{"paper_id":"e2f18012-a259-4c97-96f6-7a1b4ff35e33","body_text":"Epithelial ovarian cancers are classified into two main types. Type I tumors, including genetically more stable low-grade serous, endometrioid, clear cell, and mucinous carcinomas, are slow-growing, often large but confined to the ovary, and arise from precursor lesions such as borderline tumors. Their origins vary: low-grade serous tumors come from the distal tubal epithelium, endometrioid and clear cell tumors from endometriosis, and mucinous/Brenner tumors from the transitional epithelium at the tubal-peritoneal junction. Type II tumors, most commonly the high-grade serous ovarian carcinomas (HGSOC), are aggressive, genetically unstable, often advanced at diagnosis, and typically arise from serous tubal intraepithelial carcinoma (STIC) in the fallopian tube, with frequent  TP53  mutations [ 1 ].\nDue to difficulties in the early detection and treatment of epithelial ovarian cancer (OC) [ 2 ], the five-year overall survival rates are less than 50% [ 3 ]. Although molecularly targeted therapies, such as antiangiogenic agents, poly (ADP-ribose) polymerase (PARP) inhibitors, and folate receptor blockers, have been introduced, recurrence rates and mortality remain challenging [ 4 ]. Indeed, advancements are scarce in the detection of therapeutic targets in OC.\nThe hedgehog (HH) signaling pathway can be a potentially relevant modulator that can expand the diagnostic and therapeutic repertoire in malignant diseases. While HH signaling is inactive in mature mammalian tissues, it becomes active during tissue growth and survival in certain adult tissues [ 5 ]. Under abnormal conditions that lead to dysregulated HH signaling, these stem cells might eventually transform into cancer stem cells and trigger malignant transformation [ 6 , 7 ].\nObservations suggest that HH signaling is active in human OC and is responsible for clonal growth and proliferation in OC cells [ 8 ]. The immunohistochemical expression levels of SHH, DHH, PTCH, SMO, and GLI1 proteins in ovarian tumors correlated strongly with malignant features, being higher in carcinomas than in borderline malignant tumors and, to a lesser extent, in benign cystadenomas. Additionally, this activation was not observed in normal ovarian surface epithelium [ 9 ]. However, the clinicopathological relevance of HH signaling in OC and its potential prognostic value are not clear at present.\nPrevious studies suggested that the activation of the Sonic Hedgehog signaling pathway is associated with poor prognosis in patients with several malignancies, including head and neck cancer, human glioma, bladder cancer, and small and non-small cell lung cancer [ 10 , 11 , 12 ], increasingly gaining attention as a therapeutic target [ 13 ], with a validated anticancer effect, as shown in basal cell carcinoma [ 14 ].\nBuilding on prior research evaluating the HH pathway in other cancers, we aimed to similarly assess the relevance of the gene expression levels of key members and targets of the HH pathway to patient outcomes by assessing the progression-free and overall survival of OC patients. HH genes ( SHH ,  PTCH1 ,  PTCH2 ,  GLI1 ,  GLI2 ,  GLI3 ,  HHAT , and  SUFU ) were selected from the KEGG Database, with currently available oncological literature, as some of them have been the subject of previous research, especially the GLI family of transcriptional factors [ 15 , 16 ]. To identify the differential impact of the quantitative expression of each gene in OC on its outcome, the study also examined the prognostic value for the patient subgroups stratified by the histology, grade, and stage of the tumor, as well as the response to different therapeutic regimes, among other factors. The goal was to determine whether there is a differential impact of each gene of this pathway on the outcome of ovarian cancer, and a better understanding of the biological behavior of this cancer to pave the way for the implementation of individually targeted therapies for OC patients.\n\nTo assess their potential prognostic value, we analyzed the correlation between the expression levels of the following eight components of the Hedgehog signaling pathway on the progression-free (PFS) and overall survival (OS) in ovarian cancer:  SHH ,  GLI1 ,  GLI2 ,  GLI3 ,  PTCH1 ,  PTCH2 ,  HHAT , and  SUFU . A total of 1435 patients for PFS and 1656 patients for OS were analyzed using the KM plotter (Ovarian Cancer) database.