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Article

Association of the Single Nucleotide Polymorphisms rs11556218, rs4778889, rs4072111, and rs1131445 of the Interleukin-16 Gene with Ovarian Cancer

by
Rafał Watrowski
1,2,
Eva Schuster
3,
Toon Van Gorp
4,5,
Gerda Hofstetter
6,
Michael B. Fischer
7,8,
Sven Mahner
9,10,
Stefan Polterauer
3,
Robert Zeillinger
3 and
Eva Obermayr
3,*
1
Department of Obstetrics and Gynecology, Helios Hospital Muellheim, Teaching Hospital of the University of Freiburg, Heliosweg 1, 79379 Muellheim, Germany
2
Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
3
Molecular Oncology Group, Department of Obstetrics and Gynecology, Comprehensive Cancer Center-Gynecologic Cancer Unit, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
4
Division of Gynecologic Oncology, University Hospital Leuven, 3000 Leuven, Belgium
5
Leuven Cancer Institute, Catholic University of Leuven, 3000 Leuven, Belgium
6
Department of Pathology, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
7
Department of Blood Group Serology and Transfusion Medicine, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
8
Center for Biomedical Technology, Department for Biomedical Research, Danube University Krems, Dr.-Karl-Dorrek-Straße 30, 3500 Krems, Austria
9
Department of Gynecology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
10
Department of Obstetrics and Gynecology, University Hospital, Ludwig-Maximilians-University Munich, 81377 Munich, Germany
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2024, 25(19), 10272; https://doi.org/10.3390/ijms251910272
Submission received: 1 September 2024 / Revised: 21 September 2024 / Accepted: 22 September 2024 / Published: 24 September 2024
(This article belongs to the Special Issue A Commemorative Special Issue in Honor of Prof. Giovanni De Toni: A Pediatrician and Innovator at the Gaslini Children’s Hospital, Genoa, Italy)
Browse Figures
Versions Notes

Abstract

:
Single nucleotide polymorphisms (SNPs) of the IL-16 gene have been reported to influence the risk of several cancers, but their role in ovarian cancer (OC) has not been studied. Using the restriction fragment length polymorphism (PCR-RFLP) method, we examined four IL-16 SNPs: rs11556218 (T > G), rs4778889 (T > C), rs4072111 (C > T), and rs1131445 (T > C) in blood samples from 413 women of Central European descent, including 200 OC patients and 213 healthy controls. Among the patients, 62% were postmenopausal, 84.5% were diagnosed in late stages (FIGO IIb-IV), and 73.5% had high-grade serous OC (HGSOC). Minor allele frequencies in controls were 9.2% for rs11556218 (G allele), 13.7% for rs4778889 (C allele), 10.4% for rs4072111 (T allele), and 32.3% for rs1131445 (C allele). We found significant associations of rs11556218 (G vs. T allele: OR 2.76, 95% CI 1.84–4.14, p < 0.0001) with elevated OC risk in the whole cohort (p < 0.001) and in both premenopausal (p < 0.001) and postmenopausal (p = 0.001) subgroups. These associations remained significant across heterozygote (p < 0.001), dominant (p < 0.001), and overdominant (p < 0.001) models. IL-16 rs4778889 was associated with OC risk predominantly in premenopausal women (p < 0.0001 in almost all models). In the whole cohort, the C allele was associated with OC risk (OR 1.54, CI 95% 1.06–2.23, p = 0.024), and the association of rs4778889 was significant in dominant (p = 0.019), overdominant (p = 0.033), and heterozygote (p = 0.027) models. Furthermore, rs4778889 was linked with HGSOC (p = 0.036) and endometriosis-related OC subtypes (p = 0.002). No significant associations were found for rs4072111 or rs1131445 (p = 0.81 or 0.47, respectively). In conclusion, rs11556218 and rs4778889 SNPs are associated with OC risk, especially in premenopausal women.

1. Introduction

Ovarian cancer (OC), affecting 1.1–1.5% of women during their lifespan, is the most lethal among all gynecological malignancies [1,2,3]. In 2019, OC accounted for 3.4% of new cancer diagnoses and 4.7% of cancer-related deaths worldwide, translating to 300,000 new cases and 200,000 deaths [1]. In Europe, OC is the fifth most common cause of cancer death [3]. Globally, the incidence, related deaths, and disability-adjusted life-years of OC doubled between 1990 and 2020 [2].
Most ovarian tumors originate from ovarian or tubal epithelium (90%), with stromal cells (5–6%) and germ cells (2–3%) as less common origins [4]. Due to their similar biology and spread patterns, the term OC includes carcinomas from the ovary, fallopian tube, and peritoneum [5]. High-grade serous OC (HGSOC), arising from intraepithelial tubal precursor lesions, is the most common and aggressive subtype, making up about 75% of cases [5,6]. Endometrioid and clear cell OC subtypes, often developing from endometriotic lesions, have a comparatively better prognosis and are classified as endometriosis-related OC (EROC) subtypes [7,8]. The prognosis for OC remains poor, with 5-year survival rates between 40% and 50%, largely due to the cancer’s biological aggressiveness, frequent acquired chemotherapy resistance, and late-stage diagnosis (FIGO stage III or IV) [1,2,3]. Early OC stages, which have better outcomes, often evade detection due to non-specific symptoms and limited biomarker sensitivity and specificity [3,9,10,11,12]. OC treatment typically involves extensive cytoreductive surgery, ideally primary and complete, followed by or preceding chemotherapy and targeted therapies [3,13,14,15].
Genetic factors contribute to OC in at least 25% of cases [16,17]. Alterations in BRCA1 and BRCA2 genes account for over half of hereditary cases, while mutations in TP53, BARD1, CHEK2, RAD51, and PALB2 contribute to at least 15% of cases [16,17,18]. Besides high and moderate penetrance genes, genome-wide association studies, candidate gene studies, and next-generation sequencing have identified common alleles with low penetrance susceptibility [16,19]. Single nucleotide polymorphisms (SNPs), the most common genetic variations, result from a single nucleotide (A, T, C, or G) substitution in coding or non-coding DNA. Even minimal changes in genes regulating the cell cycle, DNA repair, immune response, angiogenesis, or metabolic pathways can influence carcinogenesis [20]. To date, around 100 SNPs have been proposed as factors potentially affecting OC risk [21].
Inflammation and inflammatory mediators play a critical role in the development, progression, metastasis, and chemoresistance of OC [22,23,24]. The communication between cells involved in the immune response occurs through cytokines (secreted messenger molecules). Among them, interleukins are named for their expression by leukocytes and their role in intercellular communication among leukocytes. However, interleukins are produced by many other body cells, and their functions are not limited to coordinating leukocyte interactions. Chemokines are a subset of cytokines primarily acting by attracting cells to sites of infection or inflammation [23,25]. Among 41 known interleukins, two—Interleukin-8 (IL-8) and Interleukin-16 (IL-16)—are classified as “interleukins with chemokine activity” [25].
IL-16, initially recognized as a “lymphocyte chemoattractant factor” for CD4-positive cells in 1982, is a multifunctional protein involved in immune response regulation, cell migration, and cell cycle control [26,27,28]. It can be synthesized by various cell types, including CD4 and CD8 T lymphocytes, eosinophils, macrophages, dendritic cells, fibroblasts, mast cells, B lymphocytes, and bronchial epithelium [27,29,30,31]. While CD4 is the primary receptor for IL-16, certain cells can respond to IL-16 through alternative receptors like CD9, for example, mastocytes or lung cancer cells [32], as well as via CD4- and CD9-independent mechanisms [33]. IL-16 attracts immunocompetent cells to sites of inflammation and can stimulate the expression of further pro-inflammatory cytokines such as TNF-α, IL-1β, IL-6, IL-8, and IL-15 [34,35].
The human IL-16 gene, located at chromosome 15q26.3, consists of 153,000 base pairs, 7 exons, and 6 introns, encoding two precursor molecules derived by alternative splicing [26,27,28,31]. The smaller isoform (Pro-IL-16) has 636 amino acids and is expressed in immunocompetent cells, while the larger 1244 amino acid isoform (n-Pro-IL-6) is identified in neuronal cells [26,27,28,31]. Mature IL-16, a 121-amino-acid product, is generated by caspase-3 cleaving the C-terminal domain of either precursor [29,31,32]. Cleaved IL-16 is secreted as a ligand for CD4 with chemoattractant, growth factor, and differentiation factor capabilities, while Pro-IL-16 translocates to the nucleus, acting as a transcriptional repressor of the cell cycle [28,32]. In addition to its role in inflammation, neuronal IL-16 (NIL-16) induces the upregulation of transcription factors, enhances neurite outgrowth, and interacts with neurotransmitter receptors and several ion channel proteins [33,36]. Initially identified in the cerebellum and hippocampus, NIL-16 has recently been identified in osteoarthritic cartilage, suggesting its further peripheral role [37]. Pro-IL-16 contains three PDZ (postsynaptic density protein, disclarge, and zonulin-1) domains for multimerization and a CcN motif for nuclear localization [27,29,38]. The larger neuronal variant has two additional PDZ domains interacting with neuronal ion channels [32,36,38]. PDZ domains bind to protein carboxyl termini or dimerize with other PDZ domains, facilitating IL-16 multimerization into homotetramers [31]. Caspase-3 cleaves IL-16 between PDZ2 and PDZ3 [31,32].
IL-16’s regulatory role in inflammation has led to its study in autoimmune diseases such as asthma [30], inflammatory bowel disease [39], multiple sclerosis [40], systemic lupus erythematosus (SLE) [41,42], and rheumatoid arthritis (RA) [42,43]. It is also implicated in diseases where inflammation modulates disease progression, including viral infections [31,44], depression [45], and cardiovascular diseases [31,46]. Both Pro-IL-16 and secreted IL-16 are relevant to carcinogenesis [32]. Within the tumor microenvironment, IL-16 can attract CD4+ T cells and other immune cells to the tumor site, promoting both pro-tumorigenic and antitumorigenic effects. The production of IL-16 correlates with the onset and progression of gastrointestinal tumors, breast cancer, cutaneous T cell lymphoma (CTCL), and multiple myeloma (MM) [32,46]. The mechanisms by which IL-16 promotes cancer growth vary. In CTCL, a sequence mutation in pro-IL-16 reduces p27KIP1 levels, enhancing cell proliferation; in MM, overexpression of secreted IL-16 induces plasma cell proliferation; and in breast cancer, increased IL-16 recruits CD4+ pro-tumor macrophages [32].
In gynecology, IL-16 is implicated in endometriosis [35,47,48,49,50,51], ovarian physiology [52], and ovarian [53,54] and cervical [55] carcinogenesis. Elevated IL-16 levels in the peritoneal fluid of women with endometriosis, especially in advanced stages, suggest its role in sustaining inflammatory responses in the peritoneal cavity [47]. The study by Zhang et al. demonstrated the pivotal role of pyroptotic T cell-derived active IL-16 as a central cytokine instigating inflammation associated with endometriosis [35]. Cells immunopositive for IL-16 were detected both in ovarian surface epithelium as well as in ovarian stroma [52]. A study using ovarian tissues from menopausal women and patients with HGSOC revealed increased IL-16 expression in the ovarian stroma during menopause [52]. An inverse relationship between IL-16 expression and levels of miR-125a-5p, an IL-16 gene regulator, was observed during ovarian aging, influenced by persistently high levels of FSH [52]. Complementary research using a laying hen model of spontaneous OC demonstrated that IL-16, known for its proangiogenic properties, is associated with OC development and tumor-associated neoangiogenesis [53].
SNPs have been related to structural and functional changes of IL-16 [46,50,55]. Four most commonly studied SNPs within the IL-16 gene are rs4778889, rs11556218, rs4072111, and rs1131445.
rs4778889 (T > C) is located in the promoter region, −295 bp upstream from the transcription start site at the GATA-3 (transcription factor) binding site [56]. The T allele reduces promoter activity compared to the C allele, increasing asthma risk [56]. Conversely, the C allele (TC/CC genotypes) is linked to a higher risk of renal cell carcinoma [57,58], nasopharyngeal cancers [59], and non-cardia gastric cancer [60]. Beyond oncology, the C allele is also associated with endometriosis [48,51] and SLE [41].
rs11556218 (T > G) is a missense mutation in exon 6, causing a substitution of asparagine by lysine in the PDZ2 domain of both pro-IL-16 (Asn446Lys) and n-Pro-IL-16 (Asn1147Lys) [50]. This SNP alters the structural configuration of the PDZ2 domain and disrupts protein–protein recognition by Pro-IL-16 [50]. It is the most studied IL-16 SNP, associated with several benign and malignant conditions. The TG/GG genotypes are linked to higher IL-16 levels [34,59]. The presence of the G allele and G-containing genotypes are strong predictors for developing lung cancer [61], oral cancer [62], nasopharyngeal carcinoma [59,63], gastric cancer [46,60,64,65], colorectal cancer [64], osteosarcoma [34], as well as endometriosis [49,50], coronary artery disease [66,67], ischemic stroke [68], SLE [41], Alzheimer’s disease [69], osteoporosis [70], RA [71], type 2 diabetes mellitus [72,73], and periodontitis [74].
rs4072111 (C > T) is a missense mutation in exon 6, causing a proline to serine change in the nPDZ2 domain of nPro-IL-16 (Pro434Ser) [50]. The T allele has been associated with increased risk of Parkinson’s disease [75], Alzheimer’s [76] disease, and SLE [41], with mixed findings for endometriosis [49,50] and gastric cancer [62,77].
rs1131445 (T > C) affects the miR-135b binding site within the 3′-untranslated region (3′UTR). miRNAs participate in carcinogenesis via post-transcriptional regulation of oncogenes and tumor suppressor genes. The T > C change disrupts the suppressive interaction of miR-135b with IL-16 mRNA, leading to upregulation of IL-16 expression [42,55]. The C allele and C-containing genotypes of rs1131445 are linked to increased risk of cervical cancer [55], colorectal cancer [78], RA, and SLE [42]. In women with cervical cancer, the C allele was associated with higher serum IL-16 concentrations [55].
To date, no study has investigated the role of IL-16 SNPs in OC. To address this gap, we examined the relationship between four common IL-16 polymorphisms and OC risk. We additionally analyzed the role of IL-16 SNPs in relation to menopausal status and specific OC subtypes, HGSOC, and EROC.

