Abstract
Background
Inflammation is one of the main events in ovarian cancer pathogenesis. Ovarian cancer secreting interleukin-6 (IL-6) as a pro-inflammatory cytokine can have altered genetic profiles that contribute to the progression of the disease. The lack of integrative studies combining quantitative IL-6 measurement with genomic profiling in ovarian cancer has not been further assessed. Therefore, this study aimed to explore genomic profile of ovarian cancer using the most current sequencing method to identify fundamental alterations in the pathobiology. Genomic profiles further may provide candidate biomarkers for future validation.
Methods
A total of 45 subjects were recruited and received surgery for indications of an ovarian tumor. Among these subjects, 18 were diagnosed as malignant disease, 19 were considered benign and 8 as borderline ovarian cancer. IL-6 level was measured from both serum and intratumoral with ELISA. Ovarian cancer with a high secretion level of IL-6 received deep whole-genome sequencing performed with ONT.
Results
The results showed that the secretion level of IL-6 was consistent in serum and intratumoral fluid. In addition, patients with malignancy showed significant secretion of IL-6 compared to benign disease (p = 0.027), according to systemic inflammation markers (NLR, PLR, and SII). Based on sequencing, IL-6-secreting ovarian cancer showed alteration of genes for MTRF1, IL-10, IL-6, APC, PAX8, PTGS2, and PACERR.
Conclusion
Ovarian malignancy has a significant secretion of pro-inflammatory cytokines. Additionally, the inflammatory condition contributes to the disruption of genetic landscapes in the ovarian cancer pathogenesis. As an exploratory cross-sectional analysis, these findings identify potential associations and candidate genomic features involved in ovarian cancer pathogenesis, diagnosis and treatment strategies.
Keywords: interleukin-6, ovarian cancer, genome sequencing
Introduction
Ovarian cancer (OC) is one of the leading causes of global mortality rates, with 294,422 cases per year. The majority of women are diagnosed with ovarian cancer each year, placing the disease as the seventh most common malignancy overall.1,2 According to Globocan data from 2022, approximately 15,000 Indonesian women were diagnosed with ovarian cancer.1 The disease also has a high mortality rate among gynecological cancer, ranking third after cervical and uterine.3 Although survival rates have improved in developed countries over the past several decades, there is still a significant health issue in developing nations like Indonesia due to the high prevalence and associated mortality rates. Ovarian cancer remains one of the most lethal gynecological malignancies, with a 5-year survival rate of less than 50% due to late diagnosis and chemoresistance.4 This phenomenon is related to the stage at diagnosis, since most patients are in the late stage. In the earlier stage, ovarian cancer remains asymptomatic or without specific symptoms, leading to diagnosis difficulty. There are also very limited reliable markers for screening in early conditions. Therefore, understanding of the fundamental pathogenesis of the disease can be further applied to develop accurate and feasible screening strategies.
The progression of ovarian cancer is related to the tumor microenvironment (TME) and cytokines like interleukin-6 (IL-6), which are becoming important for growth, angiogenesis, and immune evasion. IL-6 is multifunctional cytokine that help cancer cells survive and spread through the Janus kinases JAK/STAT3 pathway. Additionally, a significant downstream pathway, namely RAS/RAF/MEK/MAPK, is upregulated to induce survival ability of tumor cells. Vascular endothelial growth factor (VEGF) also contributes by promoting neovascularization and invasiveness. Generally, IL-6 is produced in response to local proinflammatory cytokines such as TNF-α, a principal cytokine consistently found in several malignant ovarian carcinomas. It transmits signals to an extensive network of cytokines, chemokines, angiogenic factors, and transcription factors that facilitate tumor proliferation and dissemination through both autocrine and paracrine mechanisms.5 In addition to breast cancer and multiple myeloma, ovarian cancer has been linked to the overexpression and dysregulation of IL-6, as shown by high serum levels.6 Therefore, high IL-6 may not only effected the tumor progression but serve as poor prognosis predictor when IL-6 are found not only in serum but in different form as ascitic fluids, for instance.7
Multiple clinical studies have shown a link between IL-6 levels and patient’s prognosis across diverse cancer types. Serum IL-6 has been proposed as a non-invasive biomarker for ovarian cancer, with intratumoral IL-6 levels providing deeper insights into tumor biology and patient outcomes. In previous studies, Clemens Tempfer found a significant correlation between high IL-6 preoperative serum with a poorer overall and disease-free survival.7 Furthermore, Moradi et al found a significant increase in the amount of IL-6 from patient’s ascites compared to the serum.8 Although previous studies have reported significantly elevated serum IL-6 levels in patients, the intratumoral expression remains insufficiently investigated. The molecular mechanisms driving IL-6 overexpression and its interaction with tumor-associated inflammatory pathways in ovarian cancer also remain incompletely understood. In particular, there is a lack of integrative studies that combine quantitative IL-6 measurement with genomic profiling to identify key regulatory genes and signaling networks associated with inflammatory tumor phenotypes. Therefore, this study provides a comprehensive comparison of serum and intratumoral IL-6 levels in ovarian cancer patients by analyze existing data, investigate its underlying molecular regulation and exploring the clinical implications.
