CN109880894A - 基于RNAseq的肿瘤免疫微环境预测模型的构建方法 - Google Patents
基于RNAseq的肿瘤免疫微环境预测模型的构建方法 Download PDFInfo
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Abstract
本发明公开了一种基于RNAseq的肿瘤免疫微环境预测模型的构建方法，包括以下步骤：步骤一：确定24种肿瘤微环境相关的细胞类型；步骤二：根据上述步骤得到的细胞类型，优选出301个marker基因；步骤三：使用kallisto软件计算基因表达量TPM，计算其各个细胞类型对应marker基因的平均表达量，然后取log值作为该细胞类型的相对丰度。本发明计算其各个细胞类型对应marker基因的平均表达量，即计算的是一组marker基因的平均值，相对于计算单个marker基因，其结果相对更稳定，受相对误差的影响较小。
Description
技术领域
本发明涉及基于RNAseq的肿瘤免疫微环境预测模型的构建方法。
背景技术
免疫治疗通过激活人体本身的免疫系统，依靠自身的免疫机能杀灭癌细胞。目前已在多种肿瘤如黑色素瘤，非小细胞肺癌、肾癌和前列腺癌等实体瘤的治疗中展示出了强大的抗肿瘤活性。虽然免疫疗法的成功令人振奋，无数患者在它的干预下取得了显著疗效。但仍有一部分患者对免疫疗法没有反应。而随着技术的进步，人们逐渐了解到肿瘤微环境的免疫环境的复杂性和多样性以及它对免疫治疗的重要影响。通过进一步分析和了解肿瘤免疫微环境将有助于免疫治疗反应性的改善。
目前肿瘤微环境主要靠流式细胞实验，但流式细胞仪较贵且患者不一定有样本来进行流式细胞分析。
当前用于预测免疫微环境的方法如Cibersort、MCPcounter、TIMER等都有或多或少的缺点。其中Cibersort是基于MicroArray数据开发的，对于RNAseq数据的效果并不够理想，而MCPcounter和TIMER本身计算的只是几种免疫细胞的相对丰度。而且当前几种方法预测结果的准确性并不算高，且其本身细胞类型划分对免疫治疗疗效预测不够具有针对性。
发明内容
本发明的目的是提供一种基于RNAseq的肿瘤免疫微环境预测模型的构建方法。
实现本发明目的的技术方案是：基于RNAseq的肿瘤免疫微环境预测模型的构建方法，包括以下步骤：
步骤一：确定24种肿瘤微环境相关的细胞类型；
步骤二：根据上述步骤得到的细胞类型，优选出301个marker基因；
步骤三：使用kallisto软件计算基因表达量TPM，计算其各个细胞类型对应marker基因的平均表达量，然后取log值作为该细胞类型的相对丰度。
所述步骤一具体为：检索现有文献，整合得到32种肿瘤微环境相关细胞，根据其功能和重要性，将每个微环境细胞分配到一个单一的细胞类型，确定24种免疫微环境相关细胞。
所述24种肿瘤微环境相关的细胞类型包括：
B cells
B cells memory；
Plasma cells；
T cells CD8；
T cells CD4
T cells CD4memory resting；
T cells CD4memory activated；
T cells follicular helper；
T cells regulatory(Tregs)；
T cells gamma delta；
NK cells resting；
NK cells activated；
Monocytes；
Macrophages M0；
Macrophages M1；
Macrophages M2；
Dendritic cells resting；
Dendritic cells activated；
Mast cells resting；
Mast cells activated；
Eosinophils；
Neutrophils；
Endothelial cells；
Fibroblasts；
所述步骤二具体包括以下步骤：
S1，全面查找文献搜集24种肿瘤微环境细胞对应的所有相关潜在marker基因共583个；
S2，使用TCGA的RNA-seq数据对这些候选marker基因进行聚类分析；
S3，根据聚类结果剔除普遍低表达的，剔除在多个细胞类型之间无差异的相关基因，筛选得到301个marker基因。