2. Results

Within this ethnically homogeneous cohort, 200 women were diagnosed with OC, and 213 were healthy controls. The mean age of cases was 55.8 years (SD 12.3) and the mean age of controls was 51.4 years (SD 13.3), as shown in Figure A1 (Appendix A). The proportion of postmenopausal patients, defined by age ≥ 51 years, did not differ significantly (p = 0.12) between cases (62%; n = 124/200) and controls (54.5%; n = 116/213) (see Figure A2). Regarding OC cases, 169/200 (84.5%) were diagnosed with advanced FIGO stages (IIb-IV). The predominant histological subtype was HGSOC, diagnosed in 73.5% of cases, while 28 out of 200 patients (14%) had endometrioid or clear cell subtypes (collapsed to EROC). The characteristics of the study population are provided in Table 1, while the detailed distribution of histological types is shown in Table A1.
In the control group, the minor allele frequencies (MAF) were 9.2% for rs11556218 (G allele), 13.6% for rs4778889 (C allele), 10.3% for rs4072111 (T allele), and 32.4% for rs1131445 (C allele). The distribution of the respective genotypes did not deviate from Hardy–Weinberg equilibrium for any SNP (see Table 2). These MAFs were comparable to those reported in the gnomAD (Genome Aggregation Database) for the European population, which are 8% for rs11556218 (G allele), 17.8% for rs4778889 (C allele), 10.7% for rs4072111 (T allele), and 34.8% for rs1131445 (C allele) [79].
We are the first to report a strong and significant association between IL-16 rs11556218 (T > G) and OC risk in the general (co-dominant) model (χ2 = 28.3, p < 0.0001), as well as in the dominant (p < 0.0001) and overdominant models (p < 0.0001). As presented in Table 2, the G allele was significantly more prevalent in OC cases compared to controls (21.7% vs. 9.2%, OR 2.76, 95% CI 1.84–4.14, p < 0.0001). Notably, the significant impact of the G allele on OC risk was observed even in the presence of a single G copy (in GT heterozygotes), as GG homozygotes were rare (2% of cases and no GG homozygotes in controls). Moreover, the significance of the G allele for increased OC risk was observed in both postmenopausal (p = 0.001) and premenopausal women (p < 0.0001) (see Table 3).
For the entire cohort, the association of IL-16 rs4778889 (T > C) with OC risk was significant in the dominant (p = 0.019), overdominant (p = 0.033), heterozygote comparison (p = 0.027), and allele comparison (C vs. T, OR 1.54, 95% CI 1.06–2.23, p = 0.024) models, but narrowly missed significance in the co-dominant model (p = 0.057) (Table 2). Notably, the significance of rs4778889 and its C allele was strong in premenopausal women (p = 0.012) but diminished after menopause (p = 0.29) (Table 3).
In addition, in the co-dominant, recessive, and homozygote models, rs4778889 revealed associations with the EROC subtypes (p = 0.002, p = 0.006, and p = 0.007, respectively) and, in the co-dominant and recessive models, with HGSOS (p = 0.036 and p = 0.043, respectively). The results in regard to OC subtypes are presented in Table 4 and Table 5.
In contrast, we did not find any significant associations between IL-16 rs4072111 (p = 0.814) and IL-16 rs1131445 (p = 0.474) and the risk of OC, neither in the entire cohort nor when—as displayed in Table A2—broken down by menopausal status.
The images of the fragment analyzer electropherograms are provided in Supplementary Figures S1–S4.

3. Discussion

Inflammatory processes are involved in ovarian pathology, including endometriosis and malignancy [23,24,52,80]. IL-16 expression in tissues and serum correlates with OC development, progression to advanced stages, and peritumoral neoangiogenesis [54]. The high evolutionary conservancy of IL-16 and its CD4 receptor indicates their crucial role in regulating inflammatory responses [26,32]. Thus, even discrete changes in IL-16 structure and function can modulate immune response, influence the tumor microenvironment, and impact cancer cell behavior [23,52].
In this study, we investigated four SNPs within the IL-16 gene: rs4778889 (promoter), rs11556218 (exon 6), rs4072111 (exon 6), and rs1131445 (miRNA binding site in the 3′-UTR). Two of these, rs11556218 (T > G) and rs4778889 (T > C), showed significant associations with OC. The impact of rs11556218 was significant across all age groups and genetic models, while rs4778889 influenced OC risk predominantly in premenopausal women.
The strong association between IL-16 rs11556218 (T > G) and OC risk is attributed to the G allele, which was significantly more prevalent in OC cases (22%) compared to controls (9%). The importance of the G allele was evident even with a single copy (in GT heterozygotes), as GG homozygotes constituted only 2% of cases and were not detectable in controls. Additionally, the association of the G allele with OC risk was highly significant in both postmenopausal and premenopausal women, although stronger in younger patients. While our findings are pioneering for OC, the G allele of rs11556218 is a well-known “disease marker” among IL-16 SNPs. Meta-analyses have confirmed an overall association of this SNP with cancer risk [46,81]. Specifically, individuals carrying the G allele of rs11556218 are at a higher risk for osteosarcoma [34], lung [61], oral [62], nasopharyngeal [59,63], gastric [46,60,64,65], and colorectal [64] cancers. Importantly, the protective effect of not carrying the G allele persisted in TT homozygotes even after correction for risk factors (like smoking, alcohol consumption, and H. pylori infection) in gastric cancer [65]. Among benign diseases, most studies confirm a contribution of the G allele to increased risk of cardiovascular diseases [46,66,67,68] and endometriosis [49,50], although results vary across different populations [34,51]. The ultrastructural study by Matalliotakis et al. confirmed that the substitution of asparagine by lysine in the PDZ2 domain of Pro-IL-16 and nPro-IL-16 alters the protein structure and results in an aberrant expression of IL-16 [50]. Higher IL-16 levels, as a functional consequence of rs11556218 TG/GG genotypes, were confirmed in patients with osteosarcoma [34], nasopharyngeal carcinoma [59], and benign conditions like osteoporosis [70]. However, this correlation could not be confirmed in colorectal and gastric carcinomas or healthy controls [64].
A further novel observation is the association of IL-16 rs4778889 (T > C) with OC risk, significant in all but one model for the entire cohort. Notably, the C allele’s association with OC risk was stronger in premenopausal women and fell below the significance threshold after menopause. The next finding specifically observed for rs4778889 was its association with histological subtypes HGSOC and EROC.
These observations provoke several reflections. For both SNPs, rs11556218 and rs4778889, the significance of the results increased in the group of premenopausal patients, indicating an increased relevance of IL-16-mediated processes for ovarian carcinogenesis during the reproductive years. As age increases, immune status shifts, chronic inflammation rises, and the accumulation of mutagens, toxins, and DNA damage occurs, accompanied by impaired repair mechanisms, stem cell exhaustion, and senescence [80,82]. These factors contribute to the rising cancer incidence, including OC, which has been termed an “Inflamm-Aging” condition [80]. Menopause, regardless of the age at which it occurs, signifies fundamental changes in hormonal and immunological homeostasis [52]. Ramirez et al. measured IL-16 expression, macrophage frequency (a source of IL-16), and miR-125a-5p expression (a regulator of the IL-16 gene) in ovarian tissues from women before menopause, at early menopause, and at late menopause. IL-16-expressing cell frequencies were significantly higher in the ovarian stroma of women at early and late menopause compared to premenopausal women. Additionally, miR-125a-5p expression significantly decreased as IL-16 expression increased with aging [52]. The effects of SNPs can vary depending on menopausal status. In our previous study concerning IL-8 polymorphisms, we observed an association of three out of four investigated SNPs—rs4073 (A > T), rs2227306 (C > T), and rs2227543 (C > T)—with OC risk limited to postmenopausal women, emphasizing the role of compensatory mechanisms active in the premenopause [83]. The opposite effects we observed in the present study regarding IL-16 SNPs are especially interesting from a clinical point of view. A younger age of manifestation is the typical hallmark of cancers with a genetic background [84,85]. OC is often missed in younger patients due to the dynamic ultrasound appearance of active ovaries and the escape of early stages from biomarker-based diagnostic strategies [12]. Identifying individuals at risk in this group could enhance personalized detection and treatment strategies.
In the present IL-16 study, we observed associations between rs4778889 and both HGSOC and EROC subtypes. If these results could be confirmed in larger cohorts, rs4778889 might be revealed as a genetic marker of particularly aggressive cancers that occur at a younger age and present with HGSOC morphology. When interpreting the genetic background of EROC, it is difficult to determine whether the particular SNP indicates an association with preexisting endometriosis or a predisposition to the cancerous transformation of endometriosis. Notably, in our IL-8 study, we demonstrated a unique association of the T allele of rs1126647 IL-8 with EROC subtypes [83]. Other studies have shown an association between SNPs involved in the pathogenesis of endometriosis and HGSOC or clear cell OC (the latter belonging to EROC), but not with the endometrioid EROC subtype [86]. The complexity of OC pathology shows that specific OC risk factors can play different roles in women with and without endometriosis [87]. Moreover, endometriosis is not an obligatory precursor for EROC subtypes, and both EROC subtypes can develop through distinct pathways; while clear cell OC may arise from pre-existing endometriotic lesions, endometrioid OC may originate from ovarian Mullerian metaplasia [7]. In this context, we consider it a shortcoming of the study that we did not know the endometriosis history of individual patients.
In contrast to rs11556218 and rs4778889, we did not find any significant associations between IL-16 rs4072111 and IL-16 rs1131445 and OC cancer susceptibility, either in the entire cohort or when broken down by menopausal status.
Notably, rs4072111 is a SNP within the precursor molecule of neuronal Pro-IL-16. However, the cleavage product of n-Pro-IL-16 is IL-16, identical to that of Pro-IL-16, so the extracellular action of secreted IL-16 remains unchanged, regardless of its source. Associations of the T allele with increased risk have been reported for neurological diseases like Parkinson’s [75] and Alzheimer’s [76], as well as SLE [41], and inconsistently with endometriosis [49,50] or gastric cancer [62,77]. Regarding endometriosis and gastric cancer, these associations are not surprising, as endometriosis is accompanied by dysfunctions of the peripheral [88,89] and central [89] nervous systems, and the gastrointestinal system is rich in neuronal cells (often summarized as an independent enteric nervous system) [90]. However, rs4072111 does not seem to be primarily an oncogenic SNP.
The last investigated SNP, rs1131445, is located in a miRNA binding site of the 3′-UTR region and impairs the interaction of miR-135b with IL-16 3′-UTR [55]. MiRNAs are short (18–30 nucleotides) non-coding RNAs involved in cell development and proliferation, tissue differentiation, and programmed cell death [91]. As negative regulators of gene expression, they play an essential role in carcinogenesis. They typically interact with the 3′ UTR of target mRNAs to induce mRNA degradation and translational repression, but can also interact with other regions to regulate transcription or activate translation [92]. The rs1131445 SNP has been linked to colorectal [78] and cervical [55] cancer, with carriers of the rs1131445 C allele showing higher serum IL-16 levels. Mi et al. demonstrated that rs1131445, located in the miR-135b binding site of the IL-16 3′-UTR, affects IL-16 protein expression by interfering with the suppressive function of miR-135b. This interference is significantly associated with an increased risk of cervical cancer in Asian patients [55]. The association of rs1131445 with OC deserves further investigation, as cell-line studies have shown that overexpression of miR-135b promotes growth, migration, and invasion of OC cells. Additionally, overexpression of miR-135b increased cisplatin resistance in selected OC cell lines [93]. The lack of association of rs1131445 with OC risk in the present study may reflect the heterogeneity of OC in vivo and/or the modulating effects of epigenetic and environmental influences on genetic risk factors.
To summarize, our study, which includes all four commonly investigated IL-16 SNPs, has several strengths. First, we are the first to describe an association between IL-16 SNPs and OC risk. In the case of rs11556218, the association is highly significant and consistent with results obtained in other cancer types. Adding the G allele of rs11556218 to the catalog of genetic OC markers could aid in identifying individuals at risk. Second, the subgroup analysis revealed that the relevance of the IL-16 SNPs is particularly significant in premenopausal patients, a finding of potential clinical importance. Third, the possible association with HGSOC and EROC suggests the need for confirmation in larger cohorts. Finally, based on the MAFs reported for the European (non-Finnish) population, the composition of our study was representative of the European population [79]. The homogeneity of the study cohort is a strength, though it may limit the generalizability of our findings to other populations.
From our point of view, the main shortcoming of our study is the absence of data regarding tissue or circulating IL-16 levels in relation to the investigated SNPs, which limits the insights into the mechanisms underlying the observed associations with OC. Second, while the highly significant results for rs11556218 in our study population are unlikely to change with an increased sample size, it is intriguing to consider whether the impact of rs4778889 on OC risk, restricted to premenopausal women in our study, is due to underpowering or solely biological factors. Third, for evaluating the role of specific SNPs, detailed patient history (such as endometriosis history) would allow for a more nuanced interpretation of the results.