In this study, 45 subjects who received ovarian tumor surgery were included to evaluate IL-6 levels in both serum and intra-tumoral secretion. Subsequently, IL-6-secreting tumor cells are further examined for genomic profile and to obtain inflammatory-related genes. The bioinformatics analysis shows important regulatory genes controlling the carcinogenesis in highly inflammatory states. These results serve as a fundamental tool to understand ovarian malignancy regulation through inflammatory mechanisms, as well as to design better diagnostic and therapeutic methods.
Materials and Methods
The cross-sectional study was conducted in the Clinic of Obstetrics, Gynecology, and Gynecological Oncology of the Sardjito Hospital. Data were obtained from 45 ovarian tumor patients who received primary surgery between 12 November 2024 and 15 January 2025. Informed consent was taken from all participants.
Inclusion criteria were female patients with a confirmed clinical diagnosis of ovarian cancer through histopathological examination. Patients who had not received prior neoadjuvant chemotherapy before surgery and provided written informed consent were included in this study. Meanwhile, patients were excluded when diagnosed with endometrial malignancy, cervical malignancy, or other gynecological malignancies. Additionally, patients who had undergone neoadjuvant procedures before surgery, or those with solid ovarian cancer lacking intratumor cyst fluid, were excluded.
Blood sample was collected pre-operatively in a 5 mL sample for the serological examination of IL-6. Intratumoral fluid was obtained intra-operatively in 10 mL universal container tubes. All biofluids were initially stored in a fridge at 4–7°C for approximately 2 hours. Before freezing, blood samples were centrifuged at 20°C and 2200 rpm for 15 min to obtain serum samples. Intratumoral fluid was not centrifuged, and all biofluids were stored at −80°C.
The measurement of IL-6 levels was conducted using the enzyme-linked immunosorbent assay (ELISA) method at the Biomolecular Laboratory of the Faculty of Medicine, Universitas Gadjah Mada – Dr. Sardjito General Hospital. In addition, Next Generation Sequencing (NGS) to examine mutations in the IL-6 encoding gene was performed using the Oxford Nanopore Technology (ONT) method, and a total of 6 samples consists of IL-6–secreting tumor tissues consist of malignant cases and benign cases which qualified from the Qubit Quantification received DNA extraction using the Quick-DNA Preparation protocol prior to NGS analysis used the HiFi Long Reads method (Q30+). The use of the HiFi Long Reads (Q30+) method was performed using a SMRT Cell nanofluidic chip, where circularized DNA fragments were immobilized within zero-mode waveguides (ZMWs). A DNA polymerase repeatedly read each circularized molecule, generating multiple copies while incorporating fluorescently labeled nucleotides that emitted base-specific signals detected in real time. The resulting redundant reads were then reconciled through circular consensus sequencing (CCS) to produce a single high-accuracy sequence per molecule. The HiFi Long Reads (Q30+) expected to identify novel genes or other molecular entities as a significant role in the pathobiology of ovarian cancer at the Integrated Genome Factory, Faculty of Biology, Universitas Gadjah Mada, and Yayasan Satriabudi Dharma Setia.
