CN112614546B - 一种用于预测肝细胞癌免疫治疗疗效的模型及其构建方法 - Google Patents
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Abstract
本发明属临床医学领域,涉及一种基于5‑基因的预测肝细胞癌免疫治疗疗效的模型及其构建方法。用于预测肝细胞癌免疫治疗疗效的模型,包括5个基因乳酸脱氢酶A(LDHA)、磷脂磷酸磷酰胺异质酶(PPAT)、珠子纤维结构蛋白1(BFSP1)、核受体亚纤维0组B成员1(NR0B1)和6‑磷脂‑2‑激酶/果糖酶‑2,6‑双磷酸酶4(PFPFB4)。本发明还提供了基于5基因的预测肝细胞癌免疫治疗疗效的模型的构建方法。本发明建立了一个新的基于5基因的免疫微环境模型,可以有效预测患者对HCC免疫治疗的效果,这5个基因可以作为潜在的生物标志物在临床应用。
Description
技术领域
本发明属临床医学领域,涉及一种基于5-基因的预测肝细胞癌免疫治疗疗效的模型及其构建方法。
背景技术
肝细胞癌(HCC)是一种高度恶性的癌症,是全球癌症相关死亡的第三大原因。5年生存期和总体存活率低于12%。大多数HCC患者是由肝硬化、慢性肝炎病毒感染、酒精相关肝病、非酒精性脂肪性肝病和药物引起的肝炎进展而来。由于HCC通常不能早期诊断,治疗效果较差。因此,研发新的诊断分子标志物,对改善患者预后十分重要。
免疫微环境在肿瘤发生中起着关键作用,与肿瘤进展和治疗效果相关。全身免疫疗法已显示出对HCC 的疗效,特别是对于没有机会进行切除或肝移植的患者。常见的免疫治疗策略包括嵌合抗原受体T细胞 (CAR-T细胞)、癌症疫苗、细胞因子疗法和免疫检查点抑制剂(ICIs)。目前,ICIs治疗是免疫治疗中最成功的一类,包括单一疗法和联合疗法。例如,临床使用最广泛的PD-1抗体和PD-L1抗体。由T细胞表达的 PD-L1通过PD-1调节淋巴结启动阶段和肿瘤细胞效应期阶段的免疫反应。利用针对PD-1和PD-L1的单克隆抗体,“耗竭”T细胞功能的恢复和免疫抑制调节性T淋巴细胞的耗竭为恶性肿瘤的治疗开辟了新的途径。然而,对HCC中PD-1抗体和PD-L1抗体的疗效进行了研究发现,只有大约25%的高表达PD-1的T细胞的 HCC患者对ICIs有应答,因此,识别对ICIs有良好应答的患者具有重要意义。
使用TNM(tumor-node-metastasis)分类预测HCC预后的传统策略有助于指导HCC临床治疗的决策。然而,他们的预测功效并不令人满意。随着测序技术和生物信息学的进展,结合生物信息学分析的基因组测序技术的使用提高了肿瘤诊断和预后的预测能力。建立基于基因的预后模型可以鉴别癌症和正常组织之间的mRNA表达模式差异。TCGA和NCBI基因表达综合数据库(GEO)中用于疾病预后的长链非编码RNA (lncRNAs)、调控表观遗传修饰的基因以及免疫相关基因的表达谱已得到越来越多的研究。然而,目前还没有成熟的模型可以稳定地预测患者对HCC免疫治疗的反应和预后。
发明内容
本发明提供了一种基于5基因的预测肝细胞癌免疫治疗疗效的模型。
一种用于预测肝细胞癌免疫治疗疗效的模型,包括5个基因乳酸脱氢酶A(LDHA)、磷脂磷酸磷酰胺异质酶(PPAT)、珠子纤维结构蛋白1(BFSP1)、核受体亚纤维0组B成员1(NR0B1)和6-磷脂-2-激酶/ 果糖酶-2,6-双磷酸酶4(PFPFB4)。
另外,本发明还提供了基于5基因的预测肝细胞癌免疫治疗疗效的模型的构建方法。
模型的构建方法,包括如下步骤:
1)筛选与HCC预后相关的免疫相关基因;
2)在TCGA-LIHC训练集中,免疫相关基因和生存数据使用R包“coxph”进行单变量Cox回归分析,鉴定与HCC预后相关的免疫相关性基因(DEGs);
3)采用LASSO-Cox回归分析进一步缩小目标基因,基于LASSO-Cox回归模型系数乘以mRNA表达水平建立预后基因模型。
具体为:
1)筛选了与HCC预后相关的免疫相关基因4227个;
