Abstract Objective Ovarian cancer (OC) is a highly aggressive malignancy in females. We aim to investigate the potential gene target and examine its impact on OC. Methods Hub genes were determined using protein–protein interaction networks based on differently expressed genes in [26]GSE12470 and [27]GSE14407 datasets. The impact of FAM64A on the malignant phenotype of OC cells was evaluated by cell counting kit-8, 5-ethynyl-2'-deoxyuridine staining, wound healing, and transwell assays. The epithelial-mesenchymal transition (EMT) process was assessed by determining the protein expression of E-cadherin, N-cadherin, and Vimentin. Results We identified the 18 hub genes of OC with substantial predictive value. FAM64A was selected as a candidate gene. The silencing of FAM64A suppressed the viability (si-NC: 0.78 ± 0.04, 0.95 ± 0.08; si-FAM64A: 0.58 ± 0.05, 0.64 ± 0.11), proliferation (si-NC: 100.00 ± 9.36, 100.00 ± 14.70; si-FAM64A: 34.79 ± 8.88, 44.55 ± 4.91), migration (si-NC: 61.92 ± 8.06, 60.08 ± 5.22; si-FAM64A: 45.88 ± 8.36, 37.78 ± 7.29), and invasion (si-NC: 130.00 ± 10.34, 144.00 ± 13.40; si-FAM64A: 81.00 ± 16.99, 115.60 ± 13.30) of A2780 and SKOV3 cells. FAM64A silencing reduced the EMT in OC cells. The Hippo pathway was identified as the central pathway implicated in the regulatory role of FAM64A in OC. The silencing of FAM64A caused an increase in the protein expression within the Hippo pathway in both A2780 and SKOV3 cells. Conclusion Knockdown of FAM64A emerges as a promising therapeutic target for OC, exerting an inhibitory role in OC by activating the Hippo pathway. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-025-02710-0. Keywords: FAM64A, Ovarian cancer, Hub genes, Hippo pathway Highlights 1. AURKA, CCNB2, MKI67, and FAM64A were hub genes of OC. 2. FAM64A silencing inhibits proliferation, migration, and invasion of OC cells. 3. FAM64A silencing inhibits EMT of OC cells. 4. FAM64A silencing activates Hippo pathway in OC cells. Supplementary Information The online version contains supplementary material available at 10.1007/s12672-025-02710-0. Introduction Ovarian cancer (OC) ranks as a leading cause of cancer-related deaths among women, currently holding the fifth position in terms of mortality rates due to cancer in females [[28]1]. The difficulty of early detection in OC arises from the small size of the ovaries and their concealed position deep within the pelvic cavity. When tumor cells infiltrate or metastasize to different body regions, patients may not exhibit symptoms, or they may present with non-specific symptoms [[29]2]. Only 15% of cases are detected in the early and localized stages, typically associated with better outcomes and an approximate 90% 5-year survival rate [[30]3]. Conversely, about 60% of OC diagnoses occur when the disease has already metastasized to distant sites, significantly reducing the 5-year survival rate to around 30% [[31]3]. Moreover, the primary treatment modality for OC involves surgical excision of the affected tissue, followed by chemotherapy [[32]4]. Although initially effective in most patients, nearly all individuals experience recurrence and ultimately succumb to the disease due to metastasis [[33]5]. Consequently, the identification of methodologies to impede metastatic progression emerges as a pivotal therapeutic strategy in the management of OC. The process of epithelial-mesenchymal transition (EMT) facilitates the transformation of polarized epithelial cells into mesenchymal cells through a series of biochemical changes, which is characterized by enhanced migratory and invasive properties, increased resistance to apoptosis, and a substantial upregulation of extracellular matrix components [[34]6]. EMT is a pivotal process in the transformation of early-stage ovarian tumors into aggressive and metastatic malignancies. The acquisition of mesenchymal traits and loss of epithelial characteristics during EMT enhance the invasive potential of OC, facilitating its progression and metastasis [[35]7]. Consequently, a thorough understanding of the EMT process and its cellular implications holds the potential to unveil novel perspectives on the diagnosis and therapeutic strategies for OC. The protein of family with sequence similarity 64, member A (FAM64A), also known as CATS, PIMREG, and RCS1, is originally identified as a novel clavin assembly lymph myeloid leukemia interaction factor, which plays a significant role in influencing the subcellular localization of the leukemia fusion protein [[36]8]. A recent study has indicated that FAM64A functions as a mitotic regulator, facilitating the transition from metaphase to anaphase and exhibiting high expression in a cell cycle-dependent manner [[37]9]. FAM64A exhibits a profound association with cellular proliferation, as evidenced by its elevated expression levels predominantly observed in cells undergoing rapid proliferative