解码动态 miRNA:ceRNA 相互作用,揭示主要癌症景观中的治疗见解和靶点

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-04-17 DOI:10.1186/s13040-024-00362-4
Selcen Ari Yuka, Alper Yilmaz
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引用次数: 0

摘要

竞争性内源 RNA 通过转录后相互作用的交叉作用在细胞分子机制中发挥关键作用。对 ceRNA 交叉作用的研究特别依赖于游离转录本的丰度,一般涉及大、小规模的研究,包括整合来自组织的转录组数据和相关性分析。ceRNA 相互作用的丰度依赖性表明,特定组织和条件的 ceRNA 动态可能会波动。然而,目前还没有全面的研究调查正常组织中的 ceRNA 相互作用、癌症组织中丢失和/或出现的 ceRNA 及其相互作用。在本研究中,我们全面分析了在三种高发癌症(LUAD、PRAD 和 BRCA)中观察到的肿瘤特异性 ceRNA 波动,并分别与健康肺组织、前列腺组织和乳腺组织进行了比较。我们对肿瘤特异性竞争性内源性 RNA(ceRNA)相互作用的观察结果显示,在肺腺癌(LUAD)、前列腺癌(PRAD)和乳腺浸润性癌(BRCA)病例中,分别有 3204、1233 和 406 个 ceRNA 在肿瘤组织内进行转录后互通,而在相应的健康样本中则没有。我们还发现,三种癌症类型共有 90 个 ceRNA,与正常组织相比,这些 ceRNA 参与了肿瘤组织中的 ceRNA 相互作用。在与 miRNAs 直接相互作用的 90 个 ceRNAs 中,我们发现了一个由 165 个 miRNAs 和 63 个 ceRNAs 组成的核心网络,在未来的研究中,RNA 靶向和 RNA 介导的方法应考虑这些核心网络,并可用于这三种侵袭性癌症类型。更具体地说,在这个核心相互作用网络中,GALNT7、KLF9 和 DAB2 等 ceRNA 和 miR-106a/b-5p 、miR-20a-5p 和 miR-519d-3p 等 miRNA 有可能成为这三种侵袭性癌症的共同靶点。与传统的利用与正常组织相比的差异表达基因构建 ceRNA 网络的方法不同,我们提出的方法是通过考虑 ceRNA:miRNA 相互作用中的上下文来识别 ceRNA 参与者。我们的研究结果有可能揭示癌症类型中独特和常见的 ceRNA 相互作用,并确定关键 RNA,从而为基于 RNA 的抗癌策略铺平道路。
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Decoding dynamic miRNA:ceRNA interactions unveils therapeutic insights and targets across predominant cancer landscapes
Competing endogenous RNAs play key roles in cellular molecular mechanisms through cross-talk in post-transcriptional interactions. Studies on ceRNA cross-talk, which is particularly dependent on the abundance of free transcripts, generally involve large- and small-scale studies involving the integration of transcriptomic data from tissues and correlation analyses. This abundance-dependent nature of ceRNA interactions suggests that tissue- and condition-specific ceRNA dynamics may fluctuate. However, there are no comprehensive studies investigating the ceRNA interactions in normal tissue, ceRNAs that are lost and/or appear in cancerous tissues or their interactions. In this study, we comprehensively analyzed the tumor-specific ceRNA fluctuations observed in the three highest-incidence cancers, LUAD, PRAD, and BRCA, compared to healthy lung, prostate, and breast tissues, respectively. Our observations pertaining to tumor-specific competing endogenous RNA (ceRNA) interactions revealed that, in the cases of lung adenocarcinoma (LUAD), prostate adenocarcinoma (PRAD), and breast invasive carcinoma (BRCA), 3,204, 1,233, and 406 ceRNAs, respectively, engage in post-transcriptional intercommunication within tumor tissues, in contrast to their absence in corresponding healthy samples. We also found that 90 ceRNAs are shared by the three cancer types and that these ceRNAs participate in ceRNA interactions in tumor tissues compared to those in normal tissues. Among the 90 ceRNAs that directly interact with miRNAs, we uncovered a core network of 165 miRNAs and 63 ceRNAs that should be considered in RNA-targeted and RNA-mediated approaches in future studies and could be used in these three aggressive cancer types. More specifically, in this core interaction network, ceRNAs such as GALNT7, KLF9, and DAB2 and miRNAs like miR-106a/b-5p, miR-20a-5p, and miR-519d-3p may have potential as common targets in the three critical cancers. In contrast to conventional methods that construct ceRNA networks using differentially expressed genes compared to normal tissues, our proposed approach identifies ceRNA players by considering their context within the ceRNA:miRNA interactions. Our results have the potential to reveal distinct and common ceRNA interactions in cancer types and to pinpoint critical RNAs, thereby paving the way for RNA-based strategies in the battle against cancer.
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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