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拷贝数变异:一种用于甲状腺乳头状癌的新型分子标志物。

Copy number variations: A novel molecular marker for papillary thyroid cancer.

作者信息

Lai Xingjian, Gao Luying, Zhou Gaoying, Xu Xiequn, Wang Jinhui

机构信息

Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Beijing Longer Gene Technology Co., Ltd., Beijing, China.

出版信息

Heliyon. 2022 Oct 17;8(10):e11107. doi: 10.1016/j.heliyon.2022.e11107. eCollection 2022 Oct.

Abstract

BACKGROUND

We aimed to screen tumor-associated functional genes on a large scale through copy number variation (CNV) analysis of papillary thyroid cancer (PTC).

METHODS

We analyzed 74 tissue samples from 41 patients with thyroid nodules. The samples were subjected to whole-genome resequencing and then analyzed by the 'WISECONDOR' method. Potential chromosome CNV regions were identified between the different sample groups.

RESULTS

Of the 74 samples from 41 patients, 28 were PTC tissue samples, 29 were para-carcinoma tissue samples, 13 were benign tumor tissue samples and 4 were para-benign tumor tissue samples. According to our findings, PTC can be identified by CNVs at the corresponding positions on chromosomes 5, 7, 8, 10, and 17. For carcinoma tissue, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and area under the curve (AUC) of the test method were 100%, 66.7%, 87.5%, 100.0%, 90.0% and 0.83 (95% confidence interval [CI], 0.67-1.00) and for para-carcinoma tissue, these values were 96.6%, 75.0%, 96.6%, 75.0%, 93.9% and 0.86 (95% CI, 0.60-1.00).

CONCLUSION

CNV analysis assays involving high-volume sequencing analysis can increase the identification of PTC, potentially avoiding errors caused by position deflection in sampling.

摘要

背景

我们旨在通过对甲状腺乳头状癌(PTC)的拷贝数变异(CNV)分析大规模筛选肿瘤相关功能基因。

方法

我们分析了41例甲状腺结节患者的74份组织样本。对样本进行全基因组重测序,然后采用“WISECONDOR”方法进行分析。在不同样本组之间鉴定潜在的染色体CNV区域。

结果

在41例患者的74份样本中,28份为PTC组织样本,29份为癌旁组织样本,13份为良性肿瘤组织样本,4份为良性肿瘤旁组织样本。根据我们的研究结果,PTC可通过染色体5、7、8、10和17上相应位置的CNV来识别。对于癌组织,该检测方法的灵敏度、特异度、阳性预测值(PPV)、阴性预测值(NPV)、准确度和曲线下面积(AUC)分别为100%、66.7%、87.5%、100.0%、90.0%和0.83(95%置信区间[CI],0.67 - 1.00);对于癌旁组织,这些值分别为96.6%、75.0%、96.6%、75.0%、93.9%和0.86(95%CI,0.60 - 1.00)。

结论

涉及高通量测序分析的CNV分析检测可提高PTC的识别率,可能避免采样中位置偏移导致的误差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4de0/9589167/f44ab8187232/gr1.jpg

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