所述301个marker基因分别为：ABCB4，BCL7A，BEND5，BRAF，IL4R，LINC00921，MEP1A，MICAL3，NIPSNAP3B，PSG2，SELL，TCL1A，UGT1A8，ZNF286A，AIM2，ALOX5，CLCA3P，FAM65B，IFNA10，IL7，NPIPB15，SP140，TNFRSF13B，TRAF4，ZBTB32，ABCB9，AMPD1，ANGPT4，ATXN8OS，C11orf80，CCR10，HIST1H2AE，HIST1H2BG，IGHE，KCNA3，KCNG2，LOC100130100，MAN1A1，MANEA，MAST1，MROH7，MZB1，PAX7，PDK1，RASGRP3，REN，SPAG4，ST6GALNAC4，TGM5，UGT2B17，ZBP1，ZNF165，CRTAM，DSC1，KLRC3，KLRC4，KLRF1，MAP9，NCR3，PIK3IP1，TRAV12-2，ANKRD55，CXorf57，EPHA1，FLJ13197，GAL3ST4，GALR1，MAP4K2，SERGEF，VILL，WNT7A，ZNF204P，ZNF324，EPB41，ETS1，FBXL8，PBXIP1，RPL10L，TRAV13-2，TRAV21，ZFP36L2，CDC25A，IL17A，IL26，IL4，IL9，ORC1，RRP9，SKA1，CA8，CHI3L2，FZD3，ICA1，IL21，PASK，PDCD1，SLC7A10，TRIB2，TSHR，ZBTB10，BARX2，CD5，CD70，CEMP1，CLEC2D，EFNA5，FOXP3，FRMD8，HIC1，HMGB3P30，KIRREL，LAIR2，LILRA4，LOC126987，NPAS1，NTN3，PCDHA5，PLCH2，PTPRG，RYR1，SEC31B，SEPT5，SPOCK2，SSX1，TYR，BFSP1，BRSK2，CCR5，CD300A，CDH12，COLQ，CXCR6，GYPE，KLRG1，KRT18P50，LHCGR，MAGEA11，TARDBPP1，ZNF442，AZU1，CDHR1，DEFA4，ELANE，PLEKHF1，TEP1，TTC38，ZNF135，APOBEC3G，CCND2，CDK6，FASLG，KIR2DL4，KIR2DS4，OSM，ASGR1，ASGR2，BST1，CCR2，CD1D，FCN1，HCK，HNMT，HPSE，NLRP3，UPK3A，BHLHE41，CCL7，COL8A2，CSF1，CXCL5，DCSTAMP，GPC4，MARCO，MMP9，PPBP，APOL3，ARRB1，CD40，CXCL9，CYP27B1，KIAA0754，SLAMF1，TRPM4，CCL14，CCL23，CRYBB1，FRMD4A，GSTT1，HRH1，NPL，RENBP，WNT5B，C1orf54，CD1A，DHRS11，EGR2，FLVCR2，HLA-DQA1，PPFIBP1，ARHGAP22，BIRC3，CD80，CD86，CHST7，ETV3，IL12B，MAP3K13，MSC，NR4A3，SLCO5A1，TNFRSF11A，BMP2K，CRISP3，FAM124B，FAM174B，LTC4S，PAQR5，SEPT8，HOXA1，IL1A，IL1B，IL5，LINC00597，MARCH3，TEC，BCL2A1，C5AR2，DACH1，DAPK2，DEPDC5，EMR1，EPN2，GIPR，GPR183，GPR65，IL5RA，LRMP，P2RY10，P2RY2，PDE6C，PKD2L2，RRP12，SAMSN1，SMPD3，SMPDL3B，TRPM6，ZNF222，BTNL8，CASP5，CCR3，CEACAM3，CXCR1，CXCR2，FAM212B，FCGR3B，FPR2，HAL，HSPA6，MMP25，PGLYRP1，STEAP4，TNFRSF10C，TREM1，VNN3，ACVRL1，APLN，BCL6B，BMP6，BMX，CDH5，CLEC14A，CXorf36，EDN1，ELTD1，EMCN，ESAM，ESM1，HECW2，HHIP，KDR，MMRN1，MYCT1，PALMD，PEAR1，PGF，PLXNA2，PTPRB，ROBO4，SDPR，SHANK3，SHE，TEK，TIE1，VEPH1，VWF，CA4，CYP4F3，KCNJ15，MEGF9，SLC25A37，TLE3。
所述步骤三中细胞类型的相对丰度的计算公式为：
Abundancecell＝log2ave(TPMcell)/log2ave(TPMall)。
采用了上述技术方案，本发明具有以下的有益效果：(1)本发明计算其各个细胞类型对应marker基因的平均表达量，即计算的是一组marker基因的平均值，相对于计算单个marker基因，其结果相对更稳定，受相对误差的影响较小。
(2)本发明对细胞类型的划分更合理，使得本方法构建的肿瘤免疫微环境预测模型对肿瘤免疫治疗更加具有针对性。