Statistical analysis of this study was performed with SPSS software. Mean differences between groups were analyzed using the independent t-test. Continuous variables were expressed as median and interquartile range (IQR). A p-value <0.05 was considered statistically significant. MANOVA was considered to provide the multivariate significancy of NLR, PLR, SII as markers leading to malignancy. To identify genes potentially relevant to ovarian cancer biology, candidate variants identified from sequencing were reviewed against existing literature for their reported associations with tumor progression and patient outcomes.
Results
A total of 45 patients with suspected ovarian tumor were included in the combined database. Patients demographic and clinical characteristics were given in Table 1. In this study, patients with benign ovarian tumor (N = 19), Borderline ovarian tumor (N = 8), and Ovarian cancer patients (N = 18) were assessed. The age at diagnosis were below 40 years old (N = 5) and above 40 years old (N = 13). The majority of ovarian cancer patients had advanced FIGO stage disease at diagnosis. Among the 18 patients with stage information available, 8 patients (44.4%) had stage I disease, 10 (55.56%) showed stage II–IV disease, and the remaining characteristics were presented in Table 1.
Table 1.
Clinical Parameter Differences Between Malignant and Benign Ovarian Tumors
| Parameters | N (%) | Mean ± SD |
|---|---|---|
| Age at diagnosis | ||
| Benign ovarian tumor | 19 (42.22%) | 46.53 ± 13.97 |
| Borderline ovarian tumor | 8 (17.78%) | 39.13 ± 15.59 |
| Ovarian cancer | ||
| Early onset (≤40 y.o.) | 5 (11.11%) | 36.8 ± 2.49 |
| Late onset (>40 y.o.) | 13 (28.89%) | 55.77 ± 7.74 |
| Ovarian cancer | ||
| Stage | ||
| Early stage (stage I) | 8 (44.44%) | |
| Advanced stage (II–IV) | 10 (55.56%) | |
| Grade | ||
| Low grade (I–II) | 7 (38.89%) | |
| High grade (III) | 11 (61.11) | |
| Histotype | ||
| High-grade serous carcinoma | 6 (33.33%) | |
| Endometrioid carcinoma | 4 (22.22%) | |
| Low-grade serous carcinoma | 3 (16,67%) | |
| Clear cell carcinoma | 2 (11.11%) | |
| Other types | 3 (16,67%) | |
| Lymphovascular invasion | ||
| Yes | 8 (44.44%) | |
| No | 10 (55.56%) | |
| Nodal metastasis | ||
| Yes | 7 (38.89%) | |
| No | 11 (61.11)) | |
| Tumor greatest dimension | ||
| <10 cm | 4 (22.22%) | 7.58 ± 2.47 |
| >10 cm | 14 (77.78%) | 17.04 ± 10.26 |
| Resectability | ||
| Optimal | 15 (83.33%) | |
| Suboptimal | 3 (16.67%) | |
| CA-125 | 1948.2 ± 3766.57 |
Inflammatory-related systemic markers were obtained in ovarian cancer and non-malignant ovarian tumor patients for further assessment. This was based on the correlation with high IL-6 and the poorer outcome of ovarian cancer patients in Table 2. According to the comparison of systemic inflammation markers, Neutrophil-to-lymphocyte ratio (NLR) was elevated in the malignant group, with a significant difference between groups (p = 0.003). Similarly, Platelet-to-lymphocyte ratio (PLR) (p = 0.002) and Systemic immune-inflammation index (SII) (p=0.003) mean were related to the significantly marked increase in malignant patients. Patients with malignancy showed significant secretion of IL-6 in serum compared to benign tumor (p = 0.027) and the IL-6 intratumoral showed insignificant profile compared to benign tumor (p = 0.25). Although the mean of IL-6 was higher in intratumorally fluid of malignant ovarian tumor (217.88 ± 181.87) compared with non-malignant (189.58 ± 93.05) in Figure 1, the statistical analysis did not differ. The multivariate analysis all four test yielded the same p-value of 0.0582. Since p > 0.05, the overall multivariate effect is statistically insignificant, meaning there is insufficient evidence to conclude that the group means differ across the combined dependent variables. However, the p-value is borderline (p value = 0.058), suggesting a trend toward significance that may leading to cautious interpretation, particularly with a larger sample size, the effect could potentially reach significance. The consistency across all four test statistics also strengthens the reliability of this result in Table 3. The CVA/LDA plot visually verify the borderline MANOVA result (p = 0.058). The combined inflammatory markers provide some discriminatory signal between malignant and non-malignant groups, but not enough to cleanly separate them in Figure 2.