activity, whereas it is conspicuously absent or minimally expressed in non-proliferating cell populations [[38]9]. Alterations in the genome and the acquisition of autonomous growth capabilities constitute pivotal changes that drive the onset and progression of malignant proliferation [[39]10]. Currently, researchers have indicated that FAM64A exhibits significantly elevated expression in both tumor tissues and cells of cancer patients, potentially implicating it in the disruption of cell cycle regulation [[40]11–[41]14]. The expression of FAM64A is elevated in breast cancer, cervical cancer, endometrial cancer, and OC compared to normal tissues, and its expression is negatively correlated with the overall survival and/or recurrence-free survival rates of breast and endometrial cancer patients, whereas an opposite trend is observed in patients with cervical cancer and OC [[42]15]. Nevertheless, the diagnostic and prognostic implications of FAM64A expression in OC remain unexplored. The Hippo pathway is a highly conserved signaling mechanism that transduces mechanical signals from the plasma membrane into intracellular signals to precisely control cellular processes such as differentiation and proliferation [[43]16–[44]18]. The Hippo pathway governs a myriad of fundamental cellular processes, encompassing the adhesion, migration, mitosis, polarity, and secretion of various biologically active molecules [[45]18–[46]20]. The Hippo pathway is frequently dysregulated in cancers. In this context, Yes-associated protein 1 (YAP) and Transcriptional Co-Activator with PDZ-Binding Motif (TAZ) are commonly identified as oncoproteins, while mammalian STE20-like kinases 1 and 2 (MST1/2) and large tumor suppressor kinases 1 and 2 (LATS1/2) are recognized as tumor suppressor proteins [[47]21]. The components of the Hippo pathway play a crucial role in follicle growth and activation through mechanisms involving cell proliferation, migration, and differentiation [[48]22]. Therefore, dysregulation of the Hippo pathway can lead to the loss of follicle homeostasis and reproductive disorders [[49]23–[50]27]. Furthermore, research has established that the Hippo pathway is involved in the progression of OC [[51]28–[52]30]. Nevertheless, the exact molecular mechanisms through which the Hippo pathway influences the pathogenesis of OC are still unclear and require further investigation. In this research, we applied a comprehensive bioinformatics analysis of publicly available gene datasets to identify potential key biomarkers for OC. FAM64A was identified as a candidate gene of interest. We further investigated the mechanism of action of FAM64A in OC at the cellular level and explored its regulatory effects on the Hippo pathway. The primary objective of this research is to establish a theoretical foundation and identify potential therapeutic targets for the treatment of OC. By clarifying the role of FAM64A in OC, we aim to contribute to the development of novel diagnostic and therapeutic strategies for this aggressive form of cancer. Materials and methods Data source and differentially expressed genes (DEGs) Firstly, we accessed the Gene Expression Omnibus (GEO) database ([53]https://www.ncbi.nlm.nih.gov/geo/) and input the keyword “Ovarian Cancer” to find datasets related to OC. Based on our needs and experimental design, we selected two highly relevant datasets, [54]GSE12470 and [55]GSE14407. The [56]GSE12470 dataset included normal peritoneal tissues as controls, while [57]GSE14407 utilized ovarian surface epithelium. We intentionally analyzed both datasets to identify hub genes dysregulated not only in primary tumorigenesis (via [58]GSE14407) but also in the metastatic microenvironment (via [59]GSE12470), comprehensively mapping OC-associated genes. Furthermore, we analyzed these two datasets using the GEO2R tool. We employed differential expression analysis methods and set adj.P < 0.01 and |logFC|≥ 3 as filtering criteria to identify DEGs. To visualize the determined DEGs, we generated heatmaps and volcano plots using the GEO2R tool. Heatmaps provided a visual representation of the expression levels of genes across different samples, while volcano plots allowed us to exhibit DEGs. Subsequently, we also employed the Draw Venn Diagram tool to generate a Venn diagram showing the overlap of DEGs in the two datasets. Enrichment analysis We utilized the gene annotation tool provided by Database for Annotation, Visualization, and Integrated Discovery (DAVID, [60]https://david.ncifcrf.gov/summary.jsp) to analyze the common DEGs through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of common DEGs. Subsequently, we employed the “enrichment plot” package in R software to visualize the functional enrichment analysis results. This package generated a dot plot that displayed the enrichment scores and p-values for each term in the GO/KEGG analysis. We chose the terms with the