Reassessing HPSE in Ovarian Cancer: Beneficial After All?

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Inês Guerra de Melo, Valéria Tavares, Joana Savva-Bordalo, Mariana Rei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8799366/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Heparanase (HPSE) uniquely cleaves heparan sulfate, the main component of the outer layer of endothelial cell plasma membranes, promoting tumour invasion and dissemination. However, it can also enhance tumour immune surveillance and clearance. HPSE’s versatility extends to pro-thrombotic properties, such as the promotion of tissue factor release. Interestingly, elevated HPSE levels have been found in ovarian cancer (OC), which has a notably high incidence of venous thrombosis. Previously, single-nucleotide polymorphisms (SNPs) of HPSE were shown to modulate mRNA and protein levels, possibly predicting disease outcomes. Methods and Results Given the potential role of HPSE in OC, the implications of three SNPs - rs11099592, rs4364254 and rs4693608 – were investigated on OC patients. In the discovery cohort, rs11099592 TT genotype and rs4364254 C allele carriers showed lower survival time than their counterparts (log-rank test, p = 0.025 and p = 0.001, respectively). Validation cohort analysis confirmed the worse prognosis associated with the rs11099592 T allele and rs4364254 C allele in non-serous (log-rank test, p = 0.016) and platinum-resistant (log-rank test, p = 0.044) OC patients, respectively. The rs4364254 C allele was associated with reduced HPSE expression in peripheral blood components (PBCs; χ 2 , p = 0.005), suggesting a protective role for HPSE in OC patients. Conclusions HPSE rs11099592 and rs4364254 showed prognostic value, with T and C allele carriers, respectively, displaying worse clinical outcomes. These results indicate that HPSE could enable a tumour microenvironment shift towards a less aggressive cancer behaviour, facilitating leukocyte migration and anti-tumour responses. Further research should explore the dual mechanisms of this protein to improve OC management. Ovarian Neoplasms Endothelium Heparanase Genetic Variation Gene Expression Regulation Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION Heparanase (HPSE), encoded by the homonymous gene located at 4q21.23, is the only enzyme in mammals capable of degrading heparan sulfate (HS) - the primary component of the outer layer of the plasma membrane that covers endothelial cells, as well as a crucial constituent of the extracellular matrix (ECM) [ 1 , 2 ]. Beyond its basic structure, HS can also exist in the form of a proteoglycan (HSPG) when multiple HS chains are covalently attached to a protein core [ 3 ]. Additionally, other glycosaminoglycans (GAGs) that form proteoglycans, such as hyaluronic acid, and keratan and chondroitin sulphates, along with collagens, laminin, elastin and fibronectin, also make up the ECM [ 4 ]. HS plays a critical role in the integrity and organisation of the ECM, acting as a bridging link between its components [ 3 , 5 ]. By cleaving HS, HPSE facilitates the remodelling of the subendothelial basal membrane and the ECM, an essential initial step in the formation of new vessels from pre-existing ones, consequently promoting endothelial cell migration and sprouting [ 2 , 3 , 6 ]. Furthermore, the heterogeneous structure characteristic of HS enables it to interact with various proteins and growth factors, regulating not only their storage but also their access, function, and mode of action [ 5 ]. This broad spectrum of possible interactions accounts for the wide range of signalling pathways activated upon the release of these molecules through HPSE degradation, such as cell proliferation, tissue repair and angiogenesis [ 3 , 5 , 7 ]. Uncontrolled enzymatic activity of HPSE can support pathological processes, including infections and malignancy. In the latter case, in addition to promoting the supply of nutrients and oxygen to tumour cells, HPSE can enhance cancer aggressiveness by inducing the expression of pro-angiogenic [vascular endothelial growth factors (VEGFs) A and C, and metalloproteinases 9 (MMP-9)], pro-inflammatory [tumour necrosis factor (TNFα) and interleukins 1 (IL-1) and 6 (IL-6)], and pro-coagulant factors (such as tissue factor) [ 2 , 5 ]. Notably, HPSE is frequently overexpressed in oncological contexts, such as gastric, pancreatic, bladder, lung, and notably, ovarian carcinomas, corroborating its role in aggressiveness and disease recurrence [ 8 ]. In fact, HPSE is already reported as a potential biomarker for the evaluation of surgery effects and prognosis prediction in ovarian cancer (OC) [ 9 ]. HPSE also contributes to a pro-thrombotic environment, triggered by the release of tissue factor pathway inhibitor (TFPI) from the vessel wall, and subsequent induction of tissue factor (TF), further emphasising the intricacy of its connection with OC [ 10 ]. Indeed, among solid tumours, OC together with other gynaecological malignancies is classified as a high-risk tumour for venous thromboembolism (VTE), according to cancer-associated thrombosis (CAT) scores such as Khorana Score (KS) [ 11 , 12 ]. Due to late-stage diagnosis, OC stands as the most lethal malignancy among gynaecologic tumours on a global scale. The high rates of chemoresistance and tumour recurrence combined with the high degree of heterogeneity of OC further undermine current treatment strategies, with most countries reporting 5-year survival rates below 50% [ 13 – 19 ]. In the current landscape, exploring intricate mechanisms such as the interplay between HPSE, thrombosis and OC progression may hold the key to improving these dismal outcomes [ 15 , 16 ]. Existing evidence demonstrates that genetic variations in HPSE are associated with messenger RNA (mRNA) and protein levels, possibly serving as predictors of disease outcomes [ 20 ]. Given HPSE’s prominent role in ovarian tumourigenesis, further reinforced by its pro-thrombotic activity, these variations may affect survival and CAT susceptibility among OC patients. In this context, the present study investigated the role of three relevant HPSE single-nucleotide polymorphisms (SNPs) in OC patients, aiming to advance personalised and more effective disease management. 2. MATERIAL AND METHODS 2.1. Discovery and validation cohorts A retrospective hospital-based cohort study was carried out at the Department of Gynaecology and Oncology of the Portuguese Oncology Institute of Porto (IPO Porto), enrolling histologically diagnosed epithelial OC (EOC) patients of European ancestry with admittances for first-line treatment from March 2017 to December 2023. Patients under the age of 18, seeking only a second opinion, or with follow-up elsewhere were excluded and a final cohort of 98 EOC patients (cohort A) with available biological material was established. Upon acceptance of participation, a written consent was handed to and signed by each patient according to the principles of the Helsinki Declaration. This study received approval from the ethics committee at IPO Porto (CES IPO: 69/021). The staging of all EOC cases was performed according to the International Federation of Gynecology and Obstetrics (FIGO) Cancer Report 2021 [21]. Additionally, tumour response to chemotherapy was assessed using the Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1 [22]. Demographic, clinicopathological, and follow-up data were obtained by reviewing the medical records of all patients. The average age of the participants was 63.2 years, with the majority being post-menopausal (80.6%, N=79) and diagnosed at FIGO III and IV stages (75.5%, N=74). Most tumours were serous in type (83.7%, N=82) and 41.8% of the patients (N=41) underwent standard treatment - cytoreductive surgery followed by carboplatin/cisplatin with paclitaxel. Complete/optimal surgical resection was achieved in 45 patients (45.9%). CAT was characterised as a VTE event occurring within the timeframe of six months prior to two years following an OC diagnosis [18, 23]. Among the 98 patients, information concerning CAT was available only for 80 of them, with 17 (21.3%) presenting CAT. Regarding thrombosis-related features, excluding missing values, 40 patients (46.0%) had a high-risk KS (KS≥2), 38 (49.4%) presented high activated partial thromboplastin time (aPTT; ≥27.1 s), 41 (50.6%) high prothrombin time (PT; ≥14.2 s), and 39 (48.8%) high international normalised ratio (INR; ≥1.1). The study had a median follow-up of 25.5 months. Validation for SNPs’ analysis was conducted with an independent cohort – cohort B. Another retrospective cohort study was conducted with patients admitted at the same institution for first-line treatment from January 1996 to December 2012. Inclusion and exclusion criteria, disease staging and evaluation protocols for cohort B were consistent with those used for cohort A. A total of 331 EOC patients, for whom biological material was available, were enrolled. Like cohort A, each patient provided written consent following the principles of the Helsinki Declaration before their recruitment. This study received approval from the ethics committee at IPO Porto (CES IPO:286/2014). The mean age of the enrolled patients was 55 years. The following results were obtained considering the group of patients with valid information. Like cohort A, most of the patients were post-menopausal (64.6%, N=203) and diagnosed at advanced cancer stages (FIGO III/IV; 61.3%, N=196). Regarding the histological subtype, 56.7% (N=187) were diagnosed with serous, 12.7% (N=42) with clear cell, 10.3% (N=34) with endometrioid, 9.7% (N=32) with mucinous and the other 10.6% (N=35) with less common tumour subtypes. Excluding the missing data, among the serous tumour types, 16.1% (N=24) were low grade and 83.9% (N=125) were high grade. Concerning therapeutic management, most patients were subjected to the standard treatment (92.7%, N=307), with cytoreductive surgery followed by chemotherapy with a combination of paclitaxel and carboplatin (N=171) or cisplatin (N=136). Neoadjuvant chemotherapy (N=17, 5.1%), chemotherapy alone (N=9, 2.7%) or only surgery (N=4, 1.2%) were also considered as first-line treatment options. Complete or optimal surgical resection was achieved for 49.5% of the patients (N=164). Regarding therapeutic response, most patients were highly platinum-sensitive (69.4%, N=229), 9.7% (N=32) were partially platinum-sensitive, 11.5% (N=38) were platinum-resistant and 4.8% (N=16) were platinum-refractory. Information on CAT events was not available for this cohort. The mean follow-up in this cohort was 49.4 months. 2.2. Sample collection and nucleic acid extraction Venous blood samples were collected in EDTA tubes before the initiation of the first line of chemotherapy. DNA was isolated using the QIAamp DNA Blood Mini Kit (Cat. No. 51106, Qiagen, Hilden, Germany), and RNA was extracted from peripheral blood components (PBCs) using the GRS RNA kit - Blood & cultured cells (#GK08.0100, Grisp Research Resolutions®, Porto, Portugal), following manufacturers' protocols. Nucleic acid concentration and sample purity were confirmed using a NanoDrop spectrophotometer. DNA and RNA were stored at −20 °C and −80 °C, respectively. 2.3. Polymorphism selection and genotyping HPSE SNPs were selected based on their impact on HPSE expression or activity, relevance to cancer and/or cardiovascular diseases, availability of TaqMan® SNP genotyping assays and the minor allele frequency (MAF≥10%). Variants in strong linkage disequilibrium (r²>90%) were excluded. Three SNPs were chosen: rs4364254, rs4693608 and rs11099592 ( Table 1 ). Polymorphism genotyping was performed using the StepOne Plus qRT-PCR system (Applied Biosystems®) with TaqMan allelic discrimination technology. Each PCR reaction mix (6.0 µL) contained 2.5 µL of TaqPath™ ProAmp™ Master Mix (1×), 2.375 µL of sterile water, 0.125 µL of TaqMan® SNP genotyping assay, and 1.0 µL of genomic DNA. Thermal cycling conditions were as follows: 10 min at 95°C for polymerase activation, 15 s at 95°C for DNA denaturation (45 cycles), and 1 min at 60°C for primer pairing and extension. Measures of quality control were carried out as described elsewhere [24]. Table 1 : Selected HPSE SNPs and the respective TaqMan® Genotyping Assays. SNP Functional consequence MAF in Iberians * (MA) TaqMan® Genotyping Assay rs4364254 (C/T) Intronic 32.2% (C) C___8416664_10 rs4693608 (G/A) Intronic 46.7% (A) C__30667102_10 rs11099592 (T/C) Missense 29.9% (T) C__31870510_10 Abbreviations: MA, minor allele; MAF, minor allele frequency; SNP, single-nucleotide polymorphism. * According to the Ensembl database (last accessed on the 18th of August of 2024). 