Table 2.
Inflammation Marker Differences Between Malignant and Non-Malignant Ovarian Tumor
| Ovarian Tumor | t-value | p | ||
|---|---|---|---|---|
| Malignant (Mean ± SD) |
Non-malignant and Borderline (Mean ± SD) |
|||
| N (%) | 18 (40%) | 27 (60%) | ||
| Neutrophil- to- lymphocyte ratio (NLR) | 5.77 ± 4.64 | 2.99 ± 1.75 | 2.84 | 0.003 |
| Platelet-to-lymphocyte ratio (PLR) | 355.08 ± 239.70 | 199.44 ± 99.32 | 3.02 | 0.002 |
| Systemic immune-inflammation index (SII) | 2323.56 ± 2249.06 | 1000.52 ± 715.36 | 2.86 | 0.003 |
| IL-6 serum | 138.68 ± 79.17 | 101.10 ± 48.37 | 1.98 | 0.027 |
| IL-6 intratumoral | 217.88 ± 181.87 | 189.58 ± 93.05 | 0.67 | 0.25 |
Notes: Neutrophil to lymphocyte ratio (NLR), Platelet to lymphocyte ratio (PLR), Systemic immune-inflammation index (SII), and IL-6 Serum are) statistically significant in malignant ovarian cancer with p < 0.05.
Figure 1.
Systemic inflammation in ovarian tumor patients. Box-and-whisker plots illustrating differences in inflammatory indices and cytokine levels between malignant and non-malignant groups. (A) Neutrophil-to-lymphocyte ratio (NLR) is significantly higher in the malignant group (p = 0.003). (B) Platelet-to-lymphocyte ratio (PLR) is significantly elevated in malignant tumors (p = 0.002). (C) Systemic immune-inflammation index (SII) shows a significant increase in malignant patients (p = 0.003). (D) Serum interleukin-6 (IL-6) levels are significantly higher in the malignant group compared to non-malignant tumors (p = 0.027). (E) Intratumoral IL-6 concentration (pg/mL) demonstrates a higher mean value in malignant tumors (217.88 ± 181.87) compared to non-malignant tumors (189.58 ± 93.05), although this difference is not statistically significant. (p = 0.25).
Table 3.
Multivariate Analysis of Inflammation Markers in Ovarian Tumor
| N (%) | Stat | F-Value | df (num) | df (den) | p-value |
|---|---|---|---|---|---|
| Hotelling-Lawley trace | 0.2492 | 2.4925 | 4 | 39 | 0.0582 |
| Pillai’s trace | 0.1995 | 2.4925 | 4 | 40 | 0.0582 |
| Roy’s greatest root | 0.2492 | 2.4925 | 4 | 40 | 0.0582 |
| Wilk’s lambda | 0.8005 | 2.4925 | 4 | 40 | 0.0582 |
Notes: Multivariate analysis of variance (MANOVA) test statistics evaluating simultaneous group differences in inflammatory indices (NLR, PLR, SII, and IL-6) between malignant and non-malignant ovarian tumor groups.
Figure 2.
CVA/LDA Plot of malignant and benign ovarian tumors. Canonical Component 1 (x-axis) and Canonical Component 2 (y-axis), derived from inflammatory indices (NLR, PLR, SII) and IL-6 levels. Pink and green circles indicate malignant and non-malignant cases, respectively.
