CN113957147A - 一种双基因组合及其在胃癌免疫治疗患者个性化候选评估中的应用 - Google Patents
一种双基因组合及其在胃癌免疫治疗患者个性化候选评估中的应用 Download PDFInfo
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Abstract
本发明公开了一种鉴定胃癌高风险免疫过驱动亚群的双基因组合,包括以下步骤:利用公共数据库胃癌患者肿瘤组织转录组和生存数据获取胃癌免疫检查点相关双基因组合;在待测胃癌患者队列中检测双基因的表达;根据双基因的表达和待测胃癌患者队列生存数据,鉴定出双基因各自的最显著生存的分组点;根据双基因各自的最显著生存的分组点,划分出待测胃癌患者队列的高风险免疫过驱动亚群。根据本发明鉴定出的高风险免疫过驱动亚群患者,不仅具有显著地不良预后,而且拥有相对较高的预存免疫活性和免疫检查点丰度。对于胃癌患者队列,本发明可以鉴定出适合免疫治疗的患者类群。
Description
技术领域
本发明属于生物技术和医学领域,具体涉及一种基于双基因组合表达的胃癌高风险免疫过驱动亚群鉴定方法。
背景技术
癌症是全球最主要的死亡原因之一,特别是胃癌在世界范围内仍然是重要的癌症,在包括中国在内的亚洲国家发病率和死亡率都很高。2021年发表的全球癌症报告(Global Cancer Statistics)显示,全球新发胃癌病例约108万例,占总数5.6%;新增胃癌死亡病例约76万例,占总数7.7%。2020年发表的中国癌症发病率和死亡率报告(Cancerincidence and mortality in China)显示,我国新发胃癌病例约40万例,新增胃癌死亡病例约20万例。然而,胃癌肿瘤微环境的高度异质性阻碍了有效治疗方法的发现,导致存活率降低。
高度复杂和异质的肿瘤微环境不仅包含癌细胞,还包括调节肿瘤发生和发展的各种免疫细胞。由于免疫检查点及其配体经常存在于肿瘤微环境中并诱导抗肿瘤免疫反应,肿瘤微环境将极大地影响肿瘤免疫治疗的效率,尤其是免疫检查点抑制剂的治疗。近年来,基于肿瘤微环境的分子分型得到了越来越多的倡导。其中肿瘤免疫过驱动亚群以免疫细胞高浸润、免疫检查点活化及相关反应为特征,该亚群为有效的免疫检查点抑制剂治疗或联合治疗提供了肥沃的土壤,所以肿瘤免疫过驱动亚型的鉴定有助于个性化的肿瘤免疫治疗。
基于肿瘤微环境的免疫分子亚型被开发用于包括胃癌在内的癌症,已帮助选择合适的患者候选应用个性化治疗。然而,这些分子亚型系统的特征包含许多标记基因,需要较复杂的检测手段。此外,单细胞测序和原位杂交测序等新技术价格昂贵,在大规模诊断应用中还有许多不足之处。在这种情况下,有必要开发新的分子亚型系统,只需快速、经济地检测少量的标志物基因。然而,胃癌缺乏由两个标记基因识别的肿瘤微环境分子亚型系统。
综上所述,需要在胃癌中建立检测更加方便,且能够区分出高风险免疫过驱动亚群,从而为癌症免疫治疗提供合适的候选患者。
发明内容
为了克服现有技术中存在的问题,本发明提供了一种双基因组合在胃癌个性化预后评估中的应用,基于本发明提供的双基因组合表达的胃癌高风险免疫过驱动亚群的鉴定方法,能够准确鉴定出适合免疫治疗的胃癌患者类群。
首先,本发明公开了一种双基因组合在胃癌个性化预后评估中的应用,所述双基因组合由基因CTSL和基因ZBTB7B组成。
在本发明可选的实施方案中,所述的基因CTSL,其别称还包括CATL1,MEPCTSL1,NCBI1514或ENSG00000135047。
在本发明可选的实施方案中,所述的基因ZBTB7B,其别称还包括CKROX,THPOK,ZBTB15,ZFP-67,ZFP67,ZNF857B,c-KROX,hcKROX,NCBI51043或ENSG00000135047。
