysed for RPPH1 expression in 151 paraffin-embedded samples. After deparaffinisation and protease K digestion, slides were hybridised (45 °C overnight) with digoxigenin-labelled RPPH1 probes (Geneseed, Guangzhou, China), incubated with biotinylated anti-digoxigenin antibody (4 °C overnight), and visualised with DAB (ZSGB-bio). The expression of RPPH1 was analysed according to the value of positive cell staining intensity (negative = 0, weak = 1, moderate = 2 and strong = 3) and the proportion of positively stained cells (< 10% = 0, 10–25% = 1, 26–50% =2, 51–75% = 3, > 75% = 4). ISH score equaled staining intensity × proportion score of positive cells. And the average ISH score was 5.7, so the ISH score < 6 indicated low expression, while ≥ 6 defined high expression.

Proliferation assay

For CCK-8 assay, 2 × 10⁴ cells/well (96-well plate) were analysed at 0/24/48/72 h (OD₄₅₀; Bioground). For EdU assay, ovarian cancer cells were plated at a density of 5 × 105 cells per well in a 12-well plate. Live cells were stained following the manufacturer’s instructions (Ribobio, Guangzhou, China) and subsequently analysed by fluorescence microscopy. Semi-automated analysis was performed using ImageJ. In each field, the number of positive cells was divided by the total number of DAPI-stained nuclei to calculate the percentage of positive cells. In Edu assay, 5 × 10⁵ cells/well (12-well plate) were stained per manufacturer protocol (Ribobio) and imaged. For EU assay, cells were labelled with EU for 30 min, then processed according the manufacturer protocol(Beyotime) and analysed by FACS. In colony formation assay, 1,000 cells/well (6-well plate) were cultured for 7–14 days, fixed, and stained with crystal violet.

Fluorescent In Situ Hybridization (FISH) and immunofluorescence staining

Fluorescence in situ hybridization was performed using the RNA FISH SA-Biotin kit (GenePharma, Shanghai, China) according to the manufacturer’s instructions. The experiment utilized a mixture of RPPH1 antisense DNA probes with 5’ end Biotin-TEG modifications. Sequences of the probes were in Supplementary Table S3.

Cells cultured on glass coverslips in 24-well plates were fixed with 4% paraformaldehyde for 15 min and permeabilized with 0.5% Buffer A at room temperature for 15 min. After two washes with PBS, cells were blocked with 1x blocking buffer at 37℃ for 30 min, then incubated with 2× Buffer C at 45℃ for 30 min.

For probe preparation, the specific DNA probes (2µM each, 8µL in PBS) were denatured at 85℃ for 10 min and then incubated with 2µL SA-Cy3 at 37℃ for 30 min. The probe mixture was diluted in 90µL Buffer E, added to the cells, and hybridized overnight at 45 °C in the dark.

The next day, cells were washed with 0.1% Buffer F, followed by six washes with 2× Buffer C at 45℃ for 5 min each. After three additional washes with PBS, coverslips were blocked with Immunostaining Blocking Buffer (#36320ES60, Yeasen Biotechnology), followed by incubation with primary antibodies against CDK9 (1:300, #67256-1-Ig, Proteintech, Wuhan, China) or c-Myc (1:200, #10828-1-AP, Proteintech) overnight at 4 °C. After three washes with PBST, coverslips were incubated with species-matched Alexa Fluor-conjugated secondary antibodies (1:200, #RGAR004 and #RGAM005, Proteintech) for 1 h at room temperature. Nuclei were counterstained with DAPI (1:1000 in PBS) for 10 min in the dark. Coverslips were mounted onto slides with an anti-fade mounting medium and visualized under a confocal fluorescence microscope with 63× oil objective (Leica, STELLARIS 5).