The diagnostic utility of each inflammatory biomarker was evaluated by ROC curve analysis. SII showed the highest discriminatory ability (AUC = 0.788), followed by NLR (AUC = 0.771) and PLR (AUC = 0.751), which are all within the acceptable range. Serum IL-6 had the poorest discrimination (AUC = 0.620) indicating limited diagnostic value of IL-6 serum. These results suggest that the diagnostic value of systemic inflammatory indices, especially SII, might be better than only serum IL-6 in discrimination of malignant from non-malignant ovarian tumors as can be seen in Figure 3.
Figure 3.
A Schematic figure of ONT diagram. While the helicase enzyme unwinds double-stranded DNA, a single DNA strand passes through the nanopore, and as nucleotide tranverse the pore, it induces a distinct alteration in the ionic current, which is computationally interpreted to reconstruct the DNA sequence.
These results showed that both malignant and non-malignant ovarian tumor showed high inflammation. Therefore, further evaluation of specific regulatory mechanisms in immune-inflammatory response of ovarian cancer was recommended.
In this study, genomic alterations in three malignant ovarian tumor samples and three non-malignant ovarian samples were analyzed using ONT sequencing. The results showed a subset of mutations shared across malignant groups, including MTRF1, IL10, APC, PAX8, PTGS2, PACERR, and IL6 in Table 4. This suggested distinct mutational landscapes in ovarian tissues, showing that genomic alterations could contribute to shared molecular pathways.
Table 4.
Top Seven Regulated Variants in IL-6-Secreting Ovarian Cancer
| Gene | Type | Consequence | Location |
|---|---|---|---|
| MTRF1 | SNV | Missense variant | NM_004294.4:c.711T>A |
| IL10 | SNV | Intron variant | NM_000572.3:c.-1117A>G |
| APC | SNV | Intron variant | NM_001127511.3:c.165+20896A>T |
| PAX8 | SNV | 3′ UTR variant | NM_003466.4:c.*2320G>A |
| PTGS2 | SNV | 3′ UTR variant | NM_000963.4:c.*427T>C |
| PACERR | SNV | Non-coding transcript variant | NM_000963.4:c.-899G>C |
| IL-6 | SNV | Genic upstream transcript variant | NM_000600.5:c.-237C>G |
Notes: a3′ UTR variant refers to a genetic alteration located in 3′ untranslated region (3’ UTR) of a gene.
Discussion
The inclusion of inflammation in cancer progression is well-established, as pro-inflammatory cytokines have been shown to significantly influence various stages of tumor development. Inflammation plays significant roles during tumor development, including initiation, promotion, progression, invasion, and metastasis.9 Several inflammatory mediators, such as IL-6, IL-10, and tumor necrosis factor-alpha (TNF-α), have been associated with cancer metastasis. Among these, IL-6 has become a significant immunoregulatory cytokine within ovarian cancer TME.10,11 It also activates multiple signaling pathways, leading to diverse outcomes such as tumor cell proliferation, angiogenesis, apoptosis inhibition, metastasis, malignant ascites formation, immune escape, neoantigen expression, anaerobic glycolysis, and resistance to chemotherapy. Ovarian cancer cells are known to produce IL-6 in enormous amounts within the TME.