minimum p-values, indicating the most significant enrichments, for detailed presentation. Protein–protein interaction (PPI) network and hub genes analysis To construct the PPI network and analyze hub genes, we utilized the Search Tool for the Retrieval of Interacting Genes (STRING) database ([61]https://cn.string-db.org/) based on common DEGs. By uploading the list of common DEGs to STRING, we acquired a PPI network. We then used Cytoscape software to visualize the PPI network model. Within Cytoscape, we applied the MCODE algorithm to identify different clusters within the PPI network derived from the DEGs. The resulting network diagram was directly exported from Cytoscape for further analysis. A confidence score threshold was set to 0.4 as an indicator of significance. We selected hub genes based on their significant roles in the PPI network. To validate the expression of these hub genes, we used the GEPIA2 tool on the website ([62]http://gepia2.cancer-pku.cn/#analysis). By matching the expression data of hub genes with gene expression profiles from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects, we obtained box plot results that displayed the expression of these genes under different conditions. Furthermore, we employed the Kaplan Meier (KM)-plot tool at [63]https://kmplot.com/analysis/index.php?p=background to predict the prognostic impact of hub genes in OC. Correlation analysis between FAM64A and tumor immune infiltration To explore the correlation between FAM64A and tumor immune infiltration, we employed both the Sangerbox database and the GEPIA2 database to analyze the expression profiles of the FAM64A across various types of cancers. Furthermore, we employed the immune infiltration analysis tool ([64]https://cistrome.shinyapps.io/timer/) to examine the relationship between target gene expression levels and the extent of immune cell infiltration in the microenvironment of OC. Specifically, we assessed the infiltration levels of different types of immune cells to understand how they correlated with target gene expression. Downstream pathway screening To further explore the downstream molecular mechanisms of key genes in OC, we first utilized the Genecard database to retrieve key genes that played a crucial role in OC. Subsequently, the Coxpresdb database was employed to analyze genes co-expressed with key genes. The DAVID database was used to perform functional enrichment analysis on these co-expressed genes, and KEGG bubble plots were generated for visualization, facilitating the identification of key pathways involved. Cell culture The normal human ovarian epithelial cell line IOSE-80 and the human OC cell lines A2780 and SKOV3 were purchased from iCell Bioscience Inc. (Shanghai, China). IOSE-80 was cultured in RPMI-1640 medium (Thermo Fisher Scientific, Waltham, Massachusetts, USA) containing 10% high-quality fetal bovine serum and 1% antibiotics, while A2780 and SKOV3 were cultured in RPMI-1640 medium containing 10% high-quality fetal bovine serum, 1% GlutaMAX-1 glutamine, and 1% antibiotics. All cells were maintained at 5% CO[2] and 37 °C in a cell incubator. Cell transfection To silence the expression of FAM64A in OC cells using small interfering RNA (siRNA), cells were seeded in culture plates one day before transfection. The transfection complex was prepared based on the instructions provided with the Lipofectamine 3000 kit (Thermo Fisher Scientific). First, siRNA was diluted in a certain volume of serum-free Opti-MEM medium and gently mixed. Then, Lipofectamine 3000 reagent was mixed with Opti-MEM medium, followed by the addition of P3000 reagent to enhance transfection efficiency. The diluted siRNA was added to the mixture containing Lipofectamine 3000 and P3000, gently mixed, and incubated for 10 min to form the siRNA-liposome complex. The resulting siRNA-liposome complex was added dropwise to the culture medium containing the cells, and the culture plate was gently shaken to ensure even distribution. The cells were incubated under conditions of 5% CO[2]at 37 °C for 48 h. The silencing effect of the FAM64A expression was verified by real-time quantitative polymerase chain reaction (RT-qPCR) method to further study the impact of FAM64A silencing on cell function. si-FAM64A sequence was as follows: si-FAM64A—1 sequence: CCU UGA GCA GAA GAU CAA GCC; si-FAM64A—2 sequence: GAA GCU GUC CCA AGA GCU AGA; si-FAM64A—3 sequence: CUG UGC AGA CAG UUC CAA AGG. RT-qPCR Total RNA from each group of cells was extracted using TRIZOL reagent (Thermo Fisher Scientific). Reverse transcription was performed to synthesize cDNA templates using a PCR amplifier (Applied Biosystems, Foster City, CA, USA). RT-qPCR experiments were conducted on an ABI7500 quantitative PCR instrument (Applied Biosystems) with the following conditions: pre-denaturation at 95 °C for 10 min, denaturation at 95 °C for 10 s, annealing at 60 °C for 30 s, and 36 