2.4. cDNA conversion and gene relative quantification The HPSE gene expression was conducted to assess the impact of the evaluated SNPs on gene expression levels. This analysis was performed in a subsampled cohort A comprised of 55 OC patients – cohort C. The exclusion criteria applied to cohort A were patients who: 1) had a history of malignancies before or after OC diagnosis; 2) were breastfeeding or pregnant at the time of diagnosis; 3) had a history of autoimmune diseases or were undergoing immunosuppressive therapies; 4) had acute infections at cancer diagnosis; 5) were undergoing anticoagulant treatment for diseases other than VTE; and 6) possessed the polymorphisms Factor V Leiden ( F5 rs6025) and F2 (Factor II encoding gene) rs1799963.The rationale for the cohort selection and study design is illustrated in Fig. 1 . Total RNA samples were utilised as templates to generate the complementary DNA (cDNA) strands, using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems®, Carlsbad, CA, USA), as previously described [24]. Each reaction for gene expression analysis was executed with the StepOne Plus qRT-PCR system using a 10.0 µL mixture containing: 5.0 µL of 2× TaqMan TM Gene Expression Master Mix and 0.5 µL of TaqMan TM 20× Gene Expression Assay Hs00935036_m1, both by Applied Biosystems® (Foster City, CA, USA); 3.0 µL of nuclease-free water and 1.5 µL of cDNA sample. Adding to these, glyceraldehyde-3-phosphatedehydrogenase ( GAPDH ) and hypoxanthine phosphoribosyl transferase 1 ( HPRT1 ) were evaluated as endogenous controls with the assays Hs03929097_g1 and Hs02800695_m1, respectively. Thermal cycling conditions included 50 °C for 2 min, 95 °C for 10 min, followed by 45 cycles of 95 °C for 15 s and 60 °C for 1 min. Negative controls were included, and all samples were run in triplicate. Measures of quality control were carried out as described elsewhere [24, 25]. Thermo Fisher Connect platform (Thermo Fisher Scientific, Waltham, MA, USA) was employed for data analysis. 2.5. Statistical analysis Data analysis was performed using IBM SPSS Statistics software (version 29, IBM Corp., Armonk, NY, USA) for Windows. The Kolmogorov-Smirnov test was employed to assess data distribution. Continuous variables were categorised using the mean value as the cut-off for data with a normal distribution, or the median value for data with a non-normal distribution. The genotype frequencies of each SNP in this study were compared to those reported in the Iberian population (https://www.ensembl.org/index.html, accessed on 18 th August 2024). The Hardy-Weinberg equilibrium (HWE) was assessed using the chi-square test (χ²). Gene normalised-relative expression was calculated via the Livak method using GAPDH as the most suitable control. Severe outliers were removed based on the interquartile range (IQR). Four expression profiles were defined for analysis: A) low vs. high expression based on the median value, B) low, intermediate, and high expression (terciles), C) low (first two terciles) vs. high (third tercile), and D) low (first tercile) vs. high (second and third terciles), as previously described [24]. The SNPs’ impact on the patient’s progression-free survival (PFS) and overall survival (OS) was assessed. PFS was calculated as the time from diagnosis to recurrence, progression, death, or the last clinical evaluation, while OS referred to the period from diagnosis to death or last evaluation. Survival curves were generated using the Kaplan-Meier method, and log-rank tests were used to compare survival probabilities. The most appropriate genetic model (dominant or recessive) for each variant was selected based on survival curve analysis under the additive model. Cox proportional hazard models were employed to estimate the risks of tumour progression and patient death. Validation of the SNP analysis was conducted considering the entire cohort B and subgroups based on patients’ age; hormonal status (pre- or post-menopause) at diagnosis; cancer stage; histological subtype; OC differentiation grade; surgical resection and platinum sensitivity. Statistical tests were two-sided with a 5% significance level, and p -values between 0.050 and 0.060 were considered marginally significant. 3. RESULTS 3.1. Distribution of SNP genotypes The distribution of the variants’ genotypes is represented in Table 2. Notably, all the SNPs were in HWE (χ 2 , p >0.050), demonstrating no significant deviation from expected genotype frequencies. Table 2. Genotype distribution of HPSE SNPs in cohort A (N=98). SNP MAFi * (MA) MAFs (MA) Genotype N (%) N total (%) rs4364254 32.2% (C) 29.1% (C) CC 6 (6.1) 98 (100) CT 45 (45.9) TT 47 (48.0) rs4693608 46.7% (A) 48.5% (G) GG 22 (22.4) 98 (100) AG 51 (52.0) AA 25 (25.5) rs11099592 29.9% (T) 23.9% (T) CC 55 (57.3) 96 (98) CT 36 (37.5) TT 5 (5.2) *According to the Ensembl database (last accessed on 18th of August of 2024). Abbreviations: MA, minor allele; MAFi, minor allele frequency in the Iberian population; MAFs, minor allele frequency in the study cohort; SNP, single-nucleotide polymorphism. 3.2. HPSE SNPs and gene expression In cohort C, only rs4364254 demonstrated a significant association with HPSE expression in PBCs. In the additive model, based on expression profile C (with the third tercile regarded as a high expression), the C allele was associated with a significantly lower HPSE expression than the TT genotype (CC vs. CT vs. TT; χ², p= 0.018). Under the dominant model, the presence of the C allele was similarly linked to significantly reduced HPSE expression, both considering the profile B (low vs. intermediate vs. high) (CC/CT vs. TT; χ², p= 0.014) and profile C (CC/CT vs. TT; χ², p= 0.005). Overall, the HPSE rs4364254 C allele was consistently associated with reduced HPSE gene expression in PBCs. As for the remaining SNPs, no significant association with the gene expression levels was found, nor was any discernible trend observed ( Table 3 ). Table 3. Genotype distribution of each HPSE SNP (additive model) in cohort C (N=55), according to the expression profile A of the respective gene. SNP Genotype Low Expression N (%) High Expression N (%) rs4364254 * CC 3 (5.5) 1 (1.8) CT 24 (43.6) 5 (9.1) TT 10(18.2) 12 (21.8) rs4693608 GG 6 (10.9) 7 (12.7) AG 13 (23.6) 14 (25.4) AA 8 (14.5) 9 (16.4) rs11099592 CC 15(27.3) 15 (27.3) CT 12 (21.8) 9 (16.4) TT 0 (0.0) 4 (7.3) * Statistically significant results for HPSE rs4364254 were found considering the expression profile C (χ², p= 0.018). 3.3. HPSE SNPs and clinical features of OC patients In the discovery cohort (cohort A), the SNPs rs4364254 and rs11099592 were associated with various demographic and clinicopathological features. The rs4364254 C allele was significantly associated with older age at OC diagnosis in both the additive (χ², p= 0.049) and dominant (CC/CT vs. TT; χ², p= 0.047) models. Additionally, the C allele was more common in patients with a high revised KS (KS ≥2; χ², p= 0.034). A marginal association was also observed with aPTT, suggesting that the C allele is more likely linked to lower aPTT, while the TT genotype is associated with a prolonged assay (CC/CT vs. TT; χ², p= 0.053). However, no SNP showed an association with VTE susceptibility. Like the genetic variants, KS showed a poor predictive value (χ², p ≥0.050).The T allele of HPSE rs11099592 was marginally associated with a history of other tumours in the additive model (χ², p =0.053), being more prevalent in patients without such a history. 3.4. HPSE SNPs and OC patient outcomes In cohort A, the rs4364254 C allele carriers showed lower OS compared to the TT genotype group (CC/CT vs. TT; 36.1 ± 4.5 months and 59.0 ± 5.2 months, respectively, log-rank test, p =0.001; Fig. 2 ). Moreover, rs11099592 was found to be associated with both PFS and OS. Specifically, TT genotype carriers had a lower PFS than their counterparts (CC/CT vs. TT; mean PFS of 28.8 ± 3.3 months and 11.8 ± 3.1 months, log-rank test, p =0.050; Fig. 3A ). Likewise, TT genotype carriers showed lower OS (CC/CT vs. TT; mean OS of 25.4 ± 5.8 months and 48.9 ± 3.9 months, respectively, log-rank test, p =0.025; Fig. 3B ). The significant results concerning the impact of the SNPs on patients’ prognosis in cohort A were validated in the independent cohort B. While no significant association was detected in the entire cohort B, stratified analyses confirmed the association between the rs4364254 C andrs11099259 T alleles and poorer clinical outcomes. Among the patients resistant to platinum, the rs4364254 C allele was associated with lower OS compared to the TT genotypes (CC/CT vs.TT; mean OS of 19.9 ± 2.4 months and 28.8 ± 3.4 months; log-rank test, p =0.044; Fig. 4 ). As for rs11099259, non-serous OC patients carrying the T allele presented a worse OS than their counterparts with the CC genotype (TT/CT vs. CC; mean OS of 45.1 ± 3.3 months and 53.5 ± 1.6 months, respectively; log-rank test, p =0.016; Fig. 5 ). 4. DISCUSSION Long-term survival rates for OC remain poor, despite progress in treatment options, highlighting the persistent challenges in effectively managing this disease. Late-stage diagnoses, therapy resistance and high recurrence rates continue to hinder meaningful advancements, emphasising the urgent need to rethink strategies and explore new avenues, such as the identification of robust prognostic biomarkers [15, 16, 18, 26, 27]. The interplay between HPSE, VTE, and OC could be the key to these needed advances, facilitating more personalised treatment approaches. HPSE plays a pivotal role in cancer progression, influencing multiple hallmarks of the disease, particularly in OC, where it contributes to invasion and angiogenesis. At the same time, the endothelial permeability promoted by HPSE fosters VTE, a major complication in cancer patients [28, 29]. For OC, this is reflected in high CAT scores, largely influenced by the surgery location and type of treatment involved. Building on this, the present study focused on investigating the role of HPSE SNPs in OC prognosis, aiming to further advance personalised and more effective disease management. To begin with, the variant HPSE rs4364254, located in intron 9, involves the substitution of a cytosine (C) with a thymine (T) at nucleotide position 8,718,418 [30, 31]. The T allele has been associated with higher HPSE expression levels, which was confirmed in the present study (CC/CT vs. TT; χ², p= 0.005) [32]. Notably, among the three HPSE SNPs evaluated (rs4364254, rs4693608 and rs11099592), rs4364254 was the only one to show a significant association with HPSE expression in PBCs. Although the underlying mechanism remains unclear, existing evidence suggests that HPSE rs4364254 is located within an insulator region of DNA, and thus it might alter the function of this regulatory element, affecting gene expression [33]. The combination of HPSE rs4364254 with rs4693608 can modulate the gene transcriptional activity. The latter is an intronic variant located in an enhancer, involving a guanine (G) to adenine (A) substitution, which has been reported to affect gene expression [31]. Based on haplotype analyses registered in the literature, when individual alleles are considered, the A allele of rs4693608 is linked to an increased HPSE expression [30]. However, as previously mentioned, this association was not observed in our study. Intriguingly, in a previous study also assessing HPSE SNPs and gene expression in PBCs, lower HPSE expression was associated with higher plasmatic HPSE (pHPSE), and vice-versa, in healthy individuals. This unexpected dynamic may be explained by the complex trafficking, processing, and secretion of the protein. HPSE mRNA leads to the synthesis of the protein pro-heparanase, which then undergoes rapid processing and activation in the Golgi and lysosomes, followed