Multivariate competing endogenous RNA network characterization for cancer microRNA biomarker discovery: a novel bioinformatics model with application to prostate cancer metastasis.

IF 5.1 4区 医学 Q1 MEDICINE, RESEARCH & EXPERIMENTAL
Precision Clinical Medicine Pub Date : 2022-01-10 eCollection Date: 2022-03-01 DOI:10.1093/pcmedi/pbac001
Yuxin Lin, Xin Qi, Jing Chen, Bairong Shen
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引用次数: 2

Abstract

Background: MicroRNAs (miRNAs) are post-transcriptional regulators with potential as biomarkers for cancer management. Data-driven competing endogenous RNA (ceRNA) network modeling is an effective way to decipher the complex interplay between miRNAs and spongers. However, there are currently no general rules for ceRNA network-based biomarker prioritization.

Methods and results: In this study, a novel bioinformatics model was developed by integrating gene expression with multivariate miRNA-target data for ceRNA network-based biomarker discovery. Compared with traditional methods, the structural vulnerability in the human long non-coding RNA (lncRNA)-miRNA-messenger RNAs (mRNA) network was comprehensively analyzed, and the single-line regulatory or competing mode among miRNAs, lncRNAs, and mRNAs was characterized and quantified as statistical evidence for miRNA biomarker identification. The application of this model to prostate cancer (PCa) metastasis identified a total of 12 miRNAs as putative biomarkers from the metastatic PCa-specific lncRNA-miRNA-mRNA network and nine of them have been previously reported as biomarkers for PCa metastasis. The receiver operating characteristic curve and cell line qRT-PCR experiments demonstrated the power of miR-26b-5p, miR-130a-3p, and miR-363-3p as novel candidates for predicting PCa metastasis. Moreover, PCa-associated pathways such as prostate cancer signaling, ERK/MAPK signaling, and TGF-β signaling were significantly enriched by targets of identified miRNAs, indicating the underlying mechanisms of miRNAs in PCa carcinogenesis.

Conclusions: A novel ceRNA-based bioinformatics model was proposed and applied to screen candidate miRNA biomarkers for PCa metastasis. Functional validations using human samples and clinical data will be performed for future translational studies on the identified miRNAs.

多变量竞争内源性RNA网络表征癌症microRNA生物标志物发现:一种应用于前列腺癌转移的新型生物信息学模型。
背景:MicroRNAs (miRNAs)是一种转录后调节因子,具有作为癌症治疗生物标志物的潜力。数据驱动的竞争内源性RNA (ceRNA)网络建模是破译mirna与海绵生物之间复杂相互作用的有效方法。然而,目前还没有基于ceRNA网络的生物标志物优先排序的通用规则。方法和结果:在本研究中,通过整合基因表达和多变量mirna靶点数据,建立了一种新的生物信息学模型,用于基于ceRNA网络的生物标志物发现。与传统方法相比,全面分析人类长链非编码RNA (lncRNA)-miRNA-信使RNA (mRNA)网络的结构脆弱性,并对miRNA、lncRNA和mRNA之间的单线调控或竞争模式进行表征和量化,作为鉴定miRNA生物标志物的统计证据。将该模型应用于前列腺癌(PCa)转移,共鉴定出来自转移性PCa特异性lncRNA-miRNA-mRNA网络的12种mirna作为推定的生物标志物,其中9种已被报道为前列腺癌转移的生物标志物。受体工作特性曲线和细胞系qRT-PCR实验证明了miR-26b-5p、miR-130a-3p和miR-363-3p作为预测前列腺癌转移的新候选者的能力。此外,前列腺癌相关信号通路,如前列腺癌信号通路、ERK/MAPK信号通路和TGF-β信号通路被鉴定的mirna靶点显著富集,表明mirna在前列腺癌发生中的潜在机制。结论:提出了一种新的基于cerna的生物信息学模型,并应用于筛选前列腺癌转移的候选miRNA生物标志物。将使用人类样本和临床数据进行功能验证,以便对鉴定的mirna进行未来的转化研究。
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来源期刊
Precision Clinical Medicine
Precision Clinical Medicine MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
10.80
自引率
0.00%
发文量
26
审稿时长
5 weeks
期刊介绍: Precision Clinical Medicine (PCM) is an international, peer-reviewed, open access journal that provides timely publication of original research articles, case reports, reviews, editorials, and perspectives across the spectrum of precision medicine. The journal's mission is to deliver new theories, methods, and evidence that enhance disease diagnosis, treatment, prevention, and prognosis, thereby establishing a vital communication platform for clinicians and researchers that has the potential to transform medical practice. PCM encompasses all facets of precision medicine, which involves personalized approaches to diagnosis, treatment, and prevention, tailored to individual patients or patient subgroups based on their unique genetic, phenotypic, or psychosocial profiles. The clinical conditions addressed by the journal include a wide range of areas such as cancer, infectious diseases, inherited diseases, complex diseases, and rare diseases.
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