Western blotting and co-immunoprecipitation

Proteins were separated by SDS-PAGE, transferred to PVDF membranes (Merck Millipore, Billerica, MA, USA), blocked (5% skimmed milk), and probed with antibodies. For co-IP, lysates were incubated (4 °C overnight) with anti-c-Myc, anti-CDK9, or IgG (5 µg) using Pierce™ Magnetic IP/Co-IP Kit (Thermo Fisher Scientific, Waltham, MA, USA), followed by protein A/G bead capture and immunoblotting. Antibodies included: CDK9 (#11705-1-AP, Proteintech), c-Myc (#10828-1-AP, Proteintech), eIF4E (#66655-1-ig, Proteintech), c-Jun (#A11378, Abclonal, Wuhan, China), Cdc25A (#A1173, Abclonal), RNAPⅡ (#A2107, Abclonal), p-RNAPII(S2) (#AP0749, Abclonal), p-RNAPII(S5) (#AP0828, Abclonal), Mcl1 (#A0434, Abclonal), and GAPDH (#AC002, Abclonal).

RNA-pulldown

Biotinylated RPPH1 was transcribed in vitro (T7 promoter; Ribobio) and incubated with cell lysates (Pierce™ Magnetic RNA-Protein Pull-Down Kit; Thermo Fisher Scientific). Bound proteins were analysed by Western blotting.

Xenograft mouse model

CAOV3 cells (5 × 10⁶) were implanted subcutaneously into 4-week-old female nude mice (SPF facility; First Affiliated Hospital of Army Military Medical University). After one week of implantation, all mice were randomly divided into four groups(n = 5) as follows: RPPH1-PBS, RPPH1-Comb(cisplatin (4 mg/kg) + Atuveciclib(5 mg/kg)), Vector-PBS, Vector-Comb(cisplatin (4 mg/kg) + Atuveciclib(5 mg/kg)). All the mice were administrated every other day until this experiment was ended. Tumour volume was calculated as (length × width²)/2. Mice were euthanised at 4 weeks; tumours were imaged and stored at − 80 °C.

Statistical analysis

Statistical analyses were performed using GraphPad Prism 8.0. An unpaired two-tailed Student’s t‑test was used for two-group comparisons. One‑way ANOVA followed by Tukey’s post‑hoc test was applied for multi‑group comparisons. For data that did not meet the assumptions of normality or homoscedasticity, the Kruskal-Wallis test (non-parametric one-way ANOVA) was applied. For analyses involving two independent variables, two‑way ANOVA was performed with Tukey’s or Sidak’s post‑hoc test. The Kaplan–Meier method with the log‑rank test was used for survival analysis. Data are presented as mean ± SD from at least three independent experiments. p value < 0.05 was considered statistically significant. Statistical tests and p values are specified in the figure legends.

Results

LncRNA RPPH1 regulates c-Myc signaling

To identify c-Myc-dependent lncRNAs in OC, we analysed The Cancer Genome Atlas (TCGA) data for differentially expressed genes (DEGs) (|log2FC| ≥ 1, p < 0.01). Notably, OC exhibited the highest lncRNA dysregulation among gynaecological cancers (Fig. 1a), suggesting a potential pivotal role for lncRNAs in OC pathogenesis and progression. Of the top ten overexpressed lncRNAs, RPPH1 and HAGLR correlated strongly with MYC expression (Fig. 1b, Supplementary Fig. 1a). To further elucidate this relationship, we filtered out the samples with MYC amplification and identified RPPH1 showed the strongest MYC correlation (Fig. 1c, Supplementary Fig. 1b), suggesting an RPPH1-c-Myc axis independently of genomic amplification.

Fig. 1.

LncRNA RPPH1regulates c-Myc signaling. a Comparative analysis of differentially expressed lncRNAs in gynaecological malignancies. OC exhibits the highest lncRNA dysregulation. b Top 10 upregulated lncRNAs in OC. c Correlation analysis between RPPH1 and MYC gene signatures. KEGG (d) and Hallmark (e) pathway enrichment of RPPH1-correlated genes. f RNA-seq analysis of CAOV3 cells with RPPH1 knockout (RPPH1-KO). f Gene expression profiles of CAOV3 cells and RPPH1-KO cells. KEGG and Hallmark pathway enrichment of genes significantly downregulated or upregulated following RPPH1 knockout

Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of RPPH1-correlated genes revealed enrichment in oxidative phosphorylation (OXPHOS), neurodegenerative pathways, ribosome biogenesis, and RNA polymerase activity. Hallmark analysis highlighted enrichment in oxidative phosphorylation, DNA repair, adipogenesis, MYC targets, and reactive oxygen species (ROS) signaling pathways (Fig. 1d-e). We constructed and verified the RPPH1-knockout (KO) CAOV3 cells (Supplementary Fig. 2), RNA sequencing in KO cells confirmed downregulation of “MYC targets” (Hallmark gene sets; Fig. 1f. Supplementary Table S4). Additionally, there was enrichment of E2F TARGETS, DNA REPAIR, and G2M CHECKPOINT. Apart from the downregulated genes, we also analysed the 1,015 genes that were upregulated upon RPPH1 depletion. Hallmark enrichment analysis revealed that these upregulated genes were predominantly associated with pathways related to cellular stress and metabolic adaptation, including HYPOXIA, MTORC1 SIGNALING, and GLYCOLYSIS (Fig. 1f).These findings suggest a functional interaction between RPPH1 and c-Myc signaling, prompting us to investigate its clinical significance and biological role in ovarian cancer.

RPPH1 specifically up-regulated is associated with poor prognosis of OC

To determine whether this RPPH1-c-Myc axis holds clinical relevance, we examined RPPH1 expression in OC patient samples. RPPH1 was significantly upregulated in OC versus normal tissues (TCGA; Fig. 2a). High RPPH1 correlated with shorter overall survival (OS; Fig. 2b) and advanced clinical stage (p < 0.05; Fig. 2c). To ensure the clinical relevance of RPPH1 expression in OC, RPPH1 was checked by in situ hybridization in a paraffin-embedded ovarian cancer tissue array. It was confirmed that RPPH1 upregulation in stage III/IV versus I/II tumours (Fig. 2d-e). And Kaplan–Meier analysis validated worse OS in RPPH1-high patients (Fig. 2f). However, the independent prognostic value remained unproved as the clinical variables available in our TMA were limited (Supplementary Table S5). These clinical observations reinforced our hypothesis, leading us to directly test whether RPPH1 exerts oncogenic functions in ovarian cancer cells.

Fig. 2.

RPPH1 specifically up-regulated is associated with poor prognosis of OC. a RPPH1 expression in OC (TCGA tumor samples vs. GTEx normal samples), box plots were obtained from the GEPIA database [34]. b Kaplan-Meier analysis of overall survival (High vs. Low RPPH1, log-rank p < 0.01). c Univariate regression of RPPH1 with clinicopathological characteristics. d Representative in situ hybridization (ISH) of RPPH1 in 151 HGSOC specimens. Scale bar: 100 μm. e RPPH1 expression frequency by TNM stage (stage III/IV vs. I/II). f ISH-based survival analysis (High vs. Low RPPH1). The growth curves of SKOV3 (g) and CAOV3 (h) were measured by CCK8 assay. The proliferation ability of SKOV3 (i) and CAOV3 (j) was detected by colony formation. k-l Xenograft tumour growth in control and RPPH1-knockdown models: k Tumour volume kinetics, data were analysed by two‑way ANOVA followed by Sidak’s post‑hoc test; l Excised tumours at endpoint (n = 5). The data were presented as the mean ± SD, unpaired two-tailed Student’s t‑test was used for two-group comparisons, and one-way ANOVA was conducted for multi‑group comparisons, followed by Tukey’s post hoc test. *p < 0.05, **p < 0.01, ***p < 0.001

RPPH1 regulates tumorigenesis in vitro and in vivo

RPPH1 was ubiquitously expressed in OC cell lines, with relatively higher expression levels detected in CAOV3 and SKOV3 cells (Supplementary Fig. 3a). To elucidate the function of RPPH1, gain- and loss-of-function models were established in CAOV3 and SKOV3 cells through RPPH1 overexpression or knockdown (Supplementary Fig. 3b-c). RPPH1 overexpression significantly enhanced cell proliferation and colony formation in SKOV3 (Fig. 2g-i) and CAOV3 (Fig. 2h-j) cells, while knockdown suppressed both. Additionally, 5-ethynyl-2′-deoxyuridine (EdU) incorporation assays corroborated these findings (Supplementary Fig. 3d-e).