by secretion – pHPSE. In a case of high HPSE expression, much of the active pHPSE binds to the ECM or cell surface, limiting its detection in plasma. Thus, while mRNA levels may be higher, pHPSE detection reflects only the active, secreted form, detached from ECM or cell surface. Additionally, high pHPSE levels may lead to a reduction in mRNA expression through a feedback mechanism. Assuming this reported negative correlation applies to other HPSE SNPs, this data may explain the obtained results regarding HPSE mRNA, genotype distributions and further associations, in the present study [20]. Regarding the association with patient characteristics, the rs4364254 C allele - linked to lower HPSE levels - was found to be more common among OC patients with lower aPTT, a coagulation test marker that measures the time it takes for blood to clot (χ², p= 0.053) and a higher KS (KS ≥2; χ², p= 0.034). Collectively, this suggests that the rs4364254 C allele may be reflecting a pro-thrombotic profile [34]. However, it is worth noting that neither the SNP, aPTT nor KS showed a significant association with VTE. The multifunctionality of HPSE may explain this unexpected association between a lower expression allele and a pro-thrombotic profile, as the enzyme's versatility could extend to anti-thrombotic actions as well [30]. Notably, there was a high prevalence of the rs4364254 C allele among patients diagnosed at advanced age (≥55 years) (CC/CT vs. TT; χ², p= 0.047). This age-related prevalence may partially explain the negative prognostic impact of the C allele, as disclosed further in the next paragraph. Concerning OC patients’ prognosis, HPSE rs4364254 had a significant impact. Namely, compared to TT genotype carriers, patients with the C allele - associated with lower HPSE expression - exhibited lower OS in cohort A and stratified cohort B (log-rank test, p =0.001 and p =0.044, respectively). This negative impact of the rs4364254 C allele is not consistent with previous cancer studies with endometrial and gastric cancer patients, where TT genotypes - linked to higher HPSE expression - were associated with cervical invasion and poorer survival, respectively [31, 35]. However, this effect aligns with the negative correlation between HPSE expression andpHPSE levels reported in the literature [20, 30]. The reduced OS observed among C allele carriers compared to TT genotype carriers in the validation study, specifically among platinum-resistant patients (log-rank test, p =0.044), suggests that molecular pathways driven by lower HPSE expression, potentially coupled with elevated pHPSE levels, may contribute to a tumour microenvironment (TME) that is more resilient to the cytotoxic effects of platinum-based therapies. Collectively, local action of HPSE is widely recognised for its pro-tumorigenic role, promoting tumour invasion, angiogenesis, and metastasis through the degradation of HS in the ECM. On one hand, HPSE expression by TME components, such as activated ECs, is deemed an aggressive phenotype marker, leading to poorer outcomes [5, 36, 37]. On the other hand, considering the systematic action of HPSE, the gene expression by leukocytes can paradoxically exhibit anti-tumourigenic effects under certain conditions. The cleavage of HS by HPSE is multifunctional, releasing growth factors and cytokines that, depending on the microenvironment, can enhance immune surveillance, facilitating tumour progression inhibition. Mayfosh et al. (2019) demonstrate that HPSE overexpression in T cells and NK cells enhances their migration and infiltration into tumours [38]. Whether gene and protein expression are indeed inversely correlated, or if HPSE, contrary to its well-established pro-tumourigenic role, could be exerting beneficial immunomodulatory effects need to be clarified. Considering this anti-tumourigenic role, lower HPSE associated with the rs4364254 C allele might limit this release, potentially impairing the body's ability to mount an effective defence against the tumour. Thus, HPSE may play a role in shifting OC's cold immune microenvironment to an immune-inflamed (hot) tumour phenotype [39]. Overall, additional studies are required to dissect the implications of the HPSE rs4364254 C allele in the clinical outcomes of OC patients. As for HPSE rs4693608, no prognostic value was detected. Regarding rs11099592, this missense variation located at intron 7, involves the substitution of a thymine (T) with a cytosine (C) [32]. This SNP significantly impacted OC patients’ prognosis, with C allele carriers presenting a higher PFS and OS compared to patients carrying the TT genotype in cohort A (log-rank test, p =0.050 and p =0.025, respectively). Although no association between rs11099592 and HPSE expression was observed in this study, the literature suggests that the C allele enhances its levels [20, 30, 32]. This aligns with the pattern observed for rs4364254, where the allele linked to reduced expression correlates with a poorer prognosis. Furthermore, in the validation analyses, among non-serous OC patients, the T allele showed a persistent association with a worse prognosis (log-rank test, p =0.016). Although studies should clarify the distinct HPSE roles across different histological types of OC, this finding can be attributed to how HPSE interacts with the TME [9, 40]. Once more, these results not only challenge the conventional view of HPSE as exclusively pro-tumourigenic but also question the assumed inverse correlation between its gene and protein expression [20, 38, 41]. Interestingly, in a marginal association, the rs11099592 C allele was more prevalent in patients with a history of other tumours (χ², p= 0.053), aligning with the observation that elevated HPSE may facilitate tumourigenesis in different tissues through mechanisms like ECM remodelling and angiogenesis [30, 42]. Given these complex and contradictory roles, further investigation is crucial to determine how HPSE expression and function vary across tumour contexts and whether its modulation could offer therapeutic opportunities in OC. Further studies with larger and more diverse cohorts should incorporate the evaluation of immune infiltrate composition and pHPSE levels in parallel with HPSE expression, to dissect the complex dynamics between the genetic variants, transcriptional activity, protein levels, immune response and ovarian tumourigenesis. Notably, earlier research investigated both the mRNA and serum HPSE levels in OC patients and their effects on clinical characteristics. However, the interplay between HPSE expression and circulating protein levels was not clarified, and HPSE (both gene and protein expression levels) was proposed as a diagnostic biomarker for ovarian tumours [9]. Considering the differences between serum and plasma, the results from these studies underscore the complexity of HPSE. Additionally, exploring the gene expression across different cellular compartments, beyond PBCs, could provide further clarification into its specific sources and functional implications. This study's comprehensive approach integrates genetic, molecular, and clinical data to explore the prognostic impact of HPSE SNPs on OC, providing novel insights into the potential influence of these genetic variants on tumour progression. Above all, our findings challenge the conventional perception of HPSE as solely pro-tumourigenic, prompting a reassessment of its role in the tumour microenvironment. 5. CONCLUSION Ovarian malignancy is the most lethal gynaecological cancer, primarily due to late diagnosis and the frequent presentation of chemoresistance. Furthermore, the occurrence of CAT, particularly VTE, complicates the disease prognosis. Over the last few years, searching for reliable prognostic biomarkers has become a priority to improve disease management and patient outcomes. In the cancer research field, HPSE has garnered significant interest due to its role in tumour invasion, angiogenesis, metastasis, and immune modulation. Given the reported dual role of this enzyme in VTE and cancer progression, this study aimed to assess the impact of HPSE SNPs on OC progression and patient survival. An unexpected association between HPSE SNPs and patient prognosis was revealed, challenging the conventional view of HPSE as solely pro-tumourigenic and highlighting its potential immunomodulatory effects. Notably, the findings for rs4364254 and rs11099592 SNPs suggest that the allele linked to lower HPSE expression corresponds to worse clinical outcomes. While the association between the rs4364254 C allele and reduced HPSE levels was confirmed in our cohort, the literature supports a similar effect for the rs11099592 C allele. This pattern suggests that lower HPSE expression may contribute to a more aggressive tumour microenvironment, potentially through reduced immune surveillance or alterations in ECM dynamics. Given that OC is typically an immune-cold tumour, this raises the possibility that HPSE could enable a TME shift towards an immune-inflamed phenotype, facilitating leukocyte migration and anti-tumour responses. Alternatively, these observations may reflect an inverse relationship between HPSE gene expression and its circulating protein levels, as previously reported. These findings open new perspectives on the role of HPSE in OC progression and highlight the need for further studies to elucidate the interplay between its gene and protein expression and immune infiltration. A deeper understanding of these mechanisms is crucial to determine whether HPSE could serve as a prognostic biomarker and even a potential therapeutic target in OC, addressing the urgent need to improve the patients’ clinical outcomes. Declarations Acknowledgements The authors would like to thank Ministério da Saúde de Portugal, Instituto Português de Oncologia do Porto (IPO Porto), Fundação para a Ciência e Tecnologia (FCT) and Portuguese League Against Cancer (NRNorte). Author Contributions All authors made a significant contribution to the study. Conceptualisation, IGM, VT and RM; Patient recruitment and ethical approval, JSB; Funding acquisition, IGM, VT and RM; Investigation, IGM; Review of medical files: VT, MR and JLP; Formal analysis: IGM, VT and RM; writing-original draft preparation, IGM; Writing-review and editing, IGM, VT, JA, JLP, MR, JSB, DP and RM; Supervision: VT, DP and RM. All authors have read and agreed to the published version of the manuscript. Funding This project was funded by the IPO Porto (no. PI61-CI-IPOP-22-2015) and Fundação para a Ciência e Tecnologia (FCT). IGM is a research fellowship holder (LPCC-NRN2025-IGdM) supported by the Portuguese League Against Cancer (NRNorte). VT was a PhD scholarship holder (no. 2020.08969.BD; https://doi.org/10.54499/2020.08969.BD) supported by FCT, co-financed by European Social Funds (FSE) and national funds of MCTES. She is currently a research fellowship holder (LPCC-NRN2025-VT) supported by the Portuguese League Against Cancer (NRNorte). The funders were not involved in the study design, data analysis and interpretation, and manuscript writing. Data availability The data presented in this study is available on request from the corresponding author. Competing Interests JLP has received a research Grant from GESCAT-Grupo de Estudos de Cancro e Trombose. This institution had no role in the decision to conduct the study, write and publish this manuscript. The remaining authors declare no conflict of interest. Informed Consent Statement Informed written consent according to the principals of the Helsinki Declaration was obtained from each patient before their recruitment. References Liao YE, Liu J, Arnold K (2023) Heparan sulfates and heparan sulfate binding proteins in sepsis. Front Mol Biosci 10:1146685 van Wijk XM, van Kuppevelt TH (2014) Heparan sulfate in angiogenesis: a target for therapy. Angiogenesis 17(3):443–462 Vlodavsky I et al (2007) Heparanase: structure, biological functions, and inhibition by heparin-derived mimetics of heparan sulfate. Curr Pharm Des 13(20):2057–2073 Yue B (2014) Biology of the extracellular matrix: an overview. J Glaucoma 23(8 Suppl 1):S20–S23 Vlodavsky I et al (2016) Heparanase: From basic research to therapeutic applications in cancer and inflammation. Drug Resist Updat 29:54–75 Yang Y et al (2023) Potential roles of heparanase in cancer therapy: Current trends and future direction. 