\nFirst, the impact of HH pathway constituents on PFS ( Table 1 ,  Figure 1 ), and secondly, their correlation with OS in ovarian cancer without any further classification were assessed ( Table 1 ,  Figure 2 ). We observed that  SHH  was correlated with better PFS (HR = 0.85;  p  = 0.0087), without affecting OS. In the same way, we noticed that  GLI1 ,  PTCH1 , and  PTCH2  were correlated with better PFS and OS in OC (HR = <1,  p  = ≤ 0.05). On the contrary, higher levels of expression of  GLI2 ,  GLI3 , and  SUFU  seemed to affect the PFS and OS adversely (HR > 1;  p -value < 0.05).  HHAT  overexpression did not manifest any prognostic relevance.\nFurthermore, the impact of HH members’ overexpression on the survival rates was assessed categorically in different histopathological and therapeutic subsets. The results are summarized in  Table 2 .\nExcept for SUFU, which was associated with poorer survival in serous ovarian cancer, no significant prognostic associations were observed for the target genes across different histological subtypes ( Table 2 -I). However, the validity of the analysis is limited since the vast majority of the cases in the dataset were of the serous subtype (1232 patients), and only a small number were of the endometrioid subtype (62 cases).\nIn the advanced stages of FIGO III and IV,  SHH ,  PTCH1 , and  PTCH2  overexpression are associated with better survival rates, while  SUFU, HHAT , and  GLI3  demonstrated an adverse association with the outcome in OC patients.\nSUFU , again, was the only member of the HH signaling pathway whose expression demonstrated an adverse prognostic effect on grade 3 OC with HRs of 1.38 and 1.4 and  p -values of 6.3 × 10 −5  and 0.007 for PFS and OS, respectively, as shown in  Table 2 -I.\nMoreover, the higher expression levels of  HHAT ,  PTCH1 , and  SUFU  were associated with unfavorable PFS, OS, and PFS, respectively, in TP53-mutated OC as seen in  Table 2 -I.\nSHH ,  HHAT ,  PTCH1 , and  PTCH2  overexpression further improved the survival rates of ovarian cancer patients, particularly when optimal debulking was possible. Interestingly, even in cases where optimal debulking seemed impossible,  PTCH2  overexpression was associated with better OS. Once again,  SUFU  piqued our interest by displaying an adverse correlation with PFS, even in cases with optimal debulking, as summarized in  Table 2 -II.\nTo better understand the potential impact of the HH pathway gene expression on chemosensitivity as predictive biomarkers for treatment response, we analyzed its association with clinical outcomes in patients treated with platinum-based chemotherapy. Concerning progression-free survival (PFS), higher expression levels of  SHH ,  PTCH1  (Affymetrix ID BCNS), and  PTCH2  were associated with improved PFS, while higher expression levels of  GLI3  and  SUFU  correlated with worse PFS ( Table 2 -II,  Figure 3 ). Regarding overall survival (OS), higher  PTCH2  expression was associated with improved OS, while elevated  SUFU  expression was linked to poorer OS ( Table 2 -II,  Figure 4 ).\nNotably,  SUFU  consistently demonstrated significant results across all categorical analyses, particularly within the clinically relevant subgroup ( n  = 139) of high-grade serous ovarian cancer at stages III and IV, even when the analysis was partly based on the  GSE9891  dataset.  Supplementary Figure S1  illustrates the adverse effect of  SUFU  expression on both progression-free survival (PFS) and overall survival (OS).\nThe expression profiles of the selected genes were assessed in normal ovarian tissues, as well as in tumorous and metastatic tissues from ovarian cancer by utilizing the public platform TNMplot ( http://tnmplot.com , accessed on 7 April 2025) for comparative microarray gene expression data. The differential gene expression is outlined in box plots for comparison ( Figure 5 ). The fold differences between tumorous and normal tissues, as well as metastatic and tumorous tissues, are indicated in  Table 3 .\nAs shown in  Table 3 , the expression levels of the genes  PTCH1 ,  GLI2 ,  GLI3 , and  HHAT  were significantly different among the three groups, while the differential expression levels of  SHH ,  PTCH2 , and  GLI1  in this comparative model were not significant ( p -values > 0.02). Significant differences were observed in the expression levels of  GLI2  and  GLI3  when comparing tumorous to normal, with downregulation in their expression levels in tumors (FC 0.42 and 0.67, respectively). The two-group comparison (metastasis/tumor) revealed an almost two-fold upregulation of the  HHAT  gene. Interestingly, the expression level of  PTCH1  was downregulated in the metastatic analog.