DHRS9 promotes malignant progression of ovarian cancer through SQSTM1

Abstract

Objective

This research explores the prognostic value of DHRS9 in ovarian carcinoma and elucidates its regulatory mechanisms.

Methods

Bioinformatic analyses were applied to clarify the association between DHRS9 expression level and clinical survival outcomes in ovarian cancer patients. Functional assays were conducted to evaluate cell growth, migration, and invasion. Apoptosis was quantified via flow cytometry. The expression of Dehydrogenase/Reductase Member 9 (DHRS9) and sequestosome 1 (SQSTM1) at both mRNA and protein levels was analyzed via quantitative real-time polymerase chain reaction (qRT-PCR) and western blot assays. Mass spectrometry identified SQSTM1 as a putative downstream effector of DHRS9, and their interaction was validated by co-immunoprecipitation (Co-IP). The in vivo effects of DHRS9 knockdown were examined in a subcutaneous xenograft tumor model of nude mice.

Results

Bioinformatic analysis showed that elevated DHRS9 expression correlated with reduced overall survival in ovarian cancer patients. Silencing DHRS9 attenuated cell growth, migration, and invasion, whereas promoting apoptotic activity. In contrast, DHRS9 overexpression enhanced oncogenic behaviors and suppressed apoptosis. Mass spectrometry and Co-IP analyses confirmed SQSTM1 as an interacting partner of DHRS9, and knockdown of DHRS9 decreased SQSTM1 protein levels in vivo and in vitro, while its overexpression increased SQSTM1 levels. Moreover, functional studies demonstrated that SQSTM1 knockdown reduced ovarian cell growth, migration, and invasion. Xenograft experiments further demonstrated that DHRS9 knockdown resulted in decreased tumor volume.

Conclusion

DHRS9 promotes ovarian cancer proliferation, migration, and invasion, and inhibits apoptosis through its interaction with SQSTM1. These findings indicate that DHRS9 may serve as a potential prognostic indicator and therapeutic candidate in ovarian cancer.

Introduction

Ovarian cancer ranks among the most fatal gynecological malignancies globally. In 2022, China recorded approximately 61,100 new cases and 32,600 deaths attributed to ovarian cancer (Han et al. 2024). The disease often remains asymptomatic during its early stages, resulting in delayed diagnosis, most commonly at stages III (51%) or IV (29%), where the 5-year survival rates drop to 42% and 26%, respectively (Torre et al. 2018). Surgical intervention and chemotherapy are the routine treatments for ovarian cancer. Although PARP inhibitors and anti-VEGF therapies have been integrated into treatment protocols in recent years, their impact on the 5-year survival in advanced or recurrent cases remains modest (Jelovac and Armstrong 2011). These obstacles underscore that it is urgent to explore new mechanisms underlying ovarian cancer progression and identify novel therapeutic candidates to improve patients’ outcomes.

DHRS9, a member of the short-chain dehydrogenase/reductase (SDR) superfamily, plays a key role in the biosynthesis of all-trans retinoic acid and retinol metabolism (Soref et al. 2001; Kropotova et al. 2014). Recent studies have shown that DHRS9 is dysregulated in several malignancies, including colorectal cancer, pancreatic cancer, and oral squamous cell carcinoma (Shimomura et al. 2018; Chen et al. 2022; Li et al. 2020), where its expression correlates with tumour aggressiveness and poor clinical prognosis. However, its regulatory mechanisms in ovarian cancer remain unclear. Through integrative bioinformatic and experimental analyses, the current research endeavors to elucidate the biological role of DHRS9 in ovarian cancer, potentially providing a novel viewpoint for the treatment and prognostic evaluation of ovarian cancer.

Methods

Cell culture

IOSE-80 (normal human ovarian cell line), A2780 and OVCAR-3 (two cell lines originating from human ovarian carcinoma) were purchased from Suzhou Haixin Biological Technology Co., Ltd. (China). All the cell lines were maintained in standard conditions (37 °C, 5% CO₂), with routine subculturing performed every 48 h. IOSE-80 and A2780 cells were maintained in RPMI medium (Procell, China) with 10% (v/v) fetal bovine serum (FBS, Lonsera), 2 mM L-glutamine (Thermo, USA), plus 100U/mL streptomycin and 100 U/mL penicillin solution (Solarbao, China). OVCAR-3 cells were maintained in complete RPMI medium containing 20% (v/v) FBS, 0.1 mg/mL insulin (Procell), and antibiotics.

