亚型
危险分层
基因表达谱
计算生物学
生物
肿瘤科
RNA序列
基因
弥漫性大B细胞淋巴瘤
转录组
生发中心
生物信息学
分层(种子)
预测模型
基因表达
仿形(计算机编程)
背景(考古学)
内科学
淋巴瘤
医学
列线图
免疫组织化学
DNA测序
生存分析
微阵列
预测值
队列
癌症研究
病理
相关性
作者
Veronika Navrkalová,Andrea Marečková,Lenka Radová,Klára Činátlová,Václav Kubeš,David Šálek,Michael Doubek,Šárka Pospíšilová,Leoš Křen,Jana Kotašková
出处
期刊:PubMed
[National Institutes of Health]
日期:2026-01-01
卷期号:62 (1): 43-49
摘要
Classification of diffuse large B-cell lymphoma (DLBCL) according to cell-of-origin (COO) distinguishes two main biological subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Although this distinction reflects different pathogenetic mechanisms, its prognostic impact diminishes in the context of evolving therapeutic strategies. Molecular subtyping of DLBCL, which is based on the spectrum of affected genes and aims to personalize treatment approaches, is currently gaining importance. In the study, we applied targeted gene expression profiling (GEP) using a custom Lympho-qPCR panel, enabling rapid and practically applicable ABC/GCB classification together with risk stratification of patients. RNA isolated from a cohort of 89 DLBCL tissue samples was analyzed using three GEP-based classification models. Model A compared the expression profile with immunohistochemical (IHC) COO determination and showed the expected lower correlation (62 %). Model B employed the expression scores of selected genes to predict COO regardless of IHC classification. Model C was developed as a new IHC-independent prognostic tool allowing patient stratification based on expected survival. Patients identified as high-risk by Model C had significantly worse outcomes, regardless of existing clinical prognostic indicators. In patients with early progression, parallel DNA sequencing analysis (integrative LYNX panel) confirmed complex chromosomal aberrations and defects in BCL2, TP53 and CDKN2A/B. Our results demonstrate that targeted GEP testing represents a robust, rapid, and clinically applicable method for COO determination and risk stratification in DLBCL patients. In the near future, the predictive value of ABC/GCB classification is expected to increase in relation to novel targeted therapeutic regimens. Integration of transcriptomic and genetic data will be essential for independent and individualized risk assessment in the molecular diagnostics of DLBCL.
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