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Ann Oncol 34(10):833–848 Sowamber R et al (2023) Ovarian Cancer: From Precursor Lesion Identification to Population-Based Prevention Programs. Curr Oncol 30(12):10179–10194 Moufarrij S, O'Cearbhaill RE (2023) Novel Therapeutics in Ovarian Cancer: Expanding the Toolbox. Curr Oncol 31(1):97–114 Tavares V et al (2024) Paradigm Shift: A Comprehensive Review of Ovarian Cancer Management in an Era of Advancements. Int J Mol Sci, 25(3) Sideris M, Menon U, Manchanda R (2024) Screening and prevention of ovarian cancer. Med J Aust 220(5):264–274 Marques IS et al (2023) Long Non-Coding RNAs: Bridging Cancer-Associated Thrombosis and Clinical Outcome of Ovarian Cancer Patients. Int J Mol Sci, 25(1) McKenzie ND et al (2023) Prognostic features of the tumor microenvironment in high-grade serous ovarian cancer and dietary immunomodulation. Life Sci 333:122178 Ostrovsky O et al (2009) Inverse correlation between HPSE gene single nucleotide polymorphisms and heparanase expression: possibility of multiple levels of heparanase regulation. J Leukoc Biol 86(2):445–455 Berek JS et al (2021) Cancer of the ovary, fallopian tube, and peritoneum: 2021 update. Int J Gynecol Obstet 155:61–85 Eisenhauer EA et al (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45(2):228–247 Gran OV et al (2016) Joint effects of cancer and variants in the factor 5 gene on the risk of venous thromboembolism. Haematologica 101(9):1046 de Melo IG et al (2024) Endothelial Dysfunction Markers in Ovarian Cancer: VTE Risk and Tumour Prognostic Outcomes. Life (Basel), 14(12) Tavares V et al (2024) Haemostatic Gene Expression in Cancer-Related Immunothrombosis: Contribution for Venous Thromboembolism and Ovarian Tumour Behaviour. Cancers 16(13):2356 Wang L et al (2024) Drug resistance in ovarian cancer: from mechanism to clinical trial. Mol Cancer 23(1):66 Dagogo-Jack I, Shaw AT (2018) Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol 15(2):81–94 de Melo IG et al (2024) Contribution of Endothelial Dysfunction to Cancer Susceptibility and Progression: A Comprehensive Narrative Review on the Genetic Risk Component. Curr Issues Mol Biol 46(5):4845–4873 Jayatilleke KM, Hulett MD (2020) Heparanase and the hallmarks of cancer. J Transl Med 18(1):453 Vlodavsky I, Ilan N, Sanderson RD (2020) Forty Years of Basic and Translational Heparanase Research. Adv Exp Med Biol 1221:3–59 Cao H et al (2020) Correlation between heparanase gene polymorphism and susceptibility to endometrial cancer. Mol Genet Genomic Med 8(10):e1257 Ostrovsky O et al (2007) Association of heparanase gene (HPSE) single nucleotide polymorphisms with hematological malignancies. Leukemia 21(11):2296–2303 Ostrovsky O et al (2023) Effect of HPSE and HPSE2 SNPs on the Risk of Developing Primary Paraskeletal Multiple Myeloma. Cells, 12(6) Yetkin U, Karabay O, Onol H (2004) Effects of oral anticoagulation with various INR levels in deep vein thrombosis cases. Curr Control Trials Cardiovasc Med 5(1):1 Li AL et al (2012) Polymorphisms and a haplotype in heparanase gene associations with the progression and prognosis of gastric cancer in a northern Chinese population. PLoS ONE 7(1):e30277 Ramani VC et al (2016) Chemotherapy induces expression and release of heparanase leading to changes associated with an aggressive tumor phenotype. Matrix Biol 55:22–34 Vlodavsky I et al (2007) Heparanase, heparin and the coagulation system in cancer progression. Thromb Res 120(Suppl 2):S112–S120 Mayfosh AJ, Baschuk N, Hulett MD (2019) Leukocyte Heparanase: A Double-Edged Sword in Tumor Progression. Front Oncol 9:331 Yang Y et al (2022) Nanomedicine Strategies for Heating Cold Ovarian Cancer (OC): Next Evolution in Immunotherapy of OC. Adv Sci (Weinh) 9(28):e2202797 Ginath S et al (2001) Expression of heparanase, Mdm2, and erbB2 in ovarian cancer. Int J Oncol 18(6):1133–1144 Putz EM et al (2017) NK cell heparanase controls tumor invasion and immune surveillance. J Clin Invest 127(7):2777–2788 Ostrovsky O, Vlodavsky I, Nagler A (2020) Mechanism of HPSE Gene SNPs Function: From Normal Processes to Inflammation, Cancerogenesis and Tumor Progression. Adv Exp Med Biol 1221:231–249 Additional Declarations Competing interest reported. JLP has received a research Grant from GESCAT-Grupo de Estudos de Cancro e Trombose. This institution had no role in the decision to conduct the study, write and publish this manuscript. The remaining authors declare no conflict of interest. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 19 Feb, 2026 Reviewers agreed at journal 18 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 16 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Submission checks completed at journal 09 Feb, 2026 First submitted to journal 05 Feb, 2026 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. We do this by developing innovative software and high quality services for the global research community. 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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-8799366","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593593243,"identity":"db4e4d7c-fcda-4a55-8d3f-3e5862c34a48","order_by":0,"name":"Inês Guerra de Melo","email":"","orcid":"","institution":"IPO Porto","correspondingAuthor":false,"prefix":"","firstName":"Inês","middleName":"Guerra","lastName":"de Melo","suffix":""},{"id":593593245,"identity":"633a0cba-d420-4b57-a6ec-ae01eba4d251","order_by":1,"name":"Valéria Tavares","email":"","orcid":"","institution":"IPO Porto","correspondingAuthor":false,"prefix":"","firstName":"Valéria","middleName":"","lastName":"Tavares","suffix":""},{"id":593593247,"identity":"0a243cdd-71ec-4bb2-802b-2f2b40734fe4","order_by":2,"name":"Joana Savva-Bordalo","email":"","orcid":"","institution":"IPO Porto","correspondingAuthor":false,"prefix":"","firstName":"Joana","middleName":"","lastName":"Savva-Bordalo","suffix":""},{"id":593593248,"identity":"0906d941-1868-4d60-a4ae-8b312bb60676","order_by":3,"name":"Mariana Rei","email":"","orcid":"","institution":"IPO Porto","correspondingAuthor":false,"prefix":"","firstName":"Mariana","middleName":"","lastName":"Rei","suffix":""},{"id":593593251,"identity":"ea99c58f-e040-4c52-bfdf-ce9a87fd679e","order_by":4,"name":"Joana Liz-Pimenta","email":"","orcid":"","institution":"Centro Hospitalar de Trás os Montes e Alto Douro","correspondingAuthor":false,"prefix":"","firstName":"Joana","middleName":"","lastName":"Liz-Pimenta","suffix":""},{"id":593593253,"identity":"1164d9c6-fd5c-4683-bef8-1fd168ef67e7","order_by":5,"name":"Deolinda Pereira","email":"","orcid":"","institution":"IPO Porto","correspondingAuthor":false,"prefix":"","firstName":"Deolinda","middleName":"","lastName":"Pereira","suffix":""},{"id":593593256,"identity":"233ee7cf-837a-483f-bbdb-141875f0c93a","order_by":6,"name":"Rui Medeiros","email":"data:image/png;base64,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","orcid":"","institution":"IPO Porto","correspondingAuthor":true,"prefix":"","firstName":"Rui","middleName":"","lastName":"Medeiros","suffix":""}],"badges":[],"createdAt":"2026-02-05 16:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8799366/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8799366/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103050280,"identity":"105e7453-b168-473a-b03c-2ab1b1cd5676","added_by":"auto","created_at":"2026-02-20 07:49:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":119291,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology scheme depicting the three used cohorts in the present study.\u003c/p\u003e","description":"","filename":"Figure1.jpg.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8799366/v1/672781ed64dbeb9c2bdcfb7f.jpeg"},{"id":103050346,"identity":"d5ad9b2a-7376-4a91-a1d9-f1228cff00d8","added_by":"auto","created_at":"2026-02-20 07:49:35","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival (OS) by Kaplan-Meier and log-rank test for OC patients in cohort A, according to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHPSE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e rs4364254 genotype distribution.\u003c/strong\u003e The C allele carriers showed lower OS compared to TT genotype carriers (CC/CT vs. TT; log-rank test, \u003cem\u003ep\u003c/em\u003e=0.001). The mean OS for C allele carriers was 36.1 ± 4.5 months, while for TT genotype carriers was 59.0 ± 5.2 months.\u003c/p\u003e","description":"","filename":"Figure2.jpg.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8799366/v1/e2c694bb2c8ff76e94712f15.jpeg"},{"id":103029578,"identity":"3df038e1-4f63-47aa-9102-6a59a2332969","added_by":"auto","created_at":"2026-02-19 21:41:35","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProgression-free survival (PFS) and overall survival (OS) by Kaplan-Meier and log-rank test for OC patients in cohort A, according to \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHPSE\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e rs11099592 genotype distribution.\u003c/strong\u003e TT genotype group showed lower survival times compared to C allele carriers. A) The mean PFS for C allele carriers was 28.8 ± 3.3 months while TT carriers presented a mean PFS of 11.8 ± 3.1 months (CC/CT vs. TT; log-rank test, \u003cem\u003ep\u003c/em\u003e=0.050). B) The mean OS for C allele carriers was 48.9 ± 3.9 months, while for TT genotype carriers the mean OS was 25.4 ±5.8 months (CC/CT vs. TT; log-rank test, \u003cem\u003ep\u003c/em\u003e=0.025).\u003c/p\u003e","description":"","filename":"Figure3.jpg.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8799366/v1/9af411a7a8d1e7f954a322e0.jpeg"},{"id":103050387,"identity":"cc9136e8-ad15-4d06-830b-b4c58523d917","added_by":"auto","created_at":"2026-02-20 07:49:47","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival (OS) by Kaplan-Meier and log-rank test for cohort B platinum-resistant OC patients, according to HPSE rs4364254 genotype distribution\u003c/strong\u003e. C allele carriers had a worse OS than TT allele carriers (CC/CT vs.TT; log-rank test, p=0.044). The mean OS for C allele and TT genotype carriers was 19.9 ± 2.4 months and 28.8 ± 3.4 months, respectively.\u003c/p\u003e","description":"","filename":"Figure4.jpg.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8799366/v1/ed8dea5ca022105798b2f3d1.jpeg"},{"id":103050341,"identity":"8b8129e1-ef17-4122-bde9-22446e403e36","added_by":"auto","created_at":"2026-02-20 07:49:33","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18486,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall survival (OS) by Kaplan-Meier and log-rank test for cohort B non-serous OC patients, according to genotype distribution for HPSE rs11099592\u003c/strong\u003e. T allele carriers presented a worse OS than CC allele carriers (TT/CT vs. CC; log-rank test, p=0.016). T allele and CC genotype carriers had a mean OS of 45.1 ± 3.3 months and 53.5 ± 1.6 months, respectively.\u003c/p\u003e","description":"","filename":"Figure5.jpg.