Repurposing trifluoperazine as a potential therapeutic agent for ovarian cancer: mechanistic insight via SRC/PI3K/AKT signaling pathway inhibition

Abstract

Objective

This study evaluates the efficacy of trifluoperazine in treating ovarian cancer and investigates its potential molecular mechanisms through in vitro and in vivo experiments, as well as network pharmacology analysis.

Methods

We used the CCK-8 assay to assess cell proliferation in five ovarian cancer cell lines (ES-2, SK-OV-3, OVCAR-3, OV-90, ID8). We performed scratch and Transwell assays on SK-OV-3 and ID8 cells to evaluate their migration and invasion capabilities. Apoptosis, proliferation, and reactive oxygen species (ROS) levels in ID8 cells were measured using Acridine Orange/Propidium Iodide (AO/PI) staining, EdU assays, and ROS assays, respectively. A xenograft mouse model was employed to assess the effect of trifluoperazine on tumor growth. Network pharmacology was used to identify common targets between trifluoperazine and ovarian cancer, followed by Western blot (WB) experiments to validate the protein expression levels of the SRC/PI3K/Akt signaling pathway.

Results

Trifluoperazine inhibited cell growth in a dose-dependent manner, with ES-2 cells showing the greatest sensitivity. Migration, invasion, and proliferation of SK-OV-3 and ID8 cells were significantly reduced, while apoptosis and ROS levels were increased. In vivo, trifluoperazine inhibited tumor growth, with only a slight decrease in body weight observed. Network pharmacology identified 110 common targets, with the main targets enriched in the PI3K-Akt pathway. WB analysis confirmed the downregulation of SRC, PI3K, and Akt proteins in cells treated with trifluoperazine.

Conclusion

Trifluoperazine inhibits ovarian cancer progression in vitro and in vivo by targeting the SRC/PI3K/Akt signaling pathway, providing a potential new therapeutic strategy for ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-026-01966-6.

Background

Ovarian cancer is a malignant tumor that derived from ovarian epithelium, germ cells, or stromal tissue [1]. It ranks third in incidence among tumors of the female reproductive system; however, it has the highest mortality rate, accounting for 47% of deaths from gynecological malignancies. The five-year survival rate is below 50% [2]. According to the WHO, ovarian cancer is primarily categorized into three types: epithelial carcinoma, which accounts for 90% of cases and includes subtypes such as high-grade serous carcinoma; germ cell tumors, such as immature teratoma; and sex-cord stromal tumors, such as granulosa cell tumor [3]. High-grade serous carcinoma (HGSC) is the most common subtype, which is usually diagnosed at an advanced stage, and has a poor prognosis, with a five-year survival rate of only 30% to 40% [4]. Moreover, there is a gradual increase in the incidence of endometrioid carcinoma and clear cell carcinoma, particularly among young women [5]. The clinical symptoms of ovarian cancer are often subtle, resulting in many patients being diagnosed at an advanced stage. Studies indicate that approximately 70% of ovarian cancer patients are diagnosed at Stages III or IV [6]. Treatment mainly involves surgery and platinum-based chemotherapy, while targeted therapies and immunotherapy have been shown to extend survival [7]. However, due to high tumor heterogeneity, the frequent development of platinum resistance, and suppression of the immune response within the tumor microenvironment, current treatments are often ineffective in eliminating metastatic lesions and are associated with serious toxic side effects. Therefore, the development of new treatment methods and drugs is urgently needed.

Trifluoperazine is a phenothiazine antipsychotic drug that exerts sedative, anxiolytic, and antiemetic effects by antagonizing dopamine D2 receptors and inhibiting the calmodulin signaling pathway [8]. Clinically, it is primarily used to treat schizophrenia and chemotherapy-related nausea and vomiting [9]. Recent studies have found that trifluoperazine has diverse anticancer activities. For example, it can enhance the effectiveness of cisplatin against lung cancer cells through the inhibition of autophagy [10], and it may inhibit prostate cancer proliferation by inhibiting the PI3K/AKT pathway [11]. Preclinical studies have shown that the combination of trifluoperazine with PARP inhibitors can reverse resistance to platinum-based chemotherapy in ovarian cancer [12]. However, there is currently no research on the potential of trifluoperazine alone to inhibit ovarian cancer.

The SRC signaling pathway is an important intracellular signal transduction pathway, mainly mediated by the Src family of tyrosine kinases and involved in various biological processes [13]. The Src protein is a non-receptor tyrosine kinase that contains multiple structural domains, including SH2 and SH3 domains, which allow Src to interact with various signaling molecules, thereby regulating functions such as cell proliferation, migration, and survival [14]. The PI3K/Akt signaling pathway is a complex signaling network that extends from the cell membrane to the nucleus, regulating basic cellular activities through a signaling cascade involving growth factors, receptor tyrosine kinases (RTKs), PI3K, PIP3, Akt, mTOR, and downstream targets [15]. Its abnormal activation is a key molecular driver of diseases such as cancer and is also a core target of current therapies [16]. Furthermore, studies have shown that Src can activate the PI3K/Akt signaling pathway to promote the proliferation, migration, and invasion of ovarian cancer cells [17]. However, research on its specific mechanisms is still quite limited, and there is no research demonstrating whether trifluoperazine can inhibit ovarian cancer through the Src/PI3K/Akt signaling pathway.