2)利用TCGA-LIHC训练集,对4227个免疫相关基因的进行单变量Cox回归分析,确定245个基因为HCC OS的潜在预后指标;
3)然后执行LASSO-Cox回归分析,以建立预后模型;确定5个基因:包括乳酸脱氢酶A(LDHA)、磷脂磷酸磷酰胺异质酶(PPAT)、珠子纤维结构蛋白1(BFSP1)、核受体亚纤维0组B成员1(NR0B1) 和6-磷脂-2-激酶/果糖酶-2,6-双磷酸酶4(PFPFB4);风险评分是五个基因的加权基因表达水平的总和乘以各自的LASSO系数:风险评分=[0.307×LDHA表达水平]+[0.268×PPAT表达水平+ [0.455×BFSP1表达水平]+[0.234×NROB1表达水平]+[0.109×PFKFB4表达水平];结果分析显示,5个基因的LASSO系数均为阳性。
进一步的,该构建方法还包括:利用TCGA-LIHC训练集评价基于五基因模型的预测效率。
进一步的,该构建方法还包括:使用TCGA-LIHC验证集、TCGA-LIHC全集和GSE14520数据集验证基于5基因的HCC预后模型。
本发明建立了一个新的基于5基因的免疫微环境模型,并进行生物信息学分析,以评估该模型预测HCC 免疫治疗结果的能力。我们进行了单变量Cox回归分析和(LASSO)-Cox回归分析来构建风险模型。采用风险评分、时间依赖性ROC值和Kaplan-Meier生存分析来评估模型的预后能力。结果表明,我们的模型可以有效预测患者对HCC免疫治疗的效果,这5个基因可以作为潜在的生物标志物在临床应用。
附图说明
图1基因预测模型构建和验证流程图
图2基于TCGA-LIHC训练集,5-基因模型的预测效能
图3基于TCGA-LIHC验证集,5-基因模型的预测效能
图4基于TCGA-LIHC全集,5-基因模型的预测效能
图5基于GSE14520数据集,5-基因模型的预测效能
图6免疫组化法,检测LDHA、PPAT、BFSP1、NR0B1和PFKFB4在肝癌组织中表达
图7 5-基因模型蛋白表达水平和肝癌患者预后关系
图8基于mvigor210数据集,5-基因模型对肝癌的免疫治疗效果的预测效能
具体实施方式
(一)方法和材料
1、数据采集
共从TCGA-LIHC数据集中检索365例HCC患者样本,用于分析预后基因表达特征。对365个样本(包括训练集[n=219]和验证集[n=146])进行随机抽样100次,并进行置换。TNM分期、分级、OS、性别、年龄在训练集和验证集之间无显著差异(p>0.05)。GSE14520数据集(n=221)的临床数据和mRNA表达数据来自 NCBI GEO数据库(https://www.ncbi.nlm.nih.gov/geo/),免疫治疗数据集(Imvigor210)来自于http://research-pub.gene.com/IMvigor210CoreBiologie.。本研究中使用的所有患者数据均具有完整的临床信息,包括TNM分期、分级、生存时间、性别、年龄和免疫相关基因表达水平。
2、建立基于免疫相关基因的5基因模型
我们筛选了与HCC预后相关的免疫相关基因。HCC发病机制中的免疫相关基因从已发表文献中收集。
在TCGA-LIHC训练集中,免疫相关基因和生存数据使用R包“coxph”进行单变量Cox回归分析。我们鉴定了与HCC预后相关的免疫基因。其次,采用LASSO-Cox回归分析进一步缩小目标基因。该方法能够同时进行变量选择和参数估计,并能较好地解决回归分析中的多重共线性问题。基于LASSO-Cox回归模型系数乘以mRNA表达水平建立预后基因模型。最终获得风险评分=[0.307×LDHA mRNA表达水平]+[0.268 ×PPAT mRNA表达水平+[0.455×mRNA BFSP1表达水平]+[0.234×mRNA NROB1表达水平]+[0.109 ×mRNA PFKFB4表达水平]。样本按中位数分为低风险组和高风险组。
3、验证五基因模型的性能和预测能力
