Exploring the Mechanism of Matrine in Ovarian Cancer Treatment Utilizing GEO Database and Cellular Experiments

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Methods We acquired matrine targets from the TCMSP and SwissTargetPrediction databases. Differential gene expression analysis was performed using R software on the GEO database to compare ovarian cancer tissues with normal ovarian epithelium. The Venn diagram delineated shared genes between matrine targets and differentially expressed genes (DEGs), which were subsequently analyzed using GO and KEGG pathway methodologies. Kaplan-Meier analysis was employed to identify hub targets with prognostic relevance. Molecular docking analyses involving these hub genes were conducted utilizing Autodock Vina and PyMOL. Furthermore, CCK8 and RT-qPCR assays evaluated the effective concentration of matrine and its regulatory impact on target genes in ovarian cancer cells. Results Kaplan-Meier analysis indicated that SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 significantly influenced ovarian cancer prognosis. Molecular docking validated robust binding affinities between matrine and the five targets. Cellular assays demonstrated an IC50 value of 0.5795 mg/mL for matrine in SKOV3 cells, with matrine effectively regulating mRNA levels of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 across different concentrations. Conclusions Matrine modulates the expression of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4, consequently suppressing ovarian cancer proliferation. Matrine GEO dataset Biomarker Ovarian cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Ovarian cancer is one of the deadliest gynecological cancers, with around 70% of patients diagnosed at advanced stages due to the subtle and nonspecific characteristics of early symptoms. The 5-year survival rate was less than 50% [ 1 , 2 ]. The conventional management of ovarian cancer generally involves surgical excision of the tumor, succeeded by platinum-based chemotherapy. Nonetheless, the effectiveness of this method is frequently obstructed by the development of drug resistance and the pronounced side effects linked to chemotherapy, which considerably diminish patients' quality of life and lead to a low overall survival rate[ 3 ]. The urgent demand for precision medicine has led to comprehensive clinical trials investigating novel therapies and the creation of predictive biomarkers[ 4 ]. Despite these advancements, the restricted clinical benefits realized to date underscore the need for alternative strategies. The identification of active compounds from traditional Chinese medicine (TCM) with minimal side effects signifies a promising direction for therapeutic development. Matrine, a bioactive alkaloid derived from Sophora root, exhibits notable anti-inflammatory and anti-tumor properties [ 5 – 8 ]. Matrine, a natural compound, has shown efficacy in inhibiting invasion and migration, as well as inducing apoptosis in colorectal cancer cells by downregulating miR-10b-5p expression and upregulating PTEN protein levels[ 9 ]. Matrine has been documented to modulate the circROBO1/miR-130a-5p axis, affecting the progression and Warburg effect in liver cancer[ 10 ]. Matrine induces apoptosis and autophagy through the AKT/mTOR pathway in gynecologic malignancies, exhibiting notable anti-tumor effects in breast cancer [ 11 ]. Moreover, numerous studies have demonstrated matrine's ability to impede cancer cell viability and progression [ 12 , 13 ]. Despite these findings, limited research has focused on the role of matrine in the treatment of ovarian cancer. One study reported that matrine inhibited the viability, migration, and invasion of ovarian cancer cells by downregulating CCND1 and IL1B expression[ 14 ]. However, the precise mechanisms underlying matrine's anti-ovarian cancer effects, particularly through bioinformatics analysis, remain unexplored. Given these considerations, it is reasonable to hypothesize that matrine, with its substantial anti-tumor potential, could address the diverse genetic mutations associated with ovarian cancer. This study aims to integrate GEO datasets with in vitro experiments to elucidate the molecular mechanisms underlying matrine’s anti-ovarian cancer properties, thereby paving the way for its potential clinical application. Materials and methods Obtaining the related functional genes of matrine The physical and chemical properties, along with the 2D structure (Fig. 1 ) of matrine, were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and the PubChem database[ 15 , 16 ]. The parameters of drug-likeness (DL) and oral bioavailability (OB) were crucial for further analysis. The explicit structure was incorporated into the Swiss Target Prediction database to identify associated targets[ 17 ]. Data sources and identification of DEGs The Gene Expression Omnibus (GEO) is a public repository for microarray and genomic datasets, extensively utilized in bioinformatics. This study examined two chip datasets: GSE14407 and GSE52460. These two profiles were derived from GPL570. The former comprised 12 ovarian cancer epithelial samples and 12 healthy ovarian surface epithelial samples, while the latter included 10 healthy and cancer epithelial samples, respectively. The R software (version 4.1.2) and associated R packages were employed to normalize and analyze differentially expressed genes (DEGs), which satisfied the criteria of P-value 1.5. In the interim, we selected the top 50 differentially expressed genes to create the heatmap. Common genes and enrichment analysis The genes of matrine and DEGs of ovarian cancer were processed through E Venn website to pick up the overlap[ 18 ]. These common genes may signify the effective genes of matrine against ovarian cancer. