Suppr超能文献

阴道镜引导下活检诊断为 LSIL/CIN1 的女性中,脱落细胞学检测 miRNA 对漏诊的高级别病变的检测。

MiRNA detection in cervical exfoliated cells for missed high-grade lesions in women with LSIL/CIN1 diagnosis after colposcopy-guided biopsy.

机构信息

Department of Gynecologic Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China.

Women's Reproductive Health Laboratory of Zhejiang Province, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China.

出版信息

BMC Cancer. 2019 Jan 30;19(1):112. doi: 10.1186/s12885-019-5311-3.

Abstract

BACKGROUND

Low-grade squamous intraepithelial lesion/cervical intraepithelial neoplasia grade 1 (LSIL/CIN1) preceded by colposcopy guided biopsy is recommended conservative follow-up, although some of these lesions are actually high-grade lesions, which are missed on an initial colposcopy. Therefore, in this work, we evaluate the potential role of miRNA detection in cervical exfoliated cells in a clinic-based population for predicting missed high-grade lesions in women diagnosed with LSIL/CIN1 after colposcopy-guided biopsy.

METHODS

A total number of 177 women with a diagnosis of LSIL/CIN1 obtained by colposcopy-guided biopsy were grouped into two categories according to the histology of the conization specimens: consistent LSIL/CIN1 group (surgical pathology consistent with colposcopic diagnosis) and missed high-grade lesion group (surgical pathology found high-grade lesion). The expression of eight miRNAs, such as miRNA195, miRNA424, miRNA375, miRNA218, miRNA34a, miRNA29a, miRNA16-2, and miRNA20a was detected by real time-quantitative polymerase chain reaction (RT-qPCR) in cervical exfoliated cells of the 177 patients. Pearson Chi-Square was used to compare the performance efficiency of patients' characteristics. Nonparametric Man-Whitney U test was used to assess differences in miRNA expression. The receiver operating characteristic (ROC) curve was used to assess the performance of miRNA evaluation in detecting missed high-grade lesions.

RESULTS

Among the 177 women with biopsy-confirmed CIN1, 15.3% (27/177) had CIN2+ in the conization specimen (missed high-grade lesion group) and 84.7% (150/177) had CIN1-(consistent LSIL/CIN1 group). The relative expression of miRNA-195 and miRNA-29a in the missed high-grade lesion group was significantly lower than that in the consistent LSIL/CIN1 group. The relative expression of miRNA16-2 and miRNA20a in the missed high-grade lesion group was significantly higher than that in the consistent LSIL/CIN1 group. No significant difference was observed between these two groups regarding the other four miRNAs. Of these significant miRNAs, miRNA29a detection achieved the highest Youden index (0.733), sensitivity (92.6%), positive predictive value (46.2%), negative predictive value (98.3%) and higher specificity (80.7%) when identifying missed high-grade lesions.

CONCLUSIONS

Detection of miRNA might provide a new triage for identifying a group at higher risk of missed high-grade lesions in women with colposcopy diagnosis of LSIL/CIN1.

摘要

背景

低级别鳞状上皮内病变/宫颈上皮内瘤变 1 级(LSIL/CIN1)经阴道镜引导下活检推荐保守随访,尽管这些病变中的一些实际上是高级别病变,在初次阴道镜检查时被遗漏。因此,在这项工作中,我们评估了在基于临床的人群中检测宫颈脱落细胞中 miRNA 的潜在作用,以预测经阴道镜引导活检诊断为 LSIL/CIN1 的女性中漏诊的高级别病变。

方法

根据锥形切除标本的组织病理学,将 177 例经阴道镜引导活检诊断为 LSIL/CIN1 的女性分为两组:符合 LSIL/CIN1 组(手术病理与阴道镜诊断一致)和漏诊高级别病变组(手术病理发现高级别病变)。通过实时定量聚合酶链反应(RT-qPCR)检测 177 例患者宫颈脱落细胞中 8 种 miRNA(如 miRNA195、miRNA424、miRNA375、miRNA218、miRNA34a、miRNA29a、miRNA16-2 和 miRNA20a)的表达。采用 Pearson Chi-Square 比较患者特征的表现效率。采用非参数 Man-Whitney U 检验评估 miRNA 表达的差异。采用受试者工作特征(ROC)曲线评估 miRNA 评价在检测漏诊高级别病变中的性能。

结果

在经活检证实为 CIN1 的 177 例女性中,15.3%(27/177)在锥形切除标本中为 CIN2+(漏诊高级别病变组),84.7%(150/177)为 CIN1-(符合 LSIL/CIN1 组)。漏诊高级别病变组中 miRNA-195 和 miRNA-29a 的相对表达明显低于符合 LSIL/CIN1 组。漏诊高级别病变组中 miRNA16-2 和 miRNA20a 的相对表达明显高于符合 LSIL/CIN1 组。两组间其他 4 种 miRNA 无显著差异。在这些有意义的 miRNA 中,miRNA29a 的检测在识别经阴道镜诊断为 LSIL/CIN1 的女性中漏诊的高级别病变时,具有最高的 Youden 指数(0.733)、灵敏度(92.6%)、阳性预测值(46.2%)、阴性预测值(98.3%)和更高的特异性(80.7%)。

结论

检测 miRNA 可能为经阴道镜诊断为 LSIL/CIN1 的女性中漏诊高级别病变风险较高的患者提供一种新的辅助诊断方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2dc1/6354336/0ccfec3c9f8c/12885_2019_5311_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验