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8799366/v1/cc910bcc41283b61b23b5cb6.jpeg"},{"id":103051206,"identity":"4841f39f-9ae8-40cd-8096-976e7492637e","added_by":"auto","created_at":"2026-02-20 07:58:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1319798,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8799366/v1/37b08e0d-7927-4527-b1de-4ac695ffbbb3.pdf"}],"financialInterests":"Competing interest reported. JLP has received a research Grant from GESCAT-Grupo de Estudos de Cancro e Trombose. This institution had no role in the decision to conduct the study, write and publish this manuscript. The remaining authors declare no conflict of interest.","formattedTitle":"Reassessing HPSE in Ovarian Cancer: Beneficial After All?","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eHeparanase (HPSE), encoded by the homonymous gene located at 4q21.23, is the only enzyme in mammals capable of degrading heparan sulfate (HS) - the primary component of the outer layer of the plasma membrane that covers endothelial cells, as well as a crucial constituent of the extracellular matrix (ECM) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Beyond its basic structure, HS can also exist in the form of a proteoglycan (HSPG) when multiple HS chains are covalently attached to a protein core [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, other glycosaminoglycans (GAGs) that form proteoglycans, such as hyaluronic acid, and keratan and chondroitin sulphates, along with collagens, laminin, elastin and fibronectin, also make up the ECM [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. HS plays a critical role in the integrity and organisation of the ECM, acting as a bridging link between its components [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By cleaving HS, HPSE facilitates the remodelling of the subendothelial basal membrane and the ECM, an essential initial step in the formation of new vessels from pre-existing ones, consequently promoting endothelial cell migration and sprouting [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, the heterogeneous structure characteristic of HS enables it to interact with various proteins and growth factors, regulating not only their storage but also their access, function, and mode of action [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This broad spectrum of possible interactions accounts for the wide range of signalling pathways activated upon the release of these molecules through HPSE degradation, such as cell proliferation, tissue repair and angiogenesis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUncontrolled enzymatic activity of HPSE can support pathological processes, including infections and malignancy. In the latter case, in addition to promoting the supply of nutrients and oxygen to tumour cells, HPSE can enhance cancer aggressiveness by inducing the expression of pro-angiogenic [vascular endothelial growth factors (VEGFs) A and C, and metalloproteinases 9 (MMP-9)], pro-inflammatory [tumour necrosis factor (TNFα) and interleukins 1 (IL-1) and 6 (IL-6)], and pro-coagulant factors (such as tissue factor) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Notably, \u003cem\u003eHPSE\u003c/em\u003e is frequently overexpressed in oncological contexts, such as gastric, pancreatic, bladder, lung, and notably, ovarian carcinomas, corroborating its role in aggressiveness and disease recurrence [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In fact, HPSE is already reported as a potential biomarker for the evaluation of surgery effects and prognosis prediction in ovarian cancer (OC) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. HPSE also contributes to a pro-thrombotic environment, triggered by the release of tissue factor pathway inhibitor (TFPI) from the vessel wall, and subsequent induction of tissue factor (TF), further emphasising the intricacy of its connection with OC [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Indeed, among solid tumours, OC together with other gynaecological malignancies is classified as a high-risk tumour for venous thromboembolism (VTE), according to cancer-associated thrombosis (CAT) scores such as Khorana Score (KS) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue to late-stage diagnosis, OC stands as the most lethal malignancy among gynaecologic tumours on a global scale. The high rates of chemoresistance and tumour recurrence combined with the high degree of heterogeneity of OC further undermine current treatment strategies, with most countries reporting 5-year survival rates below 50% [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the current landscape, exploring intricate mechanisms such as the interplay between HPSE, thrombosis and OC progression may hold the key to improving these dismal outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Existing evidence demonstrates that genetic variations in \u003cem\u003eHPSE\u003c/em\u003e are associated with messenger RNA (mRNA) and protein levels, possibly serving as predictors of disease outcomes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given HPSE\u0026rsquo;s prominent role in ovarian tumourigenesis, further reinforced by its pro-thrombotic activity, these variations may affect survival and CAT susceptibility among OC patients. In this context, the present study investigated the role of three relevant \u003cem\u003eHPSE\u003c/em\u003e single-nucleotide polymorphisms (SNPs) in OC patients, aiming to advance personalised and more effective disease management.\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003ch2\u003e\u003cstrong\u003e2.1. \u0026nbsp; \u0026nbsp; Discovery and validation cohorts\u0026nbsp;\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eA retrospective hospital-based cohort study was carried out at the Department of Gynaecology and Oncology of the Portuguese Oncology Institute of Porto (IPO Porto), enrolling histologically diagnosed epithelial OC (EOC) patients of European ancestry with admittances for first-line treatment from March 2017 to December 2023. Patients under the age of 18, seeking only a second opinion, or with follow-up elsewhere were excluded and a final cohort of 98 EOC patients (cohort A) with available biological material was established. Upon acceptance of participation, a written consent was handed to and signed by each patient according to the principles of the Helsinki Declaration. This study received approval from the ethics committee at IPO Porto (CES IPO: 69/021). The staging of all EOC cases was performed according to the International Federation of Gynecology and Obstetrics (FIGO) Cancer Report 2021 [21]. Additionally, tumour response to chemotherapy was assessed using the Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1 [22]. Demographic, clinicopathological, and follow-up data were obtained by reviewing the medical records of all patients. The average age of the participants was 63.2 years, with the majority being post-menopausal (80.6%, N=79) and diagnosed at FIGO III and IV stages (75.5%, N=74). Most tumours were serous in type (83.7%, N=82) and 41.8% of the patients (N=41) underwent standard treatment - cytoreductive surgery followed by carboplatin/cisplatin with paclitaxel. Complete/optimal surgical resection was achieved in 45 patients (45.9%). CAT was characterised as a VTE event occurring within the timeframe of six months prior to two years following an OC diagnosis [18, 23]. Among the 98 patients, information concerning CAT was available only for 80 of them, with 17 (21.3%) presenting CAT. Regarding thrombosis-related features, excluding missing values, 40 patients (46.0%) had a high-risk KS (KS\u0026ge;2), 38 (49.4%) presented high activated partial thromboplastin time (aPTT; \u0026ge;27.1 s), 41 (50.6%) high prothrombin time (PT; \u0026ge;14.2 s), and 39 (48.8%) high international normalised ratio (INR; \u0026ge;1.1). The study had a median follow-up of 25.5 months.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eValidation for SNPs\u0026rsquo; analysis was conducted with an independent cohort \u0026ndash; cohort B. Another retrospective cohort study was conducted with patients admitted at the same institution for first-line treatment from January 1996 to December 2012.\u0026nbsp;Inclusion and\u0026nbsp;exclusion criteria, disease staging and evaluation protocols for cohort B were consistent with those used for cohort A.\u0026nbsp;A total of 331 EOC patients, for whom biological material was available, were enrolled.\u0026nbsp;Like cohort A, each patient provided written consent following the principles of the Helsinki Declaration before their\u0026nbsp;recruitment.\u0026nbsp;This study received approval from the ethics committee at IPO Porto (CES IPO:286/2014).\u0026nbsp;The mean age of the enrolled patients was 55 years.\u0026nbsp;The following results were obtained considering the group of patients with valid information.\u0026nbsp;Like cohort A, most of the patients were post-menopausal (64.6%, N=203) and diagnosed at advanced cancer stages (FIGO\u0026nbsp;III/IV; 61.3%, N=196). Regarding the histological subtype, 56.7% (N=187) were diagnosed with serous,\u0026nbsp;12.7% (N=42) with clear cell,\u0026nbsp;10.3% (N=34) with endometrioid,\u0026nbsp;9.7% (N=32) with mucinous and the other\u0026nbsp;10.6% (N=35) with less common tumour subtypes. Excluding the missing data, among the serous tumour types, 16.1% (N=24) were low grade and 83.9% (N=125) were high grade. Concerning therapeutic management, most patients were subjected to the standard treatment (92.7%, N=307), with cytoreductive surgery followed by chemotherapy with a combination of paclitaxel and carboplatin (N=171) or cisplatin (N=136). Neoadjuvant chemotherapy (N=17, 5.1%), chemotherapy alone (N=9,\u0026nbsp;2.7%) or only surgery (N=4,\u0026nbsp;1.2%) were also considered as first-line treatment options. Complete or optimal surgical resection was achieved for\u0026nbsp;49.5% of\u0026nbsp;the patients (N=164).\u0026nbsp;Regarding therapeutic response, most patients were highly platinum-sensitive (69.4%, N=229), 9.7% (N=32) were\u0026nbsp;partially platinum-sensitive, 11.5% (N=38) were platinum-resistant and 4.8% (N=16) were platinum-refractory.\u0026nbsp;Information on CAT events was not available for this cohort. The mean follow-up in this cohort was 49.4 months.\u003c/p\u003e\n\u003ch2 id=\"_Toc185635336\"\u003e\u003cstrong\u003e2.2. \u0026nbsp; \u0026nbsp; Sample collection and nucleic acid extraction\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eVenous blood samples were collected in EDTA tubes before the initiation of the first line of chemotherapy. DNA was isolated using the QIAamp DNA Blood Mini Kit (Cat. No. 51106, Qiagen, Hilden, Germany), and RNA was extracted from peripheral blood components (PBCs) using the GRS RNA kit - Blood \u0026amp; cultured cells (#GK08.0100, Grisp Research\u0026nbsp;Resolutions\u0026reg;, Porto, Portugal), following manufacturers\u0026apos; protocols. Nucleic acid concentration and sample purity were confirmed\u0026nbsp;using a NanoDrop spectrophotometer. DNA and RNA were stored at \u0026minus;20 \u0026deg;C and \u0026minus;80 \u0026deg;C, respectively.\u003c/p\u003e\n\u003ch2 id=\"_Toc185635337\"\u003e\u003cstrong\u003e2.3. \u0026nbsp; \u0026nbsp; Polymorphism selection and genotyping\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eHPSE\u003c/em\u003e SNPs were selected based on their impact on HPSE expression or activity, relevance to cancer and/or cardiovascular diseases, availability of TaqMan\u0026reg; SNP genotyping assays and the minor allele frequency (MAF\u0026ge;10%). Variants in strong linkage disequilibrium (r\u0026sup2;\u0026gt;90%) were excluded. Three SNPs were chosen: rs4364254, rs4693608 and rs11099592 (\u003cstrong\u003eTable 1\u003c/strong\u003e). Polymorphism genotyping was performed using the StepOne Plus qRT-PCR system (Applied Biosystems\u0026reg;) with TaqMan allelic discrimination technology. Each PCR reaction mix (6.0 \u0026micro;L) contained 2.5 \u0026micro;L of TaqPath\u0026trade; ProAmp\u0026trade; Master Mix (1\u0026times;), 2.375 \u0026micro;L of sterile water, 0.125 \u0026micro;L of TaqMan\u0026reg; SNP genotyping assay, and 1.0 \u0026micro;L of genomic DNA. Thermal cycling conditions were as follows: 10 min at 95\u0026deg;C for polymerase activation, 15 s at 95\u0026deg;C for DNA denaturation (45 cycles), and 1 min at 60\u0026deg;C for primer pairing and extension. Measures of quality control were carried out as described elsewhere [24].\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc185632350\"\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eSelected HPSE SNPs and the respective TaqMan\u0026reg; Genotyping Assays.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"86%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFunctional consequence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMAF in Iberians *\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTaqMan\u0026reg; Genotyping Assay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers4364254 (C/T)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.2% (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC___8416664_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers4693608 (G/A)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIntronic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.7% (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC__30667102_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ers11099592 (T/C)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMissense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.9% (T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eC__31870510_10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MA, minor allele; MAF, minor allele frequency; SNP, single-nucleotide polymorphism. * According to the Ensembl database (last accessed on the 18th of August of 2024).