Potential of AKNA as a Predictive Biomarker for Ovarian Cancer and Its Relationship to Tumor Grading.

Abstract

Background: Ovarian cancer exhibits a significant prevalence and incidence on a global scale. Low-grade or high-grade epithelial-type ovarian cancer can be classified by using the dualistic model. Inflammation has been associated with AKNA protein by cancer researchers. The potential of AKNA as a cancer biomarker is supported by its significance and association with ovarian carcinoma. Uninvestigated is this enormous potential.

Aim: This study examines the correlation between AKNA expression in low-grade and high-grade ovarian tumors and its utility as a predictive biomarker for ovarian cancer.

Methods: This study examined a total of thirty-one samples, which were classified into three groups: cyst, low-grade, and high-grade ovarian carcinoma. The departmental archive was accessed for the following information: age, tumor size, nuclear grade, mitosis, ovary volume, implant tumor status, lymph vascular invasion status, lymph node metastasis, and tumor-infiltrating lymphocyte. The expression of AKNA was determined using IHC staining. The information was collected and analyzed via analysis of variance.

Results: The AKNA H-score shows the mean difference between all three groups (P < 0.001). Cysts had the highest AKNA expression, followed by low-grade and high-grade ovarian carcinoma.

Conclusion: Higher-grade ovarian cancer expressed less AKNA compared to cysts or low-grade forms of the disease. This considerable difference suggests that AKNA might predict ovarian cancer tumor grade.