To extend these findings in vivo, CAOV3 cells stably expressing short hairpin RNAs (shRNAs) targeting RPPH1 (shRPPH1-1 and shRPPH1-2) or a control vector were subcutaneously implanted into female nude mice. RPPH1 knockdown significantly reduced tumour weight and volume compared to controls (Fig. 2k-l). Collectively, these results from both in vitro and in vivo experiments strongly suggest that RPPH1 functions as an oncogenic lncRNA, driving tumour cell proliferation.

RPPH1 promotes OC cells proliferation through c-Myc

c-Myc protein decreased after RPPH1 knockdown but increased with RPPH1 overexpression (Fig. 3a). To investigate whether the role of RPPH1 is mediated through c-Myc, we employed a specific shRNA targeting c-Myc (shMyc) in both SKOV3 and CAOV3 cells (Fig. 3b), and examined the expression of specific MYC target gene Cdc25A regulating proliferation. RPPH1 overexpression upregulated the expression of Cdc25A, while concurrent c-Myc knockdown effectively attenuated this upregulation (Fig. 3c). Proliferation (Fig. 3d-e) and colony formation (Fig. 3f-g) driven by RPPH1 were nullified by MYC knockdown, confirming c-Myc dependence.

Fig. 3.

RPPH1 promotes OC cells proliferation through c-Myc. a Protein expression of c-Myc in the RPPH1 overexpressing or RPPH1 depleted OC cells. b c-Myc knockdown efficiency (qRT-PCR/Western blot). c Relative expression of c-Myc targets were detected in SKOV3 and CAOV3 cells expressing control, RPPH1, or both RPPH1 and c-Myc shRNA. The cell viability was evaluated in cells expressing control, RPPH1, or both RPPH1 and c-Myc shRNA by EdU (Scale bar, 100 μm) (d-e) and colony formation assay (f-g). The data were presented as the mean ± SD- and analysed by one-way ANOVA followed by Tukey’s post hoc test. *p < 0.05, **p < 0.01, ***p < 0.001

RPPH1 enhances Ser62 phosphorylation of c-Myc protein and increases its stability

RPPH1 overexpression significantly increased c-Myc protein levels without altering MYC mRNA (Fig. 4a). Given that c-Myc is a short-lived protein with a rapid turnover rate, we hypothesized that RPPH1 might regulate its stability. Cycloheximide (CHX) chase assays showed RPPH1 extended c-Myc half-life (Fig. 4b). While, RPPH1 knockdown accelerated c-Myc degradation (Fig. 4c).

Fig. 4.

RPPH1 enhances Ser62 phosphorylation of c-Myc protein and increases its stability. a MYC mRNA unchanged after the overexpression and knockdown of RPPH1 (qRT-PCR; n.s., no significance). From left to right: RPPH1 overexpression in SKOV3, RPPH1 knockdown in SKOV3, RPPH1 overexpression in CAOV3, RPPH1 knockdown in CAOV3. Cycloheximide chase (CHX, 100 µg/mL) in RPPH1-overexpressing (b) or knockdown (c) cells. d Ubiquitination levels of c-Myc in RPPH1 overexpressing or knocking down CAOV3 cells (IP: anti-c-Myc; IB: anti-Ub). e The expression of c-Myc protein after inhibition of proteasome by MG132 (20µM, 6 h). f c-Myc T58A/S62A mutant levels after RPPH1 knockdown in CAOV3 cells. g CHX chase of S62A mutant in RPPH1 overexpressing CAOV3 cells. h the effect of wildtype and S62A c-Myc on proliferation of RPPH1 KO cells was determined by Brdu assay. i Subcellular localization of RPPH1 was determined by qRT-PCR. GAPDH and U6 are used as cytoplasmic and nuclear internal control, respectively. j Subcellular localization of c-Myc in CAOV3 cells was determined by western blot. Histone (Histone H3.1), nuclear marker; GAPDH, cytoplasmic marker