\nWe next extended the gene expression analysis to a panel of 43 cell lines from the CCLE dataset, as it is still unclear whether the expression of these genes largely stems from the tumor cells or the cells in their microenvironment (e.g., cancer-associated fibroblasts, endothelial cells, pericytes, the immune cell infiltrate, etc.) [ 17 ]. Using the Mann–Whitney U test, none of the HH-related genes showed significant differential expression between metastatic and primary ovarian cancer cell lines ( Table 4 ), which may be attributed to high inter-sample variability and the relatively small sample size. Notably, however,  PTCH1  and  GLI1  exhibited elevated expression levels in the primary ovarian cancer cell lines TOV112D and COV434. In addition,  PTCH1  expression was also elevated in the primary cell lines OVK18, TOV21G, OAW28, and JHOM2B. The heatmap below ( Figure 6 ) illustrates the expression profiles of the 43 ovarian cancer cell lines analyzed.\nAs a random verification of the results retrieved from CCLE, the relative expression levels of  SHH ,  PTCH1 ,  HHAT ,  GLI1 ,  GLI2 ,  GLI3 , and  SUFU  were determined using quantitative real-time PCR (qPCR) in two widely used OC cell lines; CAOV-3 as derived from primary site with  TP53  mutation and SKOV-3 derived from ovarian metastatic ascites, without  TP53  mutation. As illustrated in the box plots in  Figure 7 ,  PTCH1 ,  GLI3 ,  HHAT , and  SUFU  showed a higher mean expression level in both non-metastatic and metastatic cell lines, with an upward trend in the primary cell line CAOV3. While  SHH ,  PTCH2 ,  GLI1 , and  GLI2  showed lower mean expression values when compared to above mentioned genes.\nComparing non-metastatic and metastatic ovarian cancer cell lines, the expression level of  SHH  and  PTCH2  was significantly higher in the primary line CAOV-3 ( p -value < 0.05) than in the metastatic line SKOV-3, while the expression level of  GLI1  and  GLI2  was higher in the metastatic line SKOV-3, as demonstrated in  Figure 7 .\n\nThe HH signaling pathway is assumed to be primarily inactive in adult tissues, nevertheless, it helps regulate adult stem cells and is also involved in tissue maintenance and repair [ 5 , 18 , 19 ]. Other studies have exposed the mammalian ovary as a novel site of active HH signaling, suggesting that the HH signaling pathway remains active or is reactivated in follicles and oocytes [ 15 , 20 ]. Few studies have demonstrated the role of the HH pathway in human ovarian carcinoma, and even fewer have examined the prognostic role of the HH signaling molecules in ovarian cancer. However, they show conflicting results. Few studies used immunohistochemistry to investigate HH pathway activation [ 21 , 22 , 23 ], while others performed in situ hybridization and/or semiquantitative qRT-PCR [ 24 ]. The involvement of HH signaling in human cancers may be context-dependent, occurring in some tissues or cell lines but not in others.\nIn this analysis, we investigated the prognostic impact of gene expression levels of the HH pathway ligands and their transcriptional factors in a large patient cohort, divided into two groups—high and low expression, recruited under identical technical conditions, increasing the statistical reliability. The expression profile of a control sample with normal ovarian tissues was also compared. Independent of tumor characteristics, such as histology and grades, higher expressions of  SHH ,  PTCH1 ,  PTCH2 , and  GLI1  have shown beneficial prognostic influence, whereas  GLI2 ,  GLI3 , and  SUFU  are correlated with adverse clinical outcomes in OC patients.  HHAT  affected the outcome in OC only in certain subsets. The current study indicates a beneficial survival impact of  GLI1 , contradicting many previous studies, reporting  GLI1  as a potent manipulator in carcinogenesis and as a negative prognostic indicator in breast, gastric, and colorectal cancers [ 25 , 26 , 27 ]. Therefore, a coherent study design is required to scrutinize the controversial results regarding  GLI1  implications in OC. Meanwhile, the current finding was quite consistent with other studies in identifying  GLI2  as a predictor for poor clinical outcomes [ 24 ]. A previous study reported that  GLI1  and  GLI2  mRNA levels, indicators of active HH signaling, were significantly higher in cancer cells isolated from persistent/chemoresistant ovarian tumors than those isolated from matched primary tumors [ 28 ].