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

Pub Date : 2024-09-01 Epub Date: 2024-09-30 DOI:10.4103/njcp.njcp_46_24

P Rustamadji, E Wiyarta, M Miftahuzzakiyah, D Sukmawati, D A Suryandari, R Kodariah

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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.

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AKNA 作为卵巢癌预测性生物标记物的潜力及其与肿瘤分级的关系

背景：卵巢癌在全球范围内的流行率和发病率都很高。低级别或高级别上皮型卵巢癌可通过二元模型进行分类。癌症研究人员认为炎症与 AKNA 蛋白有关。AKNA 作为癌症生物标志物的潜力因其与卵巢癌的重要性和关联性而得到支持。目的：本研究探讨了 AKNA 在低级别和高级别卵巢肿瘤中表达的相关性及其作为卵巢癌预测生物标志物的作用：本研究共检测了 31 份样本，将其分为三组：囊肿、低级别和高级别卵巢癌。研究人员在科室档案中查阅了以下信息：年龄、肿瘤大小、核分级、有丝分裂、卵巢体积、种植肿瘤状态、淋巴管侵犯状态、淋巴结转移和肿瘤浸润淋巴细胞。通过 IHC 染色确定 AKNA 的表达。收集的信息通过方差分析进行分析：结果：AKNA H-评分显示三组之间存在平均差异（P < 0.001）。囊肿的 AKNA 表达量最高，其次是低级别和高级别卵巢癌：结论：与囊肿或低级别卵巢癌相比，高级别卵巢癌的 AKNA 表达量较低。这一显著差异表明，AKNA可预测卵巢癌的肿瘤分级。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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