Since c-Myc is primarily degraded via the ubiquitin-proteasome pathway in the cytoplasm, we next examined c-Myc ubiquitination status in RPPH1-modulated cells. Immunoprecipitation assays demonstrated that RPPH1 overexpression reduced c-Myc ubiquitination levels, correlating with increased protein stability (Fig. 4d). In CAOV3 cells, MG132 treatment led to accumulation of c-Myc protein in the context of RPPH1 knockdown, while this effect was mild in SKOV3 cells (Fig. 4e). Usually, ubiquitin-mediated degradation requires sequential phosphorylation at Ser62 and Thr58 of c-Myc. RPPH1 knockdown reduced phosphorylation-deficient T58A c-Myc but not S62A mutants (Fig. 4f). Moreover, RPPH1 failed to stabilize S62A c-Myc (half-life unchanged, Fig. 4g), suggesting Ser62 phosphorylation is essential for RPPH1-mediated stabilization. But in functional assay, S62A c-Myc can partially rescue the proliferation defects of RPPH1 KO cells(Fig. 4h). To investigate the mechanism of RPPH1 regulating c-Myc Ser62 phosphorylation, we examined its intracellular localization. Subcellular fractionation showed cytoplasmic RPPH1 localization versus nuclear c-Myc (Fig. 4i-j) suggesting indirect regulation.

RPPH1 interacts with CDK9 to stabilize c-Myc

Our results count out a potential mechanism may involve RPPH1-mediated modulation of kinases/phosphatases governing Ser62 phosphorylation status. Given RPPH1 regulated RNA polymerase activity (Fig. 1f), and the component of positive transcription elongation factor b (pTEFb) - CDK9 could phosphorylate c-Myc Ser62. catRAPID prediction suggested an interaction between RPPH1 and CDK9 (Supplementary Fig. 4a). Immunofluorescence confirmed cytoplasmic RPPH1, nuclear c-Myc, and pan-cellular CDK9 (Fig. 5a). RNA pull-down demonstrated direct RPPH1-CDK9 binding (Fig. 5b). RPPH1 overexpression increased CDK9 protein levels, whereas RPPH1 knockdown reduced CDK9 expression (Supplementary Fig. 4b). CHX chase assays further proved RPPH1 regulated CDK9 protein stability (Fig. 5c). Co-immunoprecipitation showed RPPH1 enhanced CDK9-c-Myc interaction (Fig. 5d). This aligns with our observation of increased CDK9-mediated phosphorylation of c-Myc at Ser62 in RPPH1-overexpressing cells, thereby promoting c-Myc stabilization.

Fig. 5.

CDK9 inhibition abrogates RPPH1-driven proliferation. a RPPH1 in CAOV3 cells was detected by FISH. IF analysis of c-Myc and CDK9 protein after the transfection with RPPH1(Scale bar, 100 μm). b RPPH1 RNA pull-down with biotinylated probe. The AU-rich element and its interacting molecule HuR served as the positive control. c CHX chase (100 µg/mL) assay in CAOV3 cells transfected with RPPH1 shRNAs shows reduced CDK9 stability. d Immunoprecipitation assay was performed in CAOV3 cells to detect the interaction between CDK9 and c-Myc after RPPH1 overexpressing. e Phospho-c-Myc (Ser62) and total c-Myc protein were analysed in CAOV3 cells overexpressing RPPH1 with or without CDK9 inhibitor treatment (5µM, 6 h). f c-Myc luciferase reporter plasmid was cotransfected with vector or RPPH1 in CAOV3 cells. Combined with or without CDK9 inhibitor treatment, cells were harvested for luciferase activity assay. g CDK9 knockdown validation. h Phospho-c-Myc (Ser62) and total c-Myc protein in CAOV3 cells expressing control, RPPH1, CDK9 shRNA, or both RPPH1 and CDK9 shRNA. The proliferation of SKOV3 and CAOV3 cells co-transfected with RPPH1 and vector or shCDK9 was measured by CCK8 assay (i and j) and cell clone formation (k), data of CCK8 assay were analysed by two‑way ANOVA followed by Tukey’s post‑hoc test. Expression of downstream proteins of CDK9 were assessed in SKOV3 (l) and CAOV3 (m) cells expressing control, RPPH1, or both RPPH1 and CDK9 shRNA. n The RNA synthesis was assessed by EU assay using FACS in SKOV3 or CAOV3 cells expressing control, RPPH1, or both RPPH1 and CDK9 shRNA. The data are presented as the mean ± SD, *p < 0.05, **p< 0.01