基于此,本发明进一步公开了利用本发明提供的双基因组合表达的胃癌高风险免疫过驱动亚群的鉴定方法,包括以下步骤:
步骤1、利用公共数据库胃癌患者肿瘤组织基因表达数据和生存数据获取胃癌免疫检查点相关的双基因组合;
步骤2、在待测胃癌患者队列中获取步骤1中的双基因表达;
步骤3、根据步骤2获得的双基因表达数据和步骤1待测胃癌患者队列生存数据鉴定出双基因各自的最显著生存下的高表达和低表达分组;
步骤4、根据步骤3获取的双基因各自高表达和低表达分组,鉴定出待测胃癌患者队列的免疫过驱动亚群。
所述步骤1中的获取胃癌免疫检查点相关双基因组合具体按照以下步骤实施:
S1.1、从Genomic Data Commons Data Portal中获取胃癌患者的肿瘤组织和癌旁正常组织的基因RNA-Seq表达数据,以及临床预后数据;
S1.2、根据S1.1的RNA-Seq表达数据,选择在肿瘤组织表达均值大于10FPKM的基因,去除背景噪音;
S1.3、利用基因富集计算出S1.1的肿瘤组织RNA-Seq表达数据中每个样本的免疫检查点分数,计算所用免疫检查点基因列表如下;
基因富集计算方法来源于已公开文献:
S,Castelo R,Guinney J.GSVA:gene set variation analysis formicroarray and RNA-seq data.BMC Bioinformatics.2013;14:7.
S1.4、计算肿瘤组织中每个基因与免疫检查点分数的相关性,得出每个基因的相关系数R值,分别得到R大于2.5和R小于2.5的基因。R计算方法如下:
待检测基因表达列表为x,免疫检查点分数列表为y,有n个肿瘤组织病例,则相关系数R值等于
其中μx为x的均值,μy为y的均值,σx为x的标准偏差,σy为y的标准偏差。
S1.5、将S1.4中的候选基因进行总生存率检测,病例数为N,将待测基因表达从高到低排序,分别为EXP1、EXP2……EXPN,分为高表达组和低表达组,第1轮将EXP1划为高表达组H1,EXP2到EXPN划为低表达组L1,利用Kaplan-Meier分析和log-rank检验计算出H1组相比L1组的生存风险值(hazard ratio,HR)HR1和显著性P值P1;第2轮将EXP1到EXP2划为高表达组H2,EXP3到EXPN划为低表达组L2,计算出风险比HR2和P值P2;第3轮将EXP1到EXP3划为高表达组H3,EXP4到EXPN划为低表达组L3,计算出风险比HR3和P值P3;以此类推,在第N-1轮将EXP1到EXPN-1划为高表达组HN-1,EXPN划为低表达组LN-1,计算出风险比HRN-1和P值PN-1。根据计算获取的一系列P值P1、P2……PN-1,获取每个基因样本数的十分位数到九十分位数表达值之间最显著的P值Pmin,及其所属高低分组Hmin和Lmin。
S1.6.1、根据S1.5,从S1.4中R大于2.5的基因候选中得到风险值大于1且显著性P值小于0.05的候选基因:ADA2、ADAMTS2、APOC1、APOE、ARHGDIB、BLVRA、C1QB、C1S、C3、C5orf15、CAPG、CCL19、CD14、CNN2、COTL1、CSF1R、CTSL、CTSO、CXCR4、EPS15、EVI2B、GM2A、GNA13、GPNMB、GPSM3、GPX1、GSTO1、HLA-DQA2、IGHG2、IGHJ3、IGHV2-26、IGHV2-5、IGHV3-21、IGHV3-23、IGHV3-30、IGHV3-33、IGHV4-39、IGHV4-4、IGHV4-61、IGKJ5、IGKV1-17、IGKV1-5、IGKV3-11、IGKV3-15、IGKV3D-20、IGKV4-1、IGLC2、IGLL5、