4. Materials and Methods

4.1. Study Design and Participant Demographics

This case-control study investigated four common SNPs within the IL-16 gene: rs4778889 (−295 T > C), rs4072111 (1300 C > T), rs11556218 (3441 T > G), and rs1131445 (3′-UTR T > C), utilizing the restriction fragment length polymorphism (PCR-RFLP) technique. The study cohort comprised 413 Central European women, including 200 ovarian cancer patients and 213 healthy controls. This population and methodology are identical to our prior study concerning IL-8 gene SNPs [83].

4.2. Sample Collection and Ethical Compliance

Blood samples were obtained from the Molecular Oncology Group’s blood bank at the Medical University of Vienna. These samples were collected from 1996 to 2021 from women of Central European descent at the Medical University of Vienna and collaborating European institutions. For the present study, we retrieved blood samples originating from Austria, Poland, Germany, and Belgium. All participants provided written informed consent. The blood bank project (EK-366/2003 and EK1966/2020) and the SNP analysis in OC risk (EK-293/2011) received ethical approval from the Medical University of Vienna. Data were anonymized and managed per good scientific practice. Clinicopathological classifications followed WHO (2014) [94] and FIGO (2013) [95] guidelines. Menopausal status was approximated using the age threshold of 51 years, reflecting regional averages [96,97].

4.3. DNA Extraction and Genotyping

Peripheral blood samples collected in EDTA tubes were processed to extract genomic DNA from white blood cells using the QIAamp DNA Blood Mini Kit (QIAGEN, Hilden, Germany, cat. no. 51104). IL-16 polymorphisms were identified by analyzing fragment length polymorphisms of the respective PCR products (PCR-RFLP). Amplicons were generated from 25 ng of genomic DNA as a template in a 25 µL reaction mix, containing 5 pmol of the respective forward and reverse primers (Sigma Aldrich, St. Louis, MI, USA, Table 6) and MangoMix™ (Bioline, London, UK, cat. no. BIO-25034) providing MangoTaq™ DNA polymerase, MgCl2, and dNTPs. Amplification proceeded with an initial hot start at 95 °C for 5 min, with 45 cycles starting with a 30 s denaturation at 95 °C, followed by a 30 s annealing at the temperature indicated in Table 6, and a 60 s extension at 72 °C. Following a final extension step at 72 °C for 7 min, the PCR products were digested with the respective restriction endonuclease (all provided by New England Biolabs, Ipswich, MA, USA, cat. nos. R0584S (Ahd I), R0529S (BsmA I), R0111S (Nde I), and R0531S (BsaA I)) under conditions specified in Table 6. The restriction fragments were then separated by capillary electrophoresis using the Fragment Analyzer™ Automated CE System (Advanced Analytical, Parkersburg, WV, USA) and the dsDNA 905 Reagent Kit (Agilent, Santa Clara, CA, USA, cat. no. DNF-905). The size of the fragments was determined with PROSize® 3.0 software version 3.0.1.6 (Advanced Analytical Technologies, Orangeburg, NY, USA).

4.4. Statistical Analysis

Data analysis was conducted using JASP statistical software v.0.17.3 for Windows [98] and the VassarStats Website for Statistical Computation [99]. Differences in genotype and allele frequencies between patients and controls were evaluated using the Chi-squared (χ2) test (with Yates correction for continuity if the numbers in any of the table cells were less than 5) and Fisher’s exact test. To assess the impact of each SNP on OC risk, odds ratios (OR) with 95% confidence intervals (CIs) were calculated. Hardy–Weinberg equilibrium (HWE) in the control group was tested for all four SNPs using the goodness-of-fit χ2 test. Age differences between groups were analyzed using the Student’s t-test, with a two-sided p-value ≤ 0.05 considered statistically significant.
The association between OC risk or traits and IL-16 SNPs was explored using various genetic models, where “A” represents the major allele and “a” represents the minor allele [100,101]:
  • Co-dominant Model (General Test of Association): AA versus Aa versus aa.
  • Dominant Model: (Aa + aa) versus AA.
  • Recessive Model: aa versus (AA + Aa).
  • Overdominant Model (Heterozygote Superiority): Aa versus (AA + aa).
  • Heterozygote Comparison Model: Aa versus AA.
  • Homozygote Comparison Model: AA versus aa.
  • Allelic/Multiplicative Model (Allelic Frequency): a versus A.

5. Conclusions

The G allele and G-containing genotypes of rs11556218 represent a potential novel genetic marker for elevated risk of OC in European women. The associations of rs11556218 and rs4778889 were particularly pronounced in premenopausal women. Our results can contribute to better identification of individuals at risk and to the development of individualized diagnostic and treatment strategies. Future studies with larger cohorts are warranted to confirm and extend our findings.

Supplementary Materials

The supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms251910272/s1.

Author Contributions

Conceptualization, R.W. and R.Z.; methodology, R.W., E.O. and R.Z.; software, R.W. and E.O.; validation, G.H.; formal analysis, R.W. and E.O.; investigation, R.W., E.O. and E.S.; resources, E.O., R.W., R.Z., M.B.F., S.P., T.V.G. and S.M.; data curation, E.O., E.S. and R.W.; writing—original draft preparation, R.W.; writing—review and editing, R.W., E.O. and R.Z.; visualization, R.W.; supervision, R.Z.; project administration, E.O.; funding acquisition, E.O., R.W. and R.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Ethics Committee of the Medical University of Vienna (EK 366/2003, EK 293/2011, and EK1966/2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on reasonable request from the first (R.W.) or the corresponding (E.O.) author.