\nWe further obtained differential subset-specific survival results. The current finding that the prognostic influence of HH signaling activation is not associated with any histological subtype of OC, except for  SUFU , suggests that the morphological classification of ovarian cancer may not reflect the molecular pathogenesis of this disease. Similar to this study, Liao et al. found no significant correlation between the expression levels of  SHH ,  PTCH , and  GLI1  and the grade and clinical features of the tumor [ 29 ]. Moreover, the co-occurrence of a mutated  TP53  gene, as a highly frequent event in the HGS-OvCa [ 30 ], was associated with adverse outcomes, not only with  SUFU  but also with  PTCH1  overexpression.  PTCH1  overexpression otherwise improved the survival rates of the overall cohort and in the subgroups of optimally debulked or platinum-treated patients. This suggests possible cross-talk or a corresponding drift in the signaling transactions of the HH pathway. Possible regulatory alterations in HH activity with the co-occurrence of the highly prevalent  TP53  mutation in OvCa are still unclear.\nHigher expression levels of  SHH ,  PTCH1 , and  PTCH2  correlated with favorable prognosis in the OC subgroup treated with platinum-containing chemotherapeutic agents, which might indicate that an active HH status probably enhances chemosensitivity since higher levels of  PTCH1 ,  PTCH2 , and  GLI1  are reliable indicators of an active HH status [ 31 ]. Furthermore, this study identified a distinctly similar impact of  GLI3  and  SUFU  on patients’ survival, which both exerted adverse effects in patients who received platinum-based chemotherapy, suggesting potential chemoresistance when  SUFU  or  GLI3  is highly expressed. Remarkably,  SUFU  consistently demonstrated a subset-specific adverse effect on survival outcomes across various clinico-pathological characteristics (serous histology, grade 3, FIGO III + IV stages, and mutated  TP53 ), persistently worsening the prognosis in these categories, even with optimal surgical debulking and standard platinum-based chemotherapy.\nThe general agreement is that  GLI1  induces and  GLI3  represses HH target genes ( GLI1 ,  PTCH1 ,  Cyclin D1 ,  c-Myc , and  BCL-2 ), whereas  GLI2  can act in either a positive or negative manner depending on post-transcriptional and post-translational processing events [ 32 ], which is in agreement with the major results of our study; probably due to the enhanced chemosensitivity when the HH pathway is active.\nSUFU  is a well-known negative regulator of the HH pathway, as it prevents  GLI  translocation into the nucleus [ 31 ]. The loss of  SUFU  results in the ligand-independent activation of HH signaling, indicating the central role of  SUFU  in the repression of this pathway [ 33 ]. Considering these facts and findings together, we can suggest that OC patients with a less active HH pathway probably encounter chemoresistance, which diminishes their chances of survival. Unfortunately, no previous studies have investigated the prognostic impact of  SUFU  in OC. Nevertheless, in medulloblastoma, a mutation in  SUFU  increases  SUFU  turnover, leading to sustained HH signaling activation, which is associated with the worst prognosis [ 34 , 35 ]. Ultimately, the consistently significant adverse survival correlation with higher levels of  SUFU  sheds new light on this biomarker, suggesting the need for an integrative study design that verifies the role of  SUFU  in OC.\nSignificant differences in the expression levels of  PTCH1 ,  GLI2 ,  GLI3 , and  HHAT  were calculated among normal, metastatic, and non-metastatic tumorous tissues by utilizing the public platform  http://tnmplot.com  for comparative microarray gene expression data. The two-group comparison—tumor/normal—showed, interestingly, a tumorous downregulation of the expression levels of  GLI2  and  GLI3 . In the metastatic/tumorous comparison,  PTCH1  showed a significant downregulation, whereas  HHAT  showed an almost two-fold upregulation of their expression levels in the metastatic tissues. These changes in the expression levels indicate the involvement of the respective genes of the HH pathway in tumor progression and its metastatic potential. We searched the literature that reports comparative expression levels of the respective genes. While no differential changes in  GLI1  were observed in our study, in another study, 56 primary advanced serous ovarian cancers and 12 normal ovarian tissues from postmenopausal women were immunohistochemically evaluated, and  GLI1  immunoreactivity was absent from the surface epithelium and stromal cells of the normal ovaries; however, a positive nuclear reaction was observed in 29% of the serous ovarian carcinomas [ 32 ]. Other studies have shown that  GLI1  overexpression promoted invasion and metastasis ability, as analyzed using RT-qPCR [ 36 , 37 ]. Similarly, expression assays using ovarian cancer cell lines and patient samples, as well as pooled normal ovarian samples subjected to RT-qPCR [ 8 ], showed downregulation of  PTCH1 , consistent with our results, and upregulation of  SHH  and  GLI1  expression levels compared to their levels in normal ovarian surface epithelium.  