DNA content and clinicopathological features aid in distinguishing ameloblastic carcinoma from ameloblastoma

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-01-01 DOI:10.1111/jop.13505
Paulo Victor Mendes Penafort, André Caroli Rocha, Fernanda Viviane Mariano, Jean Nunes dos Santos, Márcio Campos Oliveira, Pablo Agustin Vargas, Marcelo Sperandio
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Abstract

Background

Ameloblastoma and ameloblastic carcinoma are epithelial odontogenic tumors that can be morphologically similar. In the present study, we evaluated the DNA content and Ki-67 index in the two tumors.

Methods

The paraffin blocks of the tumors were selected to obtain sections for the immunohistochemical reactions and preparation of the cell suspension for acquisition in a flow cytometer. The Random Forest package of the R software was used to verify the contribution of each variable to classify lesions into ameloblastoma or ameloblastic carcinoma.

Results

Thirty-two ameloblastoma and five ameloblastic carcinoma were included in the study. In our sample, we did not find statistically significant differences in Ki-67 labeling rates. A higher fraction of cells in 2c (G1) was correlated with the diagnosis of ameloblastoma, whereas higher rates of 5c-exceeding rate (5cER) were correlated with ameloblastic carcinoma. The Random Forest model highlighted histopathological findings and parameters of DNA ploidy study as important features for distinguishing ameloblastoma from ameloblastic carcinoma.

Conclusion

Our findings suggest that the parameters of the DNA ploidy study can be ancillary tools in the classification of ameloblastoma and ameloblastic carcinoma.

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DNA 含量和临床病理特征有助于区分成釉细胞癌和成釉细胞瘤。
背景:绒毛膜母细胞瘤和绒毛膜母细胞癌是上皮性牙源性肿瘤,两者在形态上可能相似。本研究评估了这两种肿瘤的 DNA 含量和 Ki-67 指数:方法:选取肿瘤的石蜡块切片,用于免疫组化反应和制备细胞悬液,以便在流式细胞仪中采集。使用R软件的随机森林软件包来验证每个变量对将病变划分为骨髓母细胞瘤或骨髓母细胞癌的贡献:研究共纳入了 32 个成釉细胞瘤和 5 个成釉细胞癌。在我们的样本中,我们没有发现 Ki-67 标记率有显著的统计学差异。2c(G1)细胞比例较高与诊断为成釉细胞瘤相关,而5c超标率(5cER)较高与成釉细胞癌相关。随机森林模型强调组织病理学结果和DNA倍性研究参数是区分成釉细胞瘤和成釉细胞癌的重要特征:我们的研究结果表明,DNA倍性研究参数可作为骨髓母细胞瘤和骨髓母细胞癌分类的辅助工具。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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