\u003c/p\u003e\n\u003ch2 id=\"_Toc185635339\"\u003e\u003cstrong\u003e2.4. \u0026nbsp; \u0026nbsp; cDNA conversion and gene relative quantification\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe \u003cem\u003eHPSE\u003c/em\u003e gene expression was conducted to assess the impact of the evaluated SNPs on gene expression levels. This analysis was performed in a subsampled cohort A comprised of 55 OC patients \u0026ndash; cohort C. The exclusion criteria applied to cohort A were patients who: 1) had a history of malignancies before or after OC diagnosis; 2) were breastfeeding or pregnant at the time of diagnosis; 3) had a history of autoimmune diseases or were undergoing immunosuppressive therapies; 4) had acute infections at cancer diagnosis; 5) were undergoing anticoagulant treatment for diseases other than VTE; and 6) possessed the polymorphisms Factor V Leiden (\u003cem\u003eF5\u003c/em\u003e rs6025) and \u003cem\u003eF2\u003c/em\u003e (Factor II encoding gene) rs1799963.The rationale for the cohort selection and study design is illustrated in \u003cstrong\u003eFig. 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eTotal RNA samples were utilised as templates to generate the complementary DNA (cDNA) strands, using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems\u0026reg;, Carlsbad, CA, USA), as previously described [24].\u003c/p\u003e\n\u003cp\u003eEach reaction for gene expression analysis was executed with the StepOne Plus qRT-PCR system using a 10.0 \u0026micro;L mixture containing: 5.0 \u0026micro;L of 2\u0026times; TaqMan\u003csup\u003eTM\u003c/sup\u003e Gene Expression Master Mix and 0.5 \u0026micro;L of TaqMan\u003csup\u003eTM\u003c/sup\u003e 20\u0026times; Gene Expression Assay Hs00935036_m1, both by Applied Biosystems\u0026reg; (Foster City, CA, USA); 3.0 \u0026micro;L of nuclease-free water and 1.5 \u0026micro;L of cDNA sample. Adding to these, \u003cem\u003eglyceraldehyde-3-phosphatedehydrogenase\u003c/em\u003e (\u003cem\u003eGAPDH\u003c/em\u003e) and \u003cem\u003ehypoxanthine phosphoribosyl transferase 1\u003c/em\u003e (\u003cem\u003eHPRT1\u003c/em\u003e) were evaluated as endogenous controls with the assays Hs03929097_g1 and Hs02800695_m1, respectively. Thermal cycling conditions included 50 \u0026deg;C for 2 min, 95 \u0026deg;C for 10 min, followed by 45 cycles of 95 \u0026deg;C for 15 s and 60 \u0026deg;C for 1 min. Negative controls were included, and all samples were run in triplicate. Measures of quality control were carried out as described elsewhere\u0026nbsp;[24, 25]. Thermo Fisher Connect platform (Thermo Fisher Scientific, Waltham, MA, USA) was employed for data analysis.\u003c/p\u003e\n\u003ch2 id=\"_Toc185635340\"\u003e\u003cstrong\u003e2.5. \u0026nbsp; \u0026nbsp; Statistical analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eData analysis was performed using IBM SPSS Statistics software (version 29, IBM Corp., Armonk, NY, USA) for Windows. The Kolmogorov-Smirnov test was employed to assess data distribution.\u0026nbsp;Continuous variables were categorised using the mean value as the cut-off for data with a normal distribution, or the median value for data with a non-normal distribution.\u0026nbsp;The genotype frequencies of each SNP in this study were compared to those reported in the Iberian population (https://www.ensembl.org/index.html, accessed\u0026nbsp;on 18\u003csup\u003eth\u003c/sup\u003e August 2024). The Hardy-Weinberg equilibrium (HWE) was assessed using the chi-square test (\u0026chi;\u0026sup2;).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGene normalised-relative expression was calculated via the Livak method using GAPDH as the most suitable control. Severe outliers were removed based on the interquartile range (IQR). Four expression profiles were defined for analysis: A) low vs. high expression based on the median value, B) low, intermediate, and high expression (terciles), C) low (first two terciles) vs. high (third tercile), and D) low (first tercile) vs. high (second and third terciles), as previously described [24].\u003c/p\u003e\n\u003cp\u003eThe SNPs\u0026rsquo; impact on the patient\u0026rsquo;s progression-free survival (PFS) and overall survival (OS) was assessed. PFS was calculated as the time from diagnosis to recurrence, progression, death, or the last clinical evaluation, while OS referred to the period from diagnosis to death or last evaluation. Survival curves were generated using the Kaplan-Meier method, and log-rank tests were used to compare survival probabilities. The most appropriate genetic model (dominant or recessive) for each variant was selected based on survival curve analysis under the additive model. Cox proportional hazard models were employed to estimate the risks of tumour progression and patient death. Validation of the SNP analysis was conducted considering the entire cohort B and subgroups based on patients\u0026rsquo; age; hormonal status (pre- or post-menopause) at diagnosis; cancer stage; histological subtype; OC differentiation grade; surgical resection and platinum sensitivity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStatistical tests were two-sided with a 5% significance level, and \u003cem\u003ep\u003c/em\u003e-values between 0.050 and 0.060 were considered marginally significant.\u0026nbsp;\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003ch2\u003e\u003cstrong\u003e3.1. \u0026nbsp; \u0026nbsp; Distribution of SNP genotypes\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe distribution of the variants\u0026rsquo; genotypes is represented in \u003cstrong\u003eTable 2.\u003c/strong\u003e Notably, all the SNPs were in HWE (\u0026chi;\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026gt;0.050), demonstrating no significant deviation from expected genotype frequencies.\u003c/p\u003e\n\u003cp id=\"_Toc174922574\"\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Genotype distribution of HPSE SNPs in cohort A (N=98).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"491\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMAFi *\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMAFs\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(MA)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN total\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4364254\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e32.2%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e29.1%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u0026nbsp;(45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;rs4693608\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e46.7%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e48.5% (G)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003cp\u003e(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;rs11099592\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e29.9%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e23.9%\u003c/p\u003e\n \u003cp\u003e(T)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55 (57.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003cp\u003e(98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e36 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*According to the Ensembl database (last accessed on 18th of August of 2024). Abbreviations: MA, minor allele; MAFi, minor allele frequency in the Iberian population; MAFs, minor allele frequency in the study cohort; SNP, single-nucleotide polymorphism.\u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc185635350\"\u003e\u003cstrong\u003e3.2. \u003cem\u003eHPSE\u003c/em\u003e SNPs and gene expression\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIn cohort C, only rs4364254 demonstrated a significant association with \u003cem\u003eHPSE\u003c/em\u003e expression in PBCs. In the additive model, based on expression profile C (with the third tercile regarded as a high expression), the C allele was associated with a significantly lower \u003cem\u003eHPSE\u003c/em\u003e expression than the TT genotype (CC vs. CT vs. TT; \u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.018). Under the dominant model, the presence of the C allele was similarly linked to significantly reduced \u003cem\u003eHPSE\u003c/em\u003e expression, both considering the profile B (low vs. intermediate vs. high) (CC/CT vs. TT; \u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.014) and profile C (CC/CT vs. TT; \u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.005). Overall, the \u003cem\u003eHPSE\u003c/em\u003e rs4364254 C allele was consistently associated with reduced \u003cem\u003eHPSE\u003c/em\u003e gene expression in PBCs. As for the remaining SNPs, no significant association with the gene expression levels was found, nor was any discernible trend observed (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp id=\"_Toc185632351\"\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Genotype distribution of each HPSE SNP (additive model) in cohort C (N=55), according to the expression profile A of the respective gene.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLow Expression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Expression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4364254 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3 (5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24\u0026nbsp;(43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5 (9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10(18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ers4693608\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6 (10.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ers11099592\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15(27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (27.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Statistically significant results for \u003cem\u003eHPSE\u003c/em\u003e rs4364254 were found considering the expression profile C (\u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.018). \u0026nbsp;\u003c/p\u003e\n\u003ch2 id=\"_Toc185635344\"\u003e\u003cstrong\u003e3.3. \u003cem\u003eHPSE\u003c/em\u003e SNPs and clinical features of OC patients\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIn the discovery cohort (cohort A), the SNPs rs4364254 and rs11099592 were associated with various demographic and clinicopathological features. The rs4364254 C allele was significantly associated with older age at OC diagnosis in both the additive (\u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.049) and dominant (CC/CT vs. TT; \u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.047) models. Additionally, the C allele was more common in patients with a high revised KS (KS \u0026ge;2; \u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.034). A marginal association was also observed with aPTT, suggesting that the C allele is more likely linked to lower aPTT, while the TT genotype is associated with a prolonged assay (CC/CT vs. TT; \u0026chi;\u0026sup2;, \u003cem\u003ep=\u003c/em\u003e0.053). However, no SNP showed an association with VTE susceptibility. Like the genetic variants, KS showed a poor predictive value (\u0026chi;\u0026sup2;, \u003cem\u003ep\u003c/em\u003e\u0026ge;0.050).The T allele of \u003cem\u003eHPSE\u003c/em\u003e rs11099592 was marginally associated with a history of other tumours in the additive model (\u0026chi;\u0026sup2;, \u003cem\u003ep\u003c/em\u003e=0.053), being more prevalent in patients without such a history.