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基于RNA测序的转录组评分在多发性骨髓瘤中的预后及诊疗价值

RNA-Sequencing-Based Transcriptomic Score with Prognostic and Theranostic Values in Multiple Myeloma.

作者信息

Alaterre Elina, Vikova Veronika, Kassambara Alboukadel, Bruyer Angélique, Robert Nicolas, Requirand Guilhem, Bret Caroline, Herbaux Charles, Vincent Laure, Cartron Guillaume, Elemento Olivier, Moreaux Jérôme

机构信息

Institute of Human Genetics, UMR 9002 CNRS-UM, 34395 Montpellier, France.

Diag2Tec, 34395 Montpellier, France.

出版信息

J Pers Med. 2021 Sep 30;11(10):988. doi: 10.3390/jpm11100988.

DOI:10.3390/jpm11100988
PMID:34683129
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8541503/
Abstract

Multiple myeloma (MM) is the second most frequent hematological cancer and is characterized by the clonal proliferation of malignant plasma cells. Genome-wide expression profiling (GEP) analysis with DNA microarrays has emerged as a powerful tool for biomedical research, generating a huge amount of data. Microarray analyses have improved our understanding of MM disease and have led to important clinical applications. In MM, GEP has been used to stratify patients, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance. In this study, we built a gene risk score for 267 genes using RNA-seq data that demonstrated a prognostic value in two independent cohorts (n = 674 and n = 76) of newly diagnosed MM patients treated with high-dose Melphalan and autologous stem cell transplantation. High-risk patients were associated with the expression of genes involved in several major pathways implicated in MM pathophysiology, including interferon response, cell proliferation, hypoxia, IL-6 signaling pathway, stem cell genes, MYC, and epigenetic deregulation. The RNA-seq-based risk score was correlated with specific MM somatic mutation profiles and responses to targeted treatment including EZH2, MELK, TOPK/PBK, and Aurora kinase inhibitors, outlining potential utility for precision medicine strategies in MM.

摘要

多发性骨髓瘤(MM)是第二常见的血液系统癌症,其特征为恶性浆细胞的克隆性增殖。利用DNA微阵列进行的全基因组表达谱(GEP)分析已成为生物医学研究的有力工具,产生了大量数据。微阵列分析增进了我们对MM疾病的理解,并带来了重要的临床应用。在MM中,GEP已被用于对患者进行分层、定义风险、识别治疗靶点、预测治疗反应以及理解耐药性。在本研究中,我们利用RNA测序数据构建了一个针对267个基因的基因风险评分,该评分在接受大剂量美法仑和自体干细胞移植治疗的两个独立队列(n = 674和n = 76)的新诊断MM患者中显示出预后价值。高危患者与MM病理生理学中涉及的几个主要通路相关基因的表达有关,包括干扰素反应、细胞增殖、缺氧、IL-6信号通路、干细胞基因、MYC以及表观遗传失调。基于RNA测序的风险评分与特定的MM体细胞突变谱以及对包括EZH2、MELK、TOPK/PBK和极光激酶抑制剂在内的靶向治疗的反应相关,为MM精准医学策略勾勒出潜在效用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c50/8541503/6d1b0a8a58e8/jpm-11-00988-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c50/8541503/5de9308dfc8b/jpm-11-00988-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c50/8541503/162a3af5b960/jpm-11-00988-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c50/8541503/6d1b0a8a58e8/jpm-11-00988-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c50/8541503/5de9308dfc8b/jpm-11-00988-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c50/8541503/8790b058732f/jpm-11-00988-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8c50/8541503/162a3af5b960/jpm-11-00988-g003.jpg
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