CN114107511A - 预测肝癌预后的标志物组合及其应用 - Google Patents
预测肝癌预后的标志物组合及其应用 Download PDFInfo
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Abstract
本发明涉及生物技术领域,尤其涉及预测肝癌预后的标志物组合及其应用。该标志物组合包括CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP中的至少两种基因。本申请基于单细胞测序和大数据分析,筛选获得上述7个基因,然后通过生物信息学分析方法建立模型(model),验证了所述7个靶点基因与肝癌的相关性,是影响肝癌预后的重要因素。
Description
技术领域
本发明涉及生物技术领域,尤其涉及预测肝癌预后的标志物组合及其应用。
背景技术
在过去的数十年中,高通量测序技术被广泛应用于生物和医学的各种领域,极大促进了相关的研究及应用。但是传统的转录组测序技术(bulk RNA-seq)是基于组织样本(细胞群体),其反映是基因在细胞群体的平均表达水平,但细胞之间存在广泛的异质性,而这种异质性对于肿瘤的靶向治疗具有非常重要意义。
近年来,单细胞转录组测序(single-cell RNA-seq,scRNA-seq)技术得到了蓬勃的发展,scRNA-seq可以在单细胞水平上揭示全基因组范围内所有基因的表达情况,可以更加直观对细胞异质性进行研究。目前scRNA-seq已广泛应用于各类物种(特别是人、小鼠等)的不同类型组织和细胞系,包括正常和病变细胞等。
肝癌是异质性高,死亡率最高的难治性恶性肿瘤,肝癌死亡率居肿瘤死亡率第二位,仅次于肺癌。目前针对肝癌的治疗有很多方法,如常规的手术切除、放疗、化疗,介入治疗,肝转移、靶向治疗和免疫治疗等等。但是五年生存率低,仅为12%左右。而肝癌的异质性可能是导致生存率下降的重要原因。
目前,现有技术主要根据肝癌分期来进行患者的预后,但这种方法无法准确的确定肝癌患者的预后。因此,有必要开发一种使用基因标志物来对肝癌患者预后进行精确预测的方法。从而及时地对患者进行特定的治疗,改进肝癌患者的治疗结果。
发明内容
有鉴于此,本发明要解决的技术问题在于提供预测肝癌预后的标志物组合及其应用。
为了实现上述发明目的,本发明提供以下技术方案:
一种预测肝癌预后的标志物组合,包括CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP中的至少两种基因。
本发明还提供了所述的标志物组合在制备预测肝癌患者预后的产品中的应用。
其中,所述预后包括对肝癌进行分类,确定治疗方案并预测治疗方案的有效性,预测患者生存时间、生存状态,和/或评估患者的免疫细胞的浸润率。
本发明所述应用中,AFP基因与预后不良相关,其余6个基因与预后良好呈正相关。
本发明所述应用中,所述分类包括TNM分型、stage分期和grade分级,其中:
CYP3A4、NR1I2、CYP2C9、TTR和APOC3在T分型中的表达具有显著性;
CYP3A4、NR1I2和TTR在N分型中的表达具有显著性;
CYP3A4、NR1I2、CYP2C9、TTR和APOC3在stage分期中的表达具有显著性;
CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP在grade分级中的表达具有显著性。
本发明研究了C1和C2两组免疫治疗靶点的评分,包括PD-L1和CTLA-4。PD-L1和CTLA-4的C1评分高于C2。结果提示,鉴于C1的高免疫浸润与患者预后不良相关,这一特殊的患者群体应接受免疫治疗,特别是抗pd-l1和CTLA-4抑制剂。因此,根据本发明可以预测治疗方案,所述治疗方案包括免疫治疗;其中,免疫治疗的药物包括但不仅限于PD-L1抑制剂和CTLA-4抑制剂。
本发明还提供了预测肝癌患者预后的产品,包括检测如下基因表达水平的产品,或检测由如下基因所编码的蛋白表达水平的产品,或检测由如下基因所转录的mRNA表达水平的产品:
所述基因包括CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP中的至少两种。
其中,所述产品包括试剂、试剂盒、芯片或其他诊断工具。所述试剂、试剂盒、芯片中包括检测所述7种基因的引物、探针,或检测所述基因的编码蛋白的抗体。
本发明还提供了所述的标志物组合在构建预测肝癌预后的模型中的应用。
本发明还提供一种预测肝癌预后的模型的构建方法,包括如下步骤:
从单细胞表达谱矩中阵筛选肝癌细胞,通过聚类分析和/或热图分析数据确定标志物与肝癌的关联性,所述的标记物为权利要求1所述的标志物组合。
本发明提供了预测肝癌预后的标志物组合及其应用,该标志物组合包括CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP中的至少两种基因。结合单细胞测序和大数据分析,以上七种基因是影响肝癌预后的重要因素,其中,AFP基因与预后不良相关,其余6个基因呈负相关,结合7种基因的表达情况可以准确预测肝癌患者的预后。本发明基于以上七种标志物组合进一步构建了肝癌患者预后的预测模型,通过ROC曲线及患者的生存时间和生存状态验证了所述预测模型具有较高的准确性和特异性,表明预测模型对于预测肝癌预后及治疗具有重要意义。