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003e3.4. \u003cem\u003eHPSE\u003c/em\u003e SNPs and OC patient outcomes\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIn cohort A, the rs4364254 C allele carriers showed lower OS compared to the TT genotype group (CC/CT vs. TT; 36.1 \u0026plusmn; 4.5 months and 59.0 \u0026plusmn; 5.2 months, respectively, log-rank test, \u003cem\u003ep\u003c/em\u003e=0.001; \u003cstrong\u003eFig. 2\u003c/strong\u003e). Moreover, rs11099592 was found to be associated with both PFS and OS. Specifically, TT genotype carriers had a lower PFS than their counterparts (CC/CT vs. TT; mean PFS of 28.8 \u0026plusmn; 3.3 months and 11.8 \u0026plusmn; 3.1 months, log-rank test, \u003cem\u003ep\u003c/em\u003e=0.050; \u003cstrong\u003eFig. 3A\u003c/strong\u003e). Likewise, TT genotype carriers showed lower OS (CC/CT vs. TT; mean OS of 25.4 \u0026plusmn; 5.8 months and 48.9 \u003cem\u003e\u0026plusmn;\u0026nbsp;\u003c/em\u003e3.9 months, respectively, log-rank test, \u003cem\u003ep\u003c/em\u003e=0.025; \u003cstrong\u003eFig. 3B\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe significant results concerning the impact of the SNPs on patients\u0026rsquo; prognosis in cohort A were validated in the independent cohort B. While no significant association was detected in the entire cohort B, stratified analyses confirmed the association between the rs4364254 C andrs11099259 T alleles and poorer clinical outcomes. Among the patients resistant to platinum, the rs4364254 C allele was associated with lower OS compared to the TT genotypes (CC/CT vs.TT; mean OS of 19.9 \u0026plusmn; 2.4 months and 28.8 \u0026plusmn; 3.4 months; log-rank test, \u003cem\u003ep\u003c/em\u003e=0.044; \u003cstrong\u003eFig. 4\u003c/strong\u003e). As for rs11099259, non-serous OC patients carrying the T allele presented a worse OS than their counterparts with the CC genotype (TT/CT vs. CC; mean OS of 45.1 \u0026plusmn; 3.3 months and 53.5 \u0026plusmn; 1.6 months, respectively; log-rank test, \u003cem\u003ep\u003c/em\u003e=0.016; \u003cstrong\u003eFig. 5\u003c/strong\u003e).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eLong-term survival rates for OC remain poor, despite progress in treatment options, highlighting the persistent challenges in effectively managing this disease. Late-stage diagnoses, therapy resistance and high recurrence rates continue to hinder meaningful advancements, emphasising the urgent need to rethink strategies and explore new avenues, such as the identification of robust prognostic biomarkers [15, 16, 18, 26, 27]. The interplay between HPSE, VTE, and OC could be the key to these needed advances, facilitating more personalised treatment approaches. HPSE plays a pivotal role in cancer progression, influencing multiple hallmarks of the disease, particularly in OC, where it contributes to invasion and angiogenesis. At the same time, the endothelial permeability promoted by HPSE fosters VTE, a major complication in cancer patients [28, 29]. For OC, this is reflected in high CAT scores, largely influenced by the surgery location and type of treatment involved.\u0026nbsp;Building on this, the present study focused on investigating the role of \u003cem\u003eHPSE\u003c/em\u003e SNPs in OC prognosis, aiming to further advance personalised and more effective disease management.\u003c/p\u003e\n\u003cp\u003eTo begin with, the variant \u003cem\u003eHPSE\u003c/em\u003e rs4364254, located in intron 9, involves the substitution of a cytosine (C) with a thymine (T) at nucleotide position 8,718,418 [30, 31]. The T allele has been associated with higher \u003cem\u003eHPSE\u003c/em\u003e expression levels, which was confirmed in the present study (CC/CT vs. TT; χ², \u003cem\u003ep=\u003c/em\u003e0.005) [32]. Notably, among the three \u003cem\u003eHPSE\u0026nbsp;\u003c/em\u003eSNPs evaluated (rs4364254, rs4693608 and rs11099592), rs4364254 was the only one to show a significant association with \u003cem\u003eHPSE\u0026nbsp;\u003c/em\u003eexpression in PBCs. Although the underlying mechanism remains unclear, existing evidence suggests that \u003cem\u003eHPSE\u003c/em\u003e rs4364254 is located within an insulator region of DNA, and thus it might alter the function of this regulatory element, affecting gene expression [33]. The combination of \u003cem\u003eHPSE\u003c/em\u003e rs4364254 with rs4693608 can modulate the gene transcriptional activity. The latter is an intronic variant located in an enhancer, involving a guanine (G) to adenine (A) substitution, which has been reported to affect gene expression [31]. Based on haplotype analyses registered in the literature, when individual alleles are considered, the A allele of rs4693608 is linked to an increased HPSE expression [30]. However, as previously mentioned, this association was not observed in our study.\u003c/p\u003e\n\u003cp\u003eIntriguingly, in a previous study also assessing \u003cem\u003eHPSE\u003c/em\u003e SNPs and gene expression in PBCs, lower \u003cem\u003eHPSE\u003c/em\u003e expression was associated with higher plasmatic HPSE (pHPSE), and vice-versa, in healthy individuals. This unexpected dynamic may be explained by the complex trafficking, processing, and secretion of the protein. \u003cem\u003eHPSE\u003c/em\u003e mRNA leads to the synthesis of the protein pro-heparanase, which then undergoes rapid processing and activation in the Golgi and lysosomes, followed by secretion – pHPSE. In a case of high \u003cem\u003eHPSE\u003c/em\u003e expression, much of the active pHPSE binds to the ECM or cell surface, limiting its detection in plasma. Thus, while mRNA levels may be higher, pHPSE detection reflects only the active, secreted form, detached from ECM or cell surface. Additionally, high pHPSE levels may lead to a reduction in mRNA expression through a feedback mechanism. Assuming this reported negative correlation applies to other \u003cem\u003eHPSE\u003c/em\u003e SNPs, this data may explain the obtained results regarding \u003cem\u003eHPSE\u003c/em\u003e mRNA, genotype distributions and further associations, in the present study [20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding the association with patient characteristics, the rs4364254 C allele - linked to lower \u003cem\u003eHPSE\u003c/em\u003e levels - was found to be more common among OC patients with lower aPTT, a coagulation test marker that measures the time it takes for blood to clot (χ², \u003cem\u003ep=\u003c/em\u003e0.053) and a higher KS (KS ≥2; χ², \u003cem\u003ep=\u003c/em\u003e0.034). Collectively, this suggests that the rs4364254 C allele may be reflecting a pro-thrombotic profile [34]. However, it is worth noting that neither the SNP, aPTT nor KS showed a significant association with VTE. The multifunctionality of HPSE may explain this unexpected association between a lower expression allele and a pro-thrombotic profile, as the enzyme's versatility could extend to anti-thrombotic actions as well [30]. Notably, there was a high prevalence of the rs4364254 C allele among patients diagnosed at advanced age (≥55 years) (CC/CT vs. TT; χ², \u003cem\u003ep=\u003c/em\u003e0.047). This age-related prevalence may partially explain the negative prognostic impact of the C allele, as disclosed further in the next paragraph.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConcerning OC patients’ prognosis, \u003cem\u003eHPSE\u003c/em\u003e rs4364254 had a significant impact. Namely, compared to TT genotype carriers, patients with the C allele - associated with lower \u003cem\u003eHPSE\u0026nbsp;\u003c/em\u003eexpression - exhibited lower OS in cohort A and stratified cohort B (log-rank test, \u003cem\u003ep\u003c/em\u003e=0.001 and \u003cem\u003ep\u003c/em\u003e=0.044, respectively). This negative impact of the rs4364254 C allele is not consistent with previous cancer studies with endometrial and gastric cancer patients, where TT genotypes - linked to higher \u003cem\u003eHPSE\u003c/em\u003e expression - were associated with cervical invasion and poorer survival, respectively [31, 35]. However, this effect aligns with the negative correlation between \u003cem\u003eHPSE\u0026nbsp;\u003c/em\u003eexpression andpHPSE levels reported in the literature [20, 30]. The reduced OS observed among C allele carriers compared to TT genotype carriers in the validation study, specifically among platinum-resistant patients (log-rank test, \u003cem\u003ep\u003c/em\u003e=0.044), suggests that molecular pathways driven by lower \u003cem\u003eHPSE\u003c/em\u003e expression, potentially coupled with elevated pHPSE levels, may contribute to a tumour microenvironment (TME) that is more resilient to the cytotoxic effects of platinum-based therapies. Collectively, local action of HPSE is widely recognised for its pro-tumorigenic role, promoting tumour invasion, angiogenesis, and metastasis through the degradation of HS in the ECM. On one hand, \u003cem\u003eHPSE\u003c/em\u003e expression by TME components, such as activated ECs, is deemed an aggressive phenotype marker, leading to poorer outcomes [5, 36, 37]. On the other hand, considering the systematic action of HPSE, the gene expression by leukocytes can paradoxically exhibit anti-tumourigenic effects under certain conditions. The cleavage of HS by HPSE is multifunctional, releasing growth factors and cytokines that, depending on the microenvironment, can enhance immune surveillance, facilitating tumour progression inhibition. Mayfosh et al. (2019) demonstrate that HPSE overexpression in T cells and NK cells enhances their migration and infiltration into tumours [38]. Whether gene and protein expression are indeed inversely correlated, or if HPSE, contrary to its well-established pro-tumourigenic role, could be exerting beneficial immunomodulatory effects need to be clarified. Considering this anti-tumourigenic role, lower \u003cem\u003eHPSE\u003c/em\u003e associated with the rs4364254 C allele might limit this release, potentially impairing the body's ability to mount an effective defence against the tumour. Thus, HPSE may play a role in shifting OC's cold immune microenvironment to an immune-inflamed (hot) tumour phenotype [39].\u0026nbsp;Overall, additional studies are required to dissect the implications of the \u003cem\u003eHPSE\u003c/em\u003e rs4364254 C allele in the clinical outcomes of OC patients. As for \u003cem\u003eHPSE\u003c/em\u003e rs4693608, no prognostic value was detected.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRegarding rs11099592, this missense variation located at intron 7, involves the substitution of a thymine (T) with a cytosine (C) [32]. This SNP significantly impacted OC patients’ prognosis, with C allele carriers presenting a higher PFS and OS compared to patients carrying the TT genotype in cohort A (log-rank test, \u003cem\u003ep\u003c/em\u003e=0.050 and \u003cem\u003ep\u003c/em\u003e=0.025, respectively). Although no association between rs11099592 and \u003cem\u003eHPSE\u003c/em\u003e expression was observed in this study, the literature suggests that the C allele enhances its levels [20, 30, 32]. This aligns with the pattern observed for rs4364254, where the allele linked to reduced expression correlates with a poorer prognosis. Furthermore, in the validation analyses, among non-serous OC patients, the T allele showed a persistent association with a worse prognosis (log-rank test, \u003cem\u003ep\u003c/em\u003e=0.016). Although studies should clarify the distinct HPSE roles across different histological types of OC, this finding can be attributed to how \u003cem\u003eHPSE\u003c/em\u003e interacts with the TME [9, 40]. Once more, these results not only challenge the conventional view of HPSE as exclusively pro-tumourigenic but also question the assumed inverse correlation between its gene and protein expression [20, 38, 41]. Interestingly, in a marginal association, the rs11099592 C allele was more prevalent in patients with a history of other tumours (χ², \u003cem\u003ep=\u003c/em\u003e0.053), aligning with the observation that elevated \u003cem\u003eHPSE\u003c/em\u003e may facilitate tumourigenesis in different tissues through mechanisms like ECM remodelling and angiogenesis [30, 42]. Given these complex and contradictory roles, further investigation is crucial to determine how HPSE expression and function vary across tumour contexts and whether its modulation could offer therapeutic opportunities in OC. Further studies with larger and more diverse cohorts should incorporate the evaluation of immune infiltrate composition and pHPSE levels in parallel with \u003cem\u003eHPSE\u003c/em\u003e expression, to dissect the complex dynamics between the genetic variants, transcriptional activity, protein levels, immune response and ovarian tumourigenesis. Notably, earlier research investigated both the mRNA and serum HPSE levels in OC patients and their effects on clinical characteristics. However, the interplay between \u003cem\u003eHPSE\u003c/em\u003e expression and circulating protein levels was not clarified, and HPSE (both gene and protein expression levels) was proposed as a diagnostic biomarker for ovarian tumours [9]. Considering the differences between serum and plasma, the results from these studies underscore the complexity of HPSE. Additionally, exploring the gene expression across different cellular compartments, beyond PBCs, could provide further clarification into its specific sources and functional implications. This study's comprehensive approach integrates genetic, molecular, and clinical data to explore the prognostic impact of \u003cem\u003eHPSE\u003c/em\u003e SNPs on OC, providing novel insights into the potential influence of these genetic variants on tumour progression. Above all, our findings challenge the conventional perception of HPSE as solely pro-tumourigenic, prompting a reassessment of its role in the tumour microenvironment.