GLI2  displayed a decrease in the expression level in non-metastatic tumors compared to normal tissues, which may again reflect its inhibitory effects on HH pathway activation, assuming that an otherwise active HH pathway promotes carcinogenesis. Representatively, the relative gene expression levels of HH pathway genes in 16 primary grade 3 serous ovarian cancer samples standardized against normal ovarian cell lines demonstrated higher  GLI2  levels only in 25% of the samples [ 16 ]. Even in tumors with elevated expressions of the HH target genes  GLI1  and  PTCH1 , the expression of  SHH , as a canonical driver of the HH pathway, is not necessarily high, suggesting other mechanisms of HH signaling activation in cancer [ 38 ]. Comparable studies that could further verify our results regarding the upregulated levels of  HHAT  in metastatic compared to non-metastatic tumors are, unfortunately, lacking.\nWe could not show significant differences in the expression levels of our study genes between ovarian cancer lines of non-metastatic and metastatic origin. In the recent gene expression studies, efforts have been made to compare normal, benign, and non-metastatic cells as well as metastatic tissues of aggressive tumors. In most of these studies, it is not clear where the drift in gene expression occurs. In the context of short- and long-term survival in high-grade serous ovarian cancer, a recent study suggested that the transcriptomes of primary and metastatic tumors were similar to each other [ 39 ], however, the authors suggested that tumors from short-term survivors may be more clonal and genetically similar than tumors from long-term survivors, indicating an inherent resistance to treatment due to the genetic similarity between primary and metastatic tumors. As a conclusion here, the comparable gene expression levels among the 43 ovarian cancer lines can be principally accepted, assuming that momentary measurement of the gene expression levels does not necessarily reflect their ongoing interplay that orchestrates the following invasiveness and progression of the tumor, hence their prognostic implication.\nWe analyzed the mRNA expression profiles of HH-related genes— SHH ,  HHAT ,  PTCH1 ,  GLI1 ,  GLI2 ,  GLI3 , and  SUFU —in two ovarian cancer cell lines. To the best of our knowledge, our study is the first work to comprehensively profile a wide range of HH pathway-related genes in primary vs. metastatic cell lines, comparing the findings with mRNA results from the online web tool  http://tnmplot.com  and the CCLE Gene Expression Data. Notably, the expression data of SKOV-3 and CAOV-3 from the CCLE dataset match our independent qPCR analysis data. However, a clear expression pattern of the genes was not found among these gene expression datasets.\nA representative comparison of the gene expression profile between the primary cancer cell line with the  TP53  mutation (CAOV-3) and a metastatic cell line without the  TP53  mutation (SKOV-3) was made. A distinct expression pattern of these genes associated with a certain genetic mutation in these cancer cell lines (in this case, the  TP53  mutation) remains unknown and likely uncertain due to the variable genomic mutations present in the cell lines, as previously mentioned. In particular, we did not examine whether the mutations addressed in our cancer cell lines—the  TP53  mutation in CAOV-3, and the  CDKN2A  mutation in SKOV-3—possibly modulate the activation of the HH pathway.