Small interfering RNA (siRNAs) transfection

siRNAs targeting DHRS9 and SQSTM1 were synthesized by JTSBIO Co., Ltd. (China) and Sangon Biotech (China), respectively. A2780 cells at 40–60% confluence were transfected with 50 nM siRNA using jetPRIME transfection reagent in line with the guidelines provided by the producer. At 48 h post-transfection, cells were collected for subsequent RNA isolation and protein quantification. The sequences of siRNA are presented as follows:

DHRS9 siRNA

#1 CAGGAUCAACAGCUUUAAATT; UUUAAAGCUGUUGAUCCUGTT

#2 GCAGAUCCAGUAAAGGUAATT; UUACCUUUACUGGAUCUGCTT

SQSTM1 siRNA

#1 UCCUCCUGAACAGUUAUCCDTDT; GGAUAACUGUUCAGGADTDT

#2 UUCUUGGUCUGCAGGAGCDTDT; GGCUCCUGCAGACCAAGADTDT

NC siRNA

UUCUCCGAACGUGUCACGUTT; ACGUGACACGUUCGGAGAATT

Plasmid transfection

The DHRS9 overexpression plasmid (pcDNA3.1-DHRS9) and corresponding empty vector (pcDNA3.1) were obtained from JTSBIO Co., Ltd. (China). OVCAR-3 cells at 70–80% confluence were transfected using jetPRIME reagent, following standard procedures. Cells were harvested at 48 h and 72 h post-transfection for subsequent quantification of mRNA and protein expression.

CCK-8 cell viability assay

Cell viability was assessed at regular intervals of 0, 24, 48, and 72 h post-transfection, utilizing the CCK-8 kit (China) for assessment. 1.5 × 10³ cells (A2780 or OVCAR-3) were plated into 96-well culture plates. At each time point, 10 μL of CCK-8 reagent was supplemented to 100 μL of the culture medium per well and incubated in a 37 °C humidified incubator for 2 h. Optical Density (OD) was recorded at 450 nm using a microplate reader and was applied to plot cell proliferation curves.

Transwell migration assay

Cell migration was checked at 24 h post-transfection using Transwell inserts with cell-permeable membrane (8.0-μm pore; Corning, USA). Transfected A2780 and OVCAR-3 cells were plated into the upper chamber of Transwell inserts with 100 μL RPMI 1640 medium at densities of 3 × 10⁴ and 6 × 10⁴ cells per well, respectively. 600 μL of 10% (v/v) serum-containing medium was added to the lower chambers. After 24 h, cells that had migrated across the membrane were fixed in 4% (w/v) paraformaldehyde (Solarbio, China) for 20 min, stained with crystal violet (0.1%, Solarbio, China) for 15 min, and rinsed with phosphate-buffered saline (PBS). Images were captured by an Olympus microscope, and migrated cells were counted using ImageJ software.

Transwell invasion assay

For invasion assays, Matrigel (Corning, USA) was diluted 1:14 (v/v) in ice-cold serum-free RPMI-1640 medium and placed 100 μL onto the center of the upper chambers, allowing it to solidify for 2 h. Transfected cells were seeded at 5 × 10⁴ (A2780) or 8 × 10⁴ (OVCAR-3) cells/well in serum-free medium. Following a 24-h incubation period, invasive cells were fixed, stained, and quantified as described for migration assays.

Clonogenicity assay

For cell growth assays, 0.5 × 10³ transfected cells were seeded in 6-well culture plates and routinely cultured for 7–10 days, with media replenished every three days. Once colonies (> 50 cells) were visible, they were subjected to the same fixation and staining protocol as previously described for migration assays. Photographs of colonies were taken, and the numbers of colonies were counted using ImageJ software.