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted using the 'clusterProfiler' package [ 19 ]. Employing the 'bitr' function to transform the gene name into an ENTREZ ID. The commands 'enrichGO' and 'enrichKEGG' were utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A P value of less than 0.05 was deemed statistically significant. The five foremost items encompassing biological process (BP), cellular component (CC), molecular function (MF) in Gene Ontology (GO) terms, and KEGG pathways were presented using bar plots and dot plots. Survival analysis and molecular docking To identify Hub genes with prognostic disparities, we imported common genes into Kaplan-Meier Plotter for survival analysis. To assess the prognostic significance of a specific gene, ovarian cancer patients were divided into two cohorts based on different quantile expressions of the proposed biomarker. The hazard ratio (HR), along with 95% confidence intervals and the log-rank P value, was computed. An HR greater than 1 or less than 1 suggests that the biomarker may serve as an unfavorable or favorable prognostic indicator. The Logrank P value indicates whether this gene exhibits a statistically significant difference. Molecular docking was extensively utilized to predict the binding affinity between small ligands and target proteins. We chose the Autodock Vina tool, an open-source application, to identify the potential association between matrine and SRD5A1, BCHE, JAK1, PTGER3, and PTGER4[ 20 ]. Following the removal of water, the addition of hydrogen, and the calculation of the Gasteiger charge, the receptor protein was prepared for grid docking. The docking results were finalized by Vina and visualized using PyMOL 2.4.2. Cell line culture and CCK8 assay The SKOV3 human ovarian cancer cell line was acquired from Genechem Enterprise in Shanghai, China. The cell was characterized through STR profiling, free from mycoplasma contamination. The cells were cultured in RPIM 1640 (Gibco, USA) supplemented with 10% fetal bovine serum, 1% streptomycin, and penicillin at 37°C in a cell incubator with 5% CO 2 . The cytotoxic effects of matrine on SKOV3 were evaluated using the CCK8 assay. The logarithmic phase cells were inoculated at a density of 1×10 4 cells per well in 96-well plates. Subsequent to adherence, different concentrations (0, 0.1, 0.2, 0.4, 0.8, 1.6 mg/mL) of matrine were administered for a duration of 24 hours. The absorbance at a wavelength of 450 nm was quantified using a microplate reader (TECAN, Switzerland). The control group received matrine at a concentration of 0 mg/mL, while the blank group comprised wells devoid of both cells and matrine. Real-time qPCR The logarithmic phase cells were inoculated at a density of 5×10 5 cells per well in 6-well plates. Matrine at concentrations of 0, 0.1, 0.2, and 0.4 mg/mL was administered for 24 hours. Total cellular RNA was extracted using TRIzol reagent (Invitrogen, USA), and RNA purity was assessed by measuring optical density (A260/A280 ratio) with a NanoDrop spectrophotometer. cDNA was synthesized using the RNA RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher) following the manufacturer's instructions. Subsequently, cDNA was amplified using TB Green Premix Ex Taq II (Takara) in a PCR system, utilizing the primers listed in Table 1 . Relative mRNA expression was quantified utilizing the 2 −ΔΔCt method, with GAPDH as the internal standard. Table 1 RT-qPCR primers Gene Forward (5’-3’) Reverse (5’-3’) SRD5A1 GAATATGTATCTTCAGCCAAC GGTAATCTTCAAACTTCTCG BCHE GCTTTGTGGAAATTACCTTG CCTCACTTCCACATCAATTT JAK1 GTCCTCTGGATCTCTTCATGCA GCTGTTTGGCAACTTTGAATTTCC PTGER3 TCTGTGTCCGTGGCCTTCCCC CACAGGCAGCAGCGCGAAGGC PTGER4 AAGTCGCGCAAGGAGCAGAA CTTGTCCACGTAGTGGCTGT GAPDH GATGACCCAGATCATGTTTGAGAC GGAGTCCATCACGATGCCAGT Statistical analysis The data were expressed as mean ± standard deviations (SD) from a minimum of three independent experiments. Comparisons among multiple groups were conducted using one-way analysis of variance (ANOVA). P value < 0.05 was deemed statistically significant. The enrichment analysis employed the pAdjustMethod = "BH". Results The physical, chemical properties and related targets of matrine The parameters OB ≥ 30% and DL ≥ 0.18 signify the optimal availability value of a pharmaceutical agent. Through database analysis, the DL and OB of matrine were determined to be 0.25 and 63.77, respectively, satisfying the previously stated criteria (Table 2 ). Upon database integration, 111 drug targets of matrine were identified (Fig. 2 ). Table 2 Pharmacological and molecular properties of matrine Name MW AlogP Hdon Hacc OB Caco-2 BBB DL Matrine 248.41 1.42 0 3 63.77 1.39 1.52 0.25 Identification of DEGs in ovarian cancer We utilize R software to analyze GSE14407 and GSE52460 to investigate alterations in gene expression in ovarian cancer relative to normal epithelial tissue. The volcano plot revealed 779 up-regulated genes and 1132 down-regulated genes in GSE14407, as well as 568 up-regulated genes and 837 down-regulated genes in GSE52460 (Fig. 3 A, B). We selected the top 20 DEGs exhibiting the most significant changes in expression for the heat map presentation (Fig. 3 C, D). Target identification and associated enrichment analysis of matrine in relation to ovarian cancer To identify shared genes between matrine and DEGs, we utilized a Venn diagram, revealing 6 overlapping genes that signify the effective genes of matrine in combating ovarian cancer (Fig. 4 ). GO annotation indicated that these genes are primarily enriched in biological processes related to ammonium ion metabolism, antibiotic response, and diterpenoid metabolism. In CC, six genes primarily involved in the nuclear envelope lumen, nuclear envelope, and myelin sheath. The MF item predominantly exhibited prostaglandin receptor activity, prostanoid receptor activity, and icosanoid receptor activity (Fig. 5 ). KEGG analysis identified five primary categories and 42 pathways, including Human cytomegalovirus infection, Human papillomavirus infection, and Neuroactive ligand-receptor interaction (Fig. 6 ). Hub genes analysis and molecular docking with matrine We conducted a prognostic analysis using the Kaplan-Meier database to investigate the correlations between gene expression levels and clinical outcomes in patients. The results demonstrated that SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 exhibited significant statistical differences in patients' five-year survival rates. The overexpression of these genes correlates with a reduced overall survival rate and an increased mortality risk in ovarian cancer patients (Fig. 7 ). Next, autodock vina was employed for molecular docking. A more stable interaction between the ligand and the receptor corresponds to a lower binding energy. The binding affinity between the protein and matrine