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Article

Cytoplasmic Claudin 6 Expression and Copy Number Variations as a Prognosticator of Survival and Relapse in Ovarian Cancer Patients

by
Mourad Assidi
1,*,
Sahar Hakamy
1,
Mohammad A. Jafri
1,
Fatima Al-Thubaity
2,
Jaudah Al-Maghrabi
3,
Abdulmajeed F. Alrefaei
4,
Sultan F. Kadasah
5,
Taoufik Nedjadi
6,
Safia A. Messaoudi
7,
Peter N. Pushparaj
1,
Adeel Chaudhary
1,8,
Abdelbaset Buhmeida
1 and
Muhammad Abu-Elmagd
1,*
1
Institute of Genomic Medicine Sciences (IGMS), King Abdulaziz University, Jeddah 22254, Saudi Arabia
2
Department of Surgery, Faculty of Medicine, King Abdulaziz University Hospital (KAUH), Jeddah 22254, Saudi Arabia
3
Department of Pathology and Laboratory Medicine, Ministry of the National Guard—Health Affairs, Jeddah 21423, Saudi Arabia
4
Department of Biology, Jamoum University College, Umm Al-Qura University, Makkah 21955, Saudi Arabia
5
Department of Biology, Faculty of Science, University of Bisha, Bisha 61922, Saudi Arabia
6
King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Jeddah 21324, Saudi Arabia
7
Department of Forensic Sciences, College of Forensic and Investigative Sciences, Naif Arab University for Security Sciences, Riyadh 14812, Saudi Arabia
8
Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 22254, Saudi Arabia
*
Authors to whom correspondence should be addressed.
J. Mol. Pathol. 2026, 7(3), 26; https://doi.org/10.3390/jmp7030026
Submission received: 15 April 2026 / Revised: 19 June 2026 / Accepted: 30 June 2026 / Published: 8 July 2026
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Abstract

Background: Tight junctions are major components of apical junction complexes and are crucial for the maintenance of cell polarity, healthy tissue architecture, adhesion, and permeability. These junctions include the claudin family of transmembrane proteins, which act as paracellular barriers to regulate selective permeability. Abnormal claudin expression disturbs cell adhesions and is associated with cancer through promoting cell invasion, migration, and metastasis. Claudin 6 (CLDN6) overexpression, in particular, is linked to several types of cancer with malignant phenotypes. The present study aimed to investigate the association between CLDN6 protein expression and its copy number variations (CNVs) with clinicopathological features and survival outcomes of ovarian cancer (OC) patients. Methods: A total of 114 formalin-fixed paraffin-embedded blocks from primary OC patients were used to construct tissue microarray slides. Automated immunostaining was used to assess CLDN6 protein expression levels, and next-generation knowledge discovery platforms were used to further evaluate CLDN6 CNV levels using The Cancer Genome Atlas open-source data. The relationships between CLDN6 CNVs and tumor stage, overall survival, disease-specific survival (DSS), and disease-free survival (DFS) were investigated. Results: This study demonstrated that CLDN6 had a mixed membranous-cytoplasmic expression pattern. The cytoplasmic expression of CLDN6 was significantly associated with tumor stage (p = 0.05), tumor size (p = 0.04), and recurrence (p = 0.05). In Univariate analysis, Kaplan–Meier analysis demonstrated that CLDN6 expression was significantly correlated with DFS (p = 0.01). OC patients with lower cytoplasmic CLDN6 expression levels lived longer and had lower recurrence rates. These findings were further confirmed through CLDN6 CNVs analysis, where OC with lower CLDN6 cytoplasmic expression positively correlated with longer DFS and DSS. No independent prognosticator was found when using Cox-regression multivariate analysis (p > 0.05). Conclusions: These results suggest CLDN6 as an interesting prognosticator to identify OC patients at a higher risk of recurrence in order to provide personalized management, alleviate the burden of this disease on women’s health, and improve their survival outcomes.