Acknowledgments

We sincerely thank the patients who donated blood samples to the Molecular Oncology Group’s blood bank, enabling studies such as the present one.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Ijms 25 10272 g0a1
Figure A1. Age at initial diagnosis in cases and controls.
Figure A1. Age at initial diagnosis in cases and controls.
Ijms 25 10272 g0a1
Ijms 25 10272 g0a2
Figure A2. Menopausal status (cutoff point 51 years) of cases and controls.
Figure A2. Menopausal status (cutoff point 51 years) of cases and controls.
Ijms 25 10272 g0a2
Table A1. Distribution of OC histological subtypes.
Table A1. Distribution of OC histological subtypes.
HistologynPercent
Serous * 14974.5
Endometrioid 2311.5
Mucinous 84.0
Clear cell52.5
Undifferentiated 73.5
MMMT10.5
Missing or incomplete (e.g., only grading)73.5
Total200100
* Serous histology, including HGSOC and LGSOC.
Table A2. Genotype and allele distribution of rs 4072111 and rs 1131445 broken down by menopausal status.
Table A2. Genotype and allele distribution of rs 4072111 and rs 1131445 broken down by menopausal status.
ModelGenotype ControlsCasesOR (95% CI)P Fiχ2P Chi
rs4072111 (C > T)
Postmenopausal
Co-dominantCC90 (77.6%)105 (84.7%)1.00 (Ref.) 2.50.287
HeterozygoteCT23 (19.8%)18 (14.5%)0.67 (0.34–1.32)0.3031.340.247
HomozygoteTT3 (2.6%)1 (0.8%)0.29 (0.03–2.8)0.341
DominantTT + CT 26 (22.4%)19 (15.3%)0.63 (0.33–1.21)0.1871.980.159
TT + CT vs. CCCC90 (77.6%)105 (84.7%)
RecessiveTT3 (2.6%)1 (0.8%)0.31(0.03–2.99)0.3560.330.567
TT vs. CT + CCCT + CC113 (97.4%)123 (99.2%)
OverdominantCT23 (19.8%)18 (14.5%)0.69 (0.35–1.35)0.3061.190.275
CT vs. TT + CCTT + CC93 (80.2%)106 (85.5%)
Allele frequencyT29 (12.5%)20 (8.1%)0.61 (0.34–1.12)0.1312.570.108
T vs. CC203 (87.5%)228 (91.9%)
Premenopausal
Co-dominantCC82 (84.5%)61 (80.3%)1.00 (Ref.) 1.590.451
HeterozygoteCT15 (15.5%)14 (18.4%)1.25 (0.56–2.79)0.6820.310.578
HomozygoteTT0 (0%)1 (1.3%)∞ (NaN–∞)0.431
DominantTT + CT 15 (15.5%)15 (19.7%)1.34 (0.61–2.96)0.5450.540.462
TT + CT vs. CCCC82 (84.5%)61 (80.3%)
RecessiveTT0 (0%)1 (1.3%)∞ (NaN–∞)0.4390.020.902
TT vs. CT + CCCT + CC97 (100%)75 (98.7%)
OverdominantCT15 (15.5%)14 (18.4%)1.23 (0.55–2.75)0.6830.270.603
CT vs. TT + CCTT + CC82 (84.5%)62 (81.6%)
Allele frequencyT15 (7.7%)16 (10.5%)1.40 (0.67–2.94)0.4480.820.365
T vs. CC179 (92.3%)136 (89.5%)
rs1131445 (3′-UTR T > C)
Postmenopausal
Co-dominantTT55 (47.4%)52 (41.9%)1.00 (Ref.) 0.750.689
HeterozygoteTC46 (39.7%)55 (44.4%)1.26 (0.73–2.18)0.4090.710.399
HomozygoteCC15 (12.9%)17 (13.7%)1.2 (0.54–2.64)0.6910.20.655
DominantCC + CT 61 (52.6%)72 (58.1%)1.25 (0.75–2.08)0.4360.730.393
(DD, Dd) vs. ddTT55 (47.4%)52 (41.9%)
RecessiveCC15 (12.9%)17 (13.7%)1.07 (0.51–2.26)10.030.862
DD vs. (Dd, dd)TC + TT101 (87.1%)107 (86.3%)
OverdominantTC46 (39.7%)55 (44.4%)1.21 (0.73–2.03)0.5140.540.462
TT + CC70 (60.4%)69 (55.6%)
Allele frequencyT156 (67.2%)159 (64.1%)1.15 (0.79–1.68)0.5010.520.471
C vs. TC76 (32.8%)89 (35.9%)
Premenopausal
Co-dominantTT46 (47.4%)31 (40.8%)1.00 (Ref.) 0.80.672
HeterozygoteTC40 (41.2%)36 (47.4%)1.34 (0.70–2.53)0.4170.790.374
HomozygoteCC11 (11.3%)9 (11.8%)1.21 (0.45–3.27)0.8000.150.699
DominantCC + CT 51 (52.6%)45 (59.2%)1.31 (0.71–2.40)0.4420.760.383
(DD, Dd) vs. ddTT46 (47.4%)31 (40.8%)
RecessiveCC11 (11.3%)9 (11.8%)1.05 (0.41–2.68)10.010.92
DD vs. (Dd, dd)TC + TT86 (88.7%)67 (88.2%)
OverdominantTC40 (41.2%)36 (47.4%)1.28 (0.7–2.35)0.4440.650.42
TT + CC57 (58.8%)40 (52.6%)
Allele frequencyT132 (68%)98 (64.5%)1.17 (0.75–1.84)0.4940.490.484
C vs. TC62 (32%)54 (35.5%)
P Fi—p value in Fisher’s exact test, P Chi—p value in Chi squared test (for df = 1 or df = 2).

