Multi-MicroRNA Analysis Can Improve the Diagnostic Performance of Mammography in Determining Breast Cancer Risk

Ji-Eun Song, Ji Young Jang, Kyung Nam Kang, Ji Soo Jung, Chul Woo Kim, Ah Sol Kim
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Abstract

The objective of this study was to determine whether multi-microRNA analysis using a combination of four microRNA biomarkers (miR-1246, 202, 21, and 219B) could improve the diagnostic performance of mammography in determining breast cancer risk by age group (under 50 vs. over 50) and distinguish breast cancer from benign breast diseases and other cancers (thyroid, colon, stomach, lung, liver, and cervix cancers). To verify breast cancer classification performance of the four miRNA biomarkers and whether the model providing breast cancer risk score could distinguish between benign breast disease and other cancers, the model was verified using nonlinear support vector machine (SVM) and generalized linear model (GLM) and age and four miRNA qRT-PCR analysis values (dCt) were input to these models. Breast cancer risk scores for each Breast Imaging-Reporting and Data System (BI-RADS) category in multi-microRNA analysis were analyzed to examine the correlation between breast cancer risk scores and mammography categories. We generated two models using two classification algorithms, SVM and GLM, with a combination of four miRNA biomarkers showing high performance and sensitivities of 84.5% and 82.1%, a specificity of 85%, and areas under the curve (AUCs) of 0.967 and 0.965, respectively, which showed consistent performance across all stages of breast cancer and patient ages. The results of this study showed that this multi-microRNA analysis using the four miRNA biomarkers was effective in classifying breast cancer in patients under the age of 50, which is challenging to accurately diagnose. In addition, breast cancer and benign breast diseases can be classified, showing the possibility of helping with diagnosis by mammography. Verification of the performance of the four miRNA biomarkers confirmed that multi-microRNA analysis could be used as a new breast cancer screening aid to improve the accuracy of mammography. However, many factors must be considered for clinical use. Further validation with an appropriate screening population in large clinical trials is required. This trial is registered with (KNUCH 2022-04-036).
多重微量核糖核酸分析可提高乳腺 X 射线摄影在确定乳腺癌风险方面的诊断性能
这项研究的目的是确定利用四种microRNA生物标记物(miR-1246、202、21和219B)组合进行的多microRNA分析是否能提高乳腺X光造影术在按年龄组(50岁以下与50岁以上)确定乳腺癌风险方面的诊断性能,并区分乳腺癌与良性乳腺疾病和其他癌症(甲状腺癌、结肠癌、胃癌、肺癌、肝癌和宫颈癌)。为了验证四种 miRNA 生物标记物的乳腺癌分类性能,以及提供乳腺癌风险评分的模型能否区分良性乳腺疾病和其他癌症,我们使用非线性支持向量机(SVM)和广义线性模型(GLM)对模型进行了验证,并向这些模型输入了年龄和四种 miRNA qRT-PCR 分析值(dCt)。我们分析了多miRNA分析中每个乳腺影像报告和数据系统(BI-RADS)类别的乳腺癌风险评分,以研究乳腺癌风险评分与乳腺X光检查类别之间的相关性。我们使用 SVM 和 GLM 两种分类算法生成了两个模型,四个 miRNA 生物标记物的组合表现出很高的性能,灵敏度分别为 84.5% 和 82.1%,特异性为 85%,曲线下面积(AUC)分别为 0.967 和 0.965,在乳腺癌的各个阶段和患者年龄段都表现出一致的性能。研究结果表明,这种利用四种 miRNA 生物标记物进行的多 miRNA 分析能有效地对 50 岁以下的乳腺癌患者进行分类,而准确诊断 50 岁以下的乳腺癌具有一定的难度。此外,乳腺癌和良性乳腺疾病也能被分类,这显示了通过乳房 X 射线照相术帮助诊断的可能性。对四种 miRNA 生物标记物性能的验证证实,多重 miRNA 分析可作为一种新的乳腺癌筛查辅助手段,以提高乳房 X 线照相术的准确性。然而,临床应用必须考虑许多因素。需要在大型临床试验中对适当的筛查人群进行进一步验证。该试验已在(KNUCH 2022-04-036)注册。
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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