Analysis of cell apoptosis by flow cytometry

Apoptosis of cells that underwent siRNA or DNA transfections was evaluated by the FITC/PI Apoptosis Detection Kit (DOJINDO, Japan). A2780 and OVCAR-3 cells were plated in 60-mm culture plates overnight. Following transfection, cells were detached using trypsin (EDTA-free), washed twice with PBS to remove trypsin, and resuspended in binding buffer at 5 × 10⁶ cells/mL. Propidium iodide staining was conducted following the manufacturer’s guidelines. Apoptosis of cells was analyzed using a BD flow cytometer. Data analysis and quantification were performed using FCS Express software.

qRT-PCR

RNA from transfected cells was isolated via RNA Isolation Kit (Beyotime, China), then 1 μg RNA was reverse transcribed via cDNA Synthesis Kit (Vazyme, China). cDNA was further amplified by qRT-PCR using 2 × TB Green Premix (TaKaRa, Japan). Relative mRNA expressions of the target gene among different groups were calculated according to the 2^−ΔΔCt formula, with 18S as the internal control. The primer sequences for 18S, DHRS9, and SQSTM1 were as follows:

18S-F, GCAGAATCCACGCCAGTACAAGAT

18S-R, TCTTCTTCAGTCGCTCCAGGTCTT

DHRS9-F, ACTCCATCCAAATATGCAGTGGAAG

DHRS9-R, ACAATCCTGGTTCAATGCATGAGA

SQSTM1-F, AGGAACAGATGGAGTCGGATAA

SQSTM1-R, TAGGGACTGGAGTTCACCTGTAG

Western blot

Total proteins were isolated from samples using ice-cold RIPA lysis buffer (Solarbio, China). 30 μg of protein samples were deposited onto 4–12% SurePAGE gels (GenScript, China) for electrophoresis. The separated proteins were then electroblotted onto PVDF membranes (0.45 μm; Millipore, USA). After the blocking procedure, the membranes underwent a 3-h incubation with specific antibodies at room temperature (RT). After three Tris-buffered saline with 0.1% Tween-20 (TBST) wash cycles, membranes were incubated with diluted secondary antibody solution for 60 min at RT. Blot images were visualized using an enhanced chemiluminescence (ECL) detection solution (Thermo Scientific, USA), and the intensities of the bands were analyzed and quantified by ImageJ software for subsequent statistical analysis. The primary and secondary antibodies are detailed below:

Anti-DHRS9 (mouse, 1:1000; Novus, USA), anti-SQSTM1 (rabbit, 1:3000; Proteintech, China), anti-GAPDH (mouse, 1:8000; Proteintech, China), anti-β-actin (mouse, 1:8000; Proteintech, China).HRP-conjugated goat anti-mouse IgG (1:10,000; Proteintech, China); HRP-conjugated goat anti-rabbit IgG (1:10,000; Proteintech, China).

Co-immunoprecipitation (Co-IP)

For protein extraction, A2780 cells (1 × 10⁷) were lysed in 1 mL Co-IP lysis buffer (Solarbio, China).. Protein concentration was measured and diluted to approximately 1 μg/μL with PBS. For IP, 500 μg of cell lysate was incubated with 4 μg of primary antibodies (anti-DHRS9, anti-SQSTM1, or isotype-matched IgG controls) overnight (> 10 h) at 4 °C with gentle mixing. Subsequently, pre-equilibrated Protein A/G-agarose beads (MCE, USA) were added to the mixtures in the IP group (50 μL /group) and incubated for 2 h at 4 °C under slow rotation. Agarose beads were washed five times with TBST (5 min each) and centrifuged at 7000 rpm for 1 min after each wash. Protein–protein complexes were analyzed by Western blot using secondary antibodies that minimize interference from immunoglobulin chains (1:4000 dilution; Abmart, China).

Xenograft experiment

BALB/c nude mice (4 weeks of age, females) were used to establish subcutaneous tumor models. A2780 cells (5 × 10⁶) suspended in 100 μL PBS were injected into the left axilla. Upon tumor formation, mice were randomized into control (PBS, n = 6) and treatment (Cholesterol-conjugated DHRS9-#1 siRNA, JTSBIO Co., Ltd., China; 3 nmol in 30 μL PBS, n = 6) groups. Tumor size was measured every three days using the formula: Volume = 1/2 × length × width². At the study endpoint, mice were euthanized by cervical dislocation to harvest xenograft tumors. Tumors were dissected, weighed, and analyzed.