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  2. Zhou, Z.; Wang, X.; Ren, X.; Zhou, L.; Wang, N.; Kang, H. Disease Burden and Attributable Risk Factors of Ovarian Cancer From 1990 to 2017: Findings from the Global Burden of Disease Study 2017. Front. Public Health 2021, 9, 619581. [Google Scholar] [CrossRef] [PubMed]
  3. Marth, C.; Abreu, M.H.; Andersen, K.K.; Aro, K.M.; de Lurdes Batarda, M.; Boll, D.; Ekmann-Gade, A.W.; Haltia, U.-M.; Hansen, J.; Haug, A.J.; et al. Real-Life Data on Treatment and Outcomes in Advanced Ovarian Cancer: An Observational, Multinational Cohort Study (RESPONSE Trial). Cancer 2022, 128, 3080–3089. [Google Scholar] [CrossRef]
  4. McCluggage, W.G. Morphological Subtypes of Ovarian Carcinoma: A Review with Emphasis on New Developments and Pathogenesis. Pathology 2011, 43, 420–432. [Google Scholar] [CrossRef]
  5. Köbel, M.; Kang, E.Y. The Evolution of Ovarian Carcinoma Subclassification. Cancers 2022, 14, 416. [Google Scholar] [CrossRef]
  6. Weinberger, V.; Bednarikova, M.; Cibula, D.; Zikan, M. Serous Tubal Intraepithelial Carcinoma (STIC)—Clinical Impact and Management. Expert Rev. Anticancer Ther. 2016, 16, 1311–1321. [Google Scholar] [CrossRef]
  7. Bergamini, A.; Mangili, G.; Ambrosi, A.; Taccagni, G.; Rabaiotti, E.; Bocciolone, L.; Candotti, G.; Cioffi, R.; Pella, F.; Sabetta, G.; et al. Endometriosis-Related Ovarian Cancers: Evidence for a Dichotomy in the Histogenesis of the Two Associated Histotypes. Diagnostics 2023, 13, 1425. [Google Scholar] [CrossRef]
  8. Centini, G.; Schettini, G.; Pieri, E.; Giorgi, M.; Lazzeri, L.; Martire, F.G.; Mancini, V.; Raimondo, D.; Seracchioli, R.; Habib, N.; et al. Endometriosis-Related Ovarian Cancer: Where Are We Now? A Narrative Review towards a Pragmatic Approach. JCM 2024, 13, 1933. [Google Scholar] [CrossRef]
  9. Elorriaga, M.Á.; Neyro, J.L.; Mieza, J.; Cristóbal, I.; Llueca, A. Biomarkers in Ovarian Pathology: From Screening to Diagnosis. Review of the Literature. J. Personal. Med. 2021, 11, 1115. [Google Scholar] [CrossRef]
  10. Watrowski, R.; Zeillinger, R. Simple Laboratory Score Improves the Preoperative Diagnosis of Adnexal Mass. Tumour Biol. 2016, 37, 4343–4349. [Google Scholar] [CrossRef]
  11. Watrowski, R.; Heinze, G.; Jäger, C.; Forster, J.; Zeillinger, R. Usefulness of the Preoperative Platelet Count in the Diagnosis 566 of Adnexal Tumors. Tumour Biol. 2016, 37, 12079–12087. [Google Scholar] [CrossRef] [PubMed]
  12. Watrowski, R.; Obermayr, E.; Wallisch, C.; Aust, S.; Concin, N.; Braicu, E.I.; Van Gorp, T.; Hasenburg, A.; Sehouli, J.; Vergote, I.; et al. Biomarker-Based Models for Preoperative Assessment of Adnexal Mass: A Multicenter Validation Study. Cancers 2022, 14, 1780. [Google Scholar] [CrossRef]
  13. Armbrust, R.; Ledwon, P.; Von Rüsten, A.; Schneider, C.; Sehouli, J. Primary Treatment Results in Patients with Ovarian, Fallopian or Peritoneal Cancer—Results of a Clinical Cancer Registry Database Analysis in Germany. Cancers 2022, 14, 4638. [Google Scholar] [CrossRef] [PubMed]
  14. Kostov, S.; Kornovski, Y.; Watrowski, R.; Slavchev, S.; Ivanova, Y.; Yordanov, A. Surgical and Anatomical Basics of Pelvic Debulking Surgery for Advanced Ovarian Cancer—The “Hudson Procedure” as a Cornerstone of Complete Cytoreduction. Chirurgia 2023, 118, 187–201. [Google Scholar] [CrossRef] [PubMed]
  15. Kostov, S.; Selçuk, I.; Watrowski, R.; Dineva, S.; Kornovski, Y.; Slavchev, S.; Ivanova, Y.; Yordanov, A. Neglected Anatomical Areas in Ovarian Cancer: Significance for Optimal Debulking Surgery. Cancers 2024, 16, 285. [Google Scholar] [CrossRef]
  16. Flaum, N.; Crosbie, E.J.; Edmondson, R.J.; Smith, M.J.; Evans, D.G. Epithelial Ovarian Cancer Risk: A Review of the Current Genetic Landscape. Clin. Genet. 2020, 97, 54–63. [Google Scholar] [CrossRef]
  17. Toss, A.; Tomasello, C.; Razzaboni, E.; Contu, G.; Grandi, G.; Cagnacci, A.; Schilder, R.J.; Cortesi, L. Hereditary Ovarian Cancer: Not Only BRCA 1 and 2 Genes. BioMed Res. Int. 2015, 2015, 1–11. [Google Scholar] [CrossRef]
  18. Kostov, S.; Watrowski, R.; Kornovski, Y.; Dzhenkov, D.; Slavchev, S.; Ivanova, Y.; Yordanov, A. Hereditary Gynecologic Cancer Syndromes—A Narrative Review. OncoTargets Ther. 2022, 15, 381–405. [Google Scholar] [CrossRef]
  19. Vazgiourakis, V.; Zervou, M.; Papageorgiou, L.; Chaniotis, D.; Spandidos, D.; Vlachakis, D.; Eliopoulos, E.; Goulielmos, G. Association of Endometriosis with Cardiovascular Disease: Genetic Aspects (Review). Int. J. Mol. Med. 2023, 51, 29. [Google Scholar] [CrossRef]
  20. Deng, N.; Zhou, H.; Fan, H.; Yuan, Y. Single Nucleotide Polymorphisms and Cancer Susceptibility. Oncotarget 2017, 8, 110635–110649. [Google Scholar] [CrossRef]
  21. Hu, J.; Xu, Z.; Ye, Z.; Li, J.; Hao, Z.; Wang, Y. The Association between Single Nucleotide Polymorphisms and Ovarian Cancer Risk: A Systematic Review and Network Meta-Analysis. Cancer Med. 2022, 12, 541–556. [Google Scholar] [CrossRef] [PubMed]
  22. Savant, S.S.; Sriramkumar, S.; O’Hagan, H.M. The Role of Inflammation and Inflammatory Mediators in the Development, Progression, Metastasis, and Chemoresistance of Epithelial Ovarian Cancer. Cancers 2018, 10, 251. [Google Scholar] [CrossRef] [PubMed]
  23. Bose, S.; Saha, P.; Chatterjee, B.; Srivastava, A.K. Chemokines Driven Ovarian Cancer Progression, Metastasis and Chemoresistance: Potential Pharmacological Targets for Cancer Therapy. Semin. Cancer Biol. 2022, 86, 568–579. [Google Scholar] [CrossRef] [PubMed]
  24. Tossetta, G.; Piani, F.; Borghi, C.; Marzioni, D. Role of CD93 in Health and Disease. Cells 2023, 12, 1778. [Google Scholar] [CrossRef]
  25. Akdis, M.; Aab, A.; Altunbulakli, C.; Azkur, K.; Costa, R.A.; Crameri, R.; Duan, S.; Eiwegger, T.; Eljaszewicz, A.; Ferstl, R.; et al. Interleukins (from IL-1 to IL-38), Interferons, Transforming Growth Factor β, and TNF-α: Receptors, Functions, and Roles in Diseases. J. Allergy Clin. Immunol. 2016, 138, 984–1010. [Google Scholar] [CrossRef]
  26. Cruikshank, W.W.; Center, D.M.; Nisar, N.; Wu, M.; Natke, B.; Theodore, A.C.; Kornfeld, H. Molecular and Functional Analysis of a Lymphocyte Chemoattractant Factor: Association of Biologic Function with CD4 Expression. Proc. Natl. Acad. Sci. USA 1994, 91, 5109–5113. [Google Scholar] [CrossRef]
  27. Cruikshank, W.W.; Kornfeld, H.; Center, D.M. Interleukin-16. J. Leukoc. Biol. 2000, 67, 757–766. [Google Scholar] [CrossRef]
  28. Cruikshank, W.; Little, F. Lnterleukin-16: The Ins and Outs of Regulating T-Cell Activation. Crit. Rev. Immunol. 2008, 28, 467–483. [Google Scholar] [CrossRef]
  29. Wilson, K.C.; Center, D.M.; Cruikshank, W.W. The Effect of Interleukin-16 and Its Precursor on T Lymphocyte Activation and Growth. Growth Factors 2004, 22, 97–104. [Google Scholar] [CrossRef]
  30. Laberge, S.; Ernst, P.; Ghaffar, O.; Cruikshank, W.W.; Kornfeld, H.; Center, D.M.; Hamid, Q. Increased Expression of Interleukin-16 in Bronchial Mucosa of Subjects with Atopic Asthma. Am. J. Respir. Cell Mol. Biol. 1997, 17, 193–202. [Google Scholar] [CrossRef]
  31. Glass, W.G.; Sarisky, R.T.; Vecchio, A.M.D. Not-so-Sweet Sixteen: The Role of IL-16 in Infectious and Immune-Mediated Inflammatory Diseases. J. Interferon Cytokine Res. 2006, 26, 511–520. [Google Scholar] [CrossRef] [PubMed]
  32. Richmond, J.; Tuzova, M.; Cruikshank, W.; Center, D. Regulation of Cellular Processes by Interleukin-16 in Homeostasis and Cancer. J. Cell. Physiol. 2014, 229, 139–147. [Google Scholar] [CrossRef] [PubMed]
  33. Hridi, S.U.; Franssen, A.J.P.M.; Jiang, H.-R.; Bushell, T.J. Interleukin-16 Inhibits Sodium Channel Function and GluA1 Phosphorylation via CD4- and CD9-Independent Mechanisms to Reduce Hippocampal Neuronal Excitability and Synaptic Activity. Mol. Cell. Neurosci. 2019, 95, 71–78. [Google Scholar] [CrossRef] [PubMed]
  34. Tang, Y.-J.; Wang, J.-L.; Xie, K.-G.; Lan, C.-G. Association of Interleukin 16 Gene Polymorphisms and Plasma IL16 Level with Osteosarcoma Risk. Sci. Rep. 2016, 6, 34607. [Google Scholar] [CrossRef]
  35. Zhang, J.; Zhao, W.; Zhou, Y.; Xi, S.; Xu, X.; Du, X.; Zheng, X.; Hu, W.; Sun, R.; Tian, Z.; et al. Pyroptotic T Cell-Derived Active IL-16 Has a Driving Function in Ovarian Endometriosis Development. Cell Rep. Med. 2024, 5, 101476. [Google Scholar] [CrossRef]
  36. Kurschner, C.; Yuzaki, M. Neuronal Interleukin-16 (NIL-16): A Dual Function PDZ Domain Protein. J. Neurosci. 1999, 19, 7770–7780. [Google Scholar] [CrossRef]
  37. McKenna, G.; O’Flatharta, C.; Ranera, B.; Shaw, G.; Barron, V.; Barry, F.; Murphy, M. Investigation of the Role of Interleukin 16 in Chondrogenesis of Mesenchymal Stem Cells and in Osteoarthritis. Osteoarthr. Cartil. 2013, 21, S239–S240. [Google Scholar] [CrossRef]
  38. Bannert, N.; Vollhardt, K.; Asomuddinov, B.; Haag, M.; König, H.; Norley, S.; Kurth, R. PDZ Domain-Mediated Interaction of Interleukin-16 Precursor Proteins with Myosin Phosphatase Targeting Subunits. J. Biol. Chem. 2003, 278, 42190–42199. [Google Scholar] [CrossRef]
  39. Seegert, D.; Rosenstiel, P.; Pfahler, H.; Pfefferkorn, P.; Nikolaus, S.; Schreiber, S. Increased Expression of IL-16 in Inflammatory Bowel Disease. Gut 2001, 48, 326–332. [Google Scholar] [CrossRef]
  40. Skundric, D.S.; Cruikshank, W.W.; Montgomery, P.C.; Lisak, R.P.; Tse, H.Y. Emerging Role of IL-16 in Cytokine-Mediated Regulation of Multiple Sclerosis. Cytokine 2015, 75, 234–248. [Google Scholar] [CrossRef]
  41. Xue, H.; Gao, L.; Wu, Y.; Fang, W.; Wang, L.; Li, C.; Li, Y.; Liang, W.; Zhang, L. The IL-16 Gene Polymorphisms and the Risk of the Systemic Lupus Erythematosus. Clin. Chim. Acta 2009, 403, 223–225. [Google Scholar] [CrossRef]