CDK9 inhibition abrogates RPPH1-driven proliferation

CDK9 inhibitor suppressed RPPH1-induced c-Myc Ser62 phosphorylation and protein accumulation (Fig. 5e). Luciferase assays confirmed that RPPH1-mediated c-Myc transcriptional activity was potently suppressed by CDK9 inhibitor (Fig. 5f). Knockdown CDK9 similarly reduced c-Myc protein (Fig. 5g-h). Furthermore, the proliferative induced by RPPH1 overexpression in SKOV3 and CAOV3 cells were impaired by CDK9 knockdown (Fig. 5i, j and k). CDK9 is the catalytic subunit of pTEFb. RNA polymerase II (RNAPII) and Mcl-1 are CDK9 targets [21]. We found RPPH1 promoted S2 and S5 phosphorylation of RNAPII without disturbing protein abundance, and increased Mcl-1 expression both in mRNA and protein levels. And CDK9 knockdown sufficiently abrogates the increase of RNAPII S2 and S5 phosphorylation and induction of Mcl-1 expression (Fig. 5l-m). EU incorporation assays confirmed RPPH1-enhanced global transcription, attenuated by CDK9 inhibition (Fig. 5n). These results indicate that the interaction between RPPH1 and CDK9 not only increases the stability of c-Myc protein, but also promote global transcription.

Targeting CDK9 in RPPH1 high ovarian cancer cells

In view of the enhanced stability of CDK9 due to the interaction with RPPH1, we treated the ovarian cancer cells with Atuveciclib (a clinical-stage CDK9 inhibitor) following RPPH1 overexpression or knockdown. RPPH1-overexpressing cells showed heightened sensitivity to the CDK9 inhibitor Atuveciclib, while RPPH1 knockdown conferred resistance (Fig. 6a-b). Dose-response assays revealed that RPPH1-overexpressing cells exhibited significantly reduced cisplatin sensitivity compared to controls (Fig. 6c, d). Building upon our previous findings that RPPH1-high cells demonstrate enhanced sensitivity to CDK9 inhibitors, we hypothesized that combinatorial treatment might overcome cisplatin resistance. Combination therapy (cisplatin + Atuveciclib) synergistically suppressed proliferation in vitro (Fig. 6e) and tumour growth in RPPH1-high xenografts (Fig. 6f, Supplementary Fig5a, -b). These results suggest that dual targeting with cisplatin and CDK9 inhibitors may represent a promising strategy for ovarian cancer treatment, particularly for RPPH1-high tumours.

Fig. 6.

Targeting CDK9 in RPPH1 high ovarian cancer cells. The growth curves of SKOV3 (a) and CAOV3 (b) cells expressing control, RPPH1 or control shRNA, RPPH1 shRNA were assessed using CCK8 assay after treatment with CDK9 inhibitors for 24 h. The sensitivity to cisplatin in SKOV3 (c) and CAOV3 (d) cells expressing control, RPPH1 or control shRNA, RPPH1 shRNA was assessed by CCK8 assay. e Viability of CAOV3 cells was evaluated by CCK8 assay in the presence of cisplatin combined with CDK9 inhibitor at varying concentrations. f The effect of combined administration of cisplatin and CDK9 inhibitors on OC tumour growth in a nude mouse xenograft model (n = 5). Tumour volumes were meticulously measured every 4 days, and the growth curves were plotted. The data are presented as the mean ± SD, and analysed by two‑way ANOVA followed by Sidak or Tukey’s post‑hoc test, *P < 0.05, **P < 0.01, ***P < 0.001

Discussion

Long non-coding RNAs have emerged as critical regulators in carcinogenesis, yet their functional characterization in ovarian cancer remains incompletely understood. Our study identifies RPPH1 as a clinically significant oncogenic lncRNA, demonstrating its specific upregulation in OC, association with advanced-stage disease, and prognostic value for poor survival. While lncRNAs typically exert context-dependent oncogenic or tumour-suppressive roles through protein interactions or miRNA sponging, we reveal that RPPH1 drives OC progression partially via post-translational stabilization of c-Myc, a master transcriptional regulator of cancer cell proliferation. Mechanistically, RPPH1 enhances c-Myc stability via CDK9-mediated phosphorylation at Ser62, offering a therapeutic strategy for RPPH1-high OC patients exhibiting c-Myc overexpression without genomic amplification. Site-specific phosphorylation governs c-Myc proteolysis via the ubiquitin-proteasome pathway. Phosphoresidues like Ser62 modulate c-Myc stability and oncogenic activity, including apoptosis and transformation [22–25]. Our findings align with evidence that dysregulated Ser62/Thr58 phosphorylation enhances c-Myc stability in tumours, lacking gene amplification [26].