Graphical Abstract

1. Introduction

Epithelial ovarian cancers are broadly classified based on morphological and molecular characteristics into two categories, Type I and Type II [1]. Type I ovarian epithelial cancers represent approximately 25–30% of total OC. They are reported to be genetically stable and remain confined in the ovary with low malignant potential. Type II ovarian epithelial cancers, which originate in the fallopian tubes, are reported to be fast-growing and aggressive tumors with TP53 (tumor protein p53) and BRCA1/2 (BRCA1/2 DNA repair-associated) mutations. The most common Type II ovarian epithelial tumor is the high-grade serous subtype, which represents ~70–75% of all epithelial OCs [2,3]. In fact, OC is a malignant gynecological disease with one of the highest mortality rates worldwide, and its mortality rate is almost three times that of breast cancer [4,5]. Currently, there is a lack of effective screening tools while the number of new cases and death rates are increasing, and the treatment costs remain the highest among all cancer types [6].
Besides the poorly understood etiology of this disease, late diagnosis at advanced stages is another major reason for this high mortality rate [7]. Additionally, the mortality and incidence of OC vary across different regions of the world. In Saudi Arabia, the OC age-standardized incidence rate is 3 per 100,000 individuals [8], and the mean age of patients at diagnosis is 51 years [9]. Several risk factors were associated with the incidence of OC, including early menarche, delayed menopause, a higher number of ovulatory cycles, nulliparity, use of fertility drugs, and increased age [4,10], while multiparity, breastfeeding, and the use of oral contraceptives have been reported to reduce its incidence [4,11,12].
Metastasis is the main cause of mortality in patients with OC, which disrupts healthy tissue architecture, promoting cancer progression, invasion, migration, and micro-metastases to distant sites. While an efficient early diagnosis of OC is still unattainable, the 5-year overall survival rate for stage IV OC is 25–35% [4]. Therefore, early diagnosis and identification of more effective molecular biomarkers of OC are critical for successful diagnosis, increased prognosis, molecular stratification, management, and treatment of this disease.
OC diagnosis and monitoring have relied on key serum-based biomarkers. While cancer antigen 125 (CA125) represents the gold standard in epithelial OC, it has reduced sensitivity at early stages [13]. Other biomarkers, such as Human Epididymis Protein 4 (HE4), have shown promising diagnostic performance mainly in serous and endometroid subtypes; however, their clinical utility may be influenced by some lifestyle factors such as body mass index (BMI), smoking, and the use of hormonal contraceptives. Although the combination of CA125 and HE4 has allowed better OC risk prediction [14], they still lack enough accuracy. Other algorithmic combinations of biomarkers are also suggested, such as the Risk of Ovarian Malignancy Algorithm (ROMA), and some multivariate assays, such as the OVA1 Second Generation test and Overa, have shown some promise, but still require further validation before being ready for bedside use [15,16,17]. The advent of high-throughput technologies has resulted in a 5-year increase in life expectancy for patients with advanced-stage OC, which holds promise for improving patient outcomes through the identification of new therapeutic avenues and prevention of drug resistance [16,18]. Despite several laudable studies, the validation and implementation of effective molecular biomarkers for OC in clinical practice remain challenging. These studies have provided, however, a better understanding of various types of molecular pathophysiological processes involved in OC and have identified several potential diagnostic and prognostic biomarkers that require further validation. The extracellular matrix (ECM) serves as a critical barrier to maintain tissue integrity and prevent metastasis. Disruption of the surrounding ECM promotes the Epithelial-to-Mesenchymal Transition (EMT) and leads to proliferation, detachment, invasion, and metastatic spread of tumor cells [12,19,20]. The integrity of healthy tissue is maintained by several structural peripheral transmembrane proteins, mainly tight junctions (TJs), adherent junctions (AJs), and gap junctions (GJs), which hold cells together and prevent tumor progression and migration. Claudin genes are a family of transmembrane proteins and a key component of TJs. This family determines key properties of TJs, such as strength and paracellular activities, and thus, the polarity and paracellular permeability of epithelial cells [19]. These claudin genes are reported to be upregulated in OC and are correlated with metastasis and poor prognosis [21,22]. Overexpression of various claudin family members has been associated with the initiation, progression, and/or metastasis in numerous cancers, including OC, which leads to distant cell migration [23,24]. At the advanced stages of OC, the TJs are disrupted due to the overexpression of claudins, mainly claudin 6, which in turn activates some matrix metalloproteinases (like MMP2) and promotes these junctions’ disassembly and consequently the EMT [25]. These events promote the loss of epithelial cell polarity, cell–cell adhesion, and barrier integrity, and therefore, invasiveness and metastatic potential [26,27]. Compared to other claudins, there is accumulating evidence reporting important roles of CLDN6 in cancer as an oncofetal marker highly expressed during embryogenesis but is largely absent in normal adult tissues, making its re-expression in malignancies clinically relevant [28]. Several studies have also shown CLDN6 overexpression in ovarian and other gynecological cancers, and linked its expression with advanced stages, invasion, metastasis, and poor prognosis [24,29,30,31,32]. It is also important to highlight that aberrant or mislocalized CLDN6 expression and the pro-oncogenic downstream molecular pathways induced by its internalization process have been implicated in tight-junction disruption, epithelial–mesenchymal transition (EMT), and promotion of tumor aggressiveness and metastatic progression [13,33,34]. Furthermore, CLDN6 is considered a promising therapeutic target given its limited expression in normal tissue and its membranous localization on tumor cells. These therapeutic properties have positioned CLDN6 as an attractive target for the development of antibody-based therapies, cancer vaccines, adoptive cell therapies, and other precision-oncology approaches [22]. In this context, CLDN6–23-ADC is a new antibody–drug conjugate composed of a humanized anti-CLDN6 monoclonal antibody that is undergoing preclinical studies and is reported to selectively bind CLDN6-positive tumor cells, be rapidly internalized, inhibit tumor proliferation, and induce important tumor regression and prolonged survival in patient-derived xenograft mouse models from high-grade serous ovarian carcinomas (HGSOCs) [35]. These promising results have led to the ongoing clinical evaluation of CLDN6–23-ADC in phase I trials as a targeted therapy for CLDN6-positive cancers [35]. As a cell-surface protein and a target antigen for ongoing advanced immunotherapies, CLDN6 is suggested as a potential biomarker that can reinforce the risk prediction algorithms mentioned earlier (e.g., ROMA). Therefore, further studies are needed to gain a deeper understanding of the involvement of claudin 6 in OC pathogenesis and to identify its potential diagnostic, prognostic, and therapeutic value [35,36,37], particularly in specific populations/regions. It is worth mentioning here that the clinicopathological significance of the CLDN6 protein, expressed mainly in the cytoplasm, remains unclear and poorly investigated. In this study, we hypothesize that tumor aggressiveness, invasion, and metastatic behavior could be more strongly associated with dysregulated or mislocalized CLDN6 expression rather than its conventional membranous localization. Therefore, more efforts are also required to investigate this biomarker, particularly in ethno-demographic cohorts in the Middle Eastern population characterized by a noticeable early onset of cancer compared to Western societies [38,39]. Taken together, these biological, prognostic, and therapeutic promises of CLDN6 were the main reason behind our investigation of its prognostic value in our OC cohort from Saudi Arabia.

2. Materials and Methods

2.1. Patients

The present study used formalin-fixed paraffin-embedded (FFPE) tissue microarray (TMA) slides from OC patients diagnosed and treated at the Department of Pathology and Gynecology at King Abdulaziz University Hospital (KAUH), respectively, between January 1995 and December 2014. This retrospective study included a cohort of 114 patients with OC classified using histopathological features, mainly the tumor-node-metastasis (TNM) classification system. This cohort is a mixture of epithelial ovarian cancer histotypes (Serous carcinoma, mainly high-grade), mucinous carcinoma (MC), Clear cell carcinoma (CCC), and endometrioid carcinoma (EC). The inclusion criteria included all available histopathologically confirmed primary OC cases. Only well-preserved FFPE primary tumor tissues with ≥50% viable tumor content and complete clinicopathological data were included. It encompasses all patients who had undergone primary surgery with or without adjuvant chemotherapy and had not received neoadjuvant treatment. Borderline tumors, poorly fixed or necrotic samples, cases with incomplete data, and pre-treated or recurrent OC cases were excluded. The pathological and clinical data of the patient cohort were collected from medical records following ethical approval obtained from the KAUH Institutional Review Board (Reference No.: 146-25).