  42. Zeinalzadeh, S.; Kheradmand, N.; Rasouli, G.; Esmaeilzadeh, E.; Pakzad, B.; Behroozi, J.; Chamanara, M.; Zoshk, M.Y.; Ehtesham, N.; Sabet, M.N. Association of a miRNA-Binding Site Polymorphism in IL-16 Gene with Disease Risk and Clinical Characteristics of Rheumatoid Arthritis and Systemic Lupus Erythematosus. Clin. Rheumatol. 2022, 41, 2189–2196. [Google Scholar] [CrossRef] [PubMed]
  43. Blaschke, S.; Schulz, H.; Schwarz, G.; Blaschke, V.; Müller, G.A.; Reuss-Borst, M. Interleukin 16 Expression in Relation to Disease Activity in Rheumatoid Arthritis. J. Rheumatol. 2001, 28, 12–21. [Google Scholar] [PubMed]
  44. Sampson, D.L.; Fox, B.A.; Yager, T.D.; Bhide, S.; Cermelli, S.; McHugh, L.C.; Seldon, T.A.; Brandon, R.A.; Sullivan, E.; Zimmerman, J.J.; et al. A Four-Biomarker Blood Signature Discriminates Systemic Inflammation Due to Viral Infection Versus Other Etiologies. Sci. Rep. 2017, 7, 2914. [Google Scholar] [CrossRef] [PubMed]
  45. Almulla, A.F.; Abbas Abo Algon, A.; Tunvirachaisakul, C.; Al-Hakeim, H.K.; Maes, M. T Helper-1 Activation via Interleukin-16 Is a Key Phenomenon in the Acute Phase of Severe, First-Episode Major Depressive Disorder and Suicidal Behaviors. J. Adv. Res. 2023, 64, 171–181. [Google Scholar] [CrossRef]
  46. De Souza, V.H.; de Alencar, J.B.; Tiyo, B.T.; Alves, H.V.; Vendramini, E.C.L.; Sell, A.M.; Visentainer, J.E.L. Association of Functional IL16 Polymorphisms with Cancer and Cardiovascular Disease: A Meta-Analysis. Oncotarget 2020, 11, 3405–3417. [Google Scholar] [CrossRef]
  47. Koga, K.; Osuga, Y.; Yoshino, O.; Hirota, Y.; Yano, T.; Tsutsumi, O.; Taketani, Y. Elevated Interleukin-16 Levels in the Peritoneal Fluid of Women with Endometriosis May Be a Mechanism for Inflammatory Reactions Associated with Endometriosis. Fertil. Steril. 2005, 83, 878–882. [Google Scholar] [CrossRef]
  48. Gan, X.-L.; Lin, Y.-H.; Zhang, Y.; Yu, T.-H.; Hu, L.-N. Association of an Interleukin-16 Gene Polymorphism with the Risk and Pain Phenotype of Endometriosis. DNA Cell Biol. 2010, 29, 663–667. [Google Scholar] [CrossRef]
  49. Azimzadeh, P.; Khorram Khorshid, H.R.; Akhondi, M.M.; Shirazi, A. Association of Interleukin-16 Polymorphisms with Disease Progression and Susceptibility in Endometriosis. Int. J. Immunogenet. 2016, 43, 297–302. [Google Scholar] [CrossRef]
  50. Matalliotakis, M.; Zervou, M.I.; Eliopoulos, E.; Matalliotaki, C.; Rahmioglu, N.; Kalogiannidis, I.; Zondervan, K.; Spandidos, D.A.; Matalliotakis, I.; Goulielmos, G.N. The Role of IL-16 Gene Polymorphisms in Endometriosis. Int. J. Mol. Med. 2018, 41, 1469–1476. [Google Scholar] [CrossRef]
  51. Babah, O.A.; Ojewunmi, O.O.; Onwuamah, C.K.; Udenze, I.C.; Osuntoki, A.A.; Afolabi, B.B. Serum Concentrations of IL-16 and Its Genetic Polymorphism Rs4778889 Affect the Susceptibility and Severity of Endometriosis in Nigerian Women. BMC Womens Health 2023, 23, 253. [Google Scholar] [CrossRef] [PubMed]
  52. Ramirez, J.; Bitterman, P.; Basu, S.; Barua, A. Changes in IL-16 Expression in the Ovary during Aging and Its Potential Consequences to Ovarian Pathology. J. Immunol. Res. 2022, 2022, 2870389. [Google Scholar] [CrossRef] [PubMed]
  53. Yellapa, A.; Bahr, J.M.; Bitterman, P.; Abramowicz, J.S.; Edassery, S.L.; Penumatsa, K.; Basu, S.; Rotmensch, J.; Barua, A. Association of Interleukin 16 with the Development of Ovarian Tumor and Tumor-Associated Neoangiogenesis in Laying Hen Model of Spontaneous Ovarian Cancer. Int. J. Gynecol. Cancer 2012, 22, 199–207. [Google Scholar] [CrossRef] [PubMed]
  54. Yellapa, A.; Bitterman, P.; Sharma, S.; Guirguis, A.S.; Bahr, J.M.; Basu, S.; Abramowicz, J.S.; Barua, A. Interleukin 16 Expression Changes in Association with Ovarian Malignant Transformation. Am. J. Obstet. Gynecol. 2014, 210, 272.e1–272.e10. [Google Scholar] [CrossRef]
  55. Mi, Y.; Wang, L.; Zong, L.; Pei, M.; Lu, Q.; Huang, P. Genetic Variants in microRNA Target Sites of 37 Selected Cancer-Related Genes and the Risk of Cervical Cancer. PLoS ONE 2014, 9, e86061. [Google Scholar] [CrossRef]
  56. Burkart, K.M.; Barton, S.J.; Holloway, J.W.; Yang, I.A.; Cakebread, J.A.; Cruikshank, W.; Little, F.; Jin, X.; Farrer, L.A.; Clough, J.B.; et al. Association of Asthma with a Functional Promoter Polymorphism in the IL16 Gene. J. Allergy Clin. Immunol. 2006, 117, 86–91. [Google Scholar] [CrossRef]
  57. Wang, Z.; Xu, Y.; Zhu, S. Interleukin-16 Rs4778889 Polymorphism Contributes to the Development of Renal Cell Cancer in a Chinese Population. Int. J. Clin. Exp. Pathol. 2015, 8, 15228–15233. [Google Scholar]
  58. Yang, S.X.; Chen, F.; Zhang, J.W.; Sun, Z.Q.; Chen, B.P. IL-16 Rs4778889 Polymorphism Contribution to the Development of Renal Cell Cancer in a Chinese Population. Genet. Mol. Res. 2016, 15, 10-4238. [Google Scholar] [CrossRef]
  59. Qin, X.; Peng, Q.; Lao, X.; Chen, Z.; Lu, Y.; Lao, X.; Mo, C.; Sui, J.; Wu, J.; Zhai, L.; et al. The Association of Interleukin-16 Gene Polymorphisms with IL-16 Serum Levels and Risk of Nasopharyngeal Carcinoma in a Chinese Population. Tumour Biol. 2014, 35, 1917–1924. [Google Scholar] [CrossRef]
  60. Zhang, T.; Wang, H. Variants of Interleukin-16 Associated with Gastric Cancer Risk. Asian Pac. J. Cancer Prev. 2013, 14, 5269–5273. [Google Scholar] [CrossRef]
  61. Wu, M.-F.; Wang, Y.-C.; Shen, T.-C.; Chang, W.-S.; Li, H.-T.; Liao, C.-H.; Gong, C.-L.; Wang, Z.-H.; Tsai, C.-W.; Hsia, T.-C.; et al. Significant Association of Interleukin-16 Genetic Variations to Taiwanese Lung Cancer. In Vivo 2020, 34, 1117–1123. [Google Scholar] [CrossRef] [PubMed]
  62. Shih, L.-C.; Chang, W.-S.; Lee, H.-T.; Wang, Y.-C.; Wang, Z.-H.; Chao, C.-Y.; Yu, C.-C.; Lin, H.-Y.; Shen, T.-C.; Kuo, C.-C.; et al. Interaction of Interleukin-16 Genotypes With Betel Quid Chewing Behavior on Oral Cancer in Taiwan. In Vivo 2020, 34, 1759–1764. [Google Scholar] [CrossRef] [PubMed]
  63. Gao, L.-B.; Liang, W.-B.; Xue, H.; Rao, L.; Pan, X.-M.; Lv, M.-L.; Bai, P.; Fang, W.-L.; Liu, J.; Liao, M.; et al. Genetic Polymorphism of Interleukin-16 and Risk of Nasopharyngeal Carcinoma. Clin. Chim. Acta 2009, 409, 132–135. [Google Scholar] [CrossRef]
  64. Gao, L.-B.; Rao, L.; Wang, Y.-Y.; Liang, W.-B.; Li, C.; Xue, H.; Zhou, B.; Sun, H.; Li, Y.; Lv, M.-L.; et al. The Association of Interleukin-16 Polymorphisms with IL-16 Serum Levels and Risk of Colorectal and Gastric Cancer. Carcinogenesis 2009, 30, 295–299. [Google Scholar] [CrossRef]
  65. Fu, C.-K.; Mong, M.-C.; Tzeng, H.-E.; Yang, M.-D.; Chen, J.-C.; Hsia, T.-C.; Hsia, N.-Y.; Tsai, C.-W.; Chang, W.-S.; Chen, C.-P.; et al. The Significant Contribution of Interleukin-16 Genotypes, Smoking, Alcohol Drinking, and Helicobacter Pylori Infection to Gastric Cancer. In Vivo 2024, 38, 90–97. [Google Scholar] [CrossRef]
  66. Chen, Y.; Huang, H.; Liu, S.; Pan, L.-A.; Zhou, B.; Zhang, L.; Zeng, Z. IL-16 Rs11556218 Gene Polymorphism Is Associated with Coronary Artery Disease in the Chinese Han Population. Clin. Biochem. 2011, 44, 1041–1044. [Google Scholar] [CrossRef]
  67. Wu, J.; Wang, Y.; Zhang, Y.; Li, L. Association between Interleukin-16 Polymorphisms and Risk of Coronary Artery Disease. DNA Cell Biol. 2011, 30, 305–308. [Google Scholar] [CrossRef]
  68. Liu, X.; Du, J.; Zhou, Y.; Shu, Q.; Li, Y. Interleukin-16 Polymorphism Is Associated with an Increased Risk of Ischemic Stroke. Mediat. Inflamm. 2013, 2013, 564750. [Google Scholar] [CrossRef]
  69. Khoshbakht, T.; Soosanabadi, M.; Neishaboury, M.; Kamali, K.; Karimlou, M.; Bazazzadegan, N.; Khorram Khorshid, H.R. An Association Study on IL16 Gene Polymorphisms with the Risk of Sporadic Alzheimer’s Disease. Avicenna J. Med. Biotechnol. 2015, 7, 128–132. [Google Scholar]
  70. Ma, X.; Chen, Y.; Zhang, Q.; Tian, H.; Wang, J.; Liu, S.; Wang, B.; Wang, Q. Interleukin-16 Rs11556218 Is Associated with a Risk of Osteoporosis in Chinese Postmenopausal Women. Eur. J. Obstet. Gynecol. Reprod. Biol. 2017, 210, 90–93. [Google Scholar] [CrossRef]
  71. Mostafa, S.; Nasef, S.I.; Hassan, A.M.; Abdalla, A.M.; Omar, H.H. Interleukin 16 Polymorphism and Susceptibility of Rheumatoid Arthritis Disease in Egyptian Population. Egypt. J. Immunol. 2021, 28, 282–289. [Google Scholar] [CrossRef] [PubMed]
  72. Mohammad, D.G.; Omar, H.; El-Abaseri, T.B.; Omar, W.; Abdelraheem, S. Association of IL-16 Rs11556218 T/G Polymorphism with the Risk of Developing Type 2 Diabetes Mellitus. J. Diabetes Metab. Disord. 2021, 20, 649–653. [Google Scholar] [CrossRef] [PubMed]
  73. Cheng, F.; Liu, L.; Zhang, H.; Zhu, Y.; Li, X.; Li, H. Association of IL-16 Gene Polymorphisms with the Risk of Developing Type 2 Diabetes Mellitus in the Chinese Han Population. Biosci. Rep. 2019, 39, BSR20190821. [Google Scholar] [CrossRef] [PubMed]
  74. Souza, V.H.; Visentainer, J.E.L.; Zacarias, J.M.V.; Alencar, J.B.; Tsuneto, P.Y.; Silva, C.O.; Salmeron, S.; Colli, C.M.; Sell, A.M. Association of IL16 Polymorphisms with Periodontitis in Brazilians: A Case- Control Study. PLoS ONE 2020, 15, e0239101. [Google Scholar] [CrossRef] [PubMed]
  75. Zhang, X.; Xue, L.; Sun, X.; Jin, J.; Bai, X.; Xie, A. Association of IL-16 Gene Polymorphisms with Sporadic Parkinson’s Disease in a Han Chinese Population. Neurosci. Lett. 2020, 724, 134877. [Google Scholar] [CrossRef]
  76. Anvar, N.E.; Saliminejad, K.; Ohadi, M.; Kamali, K.; Daneshmand, P.; Khorshid, H.R.K. Association between Polymorphisms in Interleukin-16 Gene and Risk of Late-Onset Alzheimer’s Disease. J. Neurol. Sci. 2015, 358, 324–327. [Google Scholar] [CrossRef]
