微RNA图谱分析作为慢性淋巴细胞白血病首次治疗时间的预测指标:O-CLL1前瞻性研究的启示。

IF 3.6 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ennio Nano, Francesco Reggiani, Adriana Agnese Amaro, Paola Monti, Monica Colombo, Nadia Bertola, Fabiana Ferrero, Franco Fais, Antonella Bruzzese, Enrica Antonia Martino, Ernesto Vigna, Noemi Puccio, Mariaelena Pistoni, Federica Torricelli, Graziella D'Arrigo, Gianluigi Greco, Giovanni Tripepi, Carlo Adornetto, Massimo Gentile, Manlio Ferrarini, Massimo Negrini, Fortunato Morabito, Antonino Neri, Giovanna Cutrona
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引用次数: 0

摘要

大多数 CLL 病例都采用 "观察和等待 "策略,即推迟治疗,直到出现活动性疾病;然而,结合生物标志物的预后模型已被证明有助于预测治疗需求。在我们的前瞻性 O-CLL1 研究(包括 224 例患者)中,我们研究了 513 个微小 RNA(miRNA)对首次治疗时间(TTFT)的预测作用。在这项研究中,在一个基本的多变量模型中,六个成熟的变量(即 Rai 分期、β-2-微球蛋白水平、IGVH 突变状态、del11q、del17p 和 NOTCH1 突变)与 TTFT 保持着显著的相关性,共同产生了 75% 的 Harrell's C 指数,并解释了 TTFT 预测中 45.4% 的变异。关于 miRNA,513 个 miRNA 中有 73 个在单变量模型中与 TTFT 显著相关;其中 16 个在多变量分析中与结果保持独立关系。其中 8 个(即 miR-582-3p、miR-33a-3p、miR-516a-5p、miR-99a-5p、miR-296-3p、miR-502-5p、miR-625-5p 和 miR-29c-3p)表达较低与较短的 TTFT 相关,而其余 8 个(即 miR-150-5p、miR-625-5p 和 miR-29c-3p)表达较低与较短的 TTFT 相关、miR-150-5p、miR-148a-3p、miR-28-5p、miR-144-5p、miR-671-5p、miR-1-3p、miR-193a-3p 和 miR-124-3p)中,较高的表达量与较短的 TTFT 相关。将这些 miRNA 纳入基本模型可显著提高预测准确性,使 Harrell's C 指数提高到 81.1%,TTFT 的解释变异提高到 63.3%。此外,纳入 miRNA 评分还提高了综合鉴别改善指数(IDI)和净再分类指数(NRI),这凸显了 miRNA 在完善 CLL 预后模型方面的潜力,并为临床决策提供了启示。对不同表达的 miRNA 进行的硅学分析揭示了它们对几种通路的潜在调控功能,包括那些参与治疗反应的通路。为了给临床证据增加生物学背景,miRNA-mRNA 相关性分析表明,15 个已鉴定的 miRNA 与一组 50 个人工智能(AI)选择的基因之间至少有一个显著的负相关。总之,将特定 miRNA 鉴定为 TTFT 的预测因子有望加强 CLL 的风险分层,从而预测治疗需求。然而,还需要进一步的验证研究和深入的功能分析来证实这些观察结果的可靠性,并促进它们转化为有意义的临床实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MicroRNA Profiling as a Predictive Indicator for Time to First Treatment in Chronic Lymphocytic Leukemia: Insights from the O-CLL1 Prospective Study.

A "watch and wait" strategy, delaying treatment until active disease manifests, is adopted for most CLL cases; however, prognostic models incorporating biomarkers have shown to be useful to predict treatment requirement. In our prospective O-CLL1 study including 224 patients, we investigated the predictive role of 513 microRNAs (miRNAs) on time to first treatment (TTFT). In the context of this study, six well-established variables (i.e., Rai stage, beta-2-microglobulin levels, IGVH mutational status, del11q, del17p, and NOTCH1 mutations) maintained significant associations with TTFT in a basic multivariable model, collectively yielding a Harrell's C-index of 75% and explaining 45.4% of the variance in the prediction of TTFT. Concerning miRNAs, 73 out of 513 were significantly associated with TTFT in a univariable model; of these, 16 retained an independent relationship with the outcome in a multivariable analysis. For 8 of these (i.e., miR-582-3p, miR-33a-3p, miR-516a-5p, miR-99a-5p, and miR-296-3p, miR-502-5p, miR-625-5p, and miR-29c-3p), a lower expression correlated with a shorter TTFT, whereas in the remaining eight (i.e., miR-150-5p, miR-148a-3p, miR-28-5p, miR-144-5p, miR-671-5p, miR-1-3p, miR-193a-3p, and miR-124-3p), the higher expression was associated with shorter TTFT. Integrating these miRNAs into the basic model significantly enhanced predictive accuracy, raising the Harrell's C-index to 81.1% and the explained variation in TTFT to 63.3%. Moreover, the inclusion of the miRNA scores enhanced the integrated discrimination improvement (IDI) and the net reclassification index (NRI), underscoring the potential of miRNAs to refine CLL prognostic models and providing insights for clinical decision-making. In silico analyses on the differently expressed miRNAs revealed their potential regulatory functions of several pathways, including those involved in the therapeutic responses. To add a biological context to the clinical evidence, an miRNA-mRNA correlation analysis revealed at least one significant negative correlation between 15 of the identified miRNAs and a set of 50 artificial intelligence (AI)-selected genes, previously identified by us as relevant for TTFT prediction in the same cohort of CLL patients. In conclusion, the identification of specific miRNAs as predictors of TTFT holds promise for enhancing risk stratification in CLL to predict therapeutic needs. However, further validation studies and in-depth functional analyses are required to confirm the robustness of these observations and to facilitate their translation into meaningful clinical utility.

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来源期刊
Non-Coding RNA
Non-Coding RNA Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
6.70
自引率
4.70%
发文量
74
审稿时长
10 weeks
期刊介绍: Functional studies dealing with identification, structure-function relationships or biological activity of: small regulatory RNAs (miRNAs, siRNAs and piRNAs) associated with the RNA interference pathway small nuclear RNAs, small nucleolar and tRNAs derived small RNAs other types of small RNAs, such as those associated with splice junctions and transcription start sites long non-coding RNAs, including antisense RNAs, long ''intergenic'' RNAs, intronic RNAs and ''enhancer'' RNAs other classes of RNAs such as vault RNAs, scaRNAs, circular RNAs, 7SL RNAs, telomeric and centromeric RNAs regulatory functions of mRNAs and UTR-derived RNAs catalytic and allosteric (riboswitch) RNAs viral, transposon and repeat-derived RNAs bacterial regulatory RNAs, including CRISPR RNAS Analysis of RNA processing, RNA binding proteins, RNA signaling and RNA interaction pathways: DICER AGO, PIWI and PIWI-like proteins other classes of RNA binding and RNA transport proteins RNA interactions with chromatin-modifying complexes RNA interactions with DNA and other RNAs the role of RNA in the formation and function of specialized subnuclear organelles and other aspects of cell biology intercellular and intergenerational RNA signaling RNA processing structure-function relationships in RNA complexes RNA analyses, informatics, tools and technologies: transcriptomic analyses and technologies development of tools and technologies for RNA biology and therapeutics Translational studies involving long and short non-coding RNAs: identification of biomarkers development of new therapies involving microRNAs and other ncRNAs clinical studies involving microRNAs and other ncRNAs.
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