This study evaluates the therapeutic effect of trifluoperazine on ovarian cancer and the underlying mechanisms using both in vivo and in vitro experiments. It will analyzes the impact of trifluoperazine on the SRC/PI3K/AKT signaling pathway and investigate its potential application in treating ovarian cancer. Furthermore, this study use network pharmacology to provide a broader view, which helps to identify trifluoperazine's potential targets and the gene networks linked to ovarian cancer, thereby providing a theoretical basis and experimental evidence for future clinical studies [18]. We hope this will lead to new ideas and strategies for treating ovarian cancer and promote the advancement of personalized treatment for ovarian cancer.

Method and materials

CCK-8 assay

The CCK-8 assay was used to assess the proliferation of five ovarian cancer cell lines (ES-2, SK-OV-3, OVCART-3, OV-90, ID8). Cells from Zhejiang Baidi Biotechnology Co., Ltd. and BaiDi Biotechnology Co., Ltd. (BDBIO) were cultured in high-glucose DMEM, RPMI1640, or DMEM/F12 medium (TBDscience) with 10% FBS and 1% penicillin–streptomycin at 37 °C in 5% CO₂. Log-phase cells (0.5–1 × 10⁴ cells/well, 3–6 replicates/group) were seeded in 96-well plates, cultured overnight to 60%–80% confluence, and treated with trifluoperazine-containing medium for 48 h. Bright-field images (20 ×, 5 random fields) were captured before and after treatment to record morphology.

For detection, CCK-8 reagent (Shanghai EnaMab Technology Co., Ltd.) was tenfold diluted in medium. After removing the drug-containing medium, 100 μL of the diluted reagent was added, plates were shaken gently, incubated for 2–4 h, and absorbance was measured at 450 nm (reference 600 nm) using a microplate reader.

Wound healing assay

The wound healing assay evaluated migration of SK-OV-3 and ID8 ovarian cancer cells. Log-phase cells were trypsinized (0.25%), resuspended in 10% FBS medium, counted via hemocytometer, and seeded at 50–100 × 10⁴ cells/well in 6-well plates. Cultured at 37 °C in 5% CO₂ to 95%–100% confluence, scratches were made with a 200-μl tip (ruler-guided) to create 2–3 vertical/horizontal wounds per well, followed by 2–3 PBS washes to remove detached cells.

Cells were treated with 0, 10, 20 μM trifluoperazine (2 ml/well). Initial wound images at 0 h were captured at 20 × objective for 5 fixed fields/well, with subsequent imaging at 0, 24,48 h (or when control wound closure > 50%) to monitor migration.

Transwell invasion assay

The Transwell chamber invasion assay was performed to evaluate the invasive capacity of SK-OV-3 and ID8 ovarian cancer cells. Pre-experimentally, 50 mg/L Matrigel (Corning) was diluted 1:8 with PBS, and 50–60 μL was used to coat the upper surface of Transwell polycarbonate membranes. After 30-min incubation at 37 °C for gel solidification, the upper chambers were washed with PBS to hydrate the basement membrane.

Log-phase cells were digested with 0.25% trypsin, terminated with 10% FBS medium, centrifuged at 1000 rpm for 5 min, washed twice with PBS, and resuspended in serum-free medium. Cell density was adjusted to 3–5 × 10⁴ cells/100–200 μL (hemocytometer count) and seeded into upper chambers with serum-free medium containing trifluoperazine. Lower chambers were filled with 600 μL complete medium at 0, 10, 20 μM trifluoperazine, ensuring no air bubbles, and incubated at 37 °C in 5% CO₂ for 12–48 h.

Post-incubation, cells were fixed with 4% paraformaldehyde for 30 min, stained with 0.1% crystal violet for 20–30 min, and washed thrice with PBS. Upper-surface uninvaded cells/Matrigel were removed with a wet cotton swab until the effluent cleared. After air-drying, chambers were placed in 24-well plates, and 5 fields/well were imaged at 10 ×/20 × objective to count invaded cells on the lower membrane.

EdU incorporation assay

The EdU incorporation assay was conducted to assess the proliferative activity of ID8 ovarian cancer cells. Log-phase cells were seeded at 0.5–1 × 10⁴ cells/well in 96-well plates (3 replicates/group), cultured to 60%−80% confluence, and treated with 0, 10, 20 μM trifluoperazine for 2–3 days.

Two hours before treatment termination, 100 μL of 10 μM EdU working solution (20 μM stock from BeyoClick™ EdU Cell Proliferation Kit with Alexa Fluor 555, Beyotime Biotechnology Inc., Shanghai, China, diluted in medium) was added. After 37 °C incubation, cells were washed 1–2 times with 3% BSA-PBS, fixed with 4% paraformaldehyde for 15–30 min, permeabilized with 0.3% Triton X-100 for 10–15 min, and washed again.

Click reaction solution (containing Buffer, CuSO₄, Azide 555, and Additive Solution) was freshly prepared, 100 μL added to each well, and incubated in the dark for 30 min. Nuclei were stained with 1:1000 Hoechst 33,342 for 10 min. Fluorescent images were captured at 20 ×/40 × objective for 5 random fields/group, recording red (Alexa Fluor 555-EdU) and blue (Hoechst) fluorescence.