IGLV1-47、IGLV2-11、IGLV2-23、IGLV2-8、IGLV3-1、IGLV3-21、IGLV5-45、IGLV8-61、ISG15、LAPTM5、LDHA、LGMN、LIPA、LY96、MAFB、MBNL1、MCL1、MFSD1、MSN、NPC2、PLAUR、PLD3、PNRC1、PSAP、RGS1、RNASE6、SELPLG、SERPING1、SH2B3、STAT3、STING1、SUSD6、TGFB1、TGM2、TINF2、TMSB4X、TNFAIP3、TPP1、VAMP5和YWHAH;
S1.6.2、根据S1.5,从S1.4中R小于2.5的基因候选中得到风险值小于1且显著性P值小于0.05的候选基因:PTK6、ZBTB7B、USE1、MT-CYB和SLC44A4;
S1.7.1、利用接受者操作特征曲线分析法,以基因表达和总生存状态作为分析参数,从S1.6.1中的候选基因中得到显著性P值小于0.05的候选基因:ADA2、ADAMTS2、C1S、C3、CD14、CSF1R、CTSL、CTSO、CXCR4、GPSM3、IGHV2-5、MSN、PSAP、RGS1、SELPLG、TGFB1、TINF2和TPP1;
S1.7.2、利用接受者操作特征曲线分析法,以基因表达和总生存状态作为分析参数,从S1.6.2中的候选基因中得到显著性P值小于0.05的基因,最终得到负向基因标志物ZBTB7B;
S1.8、对S1.7.1的候选基因进行表达差异分析,得到肿瘤对比癌旁正常组织表达差异大于1且显著性P值小于0.05的基因,最终得到正向基因标志物CTSL。
S2、利用RNA-Seq、芯片或者荧光定量PCR技术,从待测胃癌患者队列中获取双基因的表达量,并获取待测胃癌患者队列的生存数据。
S3、根据S1.5,得到双基因在待测胃癌患者队列中,得到各自的高低分组Hmin和Lmin;
S4、根据S3获取的CTSL的Hmin和Lmin,将患者队列分为CTSLHigh和CTSLLow组;根据ZBTB7B的的Hmin和Lmin,将患者队列分为ZBTB7BHigh和ZBTB7BLow组,将CTSLHigh和ZBTB7BLow组取交集后得到高风险免疫过驱动亚群CTSLHighZBTB7BLow。
与现有技术相比,本发明可以获得包括以下技术效果:
1、更简便
本发明仅需要通过检测两个标志物基因的表达量,包括可以直接使用荧光定量PCR检测,手段较简单,大部分肿瘤医院拥有此项检测条件;
2、更精确
传统免疫相关预后,高免疫水平与患者良好预后相关。本发明基于双基因表达,可以区分出高风险免疫过驱动亚群患者,其拥有较高免疫水平但是预后较差。相比于传统亚群或者病例分型,可以更精确预测患者生存预后,并可以预测患者拥有预存免疫反应的水平;
3、更适用于免疫检查点相关的免疫治疗
本发明基于双基因表达,可以鉴定出高风险免疫过驱动亚群患者,该类患者预后差,免疫检查点含量高,预存了大量免疫活性反应,适合免疫检查点相关的免疫治疗手段。
附图说明
此处所说明的附图用来提供对本发明的进一步理解,构成本发明的一部分,本发明的示意性实施例及其说明用于解释本发明,并不构成对本发明的不当限定。在附图中:
图1、本发明实施原理和流程图;
图2、本发明鉴定TCGA胃癌患者队列高风险免疫过驱动亚群的双基因表达量(A)和总生存率情况(B);
图3、本发明鉴定TCGA胃癌患者队列高风险免疫过驱动亚群的免疫活性(A)和免疫检查点基因表达情况(B);
图4、本发明鉴定GSE15459胃癌患者队列高风险免疫过驱动亚群的双基因表达量(A)和总生存率情况(B);
图5、本发明鉴定GSE15459胃癌患者队列高风险免疫过驱动亚群的免疫活性(A)和免疫检查点含量情况(B);
图6、本发明鉴定验证胃癌患者队列高风险免疫过驱动亚群的双基因表达量(A)总生存率情况(B);