\nIn another study, the inhibition of cell proliferation by suppressing HH signaling was observed in the following four cell lines with different  TP53  statuses in a TP53-independent manner: A2780 (wild-type  TP53 ), A2780/CDDP (mutant vv TP53 ), SKOV3 ( TP53  deleted), and OVCAR3 (mutant  TP53 ) [ 9 ]. This is relatively consistent with another report showing that G1 arrest through the inhibition of HH signaling was not affected by  TP53  status [ 40 ]. Ultimately, the heterogeneity in the expression levels of genes between microarray gene analysis and the cancer cell lines of the same histological type can be attributed to the extrinsic regulation of HH signaling in cancer. In this process, tumor cells activate the HH pathway in the non-malignant cells of their microenvironment by secreting HH ligand proteins [ 16 ]. This complexity may be due to differences in experimental systems (e.g., cell lines are prone to genotypic and phenotypic drift during their continual culture) and/or assay methods. It is also conceivable that they might reflect the heterogeneity of autocrine and paracrine signaling in OC [ 32 ]. In a study on human epithelial OC cell lines (SKOV-3, CAOV-3, and others) and 12 human OC tissue specimens, tumor-derived SHH has been shown to stimulate stromal vascular epidermal growth factor (VEGF-C) secreted by cancer-associated fibroblasts (CAFs) to promote lymphangiogenesis in OC via the HH/VEGF-C signaling axis [ 21 ]. Recent evidence indicates that the sonic HH pathway is often recruited to stimulate the growth of cancer stem cells (CSCs) and orchestrate the reprogramming of cancer cells via the epithelial-mesenchymal transition (EMT) [ 41 ].\nDifferences in  GLI1  and  SUFU  gene expression levels among control tissue, borderline tumors, and carcinomas have been reported by other studies.  SUFU  was detected with lower expression levels in higher FIGO (International Federation of Gynaecology and Obstetrics) stages compared to lower stages [ 42 ], as the authors confirmed that the HH signaling pathway was active in ovarian tumors and that  GLI1  and  SUFU  were associated with the tumor type and FIGO stage. It seems that the activation occurs downstream of the membrane components of the pathway, suggesting non-canonical activation, especially considering that simultaneous overexpression of  GLI1  and  SUFU  under classical physiological circumstances is not typical.\nTargeting the HH pathway may hold promise as a novel approach for treatment, widening the landscape of ovarian cancer therapy. In another study, cyclopamine, a natural inhibitor of the HH pathway, and its semi-synthetic derivative IPI-926 demonstrated significant antitumor activity when administered after conventional chemotherapy in primary tumor xenografts. They mediated their effects in both stromal and epithelial cells by significantly reducing GLI1 expression in the stroma and tumor cells [ 36 ], although previously, canonical inhibition of the HH pathway could not be clinically confirmed [ 43 ]. A recent study found that  GLI1  transcriptionally regulates FANCD2 expression, a tumor suppressor gene, and a key member of the FA-BRCA and the homologous recombination (HR) pathways, implicating the importance of GLI1-dependent expression of FANCD2 genes in tumorigenesis and chemoresistance in OC [ 44 ]. Inhibition of GLI1 in OC reduced FANCD2 expression, creating a state of HR deficiency, where PARP inhibition is known to be more effective. This study addresses the rationale that GLI1 depletion or inhibition can enhance the effectiveness of PARP inhibitors in BRCA-proficient patients, as they are the largest group in OC.\nTo overcome resistance to canonical inhibitors of the HH pathway, it is necessary to design methods that can operate beyond the canonical state of Hedgehog pathway activation in ovarian cancer.\nThere were several limitations to our study. One key limitation is that the survival datasets included the mRNA levels of our study genes rather than the levels of the encoded protein as the final functional effector. However, during protein synthesis, substantial regulatory processes can occur after mRNA is produced, which may occur beyond the transcriptional and translational levels, with different protein turnover rates [ 45 ]. Therefore, it is undoubtedly important to validate these findings at the corresponding protein levels. Nevertheless, microarray-based studies focused on creating genomic biomarkers to classify and predict cancer survival have been an accepted routine practice for over 20 years in developing and enhancing the care of cancer patients [ 46 ].\nAnother limitation of this analysis relevant to TNMplot is the inability to distinguish between different histological subtypes of epithelial ovarian cancer when comparing tumor and normal samples. Given the distinct cells of origin and molecular characteristics of each subtype—particularly the dominance of p53-mutated high-grade serous carcinomas arising from fallopian tube epithelium—grouping all tumor types together may obscure subtype-specific gene expression patterns. Therefore, while TNMplot is valuable for preliminary assessments, findings should be interpreted with caution in light of these constraints.