Statistical analysis

Data are shown as mean ± SD. Graphs and statistical tests were executed with GraphPad Prism (Version 9.5). For between-group comparisons, Student’s t-test was applied. For differences among the three groups, one-way or two-way analysis of variance (ANOVA) was applied. Statistical significance was established when p-values fell below 0.05. All experimental procedures were performed in triplicate with independent biological replicates.

Results

Elevated DHRS9 expression correlates with poor prognosis in ovarian cancer

To investigate the clinical significance of DHRS9 in ovarian cancer, survival analyses were conducted using the GEPIA and KMplot databases. Results revealed that patients with higher DHRS9 expression exhibited shorter overall survival in contrast to individuals with lower expression levels (Fig. 1a, b).

Fig. 1.

DHRS9 expression levels correlate with overall survival in ovarian cancer patients. Kaplan–Meier curves were constructed from GEPIA (a) and KMplot (b) databases. Patients with higher DHRS9 expression exhibited reduced survival times relative to those with lower expression

DHRS9 knockdown attenuates ovarian cancer aggressive phenotypes and enhances apoptosis

To ascertain the role of DHRS9 in ovarian cancer, the mRNA and protein expression levels of DHRS9 in ovarian cancer cell lines (A2780 and OVCAR-3) and normal ovarian cell line (IOSE-80) were first evaluated by qRT-PCR and Western blot analysis, respectively. Compared with the IOSE-80 cell line, DHRS9 was overexpressed in ovarian cancer cell lines, with higher mRNA and protein levels in A2780 than in OVCAR-3 cells (Fig. 2a, b). Given this expression pattern, A2780 was used for knockdown and OVCAR-3 for overexpression experiments. Then DHRS9 was silenced in A2780 cells using siRNA. Results from qRT-PCR and western blot showed that the siRNA interference group suppressed both mRNA and protein expression levels of DHRS9 in A2780 cells (Fig. 2c, d). CCK-8 assays showed reduced cell growth following DHRS9 knockdown in A2780 cells (Fig. 2e). Colony formation assays revealed fewer colonies in DHRS9-knockdown A2780 cells compared to control groups (Fig. 2f). Transwell assays showed a decrease in migration and invasion capabilities following DHRS9 knockdown (Fig. 2g, h). Flow cytometry analysis showed elevated apoptosis levels in DHRS9-knockdown A2780 cells relative to controls (Fig. 2i).

Fig. 2.

Effects of DHRS9 knockdown on aggressive phenotypes and apoptosis of A2780 cells. a, b qRT-PCR and western blot evaluating mRNA and protein expression of DHRS9 in IOSE-80, A2780 and OVCAR-3 cell lines. c, d qRT-PCR and western blot verification of DHRS9 knockdown efficiency. e, f CCK-8 and colony formation assays examining the effects of DHRS9 depletion on A2780 cell growth. g, h Transwell assays assessing the impact of DHRS9 knockdown on A2780 cell migration and invasion capabilities. i Flow cytometry-based apoptosis detection in DHRS9-silencing A2780 cells.Data are shown as mean ± SD. Scale bar = 200 μm. **P < 0.01; ***P < 0.001;****P < 0.0001

DHRS9 overexpression promotes ovarian cancer aggressive phenotypes and inhibits apoptosis

To further characterize DHRS9’s oncogenic role, its expression was upregulated in OVCAR-3 cells via plasmid transfection.qRT-PCR and western blot analysis confirmed upregulation of DHRS9 mRNA and protein levels in the overexpression group relative to controls (Fig. 3a, b). CCK-8 and colony formation assays demonstrated that DHRS9 overexpression enhanced OVCAR-3 cell growth (Fig. 3c, d). Transwell assays revealed increased migration and invasion of DHRS9-overexpressing OVCAR-3 cells compared to controls (Fig. 3e, f). Flow cytometry analysis showed reduced apoptosis in DHRS9-overexpressing OVCAR-3 cells compared to controls (Fig. 3g).

Fig. 3.