2.2. Tissue Microarray (TMA) Preparation

FFPE blocks were prepared from OC tissue leftover, according to the reported protocol by Gong et al. [40], from consented patients without compromising their diagnosis and treatment procedures. These FFPE blocks were converted to recipient TMA FFPE blocks. Sections were thereafter prepared from each tissue block and stained with Hematoxylin and Eosin (Cat # ab245880, Abcam, Cambridge, UK) to determine the regions of the tumor, from which the 0.6 mm diameter cylinders of tissue were punched and transferred into a recipient block using a fully automated instrument (TMA Master 1.14 SP3; 3DHISTECH Ltd., Budapest, Hungary) to produce five TMA paraffin blocks. Subsequently, 4 μm sections were taken from the TMA blocks using a fully automated rotary microtome (Cat # RM2255, Leica Biosystems Nussloch GmbH, Nussloch, Germany) and mounted with a xylene-based mounting media (Cat # 3801731, Leica Biosystems, Amsterdam, The Netherlands) on glass slides, as further detailed elsewhere [41].

2.3. Immunohistochemistry (IHC)

IHC was performed on OC tissue section slides using the BenchMark XT automated Ventana staining system (BenchMark XT ULTRA IHC/ISH Ventana Medical System, Roche Diagnostics, Tucson, AZ, USA). The antibody application step was performed manually after suitable optimization. Rabbit CLDN6 polyclonal antibody (Cat. # ab107059, Abcam, Cambridge, UK) was used at a dilution of 1:100 and an incubation time of 60 min at 37 °C. The slides were labeled using a barcoding system (SlideMate™ AS On-Demand Printer, Thermo Fisher Scientific—Waltham, MA, USA). After the run was complete, the slides were removed from the instrument and rinsed with a mild detergent, followed by rinsing in running tap water to blue the nuclei and remove residual buffers. Slides were then immersed in ascending concentrations of alcohol buffer (70%, 95%, and 100%), followed by clearing in xylene. Subsequently, one drop of the mounting medium was applied to the slide and covered with a glass coverslip.

2.4. Evaluation of CLDN6 Cytoplasmic Protein Expression Pattern

The samples were visualized using an upright light microscope (magnification is 40×; Nikon, model no. 6132, Nikon Corporation, Shinagawa, Tokyo, Japan), and the staining intensity of CLDN6 for each sample was scored manually by two pathologists who were blinded to the clinicopathological parameters of each patient. Although CLDN6 protein expression was both membranous and cytoplasmic, the current study focused on the cytoplasmic fraction. The tumor cells exhibiting cytoplasmic staining were classified into four distinct categories based on the observed staining intensity. The categories were defined as follows: (0) Negative, where no staining was detectable; (1+) Weak staining, where the staining was faint but still observable; (2+) Moderate staining, where the cells were positive, but the intensity remained relatively weak; and (3+) Strong staining, characterized by intense and pronounced staining of the cytoplasm.
To quantify the staining results and provide a more comprehensive assessment of cytoplasmic staining, a cytoplasmic staining index was calculated. This index was designed to consider not only the intensity of staining observed in tumor cells but also the proportion/fraction (%) of cells exhibiting each specific level of staining intensity. The formula used for this calculation was originally proposed by Lipponen and Collan [42] and is expressed as follows:
I = 0 × f0 + 1 × f1 + 2 × f2 + 3 × f3
In this formula,
  • I: represents the overall cytoplasmic staining index;
  • f0–f3: denote the fractions (%) of cells that exhibit each of the corresponding staining intensity levels (from 0 to 3+).
The value of the cytoplasmic staining index score could theoretically range between 0 and 300 and is a valuable tool for selecting suitable cut-offs with powerful prognostic value.

2.5. CLDN6 Expression in OC Using RNA Sequencing Data and Gene Expression Profiling Interactive Analysis (GEPIA)

The UCSC Xena (http://xenabrowser.net/, accessed on 15 March 2025) platform was used to evaluate the association of CLDN6 with tumor stage, disease-free survival (DFS), and disease-specific survival (DSS) based on CNV data normalized to germline variation from The Cancer Genome Atlas (TCGA) HGSOCs samples [43]. Patients with OC were classified into low, moderate, and high expression groups as described above. Gene Expression Profiling Interactive Analysis (GEPIA2; http://gepia2.cancer-pku.cn/, accessed on 20 March 2025) was used to analyze RNA sequencing expression data of tumor and healthy samples from TCGA and Genotype-Tissue Expression repositories, respectively [44]. The CLDN6 expression profile was obtained using the GEPIA2 online tool and used to identify CLDN6 protein expression levels at the main stages of OC progression. It was also used to analyze CLDN6 expression levels at the main stages of OC progression.

2.6. Statistical Analysis

Frequency tables were analyzed using the χ2 test to assess the correlation between CLDN6 protein expression and patients’ clinicopathological features. Univariate survival analysis was performed using the Kaplan–Meier method by performing a log-rank analysis of survival, including the follow-up data (months), endpoint status (recurrence and/or death), and CLDN6 expression patterns (low versus high). Additionally, Cox-regression multivariate analysis was performed to assess the possible independent prognostic impact of CLDN6 protein expression (high vs. low) as the main covariate, in relation to key clinicopathological features such as patients’ age, grade, stage, and lymph node status. All statistical analyses were performed using SPSS software (version 22; IBM Corporation, Armonk, NY, USA). p < 0.05 was used to indicate a statistically significant difference. The free software G*Power 3.1.9.7 (Heinrich Heine University, Düsseldorf, NRW, Germany) [45], was used for statistical power analysis and sample size justification.