cycles in total. GAPDH was used as an internal reference gene. The obtained Ct values were analyzed using the 2^−ΔΔCt method. Ct represented the number of amplification cycles required for the real-time fluorescence intensity to reach the set threshold, at which point the amplification was in the logarithmic growth phase. The experiment was repeated three times. The primer sequences are shown in Supplementary Table 1. Western blot Total cellular proteins were extracted using RIPA lysis buffer (Thermo Fisher Scientific). The Pierce Bicinchoninic Acid protein assay kit (Thermo Fisher Scientific) was employed to measure protein concentrations. Proteins were separated via sodium dodecyl sulfate–polyacrylamide gel electrophoresis and subsequently transferred onto polyvinylidene fluoride membranes. After blocking the membranes with 5% skim milk, membranes were incubated overnight at 4 °C with primary antibodies against (Abcam, Cambridge, UK): GAPDH (ab181602, 1:1000), E-cadherin (ab231303, 1:1000), N-cadherin (ab76011, 1:1000), Vimentin (ab92547, 1:1000), p-MST1/2 (ab76323, 1:1000), p-LATS1/2 (ab111344, 1:1000), p-YAP (ab76252, 1:1000), and YAP (ab52771, 1:1000). Membranes were washed five times with Tris-buffered saline with Tween 20 for 15 min each and then incubated with horseradish peroxide-conjugated secondary antibody (ab288151; 1:5000; Abcam) for 1 hour. Chemiluminescence detection was performed using an enhanced chemiluminescence reagent on a Tanon 5200 chemiluminescent imaging system (Shanghai, China), followed by image acquisition through exposure. The gray value of protein bands was determined using Image J software. Cell counting kit-8 (CCK-8) detection A2780 and SKOV3 cells were seeded in a 96-well plate (1 × 10^4 cells per well), with three replicates for each cell type. After the cells adhered, 10 μL of CCK-8 solution was added to each well. The culture plate was then returned to the incubator and incubated for 1 h. The absorbance of each well at 450 nm was determined by a microplate reader. The relative viability of the cells was calculated based on the absorbance values. 5-Ethynyl-2'-deoxyuridine (EdU) assay A2780 and SKOV3 cells were cultured in a 24-well plate containing coverslips, with three replicates for each cell type. The EdU working solution (Beyotime, Beijing, China; 10 μM) was added to the culture medium. The cells were incubated for an additional 24 h to allow EdU sufficient time to incorporate into newly synthesized DNA. The cells were washed with phosphate-buffered saline (PBS) and then fixed with 4% paraformaldehyde. Click chemistry reactions from the EdU detection kit were performed to label the incorporated EdU with fluorescent groups. Then, counterstain the nuclei with 4',6-diamidino-2-phenylindole (DAPI) and subsequently image under a fluorescence microscope. Fluorescence microscopy was used to observe and count the number of cell nuclei with fluorescent labels, thereby evaluating the proliferation status of the cells. Migration ability detection The migration was assessed by wound healing assay. Briefly, the A2780 and SKOV3 cells were seeded into 6-well plates at a density of 5 × 10^5 cells per well, with three replicates for each cell type. A sterile pipette tip was used to gently create straight scratches on the monolayer. The cells were gently washed with PBS to remove any detached cell debris. Serum-free medium was added to each well to minimize the impact of cell proliferation on the migration assay results. Immediately after scratching, images of the initial scratch area were captured using an inverted microscope. The width and location of the scratch were recorded for subsequent comparison. According to the experimental design, images of the same area were taken at specific time points (0 h and 24 h) after scratching. The same magnification and lighting conditions were used for each imaging session. The degree of wound healing was measured using Image J software. The wound healing rate was calculated according to the following formula: cell migration rate = (width of scratch at 0 h—width of scratch at 24 h) / width of scratch at 24 h × 100%. Transwell assay The upper chamber of the Transwell insert, which had been pre-coated with Matrigel (BD Biosciences, New Jersey, USA), was seeded with A2780 and SKOV3 (1 × 10^5/ well). The lower chamber contained 600 μL of culture medium containing 15% fetal bovine serum. After a 24-h incubation period at 37 °C in a humidified environment supplemented with 5% CO[2], the inserts were meticulously extracted. Subsequently, non-migratory cells, along with the Matrigel, were carefully wiped away using a soft cotton swab. The inserts were then washed with PBS, fixed with 4% paraformaldehyde for 15 min, permeabilized with PBS, and stained with 0.1% crystal violet at 37 °C for 30 min. After staining, the inserts were rinsed thoroughly with distilled water, air-dried