Annexin V-EGFP/PI assay

The Annexin V-EGFP/PI apoptosis detection kit (Shanghai EnaMab Technology Co., Ltd.) was used to assess trifluoperazine-induced apoptosis in ID8 ovarian cancer cells. Log-phase cells were seeded at 0.5–1 × 10⁴ cells/well in 96-well plates, cultured to 60%−80% confluence, and treated with 0, 10, 20 μM trifluoperazine for 24–48 h. Treated cells were detached with EDTA-free trypsin, centrifuged at 300 g for 5 min at 4 °C, and washed twice with pre-chilled PBS. Cells were resuspended in 100 μL 1 × Binding Buffer, stained with 5 μL Annexin V-FITC and 10 μL PI, incubated in the dark for 10–15 min, diluted with 400 μL 1 × Binding Buffer, and kept on ice for analysis within 1 h.

ROS staining

To determine intracellular ROS levels in ID8 ovarian cancer cells, the DCFH-DA probe method was used. ROS detection was performed using DCFH-DA (Sigma-Aldrich) and a corresponding kit. Log-phase ID8 cells were seeded at < 5 × 10⁵ cells/mL one day before the assay. After adhesion, cells were treated with 0, 10, 20 μM drugs and incubated at 37 °C in the dark. H₂DCFDA was diluted 1:1000 in serum-free medium to 10 μM. Following drug removal, 1000 μL (6-well) or 100 μL (96-well) of the working solution was added, and cells were incubated for 30 min at 37 °C in the dark. Cells were washed 1–2 times with serum-free medium to remove unincorporated H₂DCFDA, and DCF fluorescence was visualized by fluorescence microscopy to assess ROS levels.

Subcutaneous xenograft model of ovarian cancer cells

BALB/c nude mice (5–6-week-old female) were obtained from Changzhou Cavens Model Animal Co., Ltd. (Changzhou, China) and acclimated for 7 days under a 12-h light/dark cycle in pathogen-free conditions with commercial mouse chow and water ad libitum. All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) of Nanchang Royo Biotech Co., Ltd., following the "Guide for the Care and Use of Laboratory Animals" (National Research Council, 8th edition, 2011). Mice were monitored daily, and euthanasia via CO₂ asphyxiation was performed if signs of distress appeared (e.g., reduced food/water intake, skin ulcers, hunched posture, weight loss, vocalization).

For xenograft establishment, 1 × 10⁷ ovarian cancer cells per mouse (suspended in a 1:1 volume mixture with Matrigel [Shanghai EnaMab Technology Co., Ltd.]) were subcutaneously injected into the right upper scapular region of 12 nude mice. The cell-Matrigel mixture (100 μL per injection) was delivered using a 27G needle under isoflurane anesthesia. Tumor growth was monitored every 3 days with calipers, and tumor volume was calculated as (length × width²)/2.

Drug testing in ovarian cancer xenograft model

Once tumor volume reached 100 mm³, mice were randomly assigned to two groups: control (0.2 mL saline daily, i.g.) and trifluoperazine (10 mg/kg daily, i.p.). Body weight and subcutaneous tumor size were measured twice weekly. After 24 days of treatment, animals were euthanized. Tumors were excised and weighed. The experiment was terminated if any mouse lost > 20% of its baseline body weight.

Network pharmacology

Target identification of Trifluoperazine

Structural annotations of trifluoperazine (PubChem CID: 5566; Canonical SMILES: CN1CCN(CC1)CCCN2C3 = CC = CC = C3SC4 = C2C = C(C = C4)C(F)(F)F) were retrieved from PubChem. Potential targets were mined using integrative database screening: SwissTargetPrediction (http://www.swisstargetprediction.ch/, probability threshold > 0); DrugBank (https://go.drugbank.com/, all approved targets); PharmMapper (https://lilab-ecust.cn/pharmmapper/, default settings for ligand-based prediction). Duplicate entries across databases were eliminated via systematic deduplication, yielding a final set of 268 unique target genes.

Ovarian cancer-associated gene curation

Ovarian cancer-related genes were compiled from three authoritative repositories: DisGeNET (https://www.disgenet.org/, Score_gda > 0.1 for disease-gene association); GeneCards (https://www.genecards.org/, Relevance score > 10 as clinical significance threshold); OMIM (https://omim.org/, manually curated disease-gene entries). Subsequent merging and deduplication of retrieved genes resulted in a comprehensive list of 2228 ovarian cancer-associated genes.

Intersection analysis of drug-target and disease-gene networks

The overlap between trifluoperazine targets and ovarian cancer genes was identified using the VennDiagram package in R 4.3.3. This analysis yielded 110 intersection genes, which were visualized via a Venn diagram to illustrate the shared molecular basis of trifluoperazine action in ovarian cancer.

Construction and Analysis of Protein–Protein Interaction (PPI) Network

STRING Database Integration: Intersection genes were uploaded to STRING (https://string-db.org/, Homo sapiens, minimum interaction score = 0.4) to generate PPI network data with confidence-weighted edges.

Cytoscape Visualization: The STRING output was imported into Cytoscape 3.8.2 for network visualization. Node attributes (size, color, font) were normalized to Degree Centrality, where higher values denoted greater topological importance in the network.

Topological Metric Calculation: The CytoNCA plugin was employed to compute network centrality indices, including Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Eigenvector Centrality (EC), Local Average Connectivity (LAC), and Network Centrality (NC), for quantitative network analysis.