3. Results

The results of the present study showed that 54% of the cohort of patients with OC were aged <50 years, and 46% of all patients were obese (Body Mass Index (BMI) > 26) (Figure 1).
CLDN6 showed a mixed membranous-cytoplasmic staining pattern. While CLDN6 is known to have mainly membranous expression, our results clearly showed that it was also well expressed in the cytoplasm (Figure 2). This study focused mainly on the analysis of the cytoplasmic protein expression levels of CLDN6 in OC samples, which were evaluated by IHC and classed as: no expression (0, no expression), 3% of all the OC patients’ cohort), weak expression (1+, 34%), moderate expression (2+, 41%), and strong expression patterns (3+, 22%).
The study results showed that CLDN6 cytoplasmic protein expression was significantly correlated with tumor stage (p = 0.05), tumor size (p = 0.04), and recurrence (p = 0.01). However, no significant correlations were found with age, subtype, grade, or lymphovascular invasion (all p > 0.05) (Table 1).
Using G*Power 3.1.9.7, we assessed the adequacy of our sample size (n = 114) for detecting correlations between CLDN6 expression and clinicopathological features. At a significant level of α = 0.05, our analysis showed an achieved power of 85% for detecting a moderate correlation (r = 0.4), confirming that the study is well-powered. Furthermore, the minimum detectable effect size with this power level was estimated at r ≈ 0.26, indicating our ability to detect even smaller correlations. For example, a sample size of only 88 participants would suffice to detect a moderate effect size of r = 0.30 with 85% power, further validating the robustness of our sample size (n = 114).
Furthermore, Kaplan–Meier analysis of DFS outcomes in patients with OC showed a significant difference between patients with higher cytoplasmic protein expression levels of CLDN6 and those with lower expression (p = 0.01) (Figure 3). Patients with OC with low cytoplasmic CLDN6 protein expression levels had increased DFS and exhibited a lower recurrence rate. By contrast, DSS was not significantly associated with the cytoplasmic CLDN6 protein expression levels (p = 0.1) (Figure 4). Cox regression analysis was used to assess the prognostic impact of clinical and molecular variables. None of the predictors reached statistical significance at the 0.05 level. However, positive lymph-node status (Hazard Ratio (HR) = 2.51; 95% CI: 0.54–11.60; p = 0.238), advanced tumor stage (HR = 1.48; 95% CI: 0.26–8.52; p = 0.66), and older age (HR = 2.27; 95% CI: 0.60–8.57; p = 0.22) were associated with increased hazard (HR > 1). Notably, higher CLDN6 expression demonstrated a strong (HR = 4.83; nearly five times), though non-significant, association with poorer outcome (HR = 4.83; 95% CI: 0.77–30.27; p = 0.09). Tumor grade showed no significant impact (HR = 0.56; p = 0.26). These findings suggest that CLDN6 expression and lymph-node status may have biological relevance as potential independent prognostic factors but warrant further validation in larger cohorts and using histotype-specific cohorts.
Gene Expression Profiling Interactive Analysis based on RNA sequencing (RNA-Seq) data (Log2 Transcripts Per Million, (TPM) + 1) showed that CLDN6 was overexpressed in HGSOC tumors. In fact, CLDN6 expression levels significantly increased in OC samples (n = 426) compared with healthy/normal tissue samples (n = 88) obtained from the TCGA database (Figure 5A). CLDN6 expression levels extracted from the GEPIA2 platform were markedly increased across the majority of advanced stages of the disease (II–IV) (Figure 5B).
The Kaplan–Meier analysis of CLDN6 gene CNV after removing germline variation (TCGA high-grade serous ovarian cancer (HGSOCs) demonstrated statistically significant associations of CLDN6 low CNV with both DFS (Figure 6) and DSS (Figure 7) compared with moderate and high CNV groups.