appropriately, and subsequently examined using a microscope. Images were captured, and the number of migrated cells was quantified. Statistical analysis All data were analyzed using GraphPad Prism 8.0 statistical software, with results presented as the mean ± standard deviation. For comparisons between two groups, a t-test was employed, while One-Way ANOVA was used for multiple-group comparisons. Post-ANOVA pairwise comparisons were conducted using Tukey’s multiple comparisons test. A P-value of less than 0.05 was deemed statistically significant. Results Identification of DEGs From the [65]GSE12470 dataset, we chose 10 normal samples (Normal) and 43 OC tumor samples (Tumor). We identified 429 DEGs based on the criteria of which 264 were upregulated and 165 were downregulated. The [66]GSE14407 dataset contained 12 normal samples (Normal) and 12 OC tumor samples (Tumor), from which we identified 368 DEGs based on the same criteria, with 155 upregulated and 213 downregulated. Cluster analysis of these DEGs generated a volcano plot to show significant changes in gene expression between different groups in the data (Fig. [67]1A). The heat map showed the expression level of DEGs in [68]GSE12470 and [69]GSE14407 and the difference of data (Fig. [70]1B) (Supplementary Tables 2, 3). A Venn diagram was drawn to show that there were 50 common DEGs at the intersection of [71]GSE12470 and [72]GSE14407, among which there were 39 upregulated DEGs and 11 downregulated DEGs (Fig. [73]1C) (Supplementary Table 4). Fig. 1. [74]Fig. 1 [75]Open in a new tab Identification of differentially expressed genes (DEGs). A Volcano plots show DEGs between control and tumor samples in [76]GSE12470 and [77]GSE14407 datasets. The horizontal coordinate is log2FoldChange and the vertical coordinate is −log10 (p-value). Red plots represent up-regulated genes and blue ones represent down-regulated genes. B Heatmap displays clustering of DEGs in [78]GSE12470 and [79]GSE14407 datasets. C The Venn diagram displayed the common DEGs between the [80]GSE12470 and [81]GSE14407 datasets Functional enrichment analysis of common DEGs The GO enrichment analysis results were classified into molecular function (MF), biological process (BP), and cellular component (CC) GO categories. The top 10 most significantly enriched GO terms were selected for display in enrichment bubble charts (Supplementary Fig. 1A–C). Based on the KEGG enrichment analysis results of the DEGs, the top 7 pathways with the smallest p-value, representing the most significant enrichment, were selected for display in the KEGG enrichment analysis bubble charts (Supplementary Fig. 1D). The DEGs were mainly enriched in the following pathways: Cell cycle, p53 signaling pathway, Human T-cell leukemia virus 1 infection, Oocyte meiosis, Glycine, serine and threonine metabolism, Cellular senescence, and Platinum drug resistance. Construction of PPI network and hub gene identification A total of 50 common DEGs were selected for PPI network analysis. The STRING online database was used to filter these genes into a PPI network containing 40 nodes and 210 edges (Fig. [82]2A). The MCODE algorithm was employed to identify the highest-scoring subnetwork modules, with MCODE 1 containing 18 hub genes, including Phosphatase and tensin homolog regulator (PIMREG or FAM64A), Aurora kinase A (AURKA), Cell division cycle 45 (CDC45), Marker of proliferation Ki-67 (MKI67), Centrosomal protein F (CENPF), Budding uninhibited by benzimidazoles 1 beta (BUB1B, SSK1), Chromosome duplication timer 1 (CDT1), Cyclin B2 (CCNB2), PCNA-associated factor (KIAA0101/PCLAF), Kinesin family member 20A (KIF20A), Dental and thymus expressed protein like (DTL), Ribonucleotide reductase M2 (RRM2), Esp1 homolog (S. cerevisiae)-like protein 1 (ESPL1), Ubiquitin-like, containing PHD and RING finger domains, 1 (UHRF1), Non-SMC condensin I complex subunit G (NCAPG, CAP-G), Nuclear spindle assembly protein 1 (NUSAP1), Baculoviral IAP repeat-containing protein 5 (BIRC5), and Non-SMC condensin I complex subunit H (NCAPH) (Fig. [83]2B). Heatmap analysis was performed to assess and visualize the pairwise correlation of expression levels among the 18 hub genes (identified from the PPI network) within each dataset ([84]GSE12470 and [85]GSE14407), supporting their potential interactions identified in the PPI network and highlighting coherent interaction patterns within the tumor microenvironments (Fig. [86]2C). Subsequently, the enrichment analysis of the 18 hub genes were significantly enriched in “mitotic cell cycle process”, “nuclear chromosome segregation”, “regulation of mitotic nuclear division”, and other GO terms (Fig. [87]2D). Fig. 2. [88]Fig. 2 [89]Open in a new tab Construction of Protein interaction network (PPI) network and identification of hub genes. A PPI network based on common DEGs. B The highly interconnected cluster and hub genes are identified within the PPI network