Development of component-target-disease multilayer network

A tripartite network integrating trifluoperazine, intersection genes, and ovarian cancer was constructed in Cytoscape 3.8.2. This network visualized direct associations among drug components, therapeutic targets, and disease entities, enabling systemic analysis of pharmacological mechanisms.

Screening of hub targets

The CytoHubba plugin in Cytoscape 3.8.2 was utilized to identify pivotal hub targets using the Maximum Clique Centrality (MCC) algorithm. The top 30 targets with the highest MCC scores were prioritized as potential key regulators in trifluoperazine-mediated ovarian cancer therapy.

Modular clustering of PPI network

Protein complex clustering was performed using the MCODE plugin in Cytoscape 3.8.2. The PPI network was partitioned into functional modules, and the top 3 modules (with the highest MCODE scores) were selected for detailed characterization, reflecting co-regulated biological processes.

Functional enrichment analysis

Gene Ontology (GO) enrichment analysis was conducted using the clusterProfile package in R, focusing on Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). Terms with p < 0.05 were considered significant, and the top 10 most enriched terms in each category were visualized using bubble plots (bubble size: gene count; color gradient: -log10(p-value)).

Pathway enrichment was performed via the KEGG database interface in clusterProfile. Significantly enriched pathways (p < 0.05) were identified, and key pathways were visualized using Pathview to map genes onto metabolic/regulatory networks, with red nodes denoting gene involvement.

Western blot analysis

SK-OV-3 cell samples treated with 0 μM and 15 μM trifluoperazine were lysed in RIPA buffer containing protease and phosphatase inhibitors. Protein concentrations were quantified using a BCA protein assay kit. Twenty to thirty micrograms of protein were separated by 10% SDS-PAGE and transferred to PVDF membranes. Membranes were blocked with 5% non-fat dry milk, incubated overnight with primary antibodies (PI3K, p-PI3K, AKT, p-AKT, GAPDH), washed, and then incubated with HRP-conjugated secondary antibodies. Protein bands were visualized using an ECL detection kit. Band intensities were analyzed by ImageJ software and normalized to GAPDH.

Antibody Information: GAPDH (PT0582R) PT™ Rabbit mAb (YM8394,Immunoway); Src rabbit monoclonal antibody (RA1399, Immunoway); Akt (Pan) rabbit monoclonal antibody (RA1463, Immunoway); AKT(Phospho Ser473)Rabbit mab(RA1304,Immunoway); PI3-Kinase p85α rabbit monoclonal antibody (RA1045, Immunoway); Phospho-PI3-kinase p85/p55(Y467/199)Polyclonai Antibody(RLP0224,Immunoway).

Statistical analysis

All calculations were performed using Prism 6. The results are presented as the means ± standard error of the mean (SEM). For comparisons among multiple groups, two-way ANOVA was employed. Additionally, comparisons between two groups were conducted using the t-test. A p-value < 0.05 was defined as indicating a statistically significant difference.

Result

Trifluoperazine inhibits proliferation, migration and invasion, and induces apoptosis and oxidative stress in ovarian cancer cells and PDX-derived organoids

The effects of trifluoperazine on the proliferation of ovarian cancer cell lines (ES-2, SK-OV-3, OVCAR-3, OV-90, ID8) and Patient-Derived Xenograft (PDX)-derived organoids were first evaluated using the CCK-8 assay (Fig. 1). Results showed that trifluoperazine exerted a dose-dependent inhibitory effect on the viability of all five cell lines and PDX-derived organoids. ES-2 cells were the most sensitive to trifluoperazine with a half-maximal inhibitory concentration (IC₅₀) of 1.134 µM, while OVCAR-3 cells were relatively insensitive (IC₅₀ = 27.94 µM). The IC₅₀ values for SK-OV-3, OV-90, and ID8 cells were 19.54 µM, 11.29 µM, and 22.90 µM(Fig. 1A-E), respectively, and that for PDX-derived organoids was 15.68 µM(Fig. 1F). Bright-field images further revealed concentration-dependent morphological changes in PDX-derived organoids following trifluoperazine treatment (Fig. 1G). These data indicate that trifluoperazine effectively inhibits the in vitro proliferation of ovarian cancer cells and PDX-derived organoids, with varying drug sensitivity across different models.

Fig. 1.

Dose-dependent inhibitory effect of trifluoperazine on the proliferation of ovarian cancer cells and Patient-Derived Xenograft (PDX)-derived organoids. A-E Dose–response curves of trifluoperazine in ovarian cancer cell lines: A ES-2, B SK-OV-3, C OVCAR-3, D OV-90, and (E) ID8. After 48 h of drug treatment, cell viability was assessed using the CCK-8 assay. Dashed lines indicate the half-maximal inhibitory concentration (IC₅₀) values, with corresponding values: ES-2: 1.134 µM; SK-OV-3: 19.54 µM; OVCAR-3: 27.94 µM; OV-90: 11.29 µM; ID8: 22.90 µM. F Dose–response curve of trifluoperazine in PDX-derived ovarian cancer organoids. Organoid viability was measured via the CCK-8 assay after 48 h of drug treatment; the dashed line denotes the IC₅₀ value (15.68 µM). G Bright-field images of PDX-derived ovarian cancer organoids treated with different concentrations of trifluoperazine (0, 10, 20, 40 μM). Scale bar = 100 μm