  77. Kashfi, S.M.H.; Behboudi Farahbakhsh, F.; Nazemalhosseini Mojarad, E.; Mashayekhi, K.; Azimzadeh, P.; Romani, S.; Derakhshani, S.; Malekpour, H.; Asadzadeh Aghdaei, H.; Zali, M.R. Interleukin-16 Polymorphisms as New Promising Biomarkers for Risk of Gastric Cancer. Tumour Biol. 2016, 37, 2119–2126. [Google Scholar] [CrossRef]
  78. Azimzadeh, P.; Romani, S.; Mohebbi, S.R.; Mahmoudi, T.; Vahedi, M.; Fatemi, S.R.; Zali, N.; Zali, M.R. Association of Polymorphisms in microRNA-Binding Sites and Colorectal Cancer in an Iranian Population. Cancer Genet. 2012, 205, 501–507. [Google Scholar] [CrossRef]
  79. The Genome Aggregation Database (gnomAD). Available online: https://gnomad.broadinstitute.org (accessed on 24 October 2023).
  80. Sánchez-Prieto, M.; Sánchez-Borrego, R.; Lubián-López, D.M.; Pérez-López, F.R. Etiopathogenesis of Ovarian Cancer. An Inflamm-Aging Entity? Gynecol. Oncol. Rep. 2022, 42, 101018. [Google Scholar] [CrossRef]
  81. Mo, C.-J.; Peng, Q.-L.; He, Y.; Wang, J.; Xie, L.; Li, T.-J.; Li, S.; Qin, X. Positive Association between IL-16 Rs11556218 T/G Polymorphism and Cancer Risk: A Meta-Analysis. Asian Pac. J. Cancer Prev. 2014, 15, 4697–4703. [Google Scholar] [CrossRef]
  82. Montégut, L.; López-Otín, C.; Kroemer, G. Aging and Cancer. Mol. Cancer 2024, 23, 106. [Google Scholar] [CrossRef] [PubMed]
  83. Watrowski, R.; Schuster, E.; Hofstetter, G.; Fischer, M.B.; Mahner, S.; Van Gorp, T.; Polterauer, S.; Zeillinger, R.; Obermayr, E. Association of Four Interleukin-8 Polymorphisms (−251 A>T, +781 C>T, +1633 C>T, +2767 A>T) with Ovarian Cancer Risk: Focus on Menopausal Status and Endometriosis-Related Subtypes. Biomedicines 2024, 12, 321. [Google Scholar] [CrossRef] [PubMed]
  84. Lynch, H.T.; Watson, P.; Lynch, J.F.; Conway, T.A.; Fili, M. Hereditary Ovarian Cancer. Heterogeneity in Age at Onset. Cancer 1993, 71, 573–581. [Google Scholar] [CrossRef]
  85. Horackova, K.; Janatova, M.; Kleiblova, P.; Kleibl, Z.; Soukupova, J. Early-Onset Ovarian Cancer <30 Years: What Do We Know about Its Genetic Predisposition? Int. J. Mol. Sci. 2023, 24, 17020. [Google Scholar] [CrossRef]
  86. Lee, A.W.; Templeman, C.; Stram, D.A.; Beesley, J.; Tyrer, J.; Berchuck, A.; Pharoah, P.P.; Chenevix-Trench, G.; Pearce, C.L.; Ness, R.B.; et al. Evidence of a Genetic Link between Endometriosis and Ovarian Cancer. Fertil. Steril. 2016, 105, 35–43.e10. [Google Scholar] [CrossRef]
  87. Phung, M.T.; Muthukumar, A.; Trabert, B.; Webb, P.M.; Jordan, S.J.; Terry, K.L.; Cramer, D.W.; Titus, L.J.; Risch, H.A.; Doherty, J.A.; et al. Effects of Risk Factors for Ovarian Cancer in Women with and without Endometriosis. Fertil. Steril. 2022, 118, 960–969. [Google Scholar] [CrossRef]
  88. Godin, S.K.; Wagner, J.; Huang, P.; Bree, D. The Role of Peripheral Nerve Signaling in Endometriosis. FASEB Bioadv. 2021, 3, 802–813. [Google Scholar] [CrossRef]
  89. Fan, P.; Li, T. Unveil the Pain of Endometriosis: From the Perspective of the Nervous System. Expert Rev. Mol. Med. 2022, 24, e36. [Google Scholar] [CrossRef]
  90. Spencer, N.J.; Hu, H. Enteric Nervous System: Sensory Transduction, Neural Circuits and Gastrointestinal Motility. Nat. Rev. Gastroenterol. Hepatol. 2020, 17, 338–351. [Google Scholar] [CrossRef]
  91. Duică, F.; Condrat, C.E.; Dănila, C.A.; Boboc, A.E.; Radu, M.R.; Xiao, J.; Li, X.; Creţoiu, S.M.; Suciu, N.; Creţoiu, D.; et al. MiRNAs: A Powerful Tool in Deciphering Gynecological Malignancies. Front. Oncol. 2020, 10, 591181. [Google Scholar] [CrossRef]
  92. O’Brien, J.; Hayder, H.; Zayed, Y.; Peng, C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front. Endocrinol. 2018, 9, 402. [Google Scholar] [CrossRef] [PubMed]
  93. Wang, J.; Zhang, R.; Zhang, B.; Zhang, L.; Jiang, W.; Liu, X.; Duan, X. MiR-135b Improves Proliferation and Regulates Chemotherapy Resistance in Ovarian Cancer. J. Mol. Histol. 2022, 53, 699–712. [Google Scholar] [CrossRef] [PubMed]
  94. WHO Classification of Tumours of Female Reproductive Organs. In World Health Organization Classification of Tumours, 4th ed.; Kurman, R.J.; Carcangiu, M.L.; Herrington, C.S.; Young, R.H. (Eds.) International Agency for Research on Cancer: Lyon, France, 2014; ISBN 978-92-832-2435-8. [Google Scholar]
  95. Prat, J. Staging Classification for Cancer of the Ovary, Fallopian Tube, and Peritoneum. Int. J. Gynecol. Obstet. 2014, 124, 1–5. [Google Scholar] [CrossRef] [PubMed]
  96. Kilaf, E.; Kirchengast, S. Menopause between nature and culture: Menopausal age and climacteric symptoms among Turkish immigrant women in Vienna, Austria. Acta Med. Litu. 2008, 15, 2–8. [Google Scholar]
  97. Krause, L.; Dini, L.; Prütz, F. Ambulante Inanspruchnahme Und Behandlungsanlässe Bei Frauen Ab 50 Jahren. J. Health Monit. 2019, 5, 3–14. [Google Scholar] [CrossRef]
  98. JASP Team. JASP Computer Software Buscar con Google; Version 0.17.3; JASP Team: Amsterdam, The Netherlands, 2023; Available online: https://jasp-stats.org/previous-versions/ (accessed on 24 October 2023).
  99. VassarStats: Website for Statistical Computation. Copyright: Richard Lowry 1998–2023. Available online: http://www.vassarstats.net (accessed on 28 August 2023).
  100. Lewis, C.M. Genetic Association Studies: Design, Analysis and Interpretation. Brief. Bioinform. 2002, 3, 146–153. [Google Scholar] [CrossRef]
  101. Horita, N.; Kaneko, T. Genetic Model Selection for a Case–Control Study and a Meta-Analysis. Meta Gene 2015, 5, 1–8. [Google Scholar] [CrossRef]
Table 1. Study population characteristics.
Table 1. Study population characteristics.
Parameter CasesControlsp
Sample size 200213
Mean age at diagnosis (years) 55.85 (SD 12.3,
IQR 47–65)		51.44 (SD 13.3,
IQR 45–58)		<0.001
Menopausal status Premenopausal76 (38%)97 (45.5%)0.12
Postmenopausal124 (62%)116 (54.5%)
High-grade serous OCHGSOC147 (73.5%)
Non-HGSOC46 (23%)
N/a7 (3.5%)
EROCEROC28 (14%)
Non-EROC165 (82.5%)
N/a7 (3.5%)
FIGO stageEarly 23 (11.5%)
Advanced169 (84.5%)
N/a8 (4%)
N/a: non-available for analysis (e.g., only grading available or stage missing), SD—standard deviation, IQR—interquartile range (25–75%), EROC—endometriosis-related OC.
Table 2. Genotype and allele distribution of rs11556218, rs4778889, rs4072111, and rs1131445.
Table 2. Genotype and allele distribution of rs11556218, rs4778889, rs4072111, and rs1131445.
ModelGenotypeControls CasesOR (95% CI)P Fiχ2P Chi
rs11556218 (T > G), pHWE = 0.141
Co-dominantTT174 (81.7%)117 (58.5%)1.00 (Ref.) 28.3 (df = 2)<0.0001
Heterozygote GT39 (18.3%)79 (39.5%)3.01 (1.92–4.72)<0.000124.06<0.0001
HomozygoteGG0 (0%)4 (2%)∞ (NaN–∞)0.0271.620.057
DominantGG + GT39 (18.3%)83 (41.5%)3.17 (2.02–4.95)<0.000126.65<0.0001
(GG + GT vs. TT)TT174 (81.7%)117 (58.5%)
RecessiveGG0 (0%)4 (2%)∞ (NaN–∞)0.0542.470.116
GG vs. GT + TTGT + TT213 (100%)196 (98%)
OverdominantGT39 (18.3%)79 (39.5%)2.91 (1.86–4.56)<0.000122.69<0.0001
(GT vs. TT + GG)TT + GG174 (81.7%)121 (60.5%)
Allele frequencyG39 (9.2%)87 (21.7%)2.76 (1.84–4.14)<0.000125.32<0.0001
(G vs. T), MAF = 0.09T387 (90.8%)313 (78.3%)
rs4778889 (T > C), pHWE = 0.256
Co-dominant TT157 (73.7%)126 (63%)1.00 (Ref.) 5.72 (df = 2)0.057
HeterozygoteCT54 (25.4%)70 (35%)1.62 (1.06–2.47)0.0314.910.027
HomozygoteCC2 (0.9%)4 (2%)2.49 (0.45–13.83)0.4140.440.507
DominantCC + CT56 (26.3%)74 (37%)1.65 (1.08–2.5)0.0205.480.019
CC + CT vs. TT TT157 (73.7%)126 (63%)
RecessiveCC2 (0.9%)4 (2%)2.15 (0.39–11.89)0.4360.240.625
CC vs. CT + TTCT + TT211 (99.1%)196 (98%)
OverdominantCT54 (25.4%)70 (35%)1.59 (1.04–2.42)0.0414.570.033
CT vs. CC + TTCC + TT159 (74.6%)130 (65%)
Allele frequencyC58 (13.6%)78 (19.5%)1.54 (1.06–2.23)0.0245.190.023
C vs. T, MAF = 0.14T368 (86.4%)322 (80.5%)
rs4072111 (C > T), pHWE = 0.59
Co-dominantCC172 (80.8%)166 (83%)1.00 (Ref.) 0.4120.814
HeterozygoteCT38 (17.8%)32 (16%)0.87 (0.52–1.46)0.6940.270.603
HomozygoteTT3 (1.4%)2 (1%)0.69 (0.11–4.19)1<0.0011
DominantTT + CT 41 (19.2%)34 (17%)0.86 (0.52–1.42)0.6100.350.554
TT + CT vs. CCCC172 (80.8%)166 (83%)
RecessiveTT3 (1.4%)2 (1%)0.71 (0.12–4.28)1<0.0011
TT vs. CT + CCCT + CC210 (98.6%)198 (99%)
OverdominantCT38 (17.8%)32 (16%)0.88 (0.52–1.47)0.6940.250.617
CT vs. TT + CCTT + CC175 (82.2%)168 (84%)
Allele frequency C382 (89.7%)364 (91%)0.86 (0.54–1.36)0.5570.420.517
T vs. C, MAF = 0.10T44 (10.3%)36 (9%)
rs1131445 (T > C), pHWE = 0.254
Co-dominantTT101 (47.4%)83 (41.5%)1.00 (Ref.) 1.49 (df = 2)0.474
HeterozygoteTC86 (40.4%)91 (45.5%)1.06 (0.57–1.96)0.8760.030.862
HomozygoteCC26 (12.2%)26 (13%)1.22 (0.66–2.25)0.6370.390.532
DominantCC + CT 112 (52.6%)117 (58.5%)1.27 (0.86–1.88)0.2361.460.227
CC + CT vs. TTTT101 (47.4%)83 (41.5%)
RecessiveCC26 (12.2%)26 (13%)1.07 (0.60–1.92)0.8820.060.806
CC vs. TC + TTTC + TT187 (87.8%)174 (87%)
OverdominantTC86 (40.4%)91 (45.5%)1.23 (0.83–1.82)0.321.110.292
TC vs. TT + CCTT + CC127 (59.6%)109 (54.5%)
Allele frequencyT288 (67.6%)257 (64.25%)1.16 (0.87–1.55)0.341.030.310
C vs. T, MAF = 0.32C138 (32.4%)143 (35.75%)
pHWE—p value for the Hardy–Weinberg Equilibrium, MAF: minor allele frequency, P Fi—p value in Fisher’s exact test, P Chi—p value in Chi squared test (for df = 1 or df = 2). Significant p-values are in bold.
Table 3. Genotype and allele distribution of rs11556218 and rs4778889 broken down by menopausal status.
Table 3. Genotype and allele distribution of rs11556218 and rs4778889 broken down by menopausal status.
ModelGenotypeControlsCasesOR (95% CI)P Fiχ2P Chi
rs11556218 (T > G)
Postmenopausal
Co-dominantTT95 (81.9%)78 (62.9%)1.00 (Ref.) 11.560.003
Heterozygote GT21 (18.1%)44 (35.5%)2.55 (1.4–4.65)0.0029.660.002
HomozygoteGG0 (0%)2 (1.6%)∞ (NaN–∞)0.208
DominantGG + GT21 (18.1%)46 (37.1%)2.67 (1.47–4.85)0.00110.740.001
