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弥漫性大B细胞淋巴瘤的细胞起源亚型分类及预后

郝婷 高倩 郗彦凤 关红卫 王彤

郝婷, 高倩, 郗彦凤, 关红卫, 王彤. 弥漫性大B细胞淋巴瘤的细胞起源亚型分类及预后[J]. 中华疾病控制杂志, 2024, 28(2): 198-202. doi: 10.16462/j.cnki.zhjbkz.2024.02.011
引用本文: 郝婷, 高倩, 郗彦凤, 关红卫, 王彤. 弥漫性大B细胞淋巴瘤的细胞起源亚型分类及预后[J]. 中华疾病控制杂志, 2024, 28(2): 198-202. doi: 10.16462/j.cnki.zhjbkz.2024.02.011
HAO Ting, GAO Qian, XI Yanfeng, GUAN Hongwei, WANG Tong. Cell-of-origin subtype classification and prognosis of diffuse large B-cell lymphoma based on variable importance analysis[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(2): 198-202. doi: 10.16462/j.cnki.zhjbkz.2024.02.011
Citation: HAO Ting, GAO Qian, XI Yanfeng, GUAN Hongwei, WANG Tong. Cell-of-origin subtype classification and prognosis of diffuse large B-cell lymphoma based on variable importance analysis[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(2): 198-202. doi: 10.16462/j.cnki.zhjbkz.2024.02.011

弥漫性大B细胞淋巴瘤的细胞起源亚型分类及预后

doi: 10.16462/j.cnki.zhjbkz.2024.02.011
基金项目: 

国家自然科学基金 82073674

国家自然科学基金 82204163

山西省基础研究计划 202203021212382

详细信息
    通讯作者:

    王彤,E-mail:tongwang@sxmu.edu.cn

  • 中图分类号: R195.1;R733.4

Cell-of-origin subtype classification and prognosis of diffuse large B-cell lymphoma based on variable importance analysis

Funds: 

National Natural Science Foundation of China 82073674

National Natural Science Foundation of China 82204163

Shanxi Province Basic Research Programme 202203021212382

More Information
  • 摘要:   目的  基因表达谱(gene expression profiling, GEP)是弥漫性大B细胞淋巴瘤(diffuse large B-cell lymphoma, DLBCL)细胞起源(cell-of-origin, COO)分类的金标准。本研究旨在建立一个基于GEP的简约模型来准确预测DLBCL的COO亚型并为其在临床上的应用提供参考。  方法  收集GEO数据库中6个DLBCL数据集中的基因和临床数据,将其中1个数据集作为训练集,其余5个作为验证集。构建基于惩罚回归分析的变量重要性分析策略,识别最优变量子集,并通过logistic回归分析确定最终用于COO分类的六基因模型,采用生存分析评估训练集和验证集预测的两种COO亚型与临床预后的关系。  结果  六基因模型在训练集预测效果较好[AUC(95% CI): 0.999(0.997~1.000),判别斜率及其95% CI为0.944(0.920~0. 966)],在验证集也表现出较好的效果[AUC及其95% CI波动范围从0.910(0.820~0.999)到1.000,判别斜率及其95% CI波动范围从0.506(0.350~0. 966)到0.927(0.841~0.987)]。预后模型显示,在训练集和验证集中6个基因预测的亚型均为风险预测因子(均P < 0.05)。  结论  六基因模型中的6个基因对DLBCL的分型和预后有重要的临床应用价值。基于变量重要性的基因排序为基因功能和靶向药物的进—步研究提供了参考依据。
  • 图  1  六基因模型性能评估

    DS:判别斜率;ABC:活化B细胞;GCB:生发中心B细胞;DLBCL:弥漫性大B细胞淋巴瘤。

    Figure  1.  Six-gene model performance assessments

    DS: discrimination slope; ABC: activated B-cell subtypes; GCB: germinal center B-cell; DLBCL: diffuse large B-cell lymphoma.

    图  2  训练数据集和验证数据集的6个基因的预测性能

    ABC:活化B细胞;GCB:生发中心B细胞。

    Figure  2.  Prognostic performance of the six genes for the training and validation datasets

    ABC: activated B-cell subtypes; GCB: germinal center B-cell.