(3)本发明使用TCGA的RNA-seq数据对候选的marker基因进行聚类分析，其结果更准确。
具体实施方式
(实施例1)
本实施例的基于RNAseq的肿瘤免疫微环境预测模型的构建方法，包括以下步骤：
步骤一：检索现有文献，整合得到32种肿瘤微环境相关细胞，根据其功能和重要性，将每个微环境细胞分配到一个单一的细胞类型，确定24种免疫微环境相关细胞。
步骤二：全面查找文献搜集24种肿瘤微环境细胞对应的所有相关潜在marker基因共583个；使用TCGA的RNA-seq数据对这些候选marker基因进行聚类分析；根据聚类结果剔除普遍低表达的，剔除在多个细胞类型之间无差异的相关基因，筛选得到301个marker基因。
步骤三：使用kallisto软件计算基因表达量TPM，计算其各个细胞类型对应marker基因的平均表达量，然后取log值作为该细胞类型的相对丰度，具体公式为：
Abundancecell＝log2ave(TPMcell)/log2ave(TPMall)。
24种免疫微环境相关细胞如下表所示：
肿瘤微环境细胞类型和相对应的marker基因如下表所示：
本方法对细胞类型的划分更合理，使得本方法构建的肿瘤免疫微环境预测模型对肿瘤免疫治疗更加具有针对性。本方法使用TCGA的RNA-seq数据对候选的marker基因进行聚类分析，其结果更准确。本方法计算其各个细胞类型对应marker基因的平均表达量，即计算的是一组marker基因的平均值，相对于计算单个marker基因，其结果相对更稳定，受相对误差的影响较小。
以上所述的具体实施例，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本发明的具体实施例而已，并不用于限制本发明，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。
Claims (6)
1.基于RNAseq的肿瘤免疫微环境预测模型的构建方法，其特征在于：包括以下步骤：
步骤一：确定24种肿瘤微环境相关的细胞类型；
步骤二：根据上述步骤得到的细胞类型，优选出301个marker基因；
步骤三：使用kallisto软件计算基因表达量TPM，计算其各个细胞类型对应marker基因的平均表达量，然后取log值作为该细胞类型的相对丰度，形成肿瘤免疫微环境预测模型。
2.根据权利要求1所述的基于RNAseq的肿瘤免疫微环境预测模型的构建方法，其特征在于：所述步骤一具体为：检索现有文献，整合得到32种肿瘤微环境相关细胞，根据其功能和重要性，将每个微环境细胞分配到一个单一的细胞类型，确定24种免疫微环境相关细胞。
3.根据权利要求2所述的基于RNAseq的肿瘤免疫微环境预测模型的构建方法，其特征在于：所述24种肿瘤微环境相关的细胞类型包括：
B cells
B cells memory；
Plasma cells；
T cells CD8；
T cells CD4
T cells CD4 memory resting；
T cells CD4 memory activated；
T cells follicular helper；
T cells regulatory(Tregs)；
T cells gamma delta；
NK cells resting；
NK cells activated；
Monocytes；
Macrophages M0；
Macrophages M1；
Macrophages M2；
Dendritic cells resting；
Dendritic cells activated；
Mast cells resting；
Mast cells activated；
Eosinophils；
Neutrophils；
Endothelial cells；
Fibroblasts。
4.根据权利要求1所述的基于RNAseq的肿瘤免疫微环境预测模型的构建方法，其特征在于：所述步骤二具体包括以下步骤：
S1，全面查找文献搜集24种肿瘤微环境细胞对应的所有相关潜在marker基因共583个；
S2，使用TCGA的RNA-seq数据对这些候选marker基因进行聚类分析；
S3，根据聚类结果剔除普遍低表达的，剔除在多个细胞类型之间无差异的相关基因，筛选得到301个marker基因。
5.根据权利要求4所述的基于RNAseq的肿瘤免疫微环境预测模型的构建方法，其特征在于：所述301个marker基因分别为：