4. Discussion

Current clinical decisions based on standard clinicopathological factors, such as age, tumor grade, lymphovascular invasion, lymph node involvement, and tumor size, are inadequate to increase survival rates and reduce recurrence rates. Therefore, the development of more effective diagnostic and therapeutic strategies is urgently required for improved OC management [46]. The failure to have effective early detection tools is among the main reasons behind the higher mortality rates of OC worldwide [47]. Therefore, integrating CLDN6 testing with CA125 and HE4 within a multivariate model like the ROMA Algorithm could enhance early detection and prognostic accuracy, given CLDN6’s tumor specificity and association with recurrence. Such multi-marker panels align with the current move toward proteogenomic diagnostics in OC, which holds promises [15,48,49]. However, there is still a necessity for technical barriers to be overcome before these biomarkers can be implemented in routine clinical practice [15,16].
In the era of precision oncology, the identification of effective biomarkers is crucial for the molecular stratification of patients and improved prognosis and treatment outcomes. CLDN6 is an essential protein component of TJs that is overexpressed in both the cell membrane and cytoplasm of OC cells. It maintains cell–cell adhesion, forms a barrier to regulate paracellular permeability, and regulates cellular defense, cell differentiation, and polarity in both epithelial and endothelial cells [50]. Disruption of the normal expression of CLDN6 in OC impairs TJ structure and function, which in turn can promote cancer cell migration and metastasis. These pro-metastatic molecular functions are thought to be promoted mainly via the cytoplasmic CLDN6 proteins and their downstream molecular and biological pathways [20,51,52]. Given its importance in cell–cell adhesion and ECM stability, CLDN6 was reported to have a promising prognostic value that remains to be fully investigated [52], particularly in ethno-demographic cohorts like the Saudi Arabian population, characterized by a noticeable early onset of the disease. Therefore, the present study investigated the levels of CLDN6 protein expression in our unique OC patient cohort, with a special focus on the cytoplasmic expression of this biomarker when internalized/sequestrated inside the cell [34].
The results showed that cytoplasmic expression of CLDN6 in OC was significantly correlated with some clinicopathological features of patients, such as tumor stage, tumor size, and disease recurrence, suggesting that CLDN6 overexpression promotes cell proliferation and tumor growth. While CLDN6 is a tight junction protein typically associated with membranous expression patterns, several studies [21,52] have reported aberrant localization in ovarian and other epithelial cancers manifested by mixed membranous-cytoplasmic staining patterns, which correlate with epithelial–mesenchymal transition (EMT) activation and poor prognosis. Mechanistically, this “mislocalization” or internalization is now confirmed (labeling and/or colocalization studies) as a known molecular process through which claudins are internalized or overexpressed in the cytoplasm to exert their functions and trigger oncogenic signaling cascades [34]. Cytoplasmic CLDN6 has been shown to interact with intracellular mediators such as Zonula Occludens-1 (ZO-1) and atypical protein kinase C, influencing pathways that regulate cell motility, EMT, resistance to apoptosis, and metastasis [53,54,55].
Therefore, we focused on cytoplasmic expression as a functionally relevant alteration in OC biology that seems to be associated with more invasive tumors. Although high-quality reports explicitly showing cytoplasmic CLDN6 expression in OC are limited in recent years, broader evidence supports the notion of internalization and altered claudin trafficking in malignancies [34,55]. Moreover, CLDN6 has been implicated in EMT-related invasiveness and matrix remodeling in multiple cancers [56,57], supporting the biological plausibility that an aberrant expression pattern is a mixed membranous-cytoplasmic pathological expression pattern that may serve as a functional oncogenic alteration rather than a mere artifact.
The mixed membranous-cytoplasmic staining pattern of CLDN6 overexpression in advanced stages of the disease is consistent with previous studies reporting a critical role for CLDN6 in OC metastasis by stimulating the process of epithelial-to-mesenchymal transition (EMT) and regulating the tumor microenvironment [20,21,24]. Alteration of CLDN6 expression leads to loss of TJ integrity through various pathways and promotes the development and progression of TJ tumors through various mechanisms; therefore, it may serve multiple roles in tumor initiation and development [20,33]. Furthermore, it has been reported that overexpression of CLDN6 in OC and its cytoplasmic internalization and/or sequestration impair both the structure and function of TJs and promote MMP2 action to degrade the ECM, thereby enhancing tumor progression, reducing the infiltration of immune cells into the tumor microenvironment, and promoting metastasis [21,33,58,59].
Moreover, the present results showed that neither tumor grade nor lymph node status significantly correlated with CLDN6 expression. These observations were consistent with those reported by Wang et al. [58]. Similarly, CLDN6 expression level was not associated with age, but 66% of the patients aged <50 years showed moderate to strong CLDN6 expression compared with those aged >50 years. This distribution was also in line with the early onset phenomenon observed but still poorly understood in Saudi Arabia and the Arabian Peninsula cancer patients compared with those in Western countries [39]. While the risk of OC generally increases with age and is higher in older women, the results of the present study showed an early onset of OC in the Saudi population, where 54% of the cohort was <50 years of age. These results were confirmed by the Saudi Cancer Registry, where most women diagnosed with OC were aged 45–59 years. As previously reported by Cancer Research UK, 53% of female patients with OC were aged ≥65 years [60]. The results of the present study demonstrated that OC was typically diagnosed 10 years earlier in female patients in Saudi Arabia when compared with the global average, which makes the investigation of these biomarkers in these particular ethno-demographic cohorts more interesting. The reasons for this difference are currently unknown.
Additionally, 46% of the present cohort were obese and 23% overweight (BMI: 23–26), which agreed with previous studies that reported a correlation between obesity and an increased risk of OC [61,62]. Moreover, as shown in Table 1, 58% of patients with OC were diagnosed at late or advanced stages (III and IV) due to the asymptomatic aspect of this disease at early stages, which leads to an increase in mortality worldwide [12,63,64]. Therefore, further studies are needed in Saudi Arabia and the Arabian Peninsula, using larger subtype-specific cohorts to gain deeper insights into the possible molecular mechanisms involved in the early onset of OC, implement early screening strategies, and spread awareness about OC risk factors.