\u003c/p\u003e"},{"header":"5.\tCONCLUSION ","content":"\u003cp\u003eOvarian malignancy is the most lethal gynaecological cancer, primarily due to late diagnosis and the frequent presentation of chemoresistance. Furthermore, the occurrence of CAT, particularly VTE, complicates the disease prognosis. Over the last few years, searching for reliable prognostic biomarkers has become a priority to improve disease management and patient outcomes. In the cancer research field, HPSE has garnered significant interest due to its role in tumour invasion, angiogenesis, metastasis, and immune modulation. Given the reported dual role of this enzyme in VTE and cancer progression, this study aimed to assess the impact of \u003cem\u003eHPSE\u003c/em\u003e SNPs on OC progression and patient survival. An unexpected association between \u003cem\u003eHPSE\u003c/em\u003e SNPs and patient prognosis was revealed, challenging the conventional view of HPSE as solely pro-tumourigenic and highlighting its potential immunomodulatory effects. Notably, the findings for rs4364254 and rs11099592 SNPs suggest that the allele linked to lower \u003cem\u003eHPSE\u003c/em\u003e expression corresponds to worse clinical outcomes. While the association between the rs4364254 C allele and reduced \u003cem\u003eHPSE\u003c/em\u003e levels was confirmed in our cohort, the literature supports a similar effect for the rs11099592 C allele. This pattern suggests that lower \u003cem\u003eHPSE\u003c/em\u003e expression may contribute to a more aggressive tumour microenvironment, potentially through reduced immune surveillance or alterations in ECM dynamics. Given that OC is typically an immune-cold tumour, this raises the possibility that HPSE could enable a TME shift towards an immune-inflamed phenotype, facilitating leukocyte migration and anti-tumour responses. Alternatively, these observations may reflect an inverse relationship between \u003cem\u003eHPSE\u003c/em\u003e gene expression and its circulating protein levels, as previously reported. These findings open new perspectives on the role of HPSE in OC progression and highlight the need for further studies to elucidate the interplay between its gene and protein expression and immune infiltration. A deeper understanding of these mechanisms is crucial to determine whether HPSE could serve as a prognostic biomarker and even a potential therapeutic target in OC, addressing the urgent need to improve the patients’ clinical outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Ministério da Saúde de Portugal, Instituto Português de Oncologia do Porto (IPO Porto), Fundação para a Ciência e Tecnologia (FCT) and Portuguese League Against Cancer (NRNorte).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the study. Conceptualisation, IGM, VT and RM; Patient recruitment and ethical approval, JSB; Funding acquisition, IGM, VT and RM; Investigation, IGM; Review of medical files: VT, MR and JLP; Formal analysis: IGM, VT and RM; writing-original draft preparation, IGM; Writing-review and editing, IGM, VT, JA, JLP, MR, JSB, DP and RM; Supervision: VT, DP and RM. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the IPO Porto (no. PI61-CI-IPOP-22-2015) and Fundação para a Ciência e Tecnologia (FCT). IGM is a research fellowship holder (LPCC-NRN2025-IGdM) supported by the Portuguese League Against Cancer (NRNorte). VT was a PhD scholarship holder (no. 2020.08969.BD; https://doi.org/10.54499/2020.08969.BD) supported by FCT, co-financed by European Social Funds (FSE) and national funds of MCTES. She is currently a research fellowship holder (LPCC-NRN2025-VT) supported by the Portuguese League Against Cancer (NRNorte). The funders were not involved in the study design, data analysis and interpretation, and manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study is available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJLP has received a research Grant from GESCAT-Grupo de Estudos de Cancro e Trombose. This institution had no role in the decision to conduct the study, write and publish this manuscript. The remaining authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed written consent according to the principals of the Helsinki Declaration was obtained from each patient before their recruitment.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLiao YE, Liu J, Arnold K (2023) Heparan sulfates and heparan sulfate binding proteins in sepsis. Front Mol Biosci 10:1146685\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Wijk XM, van Kuppevelt TH (2014) Heparan sulfate in angiogenesis: a target for therapy. Angiogenesis 17(3):443\u0026ndash;462\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVlodavsky I et al (2007) Heparanase: structure, biological functions, and inhibition by heparin-derived mimetics of heparan sulfate. 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Int J Mol Sci, 25(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSideris M, Menon U, Manchanda R (2024) Screening and prevention of ovarian cancer. Med J Aust 220(5):264\u0026ndash;274\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarques IS et al (2023) Long Non-Coding RNAs: Bridging Cancer-Associated Thrombosis and Clinical Outcome of Ovarian Cancer Patients. Int J Mol Sci, 25(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKenzie ND et al (2023) Prognostic features of the tumor microenvironment in high-grade serous ovarian cancer and dietary immunomodulation. Life Sci 333:122178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstrovsky O et al (2009) Inverse correlation between HPSE gene single nucleotide polymorphisms and heparanase expression: possibility of multiple levels of heparanase regulation. 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Leukemia 21(11):2296\u0026ndash;2303\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstrovsky O et al (2023) Effect of HPSE and HPSE2 SNPs on the Risk of Developing Primary Paraskeletal Multiple Myeloma. Cells, 12(6)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYetkin U, Karabay O, Onol H (2004) Effects of oral anticoagulation with various INR levels in deep vein thrombosis cases. Curr Control Trials Cardiovasc Med 5(1):1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi AL et al (2012) Polymorphisms and a haplotype in heparanase gene associations with the progression and prognosis of gastric cancer in a northern Chinese population. PLoS ONE 7(1):e30277\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamani VC et al (2016) Chemotherapy induces expression and release of heparanase leading to changes associated with an aggressive tumor phenotype. 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J Clin Invest 127(7):2777\u0026ndash;2788\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstrovsky O, Vlodavsky I, Nagler A (2020) Mechanism of HPSE Gene SNPs Function: From Normal Processes to Inflammation, Cancerogenesis and Tumor Progression. Adv Exp Med Biol 1221:231\u0026ndash;249\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ovarian Neoplasms, Endothelium, Heparanase, Genetic Variation, Gene Expression Regulation, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8799366/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8799366/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHeparanase (HPSE) uniquely cleaves heparan sulfate, the main component of the outer layer of endothelial cell plasma membranes, promoting tumour invasion and dissemination. However, it can also enhance tumour immune surveillance and clearance. HPSE\u0026rsquo;s versatility extends to pro-thrombotic properties, such as the promotion of tissue factor release. Interestingly, elevated HPSE levels have been found in ovarian cancer (OC), which has a notably high incidence of venous thrombosis. Previously, single-nucleotide polymorphisms (SNPs) of \u003cem\u003eHPSE\u003c/em\u003e were shown to modulate mRNA and protein levels, possibly predicting disease outcomes.\u003c/p\u003e\u003ch2\u003eMethods and Results\u003c/h2\u003e \u003cp\u003eGiven the potential role of HPSE in OC, the implications of three SNPs - rs11099592, rs4364254 and rs4693608 \u0026ndash; were investigated on OC patients. In the discovery cohort, rs11099592 TT genotype and rs4364254 C allele carriers showed lower survival time than their counterparts (log-rank test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, respectively). Validation cohort analysis confirmed the worse prognosis associated with the rs11099592 T allele and rs4364254 C allele in non-serous (log-rank test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and platinum-resistant (log-rank test, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.044) OC patients, respectively. The rs4364254 C allele was associated with reduced \u003cem\u003eHPSE\u003c/em\u003e expression in peripheral blood components (PBCs; χ\u003csup\u003e2\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), suggesting a protective role for HPSE in OC patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eHPSE\u003c/em\u003e rs11099592 and rs4364254 showed prognostic value, with T and C allele carriers, respectively, displaying worse clinical outcomes. These results indicate that HPSE could enable a tumour microenvironment shift towards a less aggressive cancer behaviour, facilitating leukocyte migration and anti-tumour responses. Further research should explore the dual mechanisms of this protein to improve OC management.\u003c/p\u003e","manuscriptTitle":"Reassessing HPSE in Ovarian Cancer: Beneficial After All?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 21:41:30","doi":"10.21203/rs.3.rs-8799366/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T11:21:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T10:26:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T15:48:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T04:38:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10914733468157005646025965193097554263","date":"2026-02-19T00:57:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"44350583901848584714187335370482176606","date":"2026-02-17T07:52:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306804479653352707108920892060354173586","date":"2026-02-17T05:48:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-16T11:55:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T03:54:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T03:52:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2026-02-05T16:21:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"05663c19-1ae9-4e13-be0b-0a4d2bf97ac2","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T10:40:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 21:41:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8799366","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8799366","identity":"rs-8799366","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}