GG + GT vs. TTTT95 (81.9%)78 (62.9%)
RecessiveGG0 (0%)2 (1.6%)∞ (NaN–∞)0.4980.440.507
GG vs. GT + TTGT + TT116 (100%)122 (98.4%)
OverdominantGT21 (18.1%)44 (35.5%)2.49 (1.37–4.53)0.0039.170.002
GT vs. TT + GGTT + GG95 (81.9%)80 (64.5%)
Allele frequencyG21 (9.1%)48 (19.4%)2.41 (1.39–4.17)0.00210.340.001
G vs. TT211 (90.9%)200 (80.6%)
Premenopausal
Co-dominantTT79 (81.4 %)39 (51.3%)1.00 (Ref.) 18.74<0.0001
Heterozygote GT18 (18.6%)35 (46.1%)3.94 (1.98–7.82)<0.000116.21<0.0001
HomozygoteGG0 (0%)2 (2.6%)∞ (NaN–∞)0.115
DominantGG + GT18 (18.6%)37 (48.7%)4.16 (2.11–8.23)<0.000117.84<0.0001
GG + GT vs. TTTT79 (81.4 %)39 (51.3%)
RecessiveGG0 (0%)2 (2.6%)∞ (NaN–∞)0.1920.790.373
GG vs. GT + TTGT + TT97 (100%)74 (97.4%)
OverdominantGT18 (18.6%)35 (46.1%)3.75 (1.89–7.41)<0.000115.16<0.0001
GT vs. TT + GGTT + GG79 (81.4%)41 (53.9%)
Allele frequencyG18 (9.3%)39 (25.7%)3.37 (1.84–6.19)<0.000116.62<0.0001
G vs. TT176 (90.7%)113 (74.3%)
rs4778889 (T > C)
Postmenopausal
Co-dominant TT91 (78.4%)87 (70.2%)1.00 (Ref.) 5.10.078
HeterozygoteCT23 (19.8%)37 (29.8%)1.68 (0.93–3.06)0.1012.940.086
HomozygoteCC2 (1.7%)0 (0%)0 (0–∞)0.4980.440.507
DominantCC + CT25 (21.6%)37 (29.8%)1.55 (0.86–2.78)0.1842.150.143
CC + CT vs. TTTT91 (78.4%)87 (70.2%)
RecessiveCC2 (1.7%)0 (0%)0 (0–∞)0.2330.570.449
CC vs. CT + TTCT + TT114 (98.3%)124 (100%)
OverdominantCT23 (19.8%)37 (29.8%)1.72 (0.95–3.12)0.1003.20.073
CT vs. CC + TTCC + TT93 (80.2%)87 (70.2%)
Allele frequencyC27 (11.6%)37 (14.9%)1.33 (0.78–2.27)0.3471.120.29
C vs. TT205 (88.4%)211 (85.1%)
Premenopausal
Co-dominantTT66 (68%)39 (51.3%)1.00 (Ref.) 8.58 0.014
HeterozygoteCT31 (32%)33 (43.4%)1.80 (0.96–3.38)0.0783.380.065
HomozygoteCC0 (0%)4 (5.3%)∞ (0–∞)0.0224.010.045
DominantCC + CT31 (32%)37 (48.7%)2.02 (1.09–3.75)0.02950.025
CC + CT vs. TTTT66 (68%)39 (51.3%)
RecessiveCC0 (0%)4 (5.3%)0 (0–∞)0.043.160.076
CC vs. CT + TTCT + TT97 (100%)76 (94.7%)
OverdominantCT31 (32%)33 (43.4%)1.63 (0.88–3.05)0.1532.40.121
CT vs. CC + TTCC + TT66 (68%)43 (56.6%)
Allele frequencyC31 (16%)41 (27%)1.94 (1.15–3.28)0.0166.250.012
C vs. TT163 (84%)111 (73%)
P Fi—p value in Fisher’s exact test, P Chi—p value in Chi squared test (for df = 1 or df = 2). Significant p-values are in bold.
Table 4. Genotypes and allele frequencies between HGSOC and non-HGSOC subtypes (n = 193).
Table 4. Genotypes and allele frequencies between HGSOC and non-HGSOC subtypes (n = 193).
ModelGenotypeNon-HGSOCHGSOCOR (95% CI)P Fiχ2P Chi
rs11556218 (T > G)
Co-dominantTT28 (60.9%)82 (55.8%)1.00 (Ref.) 0.40.821
Heterozygote GT17 (36.9%)62 (42.2%)1.25 (0.63–2.48)0.6050.390.532
HomozygoteGG1 (2.2%)3 (2%)1.02 (0.1–10.25)1<0.00011
DominantGG + GT18 (39.1%)65 (44.2%)1.23 (0.63–2.42)0.610.370.543
TT28 (60.9%)82 (55.8%)
RecessiveGG1 (2.2%)3 (2%)0.94 (0.1–9.24)1<0.00011
GT + TT45 (97.8%)144 (98%)
OverdominantGT17 (36.9%)62 (42.2%)1.24 (0.63–2.46)0.6080.390.532
TT + GG29 (63.1%)85 (57.8%)
Allele frequency (G vs. T)G19 (20.7%)68 (23.1%)1.16 (0.65–2.05)0.670.250.617
T73 (79.3%)226 (76.9%)
rs4778889 (T > C)
Co-dominantTT30 (65.2%)91 (61.9%)1.00 (Ref.) 6.660.036
HeterozygoteCT13 (28.3%)55 (37.4%)1.39 (0.67–2.9)0.470.80.371
HomozygoteCC3 (6.5%)1 (0.7%)0.11 (0.01–1.1)0.0562.770.096
DominantCC + CT16 (34.8%)56 (38.1%)1.15 (0.58–2.3)0.730.160.689
TT30 (65.2%)91 (61.9%)
RecessiveCC3 (6.5%)1 (0.7%)0.1 (0.01–0.97)0.0433.360.067
CT + TT43 (93.5%)146 (99.3%)
OverdominantCT13 (28.3%)55 (37.4%)1.52 (0.74–3.13)0.2921.290.256
CC + TT33 (71.7%)92 (62.6%)
Allele frequency (T vs. C)C73 (79.3%)237 (80.6%)1.08 (0.6–1.94)0.8810.070.791
T19 (20.7%)57 (19.4%)
rs4072111 (C > T)
Co-dominantCC37 (80.4%)122 (83%)1.00 (Ref.) 0.810.668
HeterozygoteCT8 (17.4%)24 (16.3%)
HomozygoteTT1 (2.2%)1 (0.7%)
DominantTT + CT 9 (19.6%)25 (17%)0.84 (0.36–1.96)0.8240.160.689
CC37 (80.4%)122 (83%)
RecessiveTT1 (2.2%)1 (0.7%)0.31 (0.02–5.03)0.4210.0020.97
CT + CC45 (97.8%)146 (99.3%)
OverdominantCT8 (17.4%)24 (16.3%)0.93 (0.38–2.23)10.030.862
TT + CC38 (82.6%)123 (83.7%)
Allele frequency (T vs. C)C82 (89.1%)268 (91.2%)1.26 (0.58–2.72)0.6810.340.559
T10 (10.9%)26 (8.8%)
rs1131445 (T > C)
Co-dominantTT20 (43.5%)63 (42.9%)1.00 (Ref.) 0.04 0.982
HeterozygoteTC20 (43.5%)66 (44.9%)1.05 (0.52–2.13)10.020.888
HomozygoteCC6 (13%)18 (12.2%)0.95 (0.33–2.73)10.010.920
DominantCC + CT 26 (56.5%)84 (57.1%)1.03 (0.53–2.0)10.010.920
TT20 (43.5%)63 (42.9%)
RecessiveCC6 (13%)18 (12.2%)0.93 (0.35–2.5)10.020.888
TC + TT40 (87%)129 (87.8%)
OverdominantTC20 (43.5%)66 (44.9%)1.06 (0.54–2.06)10.030.862
TT + CC26 (56.5%)81 (55.1%)
Allele frequency (C vs. T)T60 (65.2%)192 (65.3%)0.996 (0.61–1.63)101
C32 (34.8%)102 (34.7%)
P Fi—p value in Fisher’s exact test, P Chi—p value in Chi squared test (for df = 1 or df = 2). Significant p-values are in bold.
Table 5. Genotypes and allele frequencies between EROC and non-EROC subtypes (n = 193).
Table 5. Genotypes and allele frequencies between EROC and non-EROC subtypes (n = 193).
ModelGenotypeNon-EROCEROCOR (95% CI)P Fiχ2P Chi
rs11556218 (T > G)
Co-dominantTT94 (57%)18 (64.3%)1.00 (Ref.) 1.040.594
Heterozygote GT67 (40.6%)10 (35.7%)0.78 (0.34–1.79)0.6780.340.559
HomozygoteGG4 (2.4%)0 (0%)0 (0–∞)10.030.865
DominantGG + GT71 (43%)10 (35.7%)0.736 (0.32–1.69)0.5380.530.466
TT94 (57%)18 (64.3%)
RecessiveGG4 (2.4%)0 (0%)0 (0–∞)10.010.908
GT + TT161 (97.6%)28 (100%)
OverdominantGT67 (40.6%)10 (35.7%)0.81 (0.35–1.87)0.6810.240.624
TT + GG98 (59.4%)18 (64.3%)
Allele frequency (G vs. T)G79 (23.4%)10 (17.9%)0.71 (0.34–1.48)0.3950.840.359
T259 (76.6%)46 (82.1%)
rs4778889 (T > C)
Co-dominantTT106 (64.2%)16 (57.1%)1.00 (Ref.) 12.060.002
HeterozygoteCT58 (35.2%)9 (32.1%)1.03 (0.43–2.47)10.030.86
HomozygoteCC1 (0.6%)3 (10.7%)2.99 (0.67–5.31)0.0117.260.007
DominantCC + CT59 (35.8%)12 (42.9%)1.35 (0.6–3.04)0.5270.520.471
TT106 (64.2%)16 (57.1%)
RecessiveCC1 (0.6%)3 (10.7%)2.98 (0.68–5.28)0.0107.590.006
CT + TT164 (99.4%)25 (89.3%)
OverdominantCT58 (35.2%)9 (32.1%)0.87 (0.37–2.06)0.8330.10.751
CC + TT107 (64.8%)19 (67.9%)
Allele frequency (T vs. C)C60 (18.2%)15 (26.8%)1.65 (0.86–3.17)0.1452.260.133
T270 (81.8%)41 (73.2%)
rs4072111 (C > T)
Co-dominantCC136 (82.4%)23 (82.1%)1.00 (Ref.) 2.140.344
HeterozygoteCT28 (17%)4 (14.3%)0.84 (0.27–2.63)1<0.0011
HomozygoteTT1 (0.6%)1 (3.6%)5.91 (0.36–97.9)0.2770.160.687
DominantTT + CT 29 (17.6%)5 (17.9%)1.02 (0.36–2.9)1<0.0011
CC136 (82.4%)23 (82.1%)
RecessiveTT1 (0.6%)1 (3.6%)6.07 (0.37–100.04)0.270.180.672
CT + CC164 (99.4%)27 (96.4%)
OverdominantCT28 (17%)4 (14.3%)0.82 (0.26–2.53)0.7940.0060.938
TT + CC137 (83%)24 (85.7%)
Allele frequency (T vs. C)C300 (90.9%)50 (89.3%)0.83 (0.33–2.10)0.8030.150.698
T30 (9.1%)6 (10.7%)
rs1131445 (T > C)
Co-dominantTT70 (42.4%)12 (42.9%)1.00 (Ref.) 1.02 0.601
HeterozygoteTC76 (46.1%)11(39.3%)0.84 (0.35–2.04)0.8230.140.708
HomozygoteCC19 (11.5%)5 (17.9%)1.535 (0.48–4.9)0.5290.170.681
DominantCC + CT 95 (57.6%)16 (57.1%)0.98 (0.44–2.21)10.0020.966
TT70 (42.4%)12 (42.9%)
RecessiveCC19 (11.5%)5 (17.9%)1.67 (0.57–4.91)0.3560.3980.528
TC + TT146 (88.5%)23 (82.1%)
OverdominantTC76 (46.1%)11(39.3%)0.76 (0.34–1.72)0.5440.440.505
TT + CC89 (53.9%)17 (60.7%)
Allele frequency (C vs. T)T216 (65.45%)35 (62.5%)0.88 (0.49–1.58)0.7620.180.671
C114 (34.55%)21 (37.5%)
P Fi—p value in Fisher’s exact test, P Chi—p value in Chi squared test (for df = 1 or df = 2). Significant p-values are in bold.
Table 6. PCR-RFLP of IL-16 SNPs. Primers for amplification, annealing temperature, and restriction enzyme for digestion.
Table 6. PCR-RFLP of IL-16 SNPs. Primers for amplification, annealing temperature, and restriction enzyme for digestion.
SNPLocationPrimer SequenceAnnealing TemperatureDigestion (Enzyme, Temperature, Duration)Fragment Size (bp)
rs4778889 (T > C)PromoterForward: 5′-CTCCACACTCAAAGCCTTTTGTTCCTATGA-3′
Reverse: 5′-CCATGTCAAAACGGTAGCCTCAAGC-3′		60 °CAhd I
37 °C, 25 min		T: 280
C: 246 + 34
rs4072111 (C > T)Exon 6Forward: 5′-CACTGTGATCCCGGTCCAGTC-3′
Reverse: 5′-TTCAGGTACAAACCCAGCCAGC-3′		67 °CBsmA I
55 °C, 35 min		C: 164
T: 140 + 24
rs11556218 (T > G)Exon 6Forward: 5′-GCTCAGGTTCACAGAGTGTTTCCATA-3′
Reverse: 5′-TGTGACAATCACAGCTTGCCTG-3′		60 °CNde I
37 °C, 25 min		G: 171
T: 147 + 24
rs1131445 (T > C)3′UTRForward: 5′-GAGATCATTCACTCATACATCTGG-3′
Reverse: 5′-TCATATACACGCTGGTTCCTTCTG-3′		62 °CBsaA I
37 °C, 25 min		T: 460
C: 300 + 160
3′UTR—3′ untranslated region. rs number (Ref-SNP)—ID for each SNP assigned by dbSNP.
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Watrowski, R.; Schuster, E.; Van Gorp, T.; Hofstetter, G.; Fischer, M.B.; Mahner, S.; Polterauer, S.; Zeillinger, R.; Obermayr, E. Association of the Single Nucleotide Polymorphisms rs11556218, rs4778889, rs4072111, and rs1131445 of the Interleukin-16 Gene with Ovarian Cancer. Int. J. Mol. Sci. 2024, 25, 10272. https://doi.org/10.3390/ijms251910272

AMA Style

Watrowski R, Schuster E, Van Gorp T, Hofstetter G, Fischer MB, Mahner S, Polterauer S, Zeillinger R, Obermayr E. Association of the Single Nucleotide Polymorphisms rs11556218, rs4778889, rs4072111, and rs1131445 of the Interleukin-16 Gene with Ovarian Cancer. International Journal of Molecular Sciences. 2024; 25(19):10272. https://doi.org/10.3390/ijms251910272

Chicago/Turabian Style

Watrowski, Rafał, Eva Schuster, Toon Van Gorp, Gerda Hofstetter, Michael B. Fischer, Sven Mahner, Stefan Polterauer, Robert Zeillinger, and Eva Obermayr. 2024. "Association of the Single Nucleotide Polymorphisms rs11556218, rs4778889, rs4072111, and rs1131445 of the Interleukin-16 Gene with Ovarian Cancer" International Journal of Molecular Sciences 25, no. 19: 10272. https://doi.org/10.3390/ijms251910272

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