Kaplan–Meier survival analysis in the present study cohort showed a significant correlation between cytoplasmic CLDN6 protein expression levels and DFS in OC (Figure 3). Patients with OC with low cytoplasmic CLDN6 protein expression levels showed increased survival and a lower recurrence rate. Thus, high CLDN6 protein expression levels in OC were associated with a poor prognosis and decreased survival rate. However, there was no significant correlation between cytoplasmic CLDN6 expression levels and DSS (Figure 4). In addition, in silico analysis using next-generation knowledge discovery platforms showed that the CLDN6 transcripts were overexpressed in OC at the advanced stages of the disease (Figure 5A, B). In fact, RNA-seq is an extremely powerful high-throughput transcriptomic technique that is primarily used for identifying differentially expressed genes, uncovering novel transcripts, and providing broad insight into transcriptional regulation. However, mRNA levels do not always correlate with protein expression due to molecule instability, post-transcriptional modifications, mRNA degradation, translational efficiency, and protein half-life [65]. Our study used open-source RNA-seq data to support our CLDN6 candidate selection, then focused on IHC as a targeted, protein-level detection technique that allows for the localization, quantification, and contextual analysis of protein expression directly within tissue architecture. IHC is a gold-standard technique with established clinical relevance since proteins are the true effectors of biological pathways, and changes at the protein level are more reflective of pathophysiological processes. Proteins are also more chemically and structurally stable than RNA, especially in archived FFPE specimens, making IHC a more robust and clinically translatable method that we used to confirm true protein-level CLDN6 changes that are more reliable for diagnosis, prognosis, and therapeutic decision-making.
Our results showed that HGSOC patients with high CLDN6 CNVs had a poorer prognosis and significantly decreased survival outcomes (both DFS (Figure 6) and DSS (Figure 7)) compared with those with lower CNVs. Given the reported correlation between higher CNVs and CLDN6 protein or mRNA overexpression [66], these TCGA data are consistent with the results of the present study and highlight the value of CLDN6 as a potential OC cancer biomarker. Notably, the comparison between our cohort and the TCGA dataset highlights several important distinctions. While the TCGA cohort (n = 426) offers greater statistical power due to its larger sample size, our study provides novel and region-specific insights derived from an ethno-demographic population from Saudi Arabian population and includes multiple epithelial OC subtypes. Such population differences are particularly relevant, as the TCGA represents an international cohort that may exhibit ethnic and environmental heterogeneity influencing molecular and clinical outcomes, while the cross-histotype heterogeneity of our cohort may hide some findings. Moreover, the methodological approaches differ: our study employed immunohistochemistry (IHC) to assess cytoplasmic protein expression patterns at the tissue level, whereas the TCGA dataset utilized RNA sequencing and CNV analyses. These approaches, though distinct, are complementary, together offering a more comprehensive understanding of the gene-protein expression relationship across diverse populations. It should be emphasized herein that the TCGA OC dataset used for validation is restricted to high-grade serous carcinoma (HGSOCs), whereas our institutional cohort included multiple epithelial subtypes (serous, mucinous, endometrioid, and others). This histological divergence limits direct comparability but also highlights the broader relevance of this promising biomarker. While CLDN6 expression patterns may differ across subtypes and cellular expression/localization (cytoplasmic versus membranous) due to variations in molecular pathways and tumor microenvironments, the consistent association between CLDN6 overexpression and poor prognosis in both cohorts supports its overarching role in OC progression. Nevertheless, because TCGA validation primarily reflects HGSC biology, these results should be interpreted with caution. Future multicenter studies that incorporate histotype-stratified genomic and proteomic analyses are warranted to determine whether CLDN6 represents a universal prognostic marker or one influenced by specific histological contexts. Also, larger and histotype-specific cohorts used in advanced functional studies are required to further explore the potential role of the cytoplasmic CLDN6 in OC invasion and metastasis.
Taken together, these findings suggest that cytoplasmic expression of CLDN6 could potentially be used as a prognostic indicator for OC progression and recurrence in Saudi OC patients. The prognostic value shown in univariate analysis was consistent with previous reports where CLDN6 was overexpressed in OC tissue but downregulated in the normal ovarian epithelium. Overexpression of CLDN6 in OC has been suggested to inhibit apoptosis and promote cell proliferation and invasiveness [58]. Our findings are consistent with those reported by Yuceer et al. [32]. Our study extends these observations by including multiple histological subtypes and integrating both cytoplasmic protein expression patterns and CNV analyses. Together, these complementary studies confirm the robustness of CLDN6 as a prognostic biomarker across diverse populations, ethnicities, regions, and analytical methodologies. Moreover, the genomic confirmation of CLDN6 amplification in the TCGA cohort, although it is restricted to the high-grade serous carcinoma (HGSC) subtype, provides additional biological support for the overexpression observed immunohistochemically, highlighting its potential role in disease progression and as a future cross-population target for global precision therapy [32].
In addition, CLDN6 overexpression has been considered an independent prognosticator in ovarian, gastric, and endometrial cancers [30,58,67] which is not the case in our study. The findings of the present study, performed across a mixed cohort of various epithelial histological subtypes, are a strong clinical asset that further confirms previous reports suggesting that cytoplasmic CLDN6 is a crucial protein that could play important roles in the downstream molecular pathways related to EMT, immune cell infiltration, cell invasion, and signal transduction, contributing to the progression and metastatic spread of OC [68]. Of note, however, the exact mechanisms behind these “aberrant/altered/uncommon” cytoplasmic expressions and distributions of CLDN6 are not yet established and warrant future investigation. They could be due to dysregulated protein trafficking, excessive protein expression, molecular sequestration, and/or impaired membrane localization.
Furthermore, quantification of CLDN6 CNV or protein expression (either membranous and/or cytoplasmic) could serve as a companion diagnostic tool to guide patient enrollment in precision oncology trials [69], similar to Human Epidermal Growth Factor Receptor 2 (HER2) amplification testing in breast cancer and the Programmed Death-Ligand 1 (PD-L1) scoring in immunotherapy eligibility [70].
The present study showed that CLDN6 cytoplasmic overexpression patterns and CNV amplification are significantly associated with poor survival outcomes and support the integration of CLDN6 testing into future clinical trials towards advancing OC targeted therapies. These developments underscore the dual clinical utility of CLDN6 as both a prognostic biomarker that stratifies patients by recurrence risk and a therapeutic target for personalized interventions. Collectively, this positions CLDN6 and its mixed membranous-cytoplasmic staining pattern within the expanding landscape of tumor-specific surface antigens suitable for next-generation targeted and immune-based therapies in OC.
To overcome these study limitations, further large-scale and multi-institutional studies are needed to support these efforts, validate some of the results of the present study, explore the cytoplasmic expression patterns, and confirm the clinical potential of CLDN6 as an effective prognostic/predictive biomarker for each OC subtype as well as other solid tumors. We acknowledge that additional mechanistic studies employing subcellular fractionation, Western blotting, and confocal co-localization analyses are required to definitively characterize the intracellular distribution and trafficking of CLDN6 in ovarian cancer, which may help in tailoring more effective CLDN6-based theranostics.

