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基于 SHOX2 甲基化分析和血清生物标志物的新型早期肺癌诊断方法。

A Novel Diagnosis Method Based on Methylation Analysis of SHOX2 and Serum Biomarker for Early Stage Lung Cancer.

机构信息

Department of Thoracic Surgery, Jiangmen Centre Hospital, Jiangmen, Guangdong, China.

Department of Laboratory Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.

出版信息

Cancer Control. 2020 Jan-Dec;27(1):1073274820969703. doi: 10.1177/1073274820969703.

Abstract

OBJECTIVES

Lung cancer (LC) is often accompanied by significant methylation abnormalities. This study aimed to develop a decision tree (DT) accompanied the stature homeobox 2 gene (SHOX2) / prostaglandin E receptor 4 (PTGER4) gene DNA methylation with traditional tumor marker in the differential diagnosis of benign and malignant lung nodule.

METHODS

We performed a study with 104 patients enrolled in the LC group and 36 patients in the benign lung diseases group. All the clinical data of these patients were collected through electronic medical record. Total Methylation (TM) status of both SHOX2 and PTGER4 was defined as methylation levels of SHOX2 plus methylation levels of PTGER4. One-way analysis was used to compare the concentrations of serum samples and t-test was used to compare pairwise mean values between groups. Receiver operating curve (ROC) was used to evaluate the diagnostic value. Furthermore, the strategy was validated in 19 LC patients and 11 patients with benign lung diseases.

RESULTS

There were significant differences between the concentration of neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), cytokeratin 19 fragments (CYFRA21 -1) and the methylation levels of SHOX2, PTGER4 and TM in lung benign diseases and cancer group. The AUCs of NSE, CEA, CYFRA21 -1, Methylation SHOX2, Methylation PTGER4 and TM were 0.721 (95% CI: 0.627-0.816), 0.753 (95% CI: 0.673-0.833) and 0.778(95% CI: 0.700-0.856), 0.851(0.786-0.916), 0.847(0.780-0.913) and 0.861(0.800-0.922) respectively. We developed a DT model with TM and CYFRA21 -1 used in this study, and the area under the curve (AUC) of DT was 0.921 and the sensitivity up to 0.856. In the validation cohort, the AUC of SHOX2, PTGER4 and TM was also much higher than traditional serum markers.

CONCLUSIONS

Our results indicated that the DT model calculated from the TM and CYFRA21 -1 can accurately classify LC and benign diseases, which showed better diagnostic performance than traditional serum parameter.

摘要

目的

肺癌(LC)常伴有显著的甲基化异常。本研究旨在建立一个决策树(DT),结合矮小同源盒 2 基因(SHOX2)/前列腺素 E 受体 4(PTGER4)基因的 DNA 甲基化与传统肿瘤标志物,用于良恶性肺结节的鉴别诊断。

方法

我们对 104 例 LC 组患者和 36 例良性肺部疾病组患者进行了研究。通过电子病历收集了这些患者的所有临床数据。SHOX2 和 PTGER4 的总甲基化(TM)状态定义为 SHOX2 甲基化水平与 PTGER4 甲基化水平之和。采用单因素分析比较血清样本浓度,采用 t 检验比较组间均值。采用受试者工作特征曲线(ROC)评估诊断价值。此外,在 19 例 LC 患者和 11 例良性肺部疾病患者中对该策略进行了验证。

结果

肺良性疾病和癌症组之间神经元特异性烯醇化酶(NSE)、癌胚抗原(CEA)、细胞角蛋白 19 片段(CYFRA21-1)浓度和 SHOX2、PTGER4 和 TM 甲基化水平存在显著差异。NSE、CEA、CYFRA21-1、Methylation SHOX2、Methylation PTGER4 和 TM 的 AUC 分别为 0.721(95%CI:0.627-0.816)、0.753(95%CI:0.673-0.833)和 0.778(95%CI:0.700-0.856)、0.851(0.786-0.916)、0.847(0.780-0.913)和 0.861(0.800-0.922)。我们建立了一个使用本研究中 TM 和 CYFRA21-1 的 DT 模型,其 AUC 为 0.921,灵敏度高达 0.856。在验证队列中,SHOX2、PTGER4 和 TM 的 AUC 也明显高于传统的血清标志物。

结论

我们的结果表明,基于 TM 和 CYFRA21-1 计算的 DT 模型可以准确地对 LC 和良性疾病进行分类,其诊断性能优于传统的血清参数。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a664/7791477/7aef9bc7f6c6/10.1177_1073274820969703-fig1.jpg

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