Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs.
Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs.
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DOI：
10.1186/s12864-023-09203-w
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发表时间：
2023-03-02
期刊：
影响因子：
4.4
通讯作者：
Hong, Guini
中科院分区：
文献类型：
--
作者：
Li, Hongdong;Ma, Liyuan;Luo, Fengyuan;Liu, Wenkai;Li, Na;Hu, Tao;Zhong, Haijian;Guo, You;Hong, Guini
血清microRNAs （miRNAs）是诊断胶质瘤很有前途的非侵入性生物标志物。然而，大多数报道的预测模型的构建没有足够大的样本量，其组成的血清mirna的定量表达水平容易受到批量效应的影响，降低了其临床适用性。我们提出了一种基于样本内mirna相对表达顺序，使用大量mirna谱血清样本（n = 15,460）检测定性血清预测性生物标志物的通用方法。开发了两组miRNA对（miRPairs）。第一组由5对血清miRPairs （5-miRPairs）组成，在区分胶质瘤和非癌对照（n = 436：胶质瘤= 236，非癌= 200）的3个验证集中达到100%的诊断准确率。另一个没有胶质瘤样本的验证集（非癌症= 2611）的预测准确率为95.9%。第二组包括32对血清miRPairs (32-miRPairs)，在特异性区分胶质瘤和其他癌症类型的训练集中达到100%的诊断性能（敏感性= 100%，特异性= 100%，准确性= 100%），在5个验证数据集中可重复（n = 3387：胶质瘤= 236，非胶质瘤= 3151，敏感性> 97.9%，特异性> 99.5%，准确性> 95.7%）。在其他脑部疾病中，5-miRPairs将所有非肿瘤样本归类为非癌症，包括中风（n = 165）、阿尔茨海默病（n = 973）和健康样本（n = 1820），将所有肿瘤样本归类为癌症，包括脑膜瘤（n = 16）和原发性中枢神经系统淋巴瘤样本（n = 39）。32-miRPairs分别预测82.2%和92.3%的两种肿瘤样本为阳性。基于人类miRNA组织图谱数据库，胶质瘤特异性32-miRPairs在脊髓（p = 0.013）和脑（p = 0.015）中显著富集。鉴定出的5- mirpair和32- mirpair为胶质瘤临床实践提供了潜在的人群筛查和癌症特异性生物标志物。在线版本包含补充材料，可在10.1186/s12864-023-09203-w获得。
Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability. We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs. Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer’s disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015). The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice. The online version contains supplementary material available at 10.1186/s12864-023-09203-w.
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影响因子：
5.6
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