Abstract

The exact pathogenesis and influence of various cytokines in patients with ovarian lesions remains unclear. Hence, this study aimed to investigate whether IL-6, IL-8, and TNF-α could be considered as new useful markers for diagnosis of ovarian cancer. 63 women diagnosed with ovarian cancer (OC) and 53 patients with benign ovarian cystic (BOC) lesions were included in this study. Serum levels of IL-6, IL-8, and TNF-α were measured using ELISA. Statistical comparisons were made using the Mann–Whitney U test and all correlations were evaluated by Spearman’s ranks. The serum IL-8 and TNF-α concentration measured in the OC Group was significantly higher than in the BOC Group (p < 0.05). The cutoff level of IL-8 and TNF-α in the serum was set at 4.09 ng/mL and 2.63 ng/mL, respectively, with the sensitivity and specificity of 70% and 96% for IL-8 and 85.7% and 79.3% for TNF-α (p < 0.0001). These results suggest that IL-8 and TNF-α are useful biomarkers for predicting the malignant character of lesions of the ovary. The present study highlighted the importance of measuring the cytokines such as IL-8 and TNF-α in patients with ovarian lesions in predicting the clinical outcome.

Highlights

  • Ovarian carcinoma is one of the most common types of neoplasms in women

  • The serum IL-8 concentration measured in the ovarian cancer (OC) Group was significantly higher than in the benign ovarian cystic (BOC) Group

  • Our study reports the presence of correlation between IL-6 and HE4 in the OC group, which was not observed in the BOC group

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Summary

Introduction

More than 295 thousand new cases are diagnosed worldwide It is one of the main causes of death in women, with nearly 185 thousand deaths per year [1]. It has the highest case–to–mortality ratio among all gynecological malignancies [2]. The most efficient laboratory diagnostic tool for diagnosis of ovarian cancer is the combination of CA125 and HE4 called ROMA (Risk of Ovarian Malignancies Algorithm) [3]. It is crucial to search for other markers useful in the diagnosis and prognosis of ovarian cancer at an early stage of its development to combine them with the ROMA algorithm to improve its diagnostic efficiency

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