5. Conclusions

This study, carried out in an ethno-demographic cohort from Saudi Arabia involving various OC epithelial histological subtypes, showed that CLDN6 had a mixed membranous-cytoplasmic staining pattern. The CLDN6 cytoplasmic protein expression had a potentially promising prognostic value in the Univariate Kaplan-Meier analysis, but no independent prognosticator was found when using the Cox-regression multivariate analysis. Subsequent in silico analysis using TCGA databases demonstrated that high CLDN6 CNVs were also significantly associated with advanced OC stages and poor survival outcomes. However, further studies using larger cohorts are needed to understand the biological and molecular complexities of CLDN6 signaling pathways in OC, investigate further its mixed membranous-cytoplasmic expression pattern, and provide effective translational and bedside theranostic recommendations.

Author Contributions

M.A., A.B. and M.A.-E.—Conceptualization. S.H., M.A. and A.B.—Methodology. M.A., S.H., A.B. and P.N.P.—Validation. M.A., S.H., F.A.-T., J.A.-M., A.F.A., S.F.K., T.N. and A.B.—Data collection and analysis. A.B., F.A.-T., J.A.-M., S.F.K. and A.C.—Data curation. M.A., A.B., P.N.P., S.A.M. and M.A.J.—Writing—original draft preparation. M.A.-E., A.F.A., S.F.K. and T.N.—Writing—review and editing. M.A., M.A.-E., A.F.A. and S.A.M.—Visualization. M.A., A.C. and M.A.-E.—Supervision. M.A. and A.C.—Project administration. M.A., A.F.A., S.F.K. and M.A.-E.—Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

The authors would like to thank the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, for their technical and financial support to perform this project (Grant number GPIP: 1767-117-2024).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the KAUH Institutional Review Board (IRB) (Reference No.: 146-25, approval date 2025-04-10).

Informed Consent Statement

All participants have provided and signed informed consent upon admission to KAU Hospital.

Data Availability Statement

All data used in the study, including the analysis, are available in the manuscript. Any other data not included in the manuscript is available on reasonable request from the corresponding authors.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be considered a potential conflict of interest.

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Figure 1. Distribution of the OC patients’ cohort according to age and BMI. (A) Age distribution; (B) BMI distribution. BMI: Body Mass Index.
Figure 1. Distribution of the OC patients’ cohort according to age and BMI. (A) Age distribution; (B) BMI distribution. BMI: Body Mass Index.
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Figure 2. Protein expression of CLDN6 using immunohistochemical staining. (A) No expression (0), (B) weak cytoplasmic expression (1+), (C) moderate cytoplasmic expression (2+), (D) strong cytoplasmic expression (3+). Magnification is 20× in all panels. CLDN6: Claudin 6.
Figure 2. Protein expression of CLDN6 using immunohistochemical staining. (A) No expression (0), (B) weak cytoplasmic expression (1+), (C) moderate cytoplasmic expression (2+), (D) strong cytoplasmic expression (3+). Magnification is 20× in all panels. CLDN6: Claudin 6.
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Figure 3. Univariate Kaplan–Meier analysis of the cytoplasmic CLDN6 protein expression pattern (low (0, 1+) vs. high expression (2+, 3+)) in OC as a determinant of disease-free survival (DFS) (p = 0.01, log-rank). CLDN6: Claudin 6.
Figure 3. Univariate Kaplan–Meier analysis of the cytoplasmic CLDN6 protein expression pattern (low (0, 1+) vs. high expression (2+, 3+)) in OC as a determinant of disease-free survival (DFS) (p = 0.01, log-rank). CLDN6: Claudin 6.
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Figure 4. Univariate Kaplan–Meier analysis of cytoplasmic CLDN6 protein expression pattern (low: 0, 1+ vs. high expression: 2+, 3+) as a determinant of disease-specific survival (DSS) (p = 0.1, log-rank). CLDN6: Claudin 6.
Figure 4. Univariate Kaplan–Meier analysis of cytoplasmic CLDN6 protein expression pattern (low: 0, 1+ vs. high expression: 2+, 3+) as a determinant of disease-specific survival (DSS) (p = 0.1, log-rank). CLDN6: Claudin 6.
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Figure 5. Analysis of CLDN6 expression in OC cancer using the TCGA database and the gene expression profiling interactive analysis (GEPIA) online platform. (A) CLDN6 expression in high-grade serous ovarian cancer (HGSOCs) (n = 426) and normal (n = 88) samples in the TCGA database. The box represents the interquartile range (IQR), the horizontal line within the box indicates the median, the whiskers represent the data range, and the black dots represent individual samples. (B) Higher CLDN6 expression levels at advanced OC progression stages (II, III, and IV). The violin plot illustrates the distribution of expression values; the central black box represents the IQR, the white dots indicate the median, and the vertical line represents the data range. Pr (>F) < 0.05 indicates statistically significant differences. TPM: Transcripts Per Kilobase Million. *: significant difference.
Figure 5. Analysis of CLDN6 expression in OC cancer using the TCGA database and the gene expression profiling interactive analysis (GEPIA) online platform. (A) CLDN6 expression in high-grade serous ovarian cancer (HGSOCs) (n = 426) and normal (n = 88) samples in the TCGA database. The box represents the interquartile range (IQR), the horizontal line within the box indicates the median, the whiskers represent the data range, and the black dots represent individual samples. (B) Higher CLDN6 expression levels at advanced OC progression stages (II, III, and IV). The violin plot illustrates the distribution of expression values; the central black box represents the IQR, the white dots indicate the median, and the vertical line represents the data range. Pr (>F) < 0.05 indicates statistically significant differences. TPM: Transcripts Per Kilobase Million. *: significant difference.
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Figure 6. Kaplan–Meier analysis based on CLDN6 CNV status in TCGA OC cohort as a determinant of disease-free survival (DFS) (p = 0.01131, log-rank). CNV data were normalized to germline variation from TCGA HGSOC samples. HGSOCs patients were classified into three groups: high (red), moderate (white), and low (blue) CNV. Statistical significance was set at p < 0.05.
Figure 6. Kaplan–Meier analysis based on CLDN6 CNV status in TCGA OC cohort as a determinant of disease-free survival (DFS) (p = 0.01131, log-rank). CNV data were normalized to germline variation from TCGA HGSOC samples. HGSOCs patients were classified into three groups: high (red), moderate (white), and low (blue) CNV. Statistical significance was set at p < 0.05.
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Figure 7. Kaplan–Meier analysis based on CLDN6 CNV status in the TCGA OC cohort as a determinant of disease-specific survival (DSS) (p = 0.04268, log-rank). CNV data were normalized to germline variation from TCGA HGSOC samples. HGSOC patients were classified into three groups: high (red), moderate (white), and low (blue) CNV. Statistical significance was set at p < 0.05.
Figure 7. Kaplan–Meier analysis based on CLDN6 CNV status in the TCGA OC cohort as a determinant of disease-specific survival (DSS) (p = 0.04268, log-rank). CNV data were normalized to germline variation from TCGA HGSOC samples. HGSOC patients were classified into three groups: high (red), moderate (white), and low (blue) CNV. Statistical significance was set at p < 0.05.
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Table 1. Correlation between cytoplasmic CLDN6 protein expression patterns and clinicopathological features of the OC (α = 0.05) and confidence intervals generated after Bonferroni Correction at a confidence level of 99.44%.
Table 1. Correlation between cytoplasmic CLDN6 protein expression patterns and clinicopathological features of the OC (α = 0.05) and confidence intervals generated after Bonferroni Correction at a confidence level of 99.44%.
Patient FeaturesNumber of Cases (%)Cytoplasmic CLDN6 * Protein Expressionp-ValueConfidence Intervals (CIs)
Negative/Weak (%)Moderate/Strong (%)
Age (yr)
<5062 (54%)21 (34%)41 (66%)0.61[44.3–64.0]
≥5052 (46%)20 (38%)32 (62%)[36.0–55.7]
Subtype
Serous53 (46%)19 (36%)34 (64%)0.96[36.3–56.8]
Mucinous27 (24%)9 (33%)18 (67%)[15.4–33.5]
Other
histotypes **		30 (26%)10 (33%)20 (67%)[17.4–36.5]
Missing4 (4%)
Tumor Size
1–5 cm25 (22%)7 (28%)18 (72%)0.04[13.7–31.5]
6–10 cm32 (28%)17 (53%)15 (47%)[19.1–38.2]
>10 cm54 (47%)15 (28%)39 (72%)[37.1–57.7]
Missing3 (3%)
Lymphovascular invasion
Negative52 (46%)20 (38%)32 (62%)0.50[24.0–43.6]
Positive38 (33%)12 (32%)26 (68%)[35.5–55.9]
Missing24 (21%)
BMI ***
<237 (6%)3 (43%)4 (57%)0.92[1.7–13.1]
23–2626 (23%)9 (35%)17 (65%)[14.3–32.8]
>2652 (46%)19 (36%)33 (64%)[35.5–55.9]
Missing29 (25%)
Age of menarche
<1319 (17%)4 (21%)15 (79%)0.09[9.1–26.3]
≥1364 (56%)27 (42%)37 (58%)[45.8–66.0]
Missing31 (27%)
Tumor stage
Stage I27 (24%)15 (56%)12 (44%)0.05[15.2–33.5]
Stage II10 (9%)1 (10%)9 (90%)[3.3–17.2]
Stage III50 (44%)16 (32%)34 (68%)[33.8–54.4]
Stage IV16 (14%)6 (38%)10 (62%)[7.1–23.3]
Missing11 (9%)
Endpoint status
Dead37 (32%)15 (40%)22 (60%)0.40[23.3–42.7]
Alive59 (52%)19 (32%)40 (68%)[41.7–61.9]
Missing18 (16%)
Recurrence status
None50 (44%)23 (46%)27 (54%)0.01[33.8–54.4]
Yes37 (32%)7 (19%)30 (81%)[23.3–42.7]
Missing27 (24%)
* CLDN6: Claudin 6; ** Clear cell carcinoma (CCC) and endometrioid carcinoma (EC); *** BMI: Body Mass Index.
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MDPI and ACS Style