through MCODE analysis. Lines between nodes represent interactions between genes. C Pairwise correlation matrices of the 18 hub genes in cancer samples from [90]GSE12470 and [91]GSE14407 datasets. Red indicates positive correlation; blue indicates negative correlation. D Enrichment bar graphs are used to present hub gene enrichment pathways Expression and prognostic analysis of hub genes The results of expression validation of hub genes suggested that the expression of all hub genes was upregulated in OC tumor tissues, compared with the controls (Supplementary Fig. 2A). The RT-qPCR further verified the expression levels of hub genes in the OC cells. The results showed that the mRNA expression of AURKA, CCNB2, MKI67, and FAM64A was considerably upregulated in the A2780 and SKOV3 cells, compared with those in IOSE-80 cells, consistent with the database analysis results, which further supported the results of the bioinformatics analysis (Supplementary Fig. 2B). We then predicted the prognosis of hub genes in OC with respect to overall survival (Supplementary Fig. 3). The prognostic analysis revealed that, except for CCNB2, UHRF1, ESPL1, KIAA0101, PCLAF, BIRC5, and CDC45, the remaining 12 hub genes all had predictive significance for the prognosis of OC. KM curves revealed that high expression of hub gene was negatively correlated with progression-free survival in OC patients (Supplementary Fig. 4). Analysis of FAM64A Given the limited research on FAM64A in OC and its prognostic significance, we selected it as a potential target for OC. Analysis using the SangerBox database and GEPIA2 database revealed that FAM64A expression was substantially higher in tumor tissues compared to normal tissues, indicating an important role for FAM64A in cancers (Supplementary Fig. 5A, B). We examined the relationship between FAM64A and the extent of infiltration by various types of immune cells within the OC microenvironment. A significant negative correlation between FAM64A expression and CD8 + T cells was observed (Supplementary Fig. 5C). Further, CD4 + T cells and Macrophages were notably associated with FAM64A (Supplementary Fig. 5D). FAM64A knockdown suppresses viability, proliferation, migration, and invasion of OC cells We utilized siRNA transfection to silence the expression of FAM64A in the OC cell lines A2780 and SKOV3. The efficiency of FAM64A silencing was assessed by RT-qPCR and western blot. The si-FAM64A group exhibited a substantial reduction in FAM64A mRNA and protein expression, thereby validating the efficacy of the gene-silencing approach (Fig. [92]3A, B). Notably, the siRNA-1 targeting FAM64A demonstrated consistently higher transfection efficiency, resulting in more significant suppression of FAM64A at both the mRNA and protein levels. Therefore, based on these empirical data, we chose si-1 for all the following assays. Furthermore, the silencing of FAM64A notably inhibited the proliferation and viability of A2780 and SKOV3 cells (Fig. [93]3C, D). Wound healing assays showed that relative to the si-NC group, the migration ability of A2780 and SKOV3 cells in the si-FAM64A group was markedly impaired (Fig. [94]3E). Similarly, Transwell assays demonstrated that FAM64A silencing substantially reduced the invasive capabilities of A2780 and SKOV3 cells (Fig. [95]3F). Fig. 3. [96]Fig. 3 [97]Open in a new tab FAM64A knockdown suppresses viability, proliferation, migration, and invasion of A2780 and SKOV3 cells. A The mRNA expression of FAM64A was detected by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). B The protein expression of FAM64A was detected by western blot. C The viability of A2780 and SKOV3 cells was determined by Cell Counting Kit-8 (CCK-8) assay. D The proliferation of A2780 and SKOV3 cells was evaluated by 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay (scale: 50 μm). E The migration of A2780 and SKOV3 cells was determined by wound healing assay (scale: 50 μm). F The invasion of A2780 and SKOV3 cells was determined by Transwell assay (scale: 100 μm). ^*P < 0.05, ^**P < 0.01, ^***P < 0.001 vs. si-NC group FAM64A knockdown inhibits EMT in OC cells Western blot was used to detect the expression of E-cadherin, N-cadherin, and Vimentin to assess EMT in A2780 and SKOV3 cells. FAM64A silencing caused an increased expression of E-cadherin and a decreased expression of N-cadherin and Vimentin in both A2780 and SKOV3 cells (Fig. [98]4). Fig. 4. [99]Fig. 4 [100]Open in a new tab FAM64A knockdown reduces epithelial-mesenchymal transition (EMT) in A2780 and SKOV3 cells. A Western blot analysis was performed to detect the expression of E-cadherin, N-cadherin, and Vimentin in A2780 cells. B Western blot analysis was performed to detect the expression of E-cadherin, N-cadherin, and Vimentin in SKOV3 cells. ^*P < 0.05 vs. si-NC group FAM64A knockdown activates the Hippo pathway in OC cells To further explore the downstream molecular