文章：

基于基因表达谱分析的弥漫性大B细胞淋巴瘤风险预测及免疫浸润特征

Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma

原文发布日期：2021-01-07

DOI: 10.1038/s41408-020-00404-0

类型: Article

开放获取: 是

英文摘要：

The clinical risk stratification of diffuse large B-cell lymphoma (DLBCL) relies on the International Prognostic Index (IPI) for the identification of high-risk disease. Recent studies suggest that the immune microenvironment plays a role in treatment response prediction and survival in DLBCL. This study developed a risk prediction model and evaluated the model’s biological implications in association with the estimated profiles of immune infiltration. Gene-expression profiling of 718 patients with DLBCL was done, for which RNA sequencing data and clinical covariates were obtained from Reddy et al. (2017). Using unsupervised and supervised machine learning methods to identify survival-associated gene signatures, a multivariable model of survival was constructed. Tumor-infiltrating immune cell compositions were enumerated using CIBERSORT deconvolution analysis. A four gene-signature-based score was developed that separated patients into high- and low-risk groups. The combination of the gene-expression-based score with the IPI improved the discrimination on the validation and complete sets. The gene signatures were successfully validated with the deconvolution output. Correlating the deconvolution findings with the gene signatures and risk score, CD8+ T-cells and naïve CD4+ T-cells were associated with favorable prognosis. By analyzing the gene-expression data with a systematic approach, a risk prediction model that outperforms the existing risk assessment methods was developed and validated.
 

摘要翻译： 

弥漫性大B细胞淋巴瘤（DLBCL）的临床风险分层依赖于国际预后指数（IPI）来识别高危疾病。近期研究表明，免疫微环境在DLBCL的治疗反应预测和生存预后中发挥作用。本研究开发了一个风险预测模型，并评估了该模型与免疫浸润估计谱相关的生物学意义。我们对718例DLBCL患者进行了基因表达谱分析，其RNA测序数据和临床协变量来源于Reddy等人（2017）的研究。通过无监督和有监督的机器学习方法识别与生存相关的基因特征，构建了一个多变量生存模型。采用CIBERSORT反卷积分析对肿瘤浸润免疫细胞组成进行量化。基于四个基因特征构建的风险评分可将患者分为高危组和低危组。该基因表达评分与IPI结合使用后，在验证集和完整数据集上提高了区分能力。基因特征通过反卷积分析结果得到成功验证。将反卷积结果与基因特征及风险评分关联分析发现，CD8+ T细胞和初始CD4+ T细胞与良好预后相关。通过系统分析方法对基因表达数据进行解析，我们开发并验证了一种优于现有风险评估方法的风险预测模型。

原文链接：

Gene expression profiling-based risk prediction and profiles of immune infiltration in diffuse large B-cell lymphoma