is presented in Table 3 . The visualized photo demonstrates that matrine would bind multiple residues in hub genes through hydrogen bonds (Fig. 8 ). Table 3 Binding affinity of hub genes and matrine (kcal/mol) SRD5A1 BCHE JAK1 PTGER3 PTGER4 Matrine -7.9 -8.5 -6.9 -8.9 -6.5 Anti-tumor effect of matrine The CCK8 results indicated that, compared to the control, matrine treatment significantly inhibited the proliferation of SKOV3 in a dose-dependent manner. The IC50 of matrine treatment on SKOV3 cells was 0.5795 mg/mL. Consequently, we selected 0.1, 0.2, and 0.4 mg/mL as the concentrations for the experimental drug (Fig. 9 A). The RT-qPCR results demonstrated that the expression levels of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 in the matrine-treated group were diminished in comparison to the control group. While the mRNA levels did not exhibit significant alterations at concentrations of 0.1 and 0.2 mg/mL, matrine at 0.4 mg/mL demonstrated substantial anti-ovarian cancer efficacy (Fig. 9 B-F). Discussion Ovarian cancer is a perilous condition impacting women globally, with approximately 225,500 new cases diagnosed each year. The elevated incidence is ascribed to the absence of early-stage symptoms and the restricted effectiveness of existing screening techniques[ 21 , 22 ]. Platinum-based chemotherapy is the standard treatment. However, recurrent disease often results in drug resistance, presenting a considerable therapeutic challenge [ 23 ]. This study aims to investigate prognostic biomarkers and the anti-ovarian cancer effects of matrine, utilizing data from the GEO dataset, due to its established anti-tumor potential [ 24 – 26 ]. The predominant origin of ovarian cancers is the germinal epithelium, rendering it a rational target for the identification of DEGs. The GSE14407 and GSE52460 datasets were chosen to examine gene expression variations between normal and malignant epithelium. Assessing a drug's absorption, distribution, metabolism, and excretion (ADME) properties, in conjunction with its physical characteristics, is essential for determining its viability as an optimal therapeutic agent. These parameters accelerate drug discovery by guaranteeing optimal pharmacokinetics and pharmacodynamics [ 27 ]. A drug with OB of 30% or greater and DL of 0.18 or higher is deemed to possess significant absorption and developmental potential [ 28 , 29 ]. Matrine fulfills both criteria, highlighting its appropriateness for pharmaceutical development. Target prediction identified 111 genes linked to matrine, while a Venn diagram delineated six overlapping genes between matrine targets and DEGs. The six genes were subsequently selected for further investigation owing to their potential involvement in the drug-disease interaction. Enrichment analysis revealed that Human Cytomegalovirus (HCMV) and Human Papillomavirus (HPV) are associated with the progression of ovarian cancer. Previous research indicates that active HCMV infection in the ovarian tumor microenvironment may facilitate cancer progression, especially in immunocompromised individuals [ 30 , 31 ]. S Extensive evidence indicates a strong correlation between HPV infection and the onset and advancement of ovarian cancer [ 32 – 34 ]. These findings offer significant insights into the molecular interactions that drive ovarian cancer progression. In order to clarify if these common genes may be considered prognostic biomarkers, we draw survival curve via Kaplan-Meier. The overexpression of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 were correlated with unfavorable outcomes in ovarian cancer patients. SRD5A1 is one type of 5α-reductase, might control the hormone level and mediate the tumor process[ 35 ], have been reported high-expression in diverse malignancy[ 36 – 38 ]. BCHE, an enzyme secreted by the liver and detectable in the bloodstream, demonstrates low expression in colorectal carcinoma but elevated expression in ovarian cancer [ 39 , 40 ]. The JAK1/STAT3 inflammatory signaling pathway is crucial for cell growth and differentiation, exhibiting sustained activation in ovarian cancers [ 41 ], breast cancer[ 42 ]. PTGER3 and PTGER4, receptors for prostaglandin E2 (PGE2), have been associated with tumorigenesis and metastasis in various malignancies [ 43 – 45 ]. The molecular docking results indicated that matrine possesses a high binding affinity for these hub genes. Cell experiments demonstrated that matrine, in a dose-dependent manner, decreased the mRNA expression levels of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 in SKOV3 cells, highlighting its potential as an anti-ovarian cancer agent. This study did not examine alterations in the protein expression levels of these hub genes. Nonetheless, the results offer new understanding of the mechanisms through which matrine demonstrates its anti-ovarian cancer properties. Furthermore, the identified target genes exhibit potential as prognostic biomarkers, providing novel opportunities for therapeutic intervention via computer-aided drug design. The limitations of this experiment are that the correlation between in vivo experiment and protein level has not been verified, and the interaction between small molecule drugs and related genes has not been explored in depth. We will further verify this in future experiments. Conclusion This study integrated the GEO database with experimental validation to clarify matrine's anti-tumor efficacy in ovarian cancer and identify potential functional targets. The anti-tumor ability of matrine was correlated with inhibiting the expression of RD5A1, BCHE, JAK1, PTGER3, and PTGER4, mainly . The GEO database may serve as an effective strategy for identifying drug targets. Declarations Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://www.syngoportal.org/. Ethics, Consent to Participate, and Consent to Publish declarations Ethics, Consent to Participate, and Consent to Publish declarations not applicable. Author contributions H.F.C and F.F.Z contributed to the conception of the study, H.F.C performed the data analyses and wrote the manuscript, H.F.C and F.F.Z collected the data and helped to perform the data analysis. All authors reviewed and approved the final manuscript. Funding This research was supported by the Fund Project of Zhejiang Chinese Medical (2021FSYYZQ19). Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Acknowledgments The authors express their gratitude towards the participants and staff of the GEO dataset,for their noteworthy contributions. References Lheureux S, Gourley C, Vergote I, Oza AM. Epithelial ovarian cancer. 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Rodriguez-Aguayo C, Bayraktar E, Ivan C, Aslan B, Mai J, He G, Mangala LS, Jiang D, Nagaraja AS, Ozpolat B, Chavez-Reyes A, Ferrari M, Mitra R, Siddik ZH, Shen H, Yang X, Sood AK, Lopez-Berestein G. EBioMedicine. PTGER3 induces ovary tumorigenesis and confers resistance to cisplatin therapy through up-regulation Ras-MAPK/Erk-ETS1-ELK1/CFTR1 axis. 2019; 40: 290–304. Shin VY, Siu MT, Liu X, Ng EKO, Kwong A, Chu KM. MiR-92 suppresses proliferation and induces apoptosis by targeting EP4/Notch1 axis in gastric cancer. Oncotarget. 2018;9:24209–20. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5861720","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439901860,"identity":"2fbfbab0-a3aa-463b-8a71-b6e843aad6d6","order_by":0,"name":"Fangfang Zhang","email":"","orcid":"","institution":"Lishui Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Zhang","suffix":""},{"id":439901863,"identity":"a10d0f06-4956-42ab-9337-031d31bde79e","order_by":1,"name":"Huifang Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYBACNv7mg4//GNjUtzEzJD5IqKghrIVP4liyAU9FGmMfe8NjgwdnjhHWIseQYybAc+YQ4zyeg88kH7YwE+EwhmNpDJJtB5jZJJLTKhIb2Bj427sTGH5UbMOthbn52APDtjtsbBJpaTcSd8gwSJw5u4Gx58xtfLakGyS2PeNhk8gBajnDxmAgkbuBmbENn5YcM4mDbYcl2CTyvxUkAsONKC2SDWcOG7DxHEhjIE4LMJCNGSrSEtjYG5IlEs4c4wH55SA+v8j3A6OSwcAmQb6ZIfHjj4oaOf723o0PflTg1oIBeEDEAeLVj4JRMApGwSjABgBvOVpOUhlQSgAAAABJRU5ErkJggg==","orcid":"","institution":"Lishui Hospital of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Cheng","suffix":""}],"badges":[],"createdAt":"2025-01-20 01:23:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5861720/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5861720/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80346790,"identity":"2f25f258-c3b4-4b71-9742-da05ff981c62","added_by":"auto","created_at":"2025-04-10 20:16:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5411,"visible":true,"origin":"","legend":"\u003cp\u003eStructure of matrine (CAS number: 519-02-8)\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/808416bcc2b3475bba965d08.jpg"},{"id":80346791,"identity":"4f805a59-02f5-4fda-9375-51a07c4876ac","added_by":"auto","created_at":"2025-04-10 20:16:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137220,"visible":true,"origin":"","legend":"\u003cp\u003eTargets of matrine. Green square represents the drug, and red circle represents the related target genes\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/8f7dc0b4aa2a24856ab5f9cb.jpg"},{"id":80347441,"identity":"627b31db-6a02-46a5-958a-0e1717266754","added_by":"auto","created_at":"2025-04-10 20:32:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109812,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEGs of GSE14407 and GSE52460. \u003cem\u003eP\u003c/em\u003e value \u0026lt; 0.05 and |log\u003csub\u003e2\u003c/sub\u003e (FC)| \u0026gt; 1.5 were viewed as the cut off criteria. (A) Volcano plot of DEGs got from GSE014407 (B) Volcano plot of DEGs got from GSE052460 (C) Heatmap of top 50 DEGs obtained from GSE014407 (D) Heatmap of top 50 DEGs obtained from GSE052460\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/0e28e260d6e35588e643b1a6.jpg"},{"id":80346795,"identity":"105834c6-8ac3-489a-b101-5601baae6b8c","added_by":"auto","created_at":"2025-04-10 20:16:42","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7309,"visible":true,"origin":"","legend":"\u003cp\u003eSix targets of matrine and DEGs were analyzed by Draw Venn Diagram\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/1cf6b1ebca91c7a8fb85242e.jpg"},{"id":80346793,"identity":"50c80ca8-0bc7-42b7-b4aa-03660ec8e609","added_by":"auto","created_at":"2025-04-10 20:16:42","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100043,"visible":true,"origin":"","legend":"\u003cp\u003eGO annotation for 6 common genes with barplot (Top 5). Blue column represents the biological process of 6 genes. Orange column represents the cellular components of 6 genes. Green column represents the molecular function of 6 genes.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/d7969831f7f350873159c966.jpg"},{"id":80347208,"identity":"98a028f7-ddbb-4be2-881e-1751bf059911","added_by":"auto","created_at":"2025-04-10 20:24:42","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":48565,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG annotation for 6 common genes with bubble plot (Top 10). The gradual color represents the \u003cem\u003eP\u003c/em\u003e value, and the size of spots represents genes number enriched in the different pathways.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/26cfc95bf9521e689e2f3bf8.jpg"},{"id":80347639,"identity":"cad52b33-e838-493d-b280-a7d91506d3ba","added_by":"auto","created_at":"2025-04-10 20:40:43","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":223755,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of 6 common genes. Red and black curve represented high or low expression of single gene in ovarian cancer. Time restriction were 60 months. Logrank \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05 was considered statistical significance\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/17aa557ccb5707a76d68daee.jpg"},{"id":80347444,"identity":"c49bd036-74ac-4200-b7b0-7a95a3a95e40","added_by":"auto","created_at":"2025-04-10 20:32:43","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":122957,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking between matrine and hub genes. Grey mass represented hub genes in surface formation. The green represented matrine. The red represented residues linked with matrine\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/0906984ba228da73d996467e.jpg"},{"id":80346807,"identity":"9cbd3ba1-3cb8-498c-84b6-3df6db80f320","added_by":"auto","created_at":"2025-04-10 20:16:43","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":97590,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of matrine on the cell viability and the expression of mRNA level in SKOV3 cell. (A) CCK8 assay results (B-F) BCHE, SRD5A1, JAK1, PTGER4, and PTGER4 mRNA expression. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, vs Control group.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/5892b52dff0b3e6b6a19b67a.jpg"},{"id":86778342,"identity":"3453ea31-1baa-45b0-86cc-dbf5a7c67fd2","added_by":"auto","created_at":"2025-07-15 12:54:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1814455,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5861720/v1/ac60f243-4116-4eca-badd-925a2fb87999.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Mechanism of Matrine in Ovarian Cancer Treatment Utilizing GEO Database and Cellular Experiments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer is one of the deadliest gynecological cancers, with around 70% of patients diagnosed at advanced stages due to the subtle and nonspecific characteristics of early symptoms. \u003cb\u003eThe 5-year survival rate was less than 50%\u003c/b\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The conventional management of ovarian cancer generally involves surgical excision of the tumor, succeeded by platinum-based chemotherapy. Nonetheless, the effectiveness of this method is frequently obstructed by the development of drug resistance and the pronounced side effects linked to chemotherapy, which considerably diminish patients' quality of life and lead to a low overall survival rate[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The urgent demand for precision medicine has led to comprehensive clinical trials investigating novel therapies and the creation of predictive biomarkers[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite these advancements, the restricted clinical benefits realized to date underscore the need for alternative strategies. The identification of active compounds from traditional Chinese medicine (TCM) with minimal side effects signifies a promising direction for therapeutic development.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMatrine, a bioactive alkaloid derived from Sophora root, exhibits notable anti-inflammatory and anti-tumor properties\u003c/b\u003e[\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Matrine, a natural compound, has shown efficacy in inhibiting invasion and migration, as well as inducing apoptosis in colorectal cancer cells by downregulating miR-10b-5p expression and upregulating PTEN protein levels[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Matrine has been documented to modulate the circROBO1/miR-130a-5p axis, affecting the progression and Warburg effect in liver cancer[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Matrine induces apoptosis and autophagy through the AKT/mTOR pathway in gynecologic malignancies, exhibiting notable anti-tumor effects in breast cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Moreover, numerous studies have demonstrated matrine's ability to impede cancer cell viability and progression [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite these findings, limited research has focused on the role of matrine in the treatment of ovarian cancer. One study reported that matrine inhibited the viability, migration, and invasion of ovarian cancer cells by downregulating CCND1 and IL1B expression[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the precise mechanisms underlying matrine's anti-ovarian cancer effects, particularly through bioinformatics analysis, remain unexplored.\u003c/p\u003e \u003cp\u003eGiven these considerations, it is reasonable to hypothesize that matrine, with its substantial anti-tumor potential, could address the diverse genetic mutations associated with ovarian cancer. This study aims to integrate GEO datasets with in vitro experiments to elucidate the molecular mechanisms underlying matrine\u0026rsquo;s anti-ovarian cancer properties, thereby paving the way for its potential clinical application.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObtaining the related functional genes of matrine\u003c/h2\u003e \u003cp\u003eThe physical and chemical properties, along with the 2D structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) of matrine, were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and the PubChem database[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The parameters of drug-likeness (DL) and oral bioavailability (OB) were crucial for further analysis. The explicit structure was incorporated into the Swiss Target Prediction database to identify associated targets[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData sources and identification of DEGs\u003c/h3\u003e\n\u003cp\u003eThe Gene Expression Omnibus (GEO) is a public repository for microarray and genomic datasets, extensively utilized in bioinformatics. This study examined two chip datasets: GSE14407 and GSE52460. These two profiles were derived from GPL570. The former comprised 12 ovarian cancer epithelial samples and 12 healthy ovarian surface epithelial samples, while the latter included 10 healthy and cancer epithelial samples, respectively. The R software (version 4.1.2) and associated R packages were employed to normalize and analyze differentially expressed genes (DEGs), which satisfied the criteria of \u003cem\u003eP-value\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e fold change (FC)| \u0026gt; 1.5. In the interim, we selected the top 50 differentially expressed genes to create the heatmap.\u003c/p\u003e\n\u003ch3\u003eCommon genes and enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe genes of matrine and DEGs of ovarian cancer were processed through E Venn website to pick up the overlap[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These common genes may signify the effective genes of matrine against ovarian cancer.\u003c/p\u003e \u003cp\u003eGene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted using the 'clusterProfiler' package [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Employing the 'bitr' function to transform the gene name into an ENTREZ ID. The commands 'enrichGO' and 'enrichKEGG' were utilized for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A \u003cem\u003eP\u003c/em\u003e value of less than 0.05 was deemed statistically significant. The five foremost items encompassing biological process (BP), cellular component (CC), molecular function (MF) in Gene Ontology (GO) terms, and KEGG pathways were presented using bar plots and dot plots.