\nBesides, an important limitation of this study is that some of the ovarian cancer cell lines used were derived from tumor cells isolated from ascitic fluid (EFO-21, OVCAR-3, ONCO-DG-1, SKOV-3, OC-314, and HEYA8), rather than from solid peritoneal metastases. While ascites-derived tumor cells reflect advanced disease, they do not fully recapitulate the biological behavior and microenvironmental context of solid metastatic implants. Therefore, gene expression or phenotypic characteristics observed in these cell lines may not entirely represent the metastatic progression or tissue-specific interactions seen in solid peritoneal lesions. This distinction should be considered when interpreting the translational relevance of in vitro findings. Our qPCR-based and CCLE dataset cell line analysis could be useful for choosing an in vitro model to study the relevant signaling pathways; however, for this setting, the mutational status of the gene and the expression of the relevant proteins should be analyzed in addition.\n\nThis study focused on retrieving gene-related survival estimations in OC patients from the publicly accessible Kaplan-Meier Plotter web tool ( https://kmplot.com , accessed on 7 April 2025). This tool is based on mRNA expression data and survival data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), which have been integrated for analysis. To avoid differences due to different sensitivity, specificity, and dynamic range in detecting gene expression levels for specific genes by different technologies, the search has been narrowed to only tumor samples examined using the in situ oligonucleotide array platforms  GPL96  (Affymetrix Human Genome U133A Array),  GPL571  (Affymetrix Human Genome U133A 2.0 Array), and  GPL570  (Affymetrix Human Genome U133 Plus 2.0 Array). The oligonucleotide gene array files were MAS5 normalized. Then, a second scaling normalization was executed to set the mean expression in each array to 1000. Only the probes present in the  GPL96  platform were used in the scaling normalization to prevent platform-specific differences due to the higher probe number in the  GPL570  arrays [ 47 , 48 ].\nAccording to the quantile expressions of a specific gene, the patients’ collective data were divided into two groups using the web interface to analyze the prognostic value of that gene. These groups were then compared using progression-free survival ( n  = 1436) and overall survival ( n  = 1656) data. A KM survival plot was mapped, and the significance was calculated. This integrative data analysis tool was used to investigate the prognostic value of the genes involved in the HH signaling pathway and their transcriptional products ( SHH ,  PTCH1 ,  PTCH2 ,  GLI1 ,  GLI2 ,  GLI3 ,  HHAT , and  SUFU ). The Affymetrix IDs that represent our genes are as follows:  SHH : 207586_at,  HHAT : 219687_at,  GLI1 : 206646_at,  GLI2 : 228537_at,  GLI3 : 227376_at,  PTCH1 : 209816_at,  PTCH2 : 221292_at, and  SUFU : 222749_at. Only the JetSet best probe set was chosen; this set selects the optimal probe set for each gene using a scoring method established to assess each probe set for specificity, coverage, and degradation [ 47 ].\nBefore running the analysis, the patient groups were filtered based on stage, histology, grade, TP53 mutation status (mutated or wild-type), and treatment parameters, including debulking status and the applied chemotherapy. The progression-free survival and overall survival rates in each group were investigated. The clinical properties and their proportional distribution in the cohorts are summarized in  Table 5 ,  Table 6  and  Table 7 .\nThe Hazard ratio (HR) with a 95% CI and the log-rank  p -value were calculated in each KM survival plot, with the log-rank  p -value cutoff defined as <0.05. The KM plot shows the association between the investigated marker and survival, where the samples are grouped according to the median (or upper or lower quartile) expression of the selected gene. The median expression was used as the cutoff to categorize the samples into high- and low-expression cohorts in each analysis. The false discovery rate (FDR) was computed using the brainwaver library in R, version 1.6, as integrated into the KM plotter tool.