使用TCGA-LIHC的验证集、TCGA-LIHC全集、GSE14520数据集和免疫治疗数据集(Imvigor210),采用时间依赖性ROC分析和Kaplan-Meier log-rank分析来评估模型的性能和预后能力。
4、统计分析
使用SPSS v25(IBM,Chicago,IL,USA)、GraphPad Prism 7.0(GraphPadSoftware,La Jolla,CA,USA)和R软件 (version 3.5.1)进行统计分析。采用t检验进行统计学比较,采用Kaplan-Meier分析法估计OS,p<0.05认为有统计学意义。
(二)结果
1、筛选HCC预后基因表达特征构建HCC预后模型
图1显示了分析工作流程图(5-基因预测模型构建和验证流程图)。利用TCGA-LIHC训练集,对4227 个免疫相关基因的进行单变量Cox回归分析,筛选出245个免疫相关基因为HCC OS的潜在预后指标。
2、基于五基因的HCC预后模型的构建
然后执行LASSO-Cox回归分析,以建立预测模型。确定了5个基因:包括乳酸脱氢酶A(LDHA)、磷脂磷酸磷酰胺异质酶(PPAT)、珠子纤维结构蛋白1(BFSP1)、核受体亚纤维0组B成员1(NR0B1)和6- 磷脂-2-激酶/果糖酶-2,6-双磷酸酶4(PFPFB4)。风险评分是五个基因的加权基因表达水平的总和乘以各自的LASSO系数:风险评分=[0.307×mRNA LDHA表达水平]+[0.268×mRNA PPAT表达水平+[0.455 ×mRNA BFSP1表达水平]+[0.234×mRNA NROB1表达水平]+[0.109×mRNA PFKFB4表达水平]。结果分析显示,5个基因的LASSO系数均为阳性。
3、利用TCGA-LIHC训练集评价基于五基因模型的预测效率
为了确定这5个基因的基因表达特征与HCC患者生存结果之间的关系,分别计算每个样本基于5个基因的模型风险评分(AUC值)(图2.基于TCGA-LIHC训练集,5-基因模型的预测效能),并使用TCGA-LIHC训练集定义风险评分的最佳截止点(图2A)。AUC值越高,表明基于5基因的HCC预后模型的分类性能越好。在1年、3年和5年的生存率中,AUC值分别为0.80、0.77和0.73(图2B)。Kaplan-Meier生存分析显示,高危组患者预后较低危组差(p<0.0001);图2C)。综上所述,这些结果表明建立的基于5基因的模型在预测HCC预后方面表现良好。
4、使用TCGA-LIHC验证集、TCGA-LIHC全集和GSE14520数据集验证基于5-基因的HCC预后模型。
为了验证基于5基因模型的稳定性,我们对3个数据集(TCGA-LIHC验证集、TCGA-LIHC全集和GSE14520数据集)进行了类似的流程分析。通过计算各自ROC曲线的AUC值,得到基于五基因的模型的风险评分。 TCGA-LIHC验证集1年、3年和5年生存率的AUC值分别为0.70、0.69和0.60。结果显示,高危组患者的生存率明显低于低危组,(p<0.001);(图3基于TCGA-LIHC验证集,5-基因模型的预测效能)。与TCGA-LIHC 验证集的结果一致,高危组患者的OS较低(p均<0.001),整个TCGA-LIHC数据集和GSE14520数据集的AUC 均大于0.6(图4基于TCGA-LIHC全集,5-基因模型的预测效能,和图5基于GSE14520数据集,5-基因模型的预测效能)。结果显示,基于5个基因的模型可以从基因表达水平预测患者的生存时间。
实施例2
组织微阵列(TMA)结构五基因模型评估
为了评估5基因模型的预后能力,我们使用了一个90例HCC患者癌组织的组织微阵列(上海芯超生物科技有限公司)进行免疫组织化学(IHC),并测量集成光密度分析。所使用的一抗见表1。IHC评分是由三位资深病理学家在不了解患者特征的情况下独立评估得出的。IHC得分计算:包含IHC score=[0.307×protein LDHA表达水平]+[0.268×proteinPPAT表达水平+[0.455×protein BFSP1表达水平]+[0.234×protein NROB1表达水平]+[0.109×protein PFKFB4表达水平]。采用Kaplan-Meier log-rank分析评价高IHC评分组和低IHC评分组的生存率差异。