图7、本发明鉴定验证胃癌患者队列高风险免疫过驱动亚群的免苏木精-伊红染色和免疫组化染色情况;
图8、本发明鉴定验证胃癌患者队列高风险免疫过驱动亚群的CD8A和CD274多色免疫荧光染色情况。
具体实施方式
以下将配合实施例来详细说明本发明的实施方式,藉此对本发明如何应用技术手段来解决技术问题并达成技术功效的实现过程能充分理解并据以实施,但本发明不受实例的限制。
本发明实施例中涉及的名词解释如下:
高风险免疫过驱动亚群患者:该类患者预后差,免疫检查点含量高,但也预存了大量免疫反应,更适合免疫检查点相关的免疫治疗手段;
免疫浸润:肿瘤组织并不都是单纯的肿瘤细胞,其中还包括免疫相关细胞,例如各类T细胞、巨噬细胞或NK细胞等。这些细胞分泌各种因子影响肿瘤微环境,调控肿瘤发生发展;
CTSLHigh组:CTSL高表达组;
CTSLLow组:CTSL低表达组;
ZBTB7BHigh组:ZBTB7B高表达组;
ZBTB7BLow组:ZBTB7B低表达组;
CD8A:CD8+T细胞标志物基因;
CD274:免疫检查点基因之一,又称PD-L1。
实施例1:TCGA胃癌患者队列高风险免疫过驱动亚群患者的鉴定
步骤1、从Genomic Data Commons Data Portal中获取胃癌患者肿瘤组织基因的RNA-Seq表达数据,以及生存预后数据;
步骤2、分别分析双基因CTSL和ZBTB7B的总生存率,设总样本数为N,将待测基因表达从高到低排序,分别为EXP1、EXP2……EXPN,分为高表达组和低表达组,第1轮将EXP1划为高表达组H1,EXP2到EXPN划为低表达组L1,利用Kaplan-Meier分析和log-rank检验计算出H1组相比L1组的生存风险值(hazard ratio,HR)HR1和显著性P值P1;第2轮将EXP1到EXP2划为高表达组H2,EXP3到EXPN划为低表达组L2,计算出风险比HR2和P值P2;第3轮将EXP1到EXP3划为高表达组H3,EXP4到EXPN划为低表达组L3,计算出风险比HR3和P值P3;以此类推,在第N-1轮将EXP1到EXPN-1划为高表达组HN-1,EXPN划为低表达组LN-1,计算出风险比HRN-1和P值PN-1。根据计算获取的一系列P值P1、P2……PN-1,获取每个基因样本数的十分位数到九十分位数表达值之间最显著的P值Pmin,及其所属高低分组Hmin和Lmin。
步骤3、根据步骤2获取的CTSL的Hmin和Lmin,将患者队列分为CTSLHigh和CTSLLow组;根据ZBTB7B的的Hmin和Lmin,将患者队列分为ZBTB7BHigh和ZBTB7BLow组,将CTSLHigh和ZBTB7BLow组取交集后得到高风险免疫过驱动亚群CTSLHighZBTB7BLow。
如图2A箱线图所示,图中横坐标为分组情况,纵坐标为双基因各自的表达水平,高风险免疫过驱动亚群CTSLHighZBTB7BLow对比CTSLLowZBTB7BHigh亚群,CTSL在CTSLHighZBTB7BLow中显著高表达,ZBTB7B显著低表达。2B生存曲线所示,图中横坐标为年数,纵坐标为总生存率,高风险免疫过驱动亚群CTSLHighZBTB7BLow对比CTSLLowZBTB7BHigh亚群风险比为3.047,显著性P值为0.005,展现出显著不良预后。
如图3A箱线图所示,图中横坐标为分组情况,纵坐标为免疫分数,高风险免疫过驱动亚群CTSLHighZBTB7BLow的免疫分数显著高于CTSLLowZBTB7BHigh亚群,显著性P值小于0.0001;3B箱线图纵坐标为9个免疫检查点基因的表达量,高风险免疫过驱动亚群CTSLHighZBTB7BLow的免疫检查点含量显著高于CTSLLowZBTB7BHigh亚群,显著性P值均小于0.0001。