Effects of DHRS9 overexpression on aggressive phenotypes and apoptosis of OVCAR-3 cells. a, b qRT-PCR and western blot analysis verification of DHRS9 overexpression efficiency. c, d CCK-8 and colony formation assays examining the effects of DHRS9 overexpression on OVCAR-3 cell growth. e, f Transwell assays assessing the impact of DHRS9 overexpression on OVCAR-3 cell migration and invasion capabilities. g Analysis of apoptosis via flow cytometry in DHRS9-overexpressing OVCAR-3 cells. Data are shown as mean ± SD. Scale bar = 200 μm. **P < 0.01; ***P < 0.001;****P < 0.0001

DHRS9 directly interacts with SQSTM1

To investigate the molecular mechanism of DHRS9 in regulating ovarian cancer progression, we performed mass spectrometry analysis on DHRS9-knockdown samples to identify potential targets. The results identified SQSTM1 as a putative DHRS9-interacting protein. This interaction was subsequently validated by Co-IP assays (Fig. 4a). Western blot analysis showed that DHRS9 overexpression increased SQSTM1 protein levels, while its knockdown reduced SQSTM1 levels (Fig. 4b, c).

Fig. 4.

Interaction between DHRS9 and SQSTM1. a Co-IP verification of the direct interaction between DHRS9 and SQSTM1 proteins. b, c Western blot analysis of SQSTM1 protein expression following DHRS9 overexpression and knockdown. Data are shown as mean ± SD. *P < 0.05;***P < 0.001;****P < 0.0001

SQSTM1 knockdown inhibits ovarian cancer cell proliferation, invasion, and migration

To explore SQSTM1’s role in ovarian cancer, the mRNA and protein expression of SQSTM1 in ovarian cell lines was first examined. Results showed that mRNA and protein expression were highest in the A2780 cell line (Fig. 5a, b). Then, SQSTM1 was silenced in A2780 cells using siRNA. The knockdown efficiency was validated by qRT-PCR and western blot assays (Fig. 5c, d). The results showed significantly decreased mRNA and protein expressions of SQSTM1 in the knockdown group compared to controls. CCK-8 results demonstrated that SQSTM1 knockdown significantly inhibited A2780 cell growth (Fig. 5e). Colony formation assays showed reduced colony formation in SQSTM1-knockdown A2780 cells (Fig. 5f). Transwell assays revealed decreased migration and invasion capabilities in A2780 cells following SQSTM1 knockdown compared to controls (Fig. 5g, h).

Fig. 5.

Effects of SQSTM1 knockdown on A2780 ovarian cancer cell proliferation, invasion, and migration. a qRT-PCR analysis of SQSTM1 mRNA levels in IOSE-80, A2780 and OVCAR-3 cell lines normalized to 18S. b Western blot analysis of SQSTM1 protein with GAPDH as loading control. Quantification of band intensity is shown as fold change relative to IOSE-80 cells. c, d Validation of SQSTM1 knockdown efficiency by qRT-PCR and western blot. e, f CCK-8 and colony formation methods examining the effects of SQSTM1 knockdown on A2780 cell growth. g, h Transwell assays evaluating the impact of SQSTM1 knockdown on A2780 cell migration and invasion. Data are shown as mean ± SD. Scale bar = 200 μm. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001

DHRS9 knockdown inhibits ovarian cancer cell proliferation in vivo

To evaluate the tumorigenic role of DHRS9 in vivo, a subcutaneous xenograft model was established using A2780 cells. DHRS9 expression was silenced via intratumoral injection of siRNA. DHRS9 knockdown resulted in reduced tumor weight and volume compared to controls (Fig. 6a, c). Western blot analysis of tumor tissues revealed decreased DHRS9 and SQSTM1 expression at the protein level (Fig. 6d, e).

Fig. 6.