mechanisms through which FAM64A exerts its effects on OC, we first utilized the Genecard database to retrieve key genes involved in OC, resulting in a list of 10,763 genes. Subsequently, we employed the Coxpresdb database to analyze genes co-expressed with FAM64A, identifying 2000 genes. Among these, a total of 1033 genes were found to be co-expressed with FAM64A specifically in OC (Fig. [101]5A). Using DAVID for pathway enrichment analysis of the 1033 co-expressed genes, we generated a KEGG dot plot for visualization (Fig. [102]5B). The Hippo signaling pathway was selected for further investigation based on this analysis. Western blot was performed to measure the protein expression levels of key molecules in the Hippo pathway, including p-MST1/2, p-LATS1/2, p-YAP, and YAP, in A2780 and SKOV3 cells (Fig. [103]5C, D). The results indicated that the knockdown of FAM64A led to the upregulation of p-MST1/2, p-LATS1/2, and p-YAP/YAP proteins in A2780 and SKOV3 cells. Fig. 5. [104]Fig. 5 [105]Open in a new tab FAM64A knockdown activates the Hippo pathway in A2780 and SKOV3 cells. A Genecard and Coxpresdb databases were used to analyze the genes co-expressed with FAM64A in ovarian cancer. B The enrichment analysis result of co-expression genes was displayed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) bubble map. C, D Western blot was used to detect the protein expression of p-MST1/2, p-LATS1/2, p-YAP, and YAP in A2780 and SKOV3 cells. ^*P < 0.05 vs. si-NC Discussion Late diagnosis is a major factor leading to poor prognosis of ovarian cancer and usually occurs due to the lack of specific symptoms and effective biomarkers for early detection [[106]31]. Herein, we utilized bioinformatics analysis to identify potential hub genes of OC and examined the impact of FAM64A on the malignant phenotype changes of OC cells. Consequently, we identified 18 candidate genes as potential hub genes with high predictive values in OC. Silencing of FAM64A inhibited the viability, proliferation, migration, and invasion of OC cells. Moreover, silencing of FAM64A reduced the EMT of OC cells. Mechanistically, the inhibitory impact of FAM64A silencing on OC could be attributed to the activation of the Hippo pathway within OC cells. By constructing a PPI network, we identified the hub genes of OC based on [107]GSE12470 and [108]GSE14407 datasets, which held significant predictive value for the diagnosis of OC. RT-qPCR analysis suggested the mRNA expression levels of AURKA, CCNB2, MKI67, and FAM64A were upregulated in OC cells. It is noteworthy that the normal controls in [109]GSE12470 (peritoneal tissues) and [110]GSE14407 (ovarian surface epithelium) differ in anatomical origin. While the ovarian surface epithelium represents the canonical site of tumor initiation, the inclusion of peritoneal samples in [111]GSE12470 provides unique insights into metastatic adaptation. The hub genes identified through this cross-site comparison (e.g., AURKA, CCNB2, MKI67, and FAM64A) are consistently dysregulated in both primary and metastatic lesions, suggesting their dual roles in early carcinogenesis and late-stage dissemination. This observation aligns with the clinical behavior of serous ovarian cancer, which frequently presents with widespread peritoneal metastases at diagnosis [[112]32]. Based on haplotype analysis, genotyping of 22 SNPs in four genes from 287 ovarian serous cystadenocarcinoma cases and 618 age-matched controls shows that AURKA is associated with reduced OC risk [[113]33]. Using weighted gene co-expression network analysis (WGCNA) to identify key modules and genes, and analyzing signaling pathways with high expression of key genes through gene set enrichment analysis and single-cell sequencing data, CCNB2 emerges as one of the hub genes that are upregulated in OC [[114]34]. Based on TCGA mutation profiles of endometrioid and serous cancers, targeted sequencing analysis of a custom panel in 24 tumor samples reveals distinct mutation patterns for MKI67 between endometrioid and serous cancers, highlighting its significance in the histological classification of OC [[115]35]. Using the GEO, TCGA, Xiantao, the University of Alabama at Birmingham Cancer Data Analysis Portal, and the Kaplan–Meier plotter databases, a study has reported that FAM64A is elevated in cervical, endometrial, and OC, suggesting its potential as a potential therapeutic target in gynecological malignancies [[116]15]. Overall, AURKA, CCNB2, MKI67, and FAM64A are novel hub genes of OC with high prediction values. FAM64A, a gene associated with the cell cycle, has been identified as a promoter of cell proliferation across various tumor types, including OC [[117]13, [118]36–[119]38]. Our findings revealed a negative correlation between FAM64A expression and the levels of CD8 + T-cell infiltration in OC and had a significant correlation with both CD4 + T-cell and macrophage infiltration. Research