Next, the wound healing assay (Fig. 2A) showed that after treatment with different concentrations (0, 10, 20 μM) of trifluoperazine, the wound closure degree of SK-OV-3, OVCAR-3 and ID8 ovarian cancer cells decreased with increasing drug concentration. Statistical analysis of cell migration numbers (Fig. 2C) further confirmed that compared with the control group, the number of migrated cells in the trifluoperazine treatment group was significantly reduced (**p < 0.01, ***p < 0.001). In the Transwell invasion assay (Fig. 2B), the number of SK-OV-3, OVCAR-3 and ID8 cells invading the lower membrane was significantly reduced after trifluoperazine treatment, which was visually observed in crystal violet staining images. Quantitative statistics of invasive cells (Fig. 2D) showed that the proportion of invasive cells in the treatment group was significantly lower than that in the control group (**p < 0.01, ***p < 0.001). These results suggest that trifluoperazine can effectively inhibit the migration and invasion abilities of ovarian cancer cells.

Fig. 2.

Trifluoperazine inhibits invasion and migration of ovarian cancer cells. A Representative images of Transwell invasion assay for ID8, SK-OV-3, and OVCAR-3 ovarian cancer cells treated with trifluoperazine (0, 10, 20 μM). Cells that invaded through the extracellular matrix were stained and visualized; scale bar = 200 μm. B Representative wound-healing assay images of ovarian cancer cells treated with trifluoperazine (0, 10, 20 μM): ID8 and SK-OV-3 cells were observed at 0 h and 48 h post-wounding, while OVCAR-3 cells were observed at 0 h and 24 h post-wounding; scale bar = 200 μm. C Quantification of the number of invaded cells (from Transwell invasion assay) in ID8, SK-OV-3, and OVCAR-3 cultures. Data are presented as mean ± SD (n = 3); statistical significance: **p < 0.01, ***p < 0.001. D Quantification of migration area (from wound-healing assay) in ID8, SK-OV-3, and OVCAR-3 cells, expressed as a percentage of the total area. Data are shown as mean ± SD (n = 3); statistical significance: **p < 0.01, ***p < 0.001

Finally, the EdU staining assay (Fig. 3A) showed that with the increase of trifluoperazine concentration (0, 10, 20 μM), the proportion of EdU-positive cells (proliferating cells) in ID8 and OVCAR-3 ovarian cancer cells gradually decreased. Quantitative analysis (Fig. 3B) showed that compared with the control group, the proportion of EdU-positive cells in the drug treatment group was significantly reduced (**p < 0.01, ****p < 0.0001), indicating that trifluoperazine inhibits ovarian cancer cell proliferation. The Annexin V-EGFP/PI staining assay (Fig. 3C) showed that after trifluoperazine treatment, the number of early apoptotic (green fluorescence) and late apoptotic or necrotic (red fluorescence) cells in ID8 and OVCAR-3 cells increased with increasing drug concentration. Statistical analysis of apoptotic cells (Fig. 3D) confirmed that the number of apoptotic cells in the treatment group was significantly higher than that in the control group (**p < 0.01, ****p < 0.0001), indicating that trifluoperazine induces apoptosis in ovarian cancer cells. The DCFH-DA probe assay for intracellular reactive oxygen species (ROS) levels (Fig. 3E) showed that the green fluorescence intensity (ROS level) increased in the high-concentration trifluoperazine treatment group. Quantitative analysis of ROS levels (Fig. 3F) showed that intracellular ROS levels in the treatment group were significantly higher than those in the control group (**p < 0.01), suggesting that trifluoperazine triggers oxidative stress in ovarian cancer cells.

Fig. 3.

Trifluoperazine inhibits proliferation, induces apoptosis, and promotes oxidative stress in ovarian cancer cells. A Representative EdU staining images of ID8 and OVCAR-3 ovarian cancer cells following treatment with trifluoperazine (0, 10, or 20 μM). Red fluorescence indicates EdU-positive (proliferating) cells; blue fluorescence (DAPI staining) labels cell nuclei. Scale bar = 100 μm. B Quantification of the percentage of EdU-positive cells in ID8 and OVCAR-3 cell cultures treated with trifluoperazine (0, 10, or 20 μM). Data are presented as mean ± SD (n = 3; **p < 0.01, ****p < 0.0001). C Representative AO/PI staining images of ID8 and OVCAR-3 cells to assess apoptosis after trifluoperazine treatment. Green fluorescence (AO staining) indicates viable/early apoptotic cells; red fluorescence (PI staining) labels late apoptotic/necrotic cells. Scale bar = 100 μm. D Bar graph depicting the percentage of dead cells (vs. viable cells) in ID8 and OVCAR-3 cultures treated with trifluoperazine (0, 10, or 20 μM). Data are shown as mean ± SD (n = 3; **p < 0.01, ****p < 0.0001). E Representative DCFH-DA fluorescence images of ID8 and OVCAR-3 cells for intracellular reactive oxygen species (ROS) detection post-trifluoperazine treatment. Green fluorescence intensity correlates with intracellular ROS levels: stronger fluorescence corresponds to higher ROS production (evident in high-concentration trifluoperazine groups). Scale bar = 100 μm. F Quantification of intracellular ROS levels (as the percentage of ROS-positive cells relative to total cells) in OVCAR-3 and SK-OV-3 ovarian cancer cells treated with trifluoperazine (0, 10, or 20 μM). Data are presented as mean ± SD (n = 3; **p < 0.01)