\u003c/p\u003e\n\u003ch3\u003eSurvival analysis and molecular docking\u003c/h3\u003e\n\u003cp\u003eTo identify Hub genes with prognostic disparities, we imported common genes into Kaplan-Meier Plotter for survival analysis. To assess the prognostic significance of a specific gene, ovarian cancer patients were divided into two cohorts based on different quantile expressions of the proposed biomarker. The hazard ratio (HR), along with 95% confidence intervals and the log-rank \u003cem\u003eP\u003c/em\u003e value, was computed. An HR greater than 1 or less than 1 suggests that the biomarker may serve as an unfavorable or favorable prognostic indicator. The Logrank \u003cem\u003eP\u003c/em\u003e value indicates whether this gene exhibits a statistically significant difference.\u003c/p\u003e \u003cp\u003eMolecular docking was extensively utilized to predict the binding affinity between small ligands and target proteins. We chose the Autodock Vina tool, an open-source application, to identify the potential association between matrine and SRD5A1, BCHE, JAK1, PTGER3, and PTGER4[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Following the removal of water, the addition of hydrogen, and the calculation of the Gasteiger charge, the receptor protein was prepared for grid docking. The docking results were finalized by Vina and visualized using PyMOL 2.4.2.\u003c/p\u003e\n\u003ch3\u003eCell line culture and CCK8 assay\u003c/h3\u003e\n\u003cp\u003eThe SKOV3 human ovarian cancer cell line was acquired from Genechem Enterprise in Shanghai, China. The cell was characterized through STR profiling, free from mycoplasma contamination. The cells were cultured in RPIM 1640 (Gibco, USA) supplemented with 10% fetal bovine serum, 1% streptomycin, and penicillin at 37\u0026deg;C in a cell incubator with 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eThe cytotoxic effects of matrine on SKOV3 were evaluated using the CCK8 assay. The logarithmic phase cells were inoculated at a density of 1\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells per well in 96-well plates. Subsequent to adherence, different concentrations (0, 0.1, 0.2, 0.4, 0.8, 1.6 mg/mL) of matrine were administered for a duration of 24 hours. The absorbance at a wavelength of 450 nm was quantified using a microplate reader (TECAN, Switzerland). The control group received matrine at a concentration of 0 mg/mL, while the blank group comprised wells devoid of both cells and matrine.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReal-time qPCR\u003c/h2\u003e \u003cp\u003eThe logarithmic phase cells were inoculated at a density of 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells per well in 6-well plates. Matrine at concentrations of 0, 0.1, 0.2, and 0.4 mg/mL was administered for 24 hours. Total cellular RNA was extracted using TRIzol reagent (Invitrogen, USA), and RNA purity was assessed by measuring optical density (A260/A280 ratio) with a NanoDrop spectrophotometer. cDNA was synthesized using the RNA RevertAid First Strand cDNA Synthesis Kit (Thermo Fisher) following the manufacturer's instructions. Subsequently, cDNA was amplified using TB Green Premix Ex Taq II (Takara) in a PCR system, utilizing the primers listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Relative mRNA expression was quantified utilizing the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, with GAPDH as the internal standard.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRT-qPCR primers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRD5A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAATATGTATCTTCAGCCAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGTAATCTTCAAACTTCTCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCHE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCTTTGTGGAAATTACCTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCTCACTTCCACATCAATTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJAK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTCCTCTGGATCTCTTCATGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGCTGTTTGGCAACTTTGAATTTCC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTGER3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCTGTGTCCGTGGCCTTCCCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCACAGGCAGCAGCGCGAAGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTGER4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGTCGCGCAAGGAGCAGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTTGTCCACGTAGTGGCTGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATGACCCAGATCATGTTTGAGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAGTCCATCACGATGCCAGT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD) from a minimum of three independent experiments. Comparisons among multiple groups were conducted using one-way analysis of variance (ANOVA). \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was deemed statistically significant. The enrichment analysis employed the pAdjustMethod = \"BH\".\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe physical, chemical properties and related targets of matrine\u003c/h2\u003e \u003cp\u003eThe parameters OB\u0026thinsp;\u0026ge;\u0026thinsp;30% and DL\u0026thinsp;\u0026ge;\u0026thinsp;0.18 signify the optimal availability value of a pharmaceutical agent. Through database analysis, the DL and OB of matrine were determined to be 0.25 and 63.77, respectively, satisfying the previously stated criteria (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Upon database integration, 111 drug targets of matrine were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePharmacological and molecular properties of matrine\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlogP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHdon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHacc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCaco-2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBBB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e248.