Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis
Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis
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DOI：
10.1007/s10549-023-07208-3
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发表时间：
2024-01-08
影响因子：
3.8
通讯作者：
Li，Hongdong
中科院分区：
文献类型：
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作者：
Ma，Liyuan;Gao，Yaru;Li，Hongdong
目的血清microRNA（miRNA）作为诊断乳腺癌（BrC）的非侵入性生物标志物具有很大的潜力。然而，大多数诊断模型依赖于miRNAs的绝对表达水平，这容易受到批次效应的影响，并且对临床转化具有挑战性。此外，目前的液体活检诊断生物标志物的BrC的研究主要集中在区分BrC患者从健康controls.MethodsWe收集了大量的miRNA表达数据，涉及8465样本从GEO，包括13种不同的癌症类型和非癌症对照。基于相对表达排序（REO）的每个样本内的miRNA，我们采用贪婪，LASSO多元线性回归，和随机森林算法，以确定一个定性的生物标志物，具体到BrC通过比较BrC样本的其他cancers作为controls.ResultsWe的样本开发了一个BrC特异性生物标志物称为7-miRPairs，由7个miRNA对。它在我们分析的机器学习算法中表现出相当的分类性能，同时需要更少的miRNA对，准确地将BrC与其他12种癌症类型区分开来。7-miRPairs的诊断性能在训练集中是有利的（准确度= 98.47%，特异性= 98.14%，灵敏度= 99.25%），并且在测试集中获得类似的结果（准确度= 97.22%，特异性= 96.87%，灵敏度= 98.02%）。KEGG途径富集分析的11个miRNAs内的7-miRPairs揭示了显着富集的靶mRNA的途径与BrC.ConclusionOur研究提供的证据表明，利用血清miRNA对可以提供显着的优势，BrC特异性诊断在临床实践中，通过直接比较血清样本与其他癌症类型的BrC。
PurposeSerum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment.MethodsWe collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls.ResultsWe developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC.ConclusionOur study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.