表1.抗体信息表
用免疫组化法检测组织微阵列芯片(TMA)5个基因LDHA、PPAT、BFSP1、NR0B1和PFKFB4的蛋白表达水平。结果发现这5个基因的蛋白水平在HCC组织中存在显著差异(图6免疫组化法检测LDHA、PPAT、BFSP1、 NR0B1和PFKFB4在肝癌组织中表达)。免疫组化评分较高的组蛋白表达水平较高,疾病预后较差(图7 5-基因模型蛋白表达水平和肝癌患者预后关系)。结果表明,五基因模型、分级及TNM分期均有统计学意义(p< 0.05)。这5个基因的高蛋白表达水平预示着较差的预后,低蛋白表达水平预示着相对较好的预后。
目前缺乏能够有效预测免疫治疗药物疗效的生物标志物。因此,识别新的预测5-标记对进一步提高免疫治疗的精确性是必要的。检索抗PD-L1免疫治疗患者治疗反应数据的转录组数据(Imvigor210),评估基于五基因模型的免疫治疗预测效果(图8基于mvigor210数据集,5-基因模型对肝癌的免疫治疗效果的预测效能)。Kaplan-Meier分析显示,风险评分值越高,生存率越低(图8a)。ROC曲线分析显示,合并肿瘤新生抗原(NEO)组、肿瘤突变负荷(tumor mutational burden,TMB)组和风险评分预后因素组的AUC值(AUC=0.91)高于单独NEO组(AUC=0.7)、TMB组(AUC=0.65)和风险评分组(AUC=0.54)(图8b)。风险评分与免疫治疗疗效无相关性(p>0.05;图8c),g免疫细胞和肿瘤细胞亚群相关(p<0.05);图8d、8e)。这些结果表明,将基于5基因的模型与NEO和TMB相结合,可以增强免疫治疗疗效的评估,识别对免疫治疗有反应的患者。
Claims (3)
1.用于预测肝细胞癌免疫治疗疗效的模型的构建方法,所述模型 包括5个基因乳酸脱氢酶A(LDHA)、磷脂磷酸磷酰胺异质酶(PPAT)、珠子纤维结构蛋白1(BFSP1)、核受体亚纤维0组B成员1(NR0B1)和6-磷脂-2-激酶/果糖酶-2,6-双磷酸酶4(PFPFB4),包括如下步骤:
1)筛选与HCC预后相关的免疫相关基因4227 个;
2)在TCGA-LIHC训练集中,对 4227 个免疫相关基因和生存数据使用R包“coxph”进行单变量Cox回归分析,鉴定与HCC预后相关的免疫相关性基因,确定245个基因为HCC OS的潜在预后指标;
3)采用LASSO-Cox回归分析进一步缩小目标基因,基于LASSO-Cox回归模型系数乘以mRNA表达水平建立预后基因模型;确定5个基因:包括乳酸脱氢酶A(LDHA)、磷脂磷酸磷酰胺异质酶(PPAT)、珠子纤维结构蛋白1(BFSP1)、核受体亚纤维0组B成员1(NR0B1)和6-磷脂-2-激酶/果糖酶-2,6-双磷酸酶4(PFPFB4);风险评分是五个基因的加权基因表达水平的总和乘以各自的 LASSO 系数:风险评分 = [0.307 × LDHA 表达水平]+ [0.268 ×PPAT表达水平] + [0.455 × BFSP1表达水平] + [0.234 × NROB1 表达水平] + [0.109 ×PFKFB4表达水平] ;结果分析显示,5个基因的LASSO系数均为阳性。
2.根据权利要求1所述构建方法,该方法还包括:利用TCGA-LIHC训练集评价基于五基因模型的预测效率。
3.根据权利要求1所述构建方法,该方法还包括:使用TCGA-LIHC验证集、TCGA-LIHC全集和GSE14520数据集验证基于5基因的HCC预后模型。
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