所以,在TCGA胃癌队列中,基于双基因表达的高风险免疫过驱动亚群的鉴定方法可以鉴定出生存预后差,并拥有较高的免疫反应和免疫检查点含量的患者类型。
实施例2:GSE15459胃癌患者队列高风险免疫过驱动亚群患者的鉴定
步骤1、从Gene Expression Omnibus中获取GSE15459胃癌患者肿瘤组织基因的芯片表达数据,以及生存预后数据;
步骤2、分别分析双基因CTSL和ZBTB7B的总生存率,设总样本数为N,将待测基因表达从高到低排序,分别为EXP1、EXP2……EXPN,分为高表达组和低表达组,第1轮将EXP1划为高表达组H1,EXP2到EXPN划为低表达组L1,利用Kaplan-Meier分析和log-rank检验计算出H1组相比L1组的生存风险值(hazard ratio,HR)HR1和显著性P值P1;第2轮将EXP1到EXP2划为高表达组H2,EXP3到EXPN划为低表达组L2,计算出风险比HR2和P值P2;第3轮将EXP1到EXP3划为高表达组H3,EXP4到EXPN划为低表达组L3,计算出风险比HR3和P值P3;以此类推,在第N-1轮将EXP1到EXPN-1划为高表达组HN-1,EXPN划为低表达组LN-1,计算出风险比HRN-1和P值PN-1。根据计算获取的一系列P值P1、P2……PN-1,获取每个基因样本数的十分位数到九十分位数表达值之间最显著的P值Pmin,及其所属高低分组Hmin和Lmin。
步骤3、根据步骤2获取的CTSL的Hmin和Lmin,将患者队列分为CTSLHigh和CTSLLow组;根据ZBTB7B的的Hmin和Lmin,将患者队列分为ZBTB7BHigh和ZBTB7BLow组,将CTSLHigh和ZBTB7BLow组取交集后得到高风险免疫过驱动亚群CTSLHighZBTB7BLow。
如图2A箱线图所示,图中横坐标为分组情况,纵坐标为双基因各自的表达水平,高风险免疫过驱动亚群CTSLHighZBTB7BLow对比CTSLLowZBTB7BHigh亚群,CTSL在CTSLHighZBTB7BLow中显著高表达,ZBTB7B显著低表达。2B生存曲线所示,图中横坐标为年数,纵坐标为总生存率,高风险免疫过驱动亚群CTSLHighZBTB7BLow对比CTSLLowZBTB7BHigh亚群风险比为3.047,显著性P值为0.005,展现出显著不良预后。
如图3A箱线图所示,图中横坐标为分组情况,纵坐标为免疫分数,高风险免疫过驱动亚群CTSLHighZBTB7BLow的免疫分数显著高于CTSLLowZBTB7BHigh亚群,显著性P值小于0.0001;3B箱线图纵坐标为9个免疫检查点基因的表达量,高风险免疫过驱动亚群CTSLHighZBTB7BLow的免疫检查点含量显著高于CTSLLowZBTB7BHigh亚群,显著性P值均小于0.0001。
所以,在TCGA胃癌队列中,基于双基因表达的高风险免疫过驱动亚群的鉴定方法可以鉴定出生存预后差,并拥有较高的免疫反应和免疫检查点含量的患者类型。
实施例3:自行收集胃癌患者队列进行高风免疫过驱动亚群患者的鉴定
步骤1、从江南大学附属医院样本库获取120例胃癌患者肿瘤组织样本,通过随访获取临床预后数据;
步骤2、通过实时荧光定量PCR检测样本中CTSL和ZBTB7B的表达水平;