\nTo assess the expression level of the study genes among normal, non-metastatic, and metastatic ovarian tissue, we resorted to the public platform TNMplot in the comparative microarray gene expression data in normal ovarian tissues as well as in tumorous and metastatic tissues from ovarian cancer, where the study genes have been investigated for expression profiles among the above mentioned tissue types and tested for fold differences as a comparison between tumorous and normal, as well as metastatic and tumorous tissues and outlined in box plots [ 49 ]. The significance in gene expression between the three groups is provided by using the Kruskal–Wallis method with a  p -value set to <0.02. In order to compare the groups with each other, the differential expression is represented by fold change (FC).\nIn another layer of assessing the expression level of the study genes in non-metastatic and metastatic ovarian cancer, we searched the Cancer Cell Line Encyclopedia (CCLE) datasets. Gene expression data from 1019 cell lines were retrieved from the DepMap portal [ 50 ]. From these, 43 ovarian cancer cell lines have been identified, comprising 13 metastatic, 29 primary tumor-derived, and one immortalized ovarian cell line. TPM-normalized expression values were used to generate heatmaps, constructed using the ComplexHeatmap R package Bioconductor version: Development (3.22) [ 51 ]. For statistical comparison between primary and metastatic cell lines, we performed the Mann–Whitney U test, with significance set at  p  < 0.05.\nConcurrently, and as a verification of the results retrieved from CCLE, the expression levels of our target genes were assessed in two human ovarian cancer cell lines, acquired from the American Type Culture Collection (ATCC/LGC), Promochem (Wesel, Germany), featured by varying degrees of genetic complexity and mutation. One cell line was derived from the primary site (CAOV-3), displaying a TP53 mutation, and another one was derived from ovarian metastatic ascites (SKOV-3), with no TP53 mutation. Following the product manual, total RNA was isolated from the cultured cell lines using the innuPREP RNA Mini Kit (Biometra, Göttingen, Germany). One microgram of the total isolated cellular RNA was used for reverse transcription into cDNA using the First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, Schwerte, Germany), random hexamer primers, and M-MuLV reverse transcriptase. The target gene expression levels were determined using real-time reverse transcriptase–polymerase chain reaction (RT-PCR). The gene expression levels were measured using an ABI PRISM 7300 Sequence Detection System (Thermo Fisher Scientific, Schwerte, Germany) using SYBR Green dye (Thermo Fisher Scientific) and the cycling conditions recommended by the manufacturer. The cycle threshold (Ct) values from each sample were normalized to their corresponding  β-actin . Ct melting curve analysis was performed to confirm specific product amplification. The expression levels were calculated using the 2 −∆∆Ct  formula relative to the housekeeping gene  ß-actin  as an endogenous standard. All experimental samples were analyzed in triplicate, derived from three independent experiments. Student’s  t -test was used to test the significance of differential gene expression levels in each of the two cell lines. All tests were two-sided, and a  p -value of less than 0.05 was considered significant. The results were demonstrated graphically in box plots.\n\nIn conclusion, it can be validly stated that the activation of the HH pathway affects the prognosis of ovarian cancer. This analysis of survival data in such a substantially large cohort provides, for the first time, evidence that the overexpression of  SHH ,  PTCH1 ,  PTCH2 , and  GLI1 —known indicators of active HH signaling—improves progression-free and overall survival, while  GLI3  and  SUFU  worsen progression-free and overall survival in epithelial, particularly serous, ovarian carcinomas. The findings are sufficiently broad to suggest, for the first time,  SUFU  as a potential indicator for poor prognosis in ovarian cancer and a sensitive predictive marker for resistance to platinum-based chemotherapy, highlighting the need for an alternative approach to treatment for the affected patients. As a future direction, a thorough evaluation of SUFU and GLI1 as prognostic biomarkers of high sensitivity and specificity in independent discovery and validation cohorts appears worthwhile. Moreover, our data mark SUFU as a potential therapeutic target that could be evaluated in future preclinical studies.","source_license":"CC-BY-4.0","license_restricted":false}