Assidi, M.; Hakamy, S.; Jafri, M.A.; Al-Thubaity, F.; Al-Maghrabi, J.; Alrefaei, A.F.; Kadasah, S.F.; Nedjadi, T.; Messaoudi, S.A.; Pushparaj, P.N.; et al. Cytoplasmic Claudin 6 Expression and Copy Number Variations as a Prognosticator of Survival and Relapse in Ovarian Cancer Patients. J. Mol. Pathol. 2026, 7, 26. https://doi.org/10.3390/jmp7030026

AMA Style

Assidi M, Hakamy S, Jafri MA, Al-Thubaity F, Al-Maghrabi J, Alrefaei AF, Kadasah SF, Nedjadi T, Messaoudi SA, Pushparaj PN, et al. Cytoplasmic Claudin 6 Expression and Copy Number Variations as a Prognosticator of Survival and Relapse in Ovarian Cancer Patients. Journal of Molecular Pathology. 2026; 7(3):26. https://doi.org/10.3390/jmp7030026

Chicago/Turabian Style

Assidi, Mourad, Sahar Hakamy, Mohammad A. Jafri, Fatima Al-Thubaity, Jaudah Al-Maghrabi, Abdulmajeed F. Alrefaei, Sultan F. Kadasah, Taoufik Nedjadi, Safia A. Messaoudi, Peter N. Pushparaj, and et al. 2026. "Cytoplasmic Claudin 6 Expression and Copy Number Variations as a Prognosticator of Survival and Relapse in Ovarian Cancer Patients" Journal of Molecular Pathology 7, no. 3: 26. https://doi.org/10.3390/jmp7030026

APA Style

Assidi, M., Hakamy, S., Jafri, M. A., Al-Thubaity, F., Al-Maghrabi, J., Alrefaei, A. F., Kadasah, S. F., Nedjadi, T., Messaoudi, S. A., Pushparaj, P. N., Chaudhary, A., Buhmeida, A., & Abu-Elmagd, M. (2026). Cytoplasmic Claudin 6 Expression and Copy Number Variations as a Prognosticator of Survival and Relapse in Ovarian Cancer Patients. Journal of Molecular Pathology, 7(3), 26. https://doi.org/10.3390/jmp7030026

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