Trifluoperazine inhibits the growth of ovarian cancer in Mice In Vivo

We monitored the tumor volumes of mice in the model control group (n = 6) and the trifluoperazine group (n = 6) over a period of 24 days. The results showed that the average tumor volume of the model control group continuously increased from 50.14 mm³ on day 0 to 755.17 mm³ on day 24. In contrast, the average tumor volume of the trifluoperazine group was lower than that of the model control group at all time points, with values of 50.09 mm³ on day 0 and 367.32 mm³ on day 24 (Fig. 4A, B). The relatively slower growth trend suggested that trifluoperazine may have a certain inhibitory effect on tumor growth. Regarding body weight changes, the average body weight of the model control group fluctuated slightly between 20.60–21.57 g during the 0–24 day period. Meanwhile, the trifluoperazine group showed an overall decreasing trend, declining from 19.2 g on day 0 to 17.92 g on day 24, which may indicate that the drug has an impact on the body weight of the animals (Fig. 4C).

Fig. 4.

Inhibitory effect of trifluoperazine on xenograft tumor growth in SK-OV-3 ovarian cancer cells. A Representative photographs of xenograft tumors from control and trifluoperazine-treated mice. B Tumor volume growth curves of mice in the control group (0.2 mL saline daily, i.g.) and trifluoperazine group (10 mg/kg daily, i.p.) over 24 days (mean ± SD, n = 6). C Body weight change curves of mice in the control and trifluoperazine groups during the 24-day treatment period

Trifluoperazine mediates anti ovarian cancer activity via the SRC/PI3K/AKT pathway

This study systematically explored the potential mechanism by which trifluoperazine inhibits ovarian cancer through multi-database integration and bioinformatics analysis. First, 268 target proteins of trifluoperazine were obtained from databases such as PubChem and SwissTargetPrediction, and 2228 genes related to ovarian cancer were collected from databases such as DisGeNET. Through Venn analysis, 110 overlapping targets were identified (Fig. 5A). The constructed protein–protein interaction (PPI) network contained 110 nodes and 1405 interaction relationships, among which nodes such as ALB, AKT1, and EGFR had relatively high degree values, suggesting their key roles in the network (Fig. 5B). The component-target-disease network analysis further revealed the complex network relationship by which trifluoperazine acts on ovarian cancer through multiple targets (Fig. 5D).

Fig. 5.

In silico network pharmacology analysis and experimental validation of trifluoperazine in ovarian cancer therapy. A Venn diagram illustrating the intersection of trifluoperazine targets (yellow, 268 genes) and ovarian cancer-related genes (purple, 2228 genes), yielding 110 potential therapeutic targets. B Protein–protein interaction (PPI) network with 110 nodes and 1405 edges. Node size, color intensity, and text size are scaled by Degree Centrality, where higher values denote greater topological importance. C Hub target network showing the top 30 core targets (e.g., CASP3, EGFR) identified by the MCC algorithm in CytoHubba. D Component-target-disease network comprising 112 nodes and 232 edges, depicting complex relationships among trifluoperazine, intersection genes, and ovarian cancer. E Protein complex clustering Module 1 (Score = 28.364) with 34 genes and 468 edges, centered on MCL1, HSP90AA1, etc. F Module 2 (Score = 4.625) containing 17 genes and 37 edges, with key nodes EIF4E, FGFR1, etc. G Module 3 (Score = 3.333) including 4 genes and 5 edges, dominated by CYP2D6, GSTM1, etc. H Bubble plot of GO Biological Process enrichment, highlighting top 5 terms (e.g., reproductive structure development, reproductive system development). I GO Cellular Component enrichment bubble plot with top 5 terms (vesicle lumen, membrane raft, etc.). J GO Molecular Function enrichment showing top 5 terms (nuclear receptor activity, ligand-activated transcription factor activity, etc.). K KEGG pathway analysis highlighting significantly enriched pathways (e.g., PI3K-Akt signaling, EGFR tyrosine kinase inhibitor resistance). L-M Western blot analysis of PI3K, p-PI3K, AKT, p-AKT, SRC, and GAPDH expression in SK-OV-3 cells treated with 0 μM vs. 15 μM trifluoperazine, with quantitative densitometry (mean ± SD, n = 3)

The top 30 core targets, such as CASP3, EGFR, and HSP90AA1, were screened out using the Cytohubba plug-in (Fig. 5C). MCODE clustering analysis identified 3 main functional modules, among which Cluster 1, with MCL1, AKT1, etc. as the core, may be closely related to cell apoptosis and signal transduction (Fig. 5E-G). GO functional enrichment analysis showed that the overlapping genes were mainly involved in biological processes such as reproductive structure development and cellular response to chemical stress, and molecular functions included nuclear receptor activity and protein tyrosine kinase activity (Fig. 5H-J). KEGG pathway analysis indicated that these genes were significantly enriched in cancer-related pathways such as the PI3K-Akt pathway and pathways related to resistance to EGFR tyrosine kinase inhibitors, suggesting that trifluoperazine may exert an anti-ovarian cancer effect by regulating these pathways (Fig. 5K). Network pharmacology provided bioinformatics evidence support for the mechanism of trifluoperazine in the treatment of ovarian cancer.