In vivo DHRS9 knockdown suppresses tumor growth in a xenograft model. a Representative images of subcutaneous tumors from nude mice injected with A2780 cells. b Final tumor weights. c Tumor growth curves showing inhibition of tumor progression following DHRS9 knockdown. d, e Western blot analysis confirming decreased DHRS9 and SQSTM1 protein expression in tumor tissues from the treatment group. Data are shown as mean ± SD. **P < 0.01; ***P < 0.001

Discussion

This study demonstrates that elevated DHRS9 expression correlates with reduced overall survival in ovarian cancer patients. Functionally, DHRS9 knockdown suppresses tumorigenic phenotypes including proliferation, migration, and invasion while promoting apoptosis. Conversely, DHRS9 overexpression exhibits the opposite effect. Nude mouse xenograft experiments further confirmed that DHRS9 knockdown resulted in decreased tumor volume. Collectively, these findings indicate that DHRS9 may serve as both a prognostic biomarker and a functional driver of ovarian cancer progression.

To investigate the molecular mechanism by which DHRS9 drives ovarian cancer progression, we identified SQSTM1 as a direct interacting partner through integrated mass spectrometry and co-immunoprecipitation assays. Critically, DHRS9 acts as a positive regulator of SQSTM1 protein. Knockdown of DHRS9 depleted SQSTM1 in vivo and in vitro, whereas overexpression elevated SQSTM1 levels. Importantly, given that SQSTM1 is highly expressed in high-grade ovarian cancer (Jovanović et al. 2022), our functional validation revealed that SQSTM1 knockdown suppressed ovarian cancer cell proliferation, migration, and invasion, confirming its essential oncogenic role. Collectively, these data establish SQSTM1 as a key effector downstream of DHRS9, defining a DHRS9-SQSTM1 functional axis essential for sustaining ovarian cancer aggressiveness.

SQSTM1 is a multifunctional scaffold protein involved in selective autophagy, ubiquitin-mediated protein degradation, and the regulation of various oncogenic signalling pathways (Sánchez-Martín et al. 2019). SQSTM1 contains several functional domains. SQSTM1 competitively binds to KEAP1 via its KIR domain, preventing NRF2 ubiquitination and degradation (Sánchez-Martín et al. 2019). Sustained NRF2 activation promotes malignant phenotypes—including proliferation, apoptosis resistance, migration, and invasion—by inducing antioxidant genes, metabolic reprogramming, expression of CCND1 and matrix metallopeptidases (MMPs)(Lu et al. 2023; Jiang et al. 2020; Zhang et al. 2025; Mitsuishi et al. 2012). DHRS9-dependent SQSTM1 upregulation likely activates this pathway, promoting pro-tumorigenic phenotypes in ovarian cancer cells.

Previous studies demonstrate that SQSTM1 activates NF-κB signaling through distinct domains: its ZZ domain binds RIP1, while its PB1 domain recruits and activates aPKC (Sánchez-Martín et al. 2019). NF-κB drives inflammation, metastasis, and anti-apoptotic signals in ovarian cancer (Harrington and Annunziata 2019). DHRS9-SQSTM1 binding may potentiate these cascades by enhancing SQSTM1’s scaffolding efficiency or stability. SQSTM1’s role as a multi-domain scaffold allows DHRS9 to co-opt diverse oncogenic pathways simultaneously.

Notwithstanding these insights, limitations in our study require consideration. First, although SQSTM1 was identified as a key mediator of DHRS9’s pro-tumorigenic effects, the precise structural basis of their interaction remains uncharacterized. Second, the regulatory effects of the DHRS9-SQSTM1 binding on NRF2 and NF-κB pathways were not experimentally validated. Specifically, whether this interaction directly activates NRF2-mediated antioxidant responses or NF-κB-driven inflammatory signaling remains unconfirmed. Future studies will validate these axes by mapping DHRS9-SQSTM1 binding domains, identifying downstream targets and assessing pathway crosstalk in ovarian cancer.

In conclusion, DHRS9 facilitates ovarian cancer progression by regulating SQSTM1, thereby enhancing cell proliferation, migration, and invasion while inhibiting apoptosis. These findings underscore the potential of DHRS9 as both a prognostic marker and a target for therapeutic intervention.

Author contributions

Material preparation, data collection and analysis were performed by Yanju Wu, Shu Meng, Jinte Gao, Haoqi Zhao and Bowen Tan. The first draft of the manuscript was written by Yanju Wu. Xiaona Meng contributed to the study conception and design, manuscript revisions and supervised the research. All authors read and approved the final manuscript.

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Conflict of interest

The authors declare no competing interests.