ABCB4，BCL7A，BEND5，BRAF，IL4R，LINC00921，MEP1A，MICAL3，NIPSNAP3B，PSG2，SELL，TCL1A，UGT1A8，ZNF286A，AIM2，ALOX5，CLCA3P，FAM65B，IFNA10，IL7，NPIPB15，SP140，TNFRSF13B，TRAF4，ZBTB32，ABCB9，AMPD1，ANGPT4，ATXN8OS，C11orf80，CCR10，HIST1H2AE，HIST1H2BG，IGHE，KCNA3，KCNG2，LOC100130100，MAN1A1，MANEA，MAST1，MROH7，MZB1，PAX7，PDK1，RASGRP3，REN，SPAG4，ST6GALNAC4，TGM5，UGT2B17，ZBP1，ZNF165，CRTAM，DSC1，KLRC3，KLRC4，KLRF1，MAP9，NCR3，PIK3IP1，TRAV12-2，ANKRD55，CXorf57，EPHA1，FLJ13197，GAL3ST4，GALR1，MAP4K2，SERGEF，VILL，WNT7A，ZNF204P，ZNF324，EPB41，ETS1，FBXL8，PBXIP1，RPL10L，TRAV13-2，TRAV21，ZFP36L2，CDC25A，IL17A，IL26，IL4，IL9，ORC1，RRP9，SKA1，CA8，CHI3L2，FZD3，ICA1，IL21，PASK，PDCD1，SLC7A10，TRIB2，TSHR，ZBTB10，BARX2，CD5，CD70，CEMP1，CLEC2D，EFNA5，FOXP3，FRMD8，HIC1，HMGB3P30，KIRREL，LAIR2，LILRA4，LOC126987，NPAS1，NTN3，PCDHA5，PLCH2，PTPRG，RYR1，SEC31B，SEPT5，SPOCK2，SSX1，TYR，BFSP1，BRSK2，CCR5，CD300A，CDH12，COLQ，CXCR6，GYPE，KLRG1，KRT18P50，LHCGR，MAGEA11，TARDBPP1，ZNF442，AZU1，CDHR1，DEFA4，ELANE，PLEKHF1，TEP1，TTC38，ZNF135，APOBEC3G，CCND2，CDK6，FASLG，KIR2DL4，KIR2DS4，OSM，ASGR1，ASGR2，BST1，CCR2，CD1D，FCN1，HCK，HNMT，HPSE，NLRP3，UPK3A，BHLHE41，CCL7，COL8A2，CSF1，CXCL5，DCSTAMP，GPC4，MARCO，MMP9，PPBP，APOL3，ARRB1，CD40，CXCL9，CYP27B1，KIAA0754，SLAMF1，TRPM4，CCL14，CCL23，CRYBB1，FRMD4A，GSTT1，HRH1，NPL，RENBP，WNT5B，C1orf54，CD1A，DHRS11，EGR2，FLVCR2，HLA-DQA1，PPFIBP1，ARHGAP22，BIRC3，CD80，CD86，CHST7，ETV3，IL12B，MAP3K13，MSC，NR4A3，SLCO5A1，TNFRSF11A，BMP2K，CRISP3，FAM124B，FAM174B，LTC4S，PAQR5，SEPT8，HOXA1，IL1A，IL1B，IL5，LINC00597，MARCH3，TEC，BCL2A1，C5AR2，DACH1，DAPK2，DEPDC5，EMR1，EPN2，GIPR，GPR183，GPR65，IL5RA，LRMP，P2RY10，P2RY2，PDE6C，PKD2L2，RRP12，SAMSN1，SMPD3，SMPDL3B，TRPM6，ZNF222，BTNL8，CASP5，CCR3，CEACAM3，CXCR1，CXCR2，FAM212B，FCGR3B，FPR2，HAL，HSPA6，MMP25，PGLYRP1，STEAP4，TNFRSF10C，TREM1，VNN3，ACVRL1，APLN，BCL6B，BMP6，BMX，CDH5，CLEC14A，CXorf36，EDN1，ELTD1，EMCN，ESAM，ESM1，HECW2，HHIP，KDR，MMRN1，MYCT1，PALMD，PEAR1，PGF，PLXNA2，PTPRB，ROBO4，SDPR，SHANK3，SHE，TEK，TIE1，VEPH1，VWF，CA4，CYP4F3，KCNJ15，MEGF9，SLC25A37，TLE3。
6.根据权利要求1所述的基于RNAseq的肿瘤免疫微环境预测模型的构建方法，其特征在于：所述步骤三中细胞类型的相对丰度的计算公式为：
Abundancecell＝log2 ave(TPMcell)/log2 ave(TPMall)。
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