has indicated that the expression of FAM64A mRNA in breast, cervical, endometrial, and OC is positively correlated with Th2 cell infiltration but negatively related to neutrophil and Th17 cell infiltration [[120]15]. Therefore, FAM64A plays an important role in the tumor immune microenvironment of OC. In vitro experiments, we discovered that silencing FAM64A inhibited viability, migration, and invasion, as well as suppressed the EMT of A2780 and SKOV3 cells. FAM64A plays a multifaceted regulatory role in cancer. Its expression is significantly associated with the tumor stemness index in breast cancer samples and correlates with poor prognosis of patients with breast cancer [[121]39]. Overexpression of FAM64A also promotes the proliferation and migration of breast cancer cells, accompanied by the activation of EMT [[122]39]. In the U937 and MDA-MB-231 cell lines, silencing of FAM64A results in a corresponding reduction in cell proliferation and cell cycle progression [[123]40]. Additionally, the downregulation of FAM64A inhibits the proliferation of MDA-MB-231 and MCF-7 cells by blocking EMT [[124]41]. In osteosarcoma, FAM64A overexpression facilitates tumor proliferation, migration, and invasion, while being targeted and negatively regulated by miR-493 [[125]42]. The up-regulation of FAM64A is positively correlated with poor prognosis in patients with prostate cancer, and its knockdown significantly inhibits the proliferation, migration, invasion, and cell cycle of prostate cancer cells in vitro [[126]43]. All in all, FAM64A silencing suppresses malignant phenotype and EMT of OC cells, which inhibits the progression of OC. By delving further into the downstream molecular mechanisms through which FAM64A exerts its role in OC, we identified the Hippo pathway as a pivotal pathway modulated by FAM64A. Studies have shown that the Hippo pathway is involved in promoting ovarian cancer progression, chemoresistance, and metastasis, playing a non-negligible role in OC [[127]44, [128]45]. For instance, ARID1A exerts its inhibitory effect on OC by activating the Hippo pathway, which suppresses EMT and stemness in OC cells, accompanied by reduced cell viability, migration, and colony formation, thereby playing a crucial role in inhibiting OC progression [[129]46]. Furthermore, the leukemia inhibitory factor exerts its suppressive effects on the stemness, proliferation, and metastasis of osteosarcoma cells by activating YAP1 expression, which in turn upregulates the Hippo signaling pathway [[130]44]. LINC00857 exerts its regulatory effects on YAP1 in OC by competitively binding to miR-486-5p, and silencing of LINC00857 can inhibit the proliferation, migration, invasion, and glycolysis of OC cells while promoting apoptosis [[131]47]. Additionally, Piezo1 is overexpressed in OC tissues and contributes to the growth and metastasis of OC tumors, while Piezo1 inducers such as Yoda1 activate the Hippo/YAP signaling pathway in OC cells [[132]45]. Our findings demonstrated that silencing FAM64A effectively promoted the expression of p-MST1/2, p-LATS1/2, and p-YAP/YAP of the Hippo pathway in both A2780 and SKOV3 cells. Thus, FAM64A silencing activates the Hippo pathway and plays an important role in OC progression. This study made some progress in revealing the role of FAM64A in OC, but it also had some obvious limitations that need to be improved and perfected in future research. Firstly, due to the limitations of experimental conditions and resources, we only used a single siRNA sequence to silence the expression of FAM64A. To more accurately verify the function of FAM64A, future research should design and use a variety of siRNA sequences targeting different sites, and compare their interference effects, so as to ensure that the observed phenotypic changes are indeed caused by the specific silencing of FAM64A, rather than off-target effects. Another important limitation is that the main focus of this experiment was to explore the role of the Hippo pathway in OC, and no emphasis was placed on the investigation of resistance mechanisms. Drug resistance is one of the significant problems in cancer, especially in OC. However, due to the limitations of experimental design and time, in-depth exploration in this aspect was not conducted. We will focus on drug resistance as a key research direction in our subsequent research projects. In conclusion, FAM64A silencing suppresses the development of OC and inhibits EMT of OC cells, which may be related to the activation of the Hippo pathway. This research offers novel perspectives on the regulatory mechanisms for OC. Our findings suggested that FAM64A and the Hippo pathway could serve as potential targets for the treatment of OC. Supplementary Information [133]Supplementary material 1.^ (11.8KB, docx) [134]Supplementary material 2.^ (15.2KB, docx) [135]Supplementary material 3.^ (1,023.7KB, docx) Acknowledgements