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs in ovarian cancer\u003c/h2\u003e \u003cp\u003eWe utilize R software to analyze GSE14407 and GSE52460 to investigate alterations in gene expression in ovarian cancer relative to normal epithelial tissue. The volcano plot revealed 779 up-regulated genes and 1132 down-regulated genes in GSE14407, as well as 568 up-regulated genes and 837 down-regulated genes in GSE52460 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). We selected the top 20 DEGs exhibiting the most significant changes in expression for the heat map presentation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTarget identification and associated enrichment analysis of matrine in relation to ovarian cancer\u003c/h2\u003e \u003cp\u003eTo identify shared genes between matrine and DEGs, we utilized a Venn diagram, revealing 6 overlapping genes that signify the effective genes of matrine in combating ovarian cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGO annotation indicated that these genes are primarily enriched in biological processes related to ammonium ion metabolism, antibiotic response, and diterpenoid metabolism. In CC, six genes primarily involved in the nuclear envelope lumen, nuclear envelope, and myelin sheath. The MF item predominantly exhibited prostaglandin receptor activity, prostanoid receptor activity, and icosanoid receptor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKEGG analysis identified five primary categories and 42 pathways, including Human cytomegalovirus infection, Human papillomavirus infection, and Neuroactive ligand-receptor interaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eHub genes analysis and molecular docking with matrine\u003c/h2\u003e \u003cp\u003eWe conducted a prognostic analysis using the Kaplan-Meier database to investigate the correlations between gene expression levels and clinical outcomes in patients. The results demonstrated that SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 exhibited significant statistical differences in patients' five-year survival rates. The overexpression of these genes correlates with a reduced overall survival rate and an increased mortality risk in ovarian cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, autodock vina was employed for molecular docking. A more stable interaction between the ligand and the receptor corresponds to a lower binding energy. The binding affinity between the protein and matrine is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The visualized photo demonstrates that matrine would bind multiple residues in hub genes through hydrogen bonds (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBinding affinity of hub genes and matrine (kcal/mol)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRD5A1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBCHE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJAK1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePTGER3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePTGER4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAnti-tumor effect of matrine\u003c/h2\u003e \u003cp\u003eThe CCK8 results indicated that, compared to the control, matrine treatment significantly inhibited the proliferation of SKOV3 in a dose-dependent manner. The IC50 of matrine treatment on SKOV3 cells was 0.5795 mg/mL. Consequently, we selected 0.1, 0.2, and 0.4 mg/mL as the concentrations for the experimental drug (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe RT-qPCR results demonstrated that the expression levels of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 in the matrine-treated group were diminished in comparison to the control group. While the mRNA levels did not exhibit significant alterations at concentrations of 0.1 and 0.2 mg/mL, matrine at 0.4 mg/mL demonstrated substantial anti-ovarian cancer efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-F).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOvarian cancer is a perilous condition impacting women globally, with approximately 225,500 new cases diagnosed each year. The elevated incidence is ascribed to the absence of early-stage symptoms and the restricted effectiveness of existing screening techniques[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Platinum-based chemotherapy is the standard treatment. However, recurrent disease often results in drug resistance, presenting a considerable therapeutic challenge [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This study aims to investigate prognostic biomarkers and the anti-ovarian cancer effects of matrine, utilizing data from the GEO dataset, due to its established anti-tumor potential [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe predominant origin of ovarian cancers is the germinal epithelium, rendering it a rational target for the identification of DEGs. The GSE14407 and GSE52460 datasets were chosen to examine gene expression variations between normal and malignant epithelium.\u003c/p\u003e \u003cp\u003eAssessing a drug's absorption, distribution, metabolism, and excretion (ADME) properties, in conjunction with its physical characteristics, is essential for determining its viability as an optimal therapeutic agent. These parameters accelerate drug discovery by guaranteeing optimal pharmacokinetics and pharmacodynamics [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. A drug with OB of 30% or greater and DL of 0.18 or higher is deemed to possess significant absorption and developmental potential [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Matrine fulfills both criteria, highlighting its appropriateness for pharmaceutical development. Target prediction identified 111 genes linked to matrine, while a Venn diagram delineated six overlapping genes between matrine targets and DEGs. The six genes were subsequently selected for further investigation owing to their potential involvement in the drug-disease interaction.\u003c/p\u003e \u003cp\u003eEnrichment analysis revealed that Human Cytomegalovirus (HCMV) and Human Papillomavirus (HPV) are associated with the progression of ovarian cancer. Previous research indicates that active HCMV infection in the ovarian tumor microenvironment may facilitate cancer progression, especially in immunocompromised individuals [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. S Extensive evidence indicates a strong correlation between HPV infection and the onset and advancement of ovarian cancer [\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings offer significant insights into the molecular interactions that drive ovarian cancer progression.