竞争性内源性 RNA(ceRNA)与癌症治疗的耐药性。

IF 4.6 Q1 ONCOLOGY 癌症耐药(英文) Pub Date : 2024-09-25 eCollection Date: 2024-01-01 DOI:10.20517/cdr.2024.66
Kenneth K W To, Hang Zhang, William C Cho
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摘要

竞争性内源性 RNA(ceRNA)是指具有高度相似的 microRNA 响应元件(MRE)的转录本。microRNA(miRNA)是短的内源性单链非编码 RNA(ncRNA),可通过与目标 mRNA 转录本 3' 非翻译区(UTR)上的 MRE 结合抑制基因表达,从而通过促进 mRNA 降解和/或抑制蛋白质翻译来抑制基因表达。mRNA 转录本、环状 RNA(circRNA)、长非编码 RNA(lncRNA)和转录假基因可能共享相似的 MREs,它们可以竞争同一个 miRNA 池。这些 ceRNA 可通过竞争共享的 miRNA 而影响彼此的水平。不同 RNA 之间的这种相互作用构成了一个 ceRNA 网络,它调控着许多重要的生物过程。癌症耐药性是导致化疗患者治疗失败的一个主要因素。耐药性可通过遗传、表观遗传和各种肿瘤微环境机制获得。ceRNA串联及其在化疗耐药性中的作用正引起癌症研究界的关注。本综述对不同癌症类型和化疗药物类别中导致耐药性的 ceRNA 失调的最新研究进行了总结。有趣的是,越来越多的证据表明,ceRNA 可作为预后生物标志物来预测癌症化疗的临床反应。不过,还需要对计算算法生成的假定 ceRNA 网络进行详细的实验研究,以支持其在治疗和预后方面的应用。
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Competing endogenous RNAs (ceRNAs) and drug resistance to cancer therapy.

Competing endogenous RNAs (ceRNAs) are transcripts that possess highly similar microRNA response elements (MREs). microRNAs (miRNAs) are short, endogenous, single-stranded non-coding RNAs (ncRNAs) that can repress gene expression by binding to MREs on the 3' untranslated regions (UTRs) of the target mRNA transcripts to suppress gene expression by promoting mRNA degradation and/or inhibiting protein translation. mRNA transcripts, circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), and transcribed pseudogenes could share similar MREs, and they can compete for the same pool of miRNAs. These ceRNAs may affect the level of one another by competing for their shared miRNAs. This interplay between different RNAs constitutes a ceRNA network, which regulates many important biological processes. Cancer drug resistance is a major factor leading to treatment failure in patients receiving chemotherapy. It can be acquired through genetic, epigenetic, and various tumor microenvironment mechanisms. The involvement of ceRNA crosstalk and its disruption in chemotherapy resistance is attracting attention in the cancer research community. This review presents an updated summary of the latest research on ceRNA dysregulation causing drug resistance across different cancer types and chemotherapeutic drug classes. Interestingly, accumulating evidence suggests that ceRNAs may be used as prognostic biomarkers to predict clinical response to cancer chemotherapy. Nevertheless, detailed experimental investigations of the putative ceRNA networks generated by computational algorithms are needed to support their translation for therapeutic and prognostic applications.

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