Convergent evidence from the TCGA data analysis and RNA sequencing of RPPH1-knockout cells consistently points to a functional association between RPPH1 and c‑Myc signalling. Notably, genes upregulated upon RPPH1 depletion were enriched in pathways related to HYPOXIA, MTORC1 SIGNALING, and GLYCOLYSIS. This pattern is more consistent with a compensatory cellular response to the proliferative stress caused by RPPH1 loss rather than a direct oncogenic drive. For instance, the upregulation of glycolytic genes may reflect an adaptive metabolic shift aimed at maintaining energy homeostasis under conditions of impaired growth. Importantly, the absence of classical proliferation-associated signatures among the upregulated genes further reinforces the conclusion that the pro‑tumorigenic function of RPPH1 is largely mediated through the downregulation of MYC‑dependent proliferative programs.

Structurally, RPPH1 binds and stabilizes CDK9, increasing CDK9-c-Myc interaction and Ser62 phosphorylation. This subverts proteasome-mediated degradation, extending c-Myc half-life. As the catalytic core of pTEFb, CDK9/cyclin T canonically regulates transcription initiation/elongation. CDK9 is overtly employed by cancer cells to sustain production of short-lived proteins like c-Myc [27]. Although CDK9 modulates both MYC transcription and protein stability in other contexts [28], RPPH1 engagement selectively enhanced c-Myc stability without altering MYC mRNA levels. The underlying mechanism warrants further investigation.

Functionally, RPPH1-CDK9-c-Myc axis promotes key oncogenic processes: (1) Enhanced oxidative phosphorylation to meet bioenergetic demands, (2) Ribosome biogenesis to support protein synthesis, and (3) Transcriptional activation of proliferation-associated targets (Cdc25A). These findings are particularly significant, given the established roles of Cdc25A activation in OC chemoresistance [29–31]. Consistent with reports that c-Myc-addicted cancers exhibit CDK9 sensitivity [32], RPPH1-mediated c-Myc stabilization rendered OC cells more vulnerable to CDK9 inhibitor (Atuveciclib). Therapeutically, CDK9 inhibition abrogated RPPH1-mediated cisplatin resistance, and synergizes with cisplatin to inhibit tumour growth in vivo, even in RPPH1-high models. Unlike the therapeutic challenges posed by the intrinsically disordered structure of c-Myc, several CDK9 inhibitors (KB-0742 and VIP-152) have shown promise in clinical trials (NCT04718675, NCT04978779) [33].

Based on our findings, the corresponding clinical application potential is proposed. First, RPPH1 expression may serve as a biomarker for CDK9 inhibitor sensitivity, particularly in cisplatin-resistant OC. Second, combined CDK9 inhibition and cisplatin could represent a precision strategy for RPPH1-high tumours. While our study establishes the RPPH1-CDK9-c-Myc axis as a critical driver of OC pathogenesis, future investigations should delineate the structural basis of RPPH1-CDK9 interaction and explore therapeutic strategies to disrupt this oncogenic partnership.

Authors’ contributions

XC and YC designed the research. YC, LT, and HH performed research and analyszed the data. YC drafted the manuscript. XC, YW and YGC supervised the study and revised the paper. All authors contributed to the article and approved the submitted version.

Funding

This work was supported by the National Natural Science Foundation of China (81874213) and National Key Clinical Specialty Construction Project (Obstetrics and Gynaecology, 2022).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

All animal experiments were approved by Animal Care and Research Advisory Committee of Southwest Hospital. All methods were performed in accordance with its relevant guidelines and regulations.

Competing interests

The authors declare no competing interests.