In this study, IL-6 was measured in the serum and intratumor of patients with FIGO stage I to IV ovarian cancer and Benign ovarian tumor. Compared to benign tumor, ovarian cancer patients had significantly greater levels of IL-6, alongside NLR, PLR, and SII. Since PLR and NLR are higher in ovarian cancer patients, these indicators are reliable for diagnostic markers.12 The systemic inflammation from cancer cells secretes interleukin-2 (IL-2), IL-6, IL-10, tumor necrosis factor α (TNF-α) contribute to a decrease in lymphocyte counts as well as lymphocyte dysfunction and VEGF. This serves as proangiogenic factor for development in angiogenesis in response to promote infiltration of neutrophils, which is related to cancer progression.13 Based on the results, a significant correlation was found between high levels of IL-6 and with increase in systemic inflammation markers in ovarian cancer patients. Hence, Canonical Variate Analysis (CVA) of the combined inflammatory indices (NLR, PLR, SII, and IL-6) revealed partial separation between malignant and non-malignant groups along the first canonical component, with malignant cases tending toward lower canonical scores. However, the substantial overlap between groups is consistent with the borderline multivariate result (MANOVA, p = 0.058), suggesting these markers collectively contribute to distinguishing malignancy. These findings underscore the need for larger, adequately powered studies to establish the combined diagnostic utility of inflammatory indices in ovarian cancer. The ROC analysis revealed that systemic inflammation indices outperformed serum IL-6 as individual diagnostic discriminators, with SII achieving the highest AUC of 0.788. The acceptable performance of NLR, PLR, and SII suggests their potential utility as adjunctive markers in the preoperative evaluation of ovarian tumors. The relatively modest AUC of serum IL-6 (0.620) may reflect the heterogeneity of IL-6 secretion across different histologic subtypes and disease stages within this cross-sectional cohort. Future studies should explore whether combining these inflammatory indices into a composite score or integrating them with established markers such as CA-125 could improve overall diagnostic accuracy. Several limitations of this study warrant acknowledgement. First, the relatively small sample size and single-center design with heterogeneity ovarian cancer characteristic limit the generalizability of our findings, as the cross-sectional nature of this study captures data at a single point in time and may not fully represent the broader population of ovarian tumor patients across different institutional and geographic settings. Second, the short recruitment window constrains the diversity of cases captured and may introduce selection bias.
Ovarian cancer has multiple aetiologies and remaining unknown for the specified growth cause of cancer cells. Regarding various mutations over genomic in RNA or DNA of each subject, this study provides results of genomic mutations with ONT for both malignant and benign ovarian tumor. A new generation of DNA/RNA sequencing technology has been created by Oxford Nanopore. It allows for analyzing native DNA/RNA in real-time for a quick insight, in formats that are completely scalable from pocket to population scale, sequencing any length of fragment to provide short and ultra-long read lengths. Nanopore sequencing is dependent on single molecules of DNA/RNA passing through thousands of nanopores in parallel. After DNA/RNA passes through the pore, the data becomes available. A longer sequencing run enables more molecules to be sequenced, thereby increasing the potential of identifying low-abundance sequences within a sample, causing greater confidence in the result and range of analysis. This is particularly important for applications such as detection of low-abundance fusion genes and transcripts, the identification of low-frequency variants, alleles, and biomarkers, including differentiating metagenomic communities.14,15 The mechanism of Nanopore technologies is shown in Figure 4.
Figure 4.
Dysregulation of cellular function by IL-6 upregulation. Overview of IL-6–regulated genes and pathways involved in tumor progression. IL-6 upregulates PAX8, IL-10, PTGS2/COX2, PACERR, MTERF1, and APC, promoting oncogenesis, immunosuppression, metabolic reprogramming, PI3K/AKT activation, and disruption of Wnt signaling. Collectively, these effects enhance tumor cell proliferation, invasion, angiogenesis, and immune evasion via VEGF-related mechanisms.