\u003c/p\u003e \u003cp\u003eIn order to clarify if these common genes may be considered prognostic biomarkers, we draw survival curve via Kaplan-Meier. The overexpression of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 were correlated with unfavorable outcomes in ovarian cancer patients. SRD5A1 is one type of 5α-reductase, might control the hormone level and mediate the tumor process[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], have been reported high-expression in diverse malignancy[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. BCHE, an enzyme secreted by the liver and detectable in the bloodstream, demonstrates low expression in colorectal carcinoma but elevated expression in ovarian cancer [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The JAK1/STAT3 inflammatory signaling pathway is crucial for cell growth and differentiation, exhibiting sustained activation in ovarian cancers [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], breast cancer[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. PTGER3 and PTGER4, receptors for prostaglandin E2 (PGE2), have been associated with tumorigenesis and metastasis in various malignancies [\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The molecular docking results indicated that matrine possesses a high binding affinity for these hub genes.\u003c/p\u003e \u003cp\u003eCell experiments demonstrated that matrine, in a dose-dependent manner, decreased the mRNA expression levels of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 in SKOV3 cells, highlighting its potential as an anti-ovarian cancer agent. This study did not examine alterations in the protein expression levels of these hub genes. Nonetheless, the results offer new understanding of the mechanisms through which matrine demonstrates its anti-ovarian cancer properties. Furthermore, the identified target genes exhibit potential as prognostic biomarkers, providing novel opportunities for therapeutic intervention via computer-aided drug design. \u003cb\u003eThe limitations of this experiment are that the correlation between in vivo experiment and protein level has not been verified, and the interaction between small molecule drugs and related genes has not been explored in depth. We will further verify this in future experiments.\u003c/b\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study integrated the GEO database with experimental validation to clarify matrine's anti-tumor efficacy in ovarian cancer and identify potential functional targets. \u003cb\u003eThe anti-tumor ability of matrine was correlated with inhibiting the expression of RD5A1, BCHE, JAK1, PTGER3, and PTGER4, mainly\u003c/b\u003e. The GEO database may serve as an effective strategy for identifying drug targets.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.syngoportal.org/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics, Consent to Participate, and Consent to Publish declarations not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eH.F.C and F.F.Z contributed to the conception of the study, H.F.C performed the data analyses and wrote the manuscript, H.F.C and\u0026nbsp;F.F.Z \u0026nbsp; collected the data and helped to perform the data analysis. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Fund Project of Zhejiang Chinese Medical (2021FSYYZQ19).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude towards the participants and staff of the\u0026nbsp;GEO dataset,for their noteworthy contributions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLheureux S, Gourley C, Vergote I, Oza AM. Epithelial ovarian cancer. 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Oncotarget. 2018;9:24209\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Matrine, GEO dataset, Biomarker, Ovarian cancer","lastPublishedDoi":"10.21203/rs.3.rs-5861720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5861720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo assess the potential therapeutic targets of matrine in the treatment of ovarian cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe acquired matrine targets from the TCMSP and SwissTargetPrediction databases. Differential gene expression analysis was performed using R software on the GEO database to compare ovarian cancer tissues with normal ovarian epithelium. The Venn diagram delineated shared genes between matrine targets and differentially expressed genes (DEGs), which were subsequently analyzed using GO and KEGG pathway methodologies. Kaplan-Meier analysis was employed to identify hub targets with prognostic relevance. Molecular docking analyses involving these hub genes were conducted utilizing Autodock Vina and PyMOL. Furthermore, CCK8 and RT-qPCR assays evaluated the effective concentration of matrine and its regulatory impact on target genes in ovarian cancer cells.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eKaplan-Meier analysis indicated that SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 significantly influenced ovarian cancer prognosis. Molecular docking validated robust binding affinities between matrine and the five targets. Cellular assays demonstrated an IC50 value of 0.5795 mg/mL for matrine in SKOV3 cells, with matrine effectively regulating mRNA levels of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4 across different concentrations.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMatrine modulates the expression of SRD5A1, BCHE, JAK1, PTGER3, and PTGER4, consequently suppressing ovarian cancer proliferation.\u003c/p\u003e","manuscriptTitle":"Exploring the Mechanism of Matrine in Ovarian Cancer Treatment Utilizing GEO Database and Cellular Experiments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 20:16:37","doi":"10.21203/rs.3.rs-5861720/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60fb4007-a86f-41f1-bba1-be522fa0acf3","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T12:53:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-10 20:16:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5861720","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5861720","identity":"rs-5861720","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}