    表  1  不同惩罚回归方法的变量排名表和秩整合的汇总排名列表

    Table  1.   Variable ranking lists of different penalized regression methods and aggregated ranking list by rank aggregation

    最小绝对收缩选择算子
    LASSO
    自适应最小绝对收缩选择算子
    aLASSO
    弹性网
    EN
    岭回归
    Ridge regression
    极小极大凹惩罚
    MCP
    光滑链接绝对偏差惩罚
    SCAD
    秩整合
    Rank aggregation
    TNFRSF13B TNFRSF13B MYBL1 AFFX- HUMISGF3A/M97935_MB_at MYBL1 MYBL1 MYBL1
    MYBL1 MYBL1 TNFRSF13B CCL5 TNFRSF13B TNFRSF13B TNFRSF13B
    MAML3 MAML3 BATF SCARB1 MAML3 MAML3 MAML3
    CYB5R2 CYB5R2 CYB5R2 PXK CYB5R2 CYB5R2 CYB5R2
    BATF BATF ASB13 PXK S1PR2 BATF BATF
    ASB13 ASB13 MAML3 C15orf40 ENTPD1 S1PR2 S1PR2
    S1PR2 S1PR2 LIMD1 PDE7A LMO2 ASB13 ASB13
    LIMD1 LIMD1 S1PR2 CLEC12A PALD1 LIMD1 LIMD1
    SERPINA9 SERPINA9 SERPINA9 LACTB ZBTB32 LMO2 SERPINA9
    ENTPD1 ENTPD1 MME LACTB FUT8 SERPINA9 ENTPD1
    LMO2 LMO2 PIM2 NLRP11 ASB13 ENTPD1 LMO2
    FUT8 FUT8 FUT8 CDC42SE2 PTK2 FUT8 FUT8
    ZBTB32 ZBTB32 ENTPD1 1552621_at PIM2 ZBTB32 PALD1
    PALD1 PALD1 ENTPD1 1552622_s_at SERPINA9 PALD1 ZBTB32
    BCL2L10 BCL2L10 CFLAR TRNT1 ARHGAP24 BCL2L10 BCL2L10
    PIM2 PIM2 BCL6 ARHGAP5 215164_at STAG3 PDE7A
    注:LASSO,最小绝对收缩选择算子;aLASSO,自适应最小绝对收缩选择算子;EN,弹性网;SCAD,光滑链接绝对偏差惩罚;MCP,极小极大凹惩罚。
    Note:LASSO,least absolute shrinkage and selection operator;aLASSO,adaptive least absolute shrinkage and selection operator;EN,elastic net;SCAD,smoothly clipped absolute deviation penalty;MCP,minimax concave penalty.
    下载: 导出CSV

    表  2  训练数据集和验证数据集中的单变量和多变量Cox比例风险回归模型

    Table  2.   Univariate and multivariable Cox proportional hazards regression analyses in training and validation datasets

    变量  Variables 单变量分析  Univariate analysis 多变量分析  Multivariable analysis
    β
    value
    sx HR值value (95% CI) χ2
    value
    P
    value
    β
    value
    sx HR值value (95% CI) χ2
    value
    P
    value
    训练集  GSE10846 (n=350)
        六基因 Six-genes 1.040 0.179 2.830(1.993~4.018) 33.813 <0.001 0.981 0.202 2.668(1.795~3.968) 23.514 <0.001
        国际预后指数  International prognostic index 1.091 0.192 2.978(2.044~4.340) 32.252 <0.001 1.135 0.195 3.110(2.120~4.562) 33.688 <0.001
        处理  Treatment -0.554 0.177 0.574(0.406~0.812) 9.851 0.002 -0.685 0.205 0.503(0.337~0.754) 11.077 0.001
        性别  Gender 0.031 0.173 1.032(0.735~1.449) 0.032 0.858
    验证集  Combat data (n=624)
        六基因  Six-genes 0.457 0.157 1.579(1.161~2.147) 8.470 0.004 0.425 0.167 1.530(1.104~2.121) 6.500 0.011
        国际预后指数  International prognostic index 0.966 0.165 2.627(1.900~3.632) 34.121 <0.001 0.914 0.166 2.494(1.799~3.458) 30.090 <0.001
        性别 Gender -0.086 0.158 0.918(0.674~1.251) 0.292 0.588
    注:“―”表示数据无法获得。
    Note:"―" stands for date is not available.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-08-07
  • 修回日期:  2023-10-20
  • 网络出版日期:  2024-03-30
  • 刊出日期:  2024-02-10

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