The results showed mutations shared in malignant groups, with inflammatory identified as the main predominant cause of ovarian cancer provided in Figure 5. Therefore, IL-6 as the upstream gene regulation, activates the oncogenic pathway through JAK/STAT3, PI3K/AKT, and MAPK pathways.16 PTGS2 (COX-2) also plays role in tumor growth through the regulation of key oncogenic PI3K/AKT essential for cancer cells survival. Previous studies had shown that PACERR and PTGS2 were strongly positively correlated at both mRNA and protein levels. In tumour-associated macrophages, increased PTGS2 (COX-2) promotes M2 macrophage polarization, thereby enhancing tumor growth, invasion, and metastasis.17–19 Persistent IL-6 signaling promotes proliferation, suppresses apoptosis, stimulates epithelial–mesenchymal transition (EMT), and chemoresistance. Similarly, PTGS2 favors cancer progression through tumor cell proliferation, invasion, angiogenesis, and immune suppression through the production or activation of VEGF that arises from the inflammatory response. Although IL6, PACERR, and PTGS2 act as pro-inflammatory, the anti-inflammatory IL10 cytokine found in this study correlates with the Immunosuppressive tumor-microenvironment. This suggests that expression of cytokines, including IL-10 and IL-6, within the TME hinders APC maturation and downregulates major histocompatibility complex (MHC) and co-stimulatory molecule expression necessary for effective T-cell activation. Consequently, the impaired antigen presentation results in the accumulation of immature APC that fail to obtain cytotoxic immune responses. This immunologic dysfunction contributes to the formation of an immunosuppressive TME characterized by increased infiltration of regulatory T cells (Tregs) and M2-polarized macrophages, which collectively promote tumor immune evasion, proliferation, and metastasis.20 The APC gene has been extensively characterized in colorectal cancer as a tumor suppressor. Further analysis suggests that the dysregulation also contributes to the development and progression of ovarian cancer. Moreover, APC encodes a critical component of the β-catenin destruction complex, which maintains Wnt/β-catenin signaling. Loss or inactivation of APC leads to the stabilization and massive accumulation of β-catenin, causing transcriptional activation of oncogenic targets, chromosomal instability (CIN), and enhanced tumor cell growth. In ovarian carcinoma, activation of the Wnt/β-catenin pathway has been related to poor differentiation, invasive potential, and chemoresistance. Increase of β-catenin activity is often observed in HGSOC, showing the major role of APC-mediated Wnt signaling in ovarian tumorigenesis.14,21
Figure 5.
ROC/AUC of Inflammatory markers in ovarian tumor. ROC curves for serum IL-6 (AUC = 0.620), NLR (AUC = 0.771), PLR (AUC = 0.751), and SII (AUC = 0.788) are shown. The teal line represents each biomarkers ROC curve and the red diagonal line represents no discrimination (AUC = 0.5). SII demonstrated the highest discriminatory performance among the four markers.
Abbreviations: AUC, area under the curve; IL-6, interleukin-6; NLR, neutrophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; SII, systemic immune-inflammation index.
PAX8, as a transcription factor, plays an essential role in the development of the Müllerian duct and fallopian tube epithelium. The expression of PAX8 contribute to ovarian tumorigenesis and represents one of the most consistent molecular features of high-grade serous ovarian carcinoma (HGSOC). However, overexpression of PAX8 is associated with high tumor invasiveness and metastatic potential in HGSOC.22 MTERF1 contributes to tumor cell proliferation, migration, and invasion through mechanisms including high mitochondrial energy demand and reactive oxygen species (ROS) signaling.23 Dysregulated mitochondrial metabolism resulting from MTERF1 overexpression is recognized as a hallmark of aggressive tumor behavior. Meanwhile, direct and reproducible results of MTERF1 mutation or expression in ovarian cancer remain limited, despite being biologically plausible.24
Conclusion
In conclusion, ovarian malignancy in this group showed much higher levels of systemic inflammatory markers and serum IL-6, compared to non-malignant ovarian tumors. These findings suggest that serum IL-6, especially when considered alongside systemic inflammatory indices, may help distinguish between malignant and non-malignant ovarian tumors.
Exploratory long-read genomic profiling uncovered several candidate variants linked to inflammatory and oncogenic pathways, such as IL6, PTGS2, PACERR, APC, PAX8, IL10, and MTRF1. Although these findings do not prove causation, they offer initial insight into the molecular landscape related to IL-6 in ovarian tumor biology. Overall, the results indicate that inflammatory biomarkers and genomic profiling could work together to better characterize ovarian cancer and can be applied to develop diagnosis or treatment strategies. More extensive and functionally validated studies are needed before these methods can be applied in clinical settings.
Acknowledgment
The authors acknowledge supports from Yayasan Satriabudi Dharma Setia. Our study approved by the Medical and Health Research Ethics Committee of the Universitas Gadjah Mada, Faculty of Medicine, Public Health, and Nursing (KE/FK/1713/EC/2024) and our study complies with the Declaration of Helsinki. The authors express their sincere gratitude for the support that has facilitated the completion of this study.
Funding Statement
This work was funded by the Islamic Development Bank for its support to Sardjito Hospital through Project IDN 1031.
Disclosure
The authors report no conflicts of interest in this work.
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