Subsequently, we detected the expression of related proteins by WB. The protein levels of PI3K, AKT, and SRC in the control group (C/Control) and the trifluoperazine group (TFP/Trifluoperazine) were compared, with β-actin as the internal reference. The results showed that after trifluoperazine treatment, the relative positive rates of p-PI3K, p-AKT, and SRC proteins were significantly reduced (*P < 0.05, **P < 0.01). This indicated that trifluoperazine may exert an inhibitory effect on processes related to ovarian cancer by down-regulating the expression of proteins related to the SRC/PI3K/AKT signaling pathway, providing experimental evidence at the protein level for exploring its anti-cancer mechanism.

Discussion

This study is the first to systematically demonstrate the anti-tumor activity of the antipsychotic drug trifluoperazine (TFP) against ovarian cancer. Our in vitro experiments clearly showed that TFP exerts dose-dependent cytotoxicity against five ovarian cancer cell lines with distinct molecular characteristics, namely, ES-2, SK-OV-3, OVCAR-3, OV-90, and ID8. Notably, significant differences in sensitivity to TFP were observed among different ovarian cancer subtypes. Among these cell lines, the ES-2 cell line, which represents ovarian clear cell carcinoma (OCCC), exhibited the highest sensitivity. The heightened sensitivity of ES-2 cells may be attributed to their unique molecular features, including characteristic hypermethylation-induced inactivation of the 14–3-3σ gene and overexpression of HOXA10. Additionally, ES-2 cells display active glycolytic metabolism, (as evidenced by high FDG uptake), and dependence on PDGF-AA secretion and K + channel function for migration and invasion,. They also exhibit low nm23-H1 and high p-AKT expression, which may render them more responsive to the pro-metastatic effects of estrogen. Furthermore, ES-2 cells possess certain cancer stem cell-like properties, and show sensitivity to specific therapeutic agents [19–24].

To further investigate the effects of TFP on the migratory and invasive capabilities of ovarian cancer cells, we selected the SK-OV-3 (high-grade serous ovarian carcinoma, HGSOC) and ID8 (murine epithelial ovarian cancer) cell lines—both of which possess strong migratory and invasive properties—for wound healing and Transwell invasion assays. The rationale for selecting these two cell lines stems from their distinct experimental utilities. Specifically, SK-OV-3 is a well-established model for investigating ovarian cancer metastatic mechanisms, characterized by high peritoneal metastatic potential and overexpression of invasion-associated molecular markers (e.g., matrix metalloproteinases [MMPs], CD44) [25]. By contrast, the ID8 cell line offers a unique advantage in effectively mimicking key pathological processes of ovarian cancer—including intraperitoneal dissemination and ascites formation—in immunocompetent mouse models. Its murine origin ensures consistency in metabolism between in vitro and in vivo experiments, making it particularly suitable for assays that requiringe detailed analysis of cellular states (e.g., subch asequent EdU incorporation, AO/PI staining, and ROS detection) [26]. In comparison, the highly TFP-sensitive ES-2 cells may undergo rapid drug-induced apoptosis, which could obscure the dynamic process of migration and invasion inhibition. Consistent with this, our experimental results demonstrated that TFP significantly suppressed the migration and invasion capabilities of both cell lines. This inhibitory effect may involve the dual regulation of both cytoskeletal dynamics and extracellular matrix (ECM) remodeling. Previous studies have shown that inhibition of the SRC/PI3K/Akt pathway can reduce the expression of matrix metalloproteinases (MMPs, such as MMP-2/9) and decrease the phosphorylation of focal adhesion kinase (FAK), thereby impairing ECM degradation and focal adhesion turnover [27]. Compared with standard anti-metastatic drugs that target a single pathway (e.g., VEGF-targeting bevacizumab), TFP may exhibit broader anti-metastatic potential thby roegulatingh multi-ple signaling pathway regulations.

In cellular functional experiments, we focused on usemploying ID8 cells as a model to evaluate the effects of TFP on the cell cycle, apoptosis/and necrosis, and oxidative stress. ID8 cells were selected primarily dbecause to their moderate proliferation rate, which facilitates the dynamic observation of these key biological processes. Our results showed that TFP not only inhibited proliferation but also significantly induced cell apoptosis. Notably, TFP treatment led to a marked increase in intracellular reactive oxygen species (ROS) levels, suggesting that TFP may promote cell death by triggering an burst of oxidative stress burst and activating the intrinsic mitochondrial apoptotic pathway. The elevated ROS levels may act as a crucial "molecular link" connecting the anti-metastatic and pro-apoptotic effects of TFP. A high-ROS microenvironment has been demonstrated to disrupt cytoskeletal integrity, inhibit the assembly of lamellipodia and filopodia (key cellular structures underlying migration and invasion), and concurrently activate the DNA damage response pathway [28]—findings that are highly consistent with the synergistic inhibitory phenotype observed in our study. This finding provides a critical new perspective for understanding the multifunacetionaled anti-tumor properties of TFP, namely that its synergistic suppression of tumor cell metastasis and viability may be mechanistically achieved by disrupting intracellular redox homeostasis.