步骤3、分别分析双基因CTSL和ZBTB7B的总生存率,设总样本数为N,将待测基因表达从高到低排序,分别为EXP1、EXP2……EXPN,分为高表达组和低表达组,第1轮将EXP1划为高表达组H1,EXP2到EXPN划为低表达组L1,利用Kaplan-Meier分析和log-rank检验计算出H1组相比L1组的生存风险值(hazard ratio,HR)HR1和显著性P值P1;第2轮将EXP1到EXP2划为高表达组H2,EXP3到EXPN划为低表达组L2,计算出风险比HR2和P值P2;第3轮将EXP1到EXP3划为高表达组H3,EXP4到EXPN划为低表达组L3,计算出风险比HR3和P值P3;以此类推,在第N-1轮将EXP1到EXPN-1划为高表达组HN-1,EXPN划为低表达组LN-1,计算出风险比HRN-1和P值PN-1。根据计算获取的一系列P值P1、P2……PN-1,获取每个基因样本数的十分位数到九十分位数表达值之间最显著的P值Pmin,及其所属高低分组Hmin和Lmin。
步骤4、根据步骤3获取的CTSL的Hmin和Lmin,将患者队列分为CTSLHigh和CTSLLow组;根据ZBTB7B的的Hmin和Lmin,将患者队列分为ZBTB7BHigh和ZBTB7BLow组,将CTSLHigh和ZBTB7BLow组取交集后得到高风险免疫过驱动亚群CTSLHighZBTB7BLow。
如图6A箱线图所示,图中横坐标为分组情况,纵坐标为双基因各自的表达水平,高风险免疫过驱动亚群CTSLHighZBTB7BLow对比CTSLLowZBTB7BHigh亚群,CTSL在CTSLHighZBTB7BLow中显著高表达,ZBTB7B显著低表达。6B生存曲线所示,图中横坐标为年数,纵坐标为总生存率,高风险免疫过驱动亚群CTSLHighZBTB7BLow对比CTSLLowZBTB7BHigh亚群风险比为5.451,显著性P值小于0.0001,展现出显著不良预后。
如图7苏木精-伊红染色和免疫组化染色显示所示,高风险免疫过驱动亚群CTSLHighZBTB7BLow组免疫浸润较多,CTSL蛋白水平较高而ZBTB7B蛋白水平较低;
如图8多色免疫荧光染色显示所示,高风险免疫过驱动亚群CTSLHighZBTB7BLow组CD8A和CD274蛋白水平较高;
所以,在附属医院胃癌验证队列中,基于双基因表达的高风险免疫过驱动亚群的鉴定方法可以鉴定出生存预后差,并拥有较高的免疫反应和免疫检查点含量的患者类型。
上述实施例展示并描述了发明的若干优选实施例,但本发明并非局限于本文所披露的形式,不应看作是对其他实施例的排除。对于本领域人员来说,在不脱离本发明原理的前提下,还可以做出若干改进和润饰,这些改进和润饰也应视为本发明的保护范围。
Claims (6)
1.一种双基因组合在胃癌个性化预后评估中的应用,其特征在于,所述双基因组合由基因CTSL和基因ZBTB7B组成。
2.根据权利要求1所述的双基因组合在胃癌个性化预后评估中的应用,其特征在于,所述的基因CTSL,其别称还包括CATL1,MEPCTSL1,NCBI1514或ENSG00000135047。
3.根据权利要求1所述的双基因组合在胃癌个性化预后评估中的应用,其特征在于,所述的基因ZBTB7B,其别称还包括CKROX,THPOK,ZBTB15,ZFP-67,ZFP67,ZNF857B,c-KROX,hcKROX,NCBI51043或ENSG00000135047。
4.根据权利要求1所述的双基因组合在胃癌个性化预后评估中的应用,其特征在于,所述的双基因标志物组合在鉴定高风险免疫过驱动亚群患者的应用。
5.根据权利要求4所述的双基因组合在胃癌个性化预后评估中的应用,其特征在于,所述的高风险免疫过驱动亚群患者为CTSLHighZBTB7BLow患者,即CTSL高表达并且ZBTB7B低表达患者。
6.根据权利要求5所述的双基因组合在胃癌个性化预后评估中的应用,其特征在于,所述的高风险免疫过驱动亚群患者为CTSLHighZBTB7BLow患者,适合免疫检查点相关的免疫治疗。
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