At the molecular mechanism level, this study integrated network pharmacology analysis with experimental validation to identify the SRC/PI3K/AKT signaling pathway as the core target of TFP in exerting anti-ovarian cancer effects. Network pharmacology screening identified 110 common targets associated with both TFP action and ovarian cancer. KEGG pathway enrichment analysis clearly showed that the PI3K-/AktKT signaling pathway, EGFR tyrosine kinase inhibitor resistance pathway, and proteoglycan rmegutabolatic proncess were the core enriched pathways. The identified hub genes (e.g., EGFR, CASP3, HSP90AA1) indicated that TFP can simultaneously interfere with tumor cell proliferation, apoptosis, and chaperone-mediated protein homeostasis., Thdemonstrating its multi-target property. This differs from single-pathway inhibitors (e.g., selective AKT inhibitors) and may help reduce the risk of tumor adaptativon leading to drug resistance. Of particular note, the KEGG enrichment analysis revealed the EGFR tyrosine kinase inhibitor resistance pathway, suggesting that TFP may have the potential to reverse tumor resistance to EGFR-TKIs such as erlotinib or gefitinib—t. This potential warrants verification in subsequent combination therapy experiments. Subsequent Western blot (WB) assays directly confirmed that TFP treatment significantly downregulated the protein expression levels of SRC, PI3K, and AKT in SK-OV-3 cells, indicating effective inhibition of the SRC-/PI3K-/AktKT cascade signaling pathway. This pathway is persistently activated in most ovarian cancers and serves as a key molecular mechanism driving tumor progression [17]. By simultaneously targeting and inhibiting SRC (a key upstream kinase) and PI3K/AKT (core downstream effectors), TFP may exert a synergistic blocking effect. Preclinical studies have shown that inhibitors targeting only the PI3K/AKT pathway (e.g., ipatasertib) failed to meet expected outcomes in phase III clinical trials [29],. This suggestings that single-pathway inhibition may be insufficient;. iIn contrast, the multi-target synergistic mode of action of TFP may hold greater therapeutic potential than single-target inhibitors. Additionally, studies have reported that SRC is overexpressed in up to 82% of high-grade serous ovarian carcinomas (HGSOCs), and its specific inhibitor dasatinib has entered phase II clinical trials for ovarian cancer treatment [30]. CombinBased withon our results, itwe is hypothesized that TFP may bind to the SH2 domain of SRC, allosthericalleby inhibiting its kinase activity and thereby exerting anti-tumor effects.

In For the in vivo efficacy evaluation, we used SK-OV-3 cells to establish subcutaneous xenograft models in nude mice. SK-OV-3 exhibits a high tumor formation rate and stable tumor growth characteristics in nude mice, while retaining the heterogeneity of human tumors and key drug resistance features (e.g., platinum resistance caused by TP53 deletion), making it a reliable model for evaluating in vivo drug efficacy [31]. In contrast, the highly sensitive ES-2 cells mighay lead to crausepid xenografts to regress tioon rapidly under drug treactioment, making it difficult to simulate the therapeutic response of clinically drug-resistant tumors. In vivo experimental results showed that TFP significantly inhibited the growth of SK-OV-3 xenografts, which verifyinges its in vitro anti-tumor activity. However, a trend of weight loss was observed in mice in the treatment group, suggesting potential systemic toxicity at this dose. Future studies should optimize the dosing regimen or explore local administration strategies (e.g., intraperitoneal administration) to improve treatment safety. Furthermore, this study did not evaluate the inhibitory effect of TFP on in vivo metastatic foci, which represents an important limitation. Although the lipophilicity of TFP favors its penetration of the blood–brain barrier [32], its specific distribution in ovarian tissues, accumulation capacity, and metabolic stability still require in-depth exploration. The use of nanocarrier technology (e.g., nanoliposomes) to encapsulate TFP for enhancing its accumulation and selectivity in ovarian tissue args wetingll ands its bioavailability represents a highly promising direction for future research.

In conclusion, trifluoperazine reshapes ovarian cancer treatment strategies by targeting the SRC/PI3K/AKT pathway, and its potential for drug repurposing can significantly shorten the clinical translation cycle. This study provides multi-dimensional evidence to confirm its anti-tumor efficacy, offering new hope particularly for patients with platinum-resistant ovarian cancer. Future work will focus on advancing preclinical evaluations based on patient-derived xenograft (PDX) models and organoid platforms.

Conclusion

Trifluoperazine inhibits the proliferation, metastasis and survival of ovarian cancer in multiple dimensions by targeting the SRC/PI3K/Akt signaling pathway. Its synergistic nature with existing therapies and its activity against drug-resistant subtypes make it a highly promising candidate drug for translational research.

Authors’ contributions

Quan He and Jiaqi Mei wrote the main manuscript text, and Jiaqi Mei also processed the data. Desheng Cai and Qinghua Yan performed data analysis; Qinghua Yan, Yanping Zhang, Yuting Deng, Wenyan Li, and Jiaying Xiong conducted experiments. Junhui Wan and Yuanqiao He conceived the study and revised the manuscript. All authors reviewed the manuscript.

Funding

This work was supported by the Central Government-guided Local Science and Technology Development Fund Project (Project Name: Branch Center of National Clinical Research Center for Obstetric and Gynecologic Diseases; Application No.: S2024KJCXG0001).

Data availability

Data is provided within the manuscript or supplementary information files.

Declarations

Ethics approval and consent to participate

All animal experiments were conducted in accordance with the guidelines for the care and use of laboratory animals and were approved by the Institutional Animal Care and Use Committee (IACUC) of Nanchang Royo Biotech Co., Ltd. (Approval No: RTE2024083002).

Competing interests

The authors declare no competing interests.