附图说明
图1中肝癌相关基因的筛选技术路线图;
图2示过滤前后细胞质控,至少在100个细胞表达的基因,每一个细胞中至少检测到2000个基因,各样本feature的read count的情况,percent.mt为检测到的线粒体基因所占的比例。A)过滤前的细胞质控。B)过滤后的细胞质控;
图3示前10个高变异的基因分布情况;
图4中A示PCA降维之后,各样本的分布情况;B示通过筛选的marker基因构建细胞发育轨迹图,其中,图中的黑点即为筛选的marker基因;C示20个cluster细胞亚群分布;D示细胞发育轨迹图;
图5中A:1930个基因,371个肿瘤样本双向聚类热图;B:基于OS时间,km曲线;C:两个亚型GSVA分析;
图6A:两个亚型在22个免疫细胞中分布情况;B:两个亚型在基质打分、免疫打分和综合打分的分布情况;C:免疫治疗靶标PD-L1和CTLA-4(在两个亚型中分布情况;
图7示A:TCGA数据库肝癌样本在6种免疫亚型中比例;B:Cluster1亚型中4种免疫亚型中比例(C5和C6两个亚型没有);C:cluster2亚型中5种免疫亚型中比例(C5亚型没有);D:基于OS时间的km曲线;
图8示两个亚型临床特征的比较。A:性别在两个亚型中分布情况;B:T分期在两个亚型中分布情况;C:N分期在两个亚型中分布情况;D:M分期在两个亚型中分布情况;E:stage分期在两个亚型中分布情况;F:grade分级在两个亚型中分布情况;
图9示两个亚型临床特征的比较;A:性别在两个亚型中分布情况;B:T分期在两个亚型中分布情况;C:N分期在两个亚型中分布情况;D:M分期在两个亚型中分布情况;E:stage分期在两个亚型中分布情况;F:grade分级在两个亚型中分布情况;
图10示A:121个重要基因表达热图;B:gene-gene互作网络图;C:degree不小于20的7个基因的相关性分析;D:7个重要基因在两个亚型中表达情况;
图11示A:7个基因在T分期中表达情况;B:7个基因在N分期中表达情况;C:7个基因在stage分期中表达情况;D:7个基因在grade分级中表达情况;
图12示利用KM对7个关键基因进行单细胞RNA测序筛选分析;
图13示预测模型的验证以确定其临床预测价值;A:预后模型风险评分、生存时间、生存状态与7个关键基因表达之间的关系;B:基于高、低风险的HCC患者KM分析;C:ROC曲线评价预后模型的敏感性;
图14示通过与其他临床特征的比较验证预后模型的预测效率;A:ROC曲线用于描述预后模型、TNM、年龄以及结合所有现有特征的预测潜力;B:Nomogram预后预测模型与TNM分期比较,采用Nomogram预后预测模型;C:列线图1年、3年、5年的校准图;
图15示森林图单变量和多变量生存分析与临床特征和预后模型;采用(A)单因素和(B)多因素生存分析森林图验证临床特征和预后模型的独立性和有效性。
具体实施方式
本发明提供了预测肝癌预后的标志物组合及其应用,本领域技术人员可以借鉴本文内容,适当改进工艺参数实现。特别需要指出的是,所有类似的替换和改动对本领域技术人员来说是显而易见的,它们都被视为包括在本发明。本发明的方法及应用已经通过较佳实施例进行了描述,相关人员明显能在不脱离本发明内容、精神和范围内对本文的方法和应用进行改动或适当变更与组合,来实现和应用本发明技术。
本发明采用的试材皆为普通市售品,皆可于市场购得。
本发明的数据来源:
下载geo数据库中GSE149614数据集,包括10个病人,71915个细胞(包括了34414个癌细胞)的normalized数据。
下载TCGA中RNA-Seq的FPKM数据,进一步的我们将其转换为TPM表达谱,如tcga.exp.LIHC.txt。
TCGA的临床随访信息数据,包括生存时间,生存状态,stage分期、TNM分型、grade分级等。如:cli_tcga.txt。
下面结合实施例,进一步阐述本发明:
实施例
1、数据分析
1.1单细胞分析
首先,我们在单细胞表达谱矩阵71915个细胞筛选出34414个癌细胞,利用R语言Seurat包进行分析,过滤剔除表达过高或者过低的细胞(图2)(此时细胞只剩下16880个细胞)。
通过NormalizeData函数标准化,利用FindVariableFeatures函数筛选高变异基因(图3)。
下一步对全部基因进行z-score标准化,使用RunPCA对单细胞数据进行降维。取贡献最大的前11个主成分(PC)进行UMAP非线性降维。通过FindNeighbors和FindClusters函数对细胞进行聚类,结果将16880个细胞聚成了20个clust(说明在本次研究中的肝癌的癌细胞可能存在20个细胞亚群,同时也说明本次研究的单细胞异质性较高)。使用FindAllMarkers函数筛选marker基因,最终得到4121个marker基因。通过去重,我们发现,总共有2038个基因重要基因。接着,通过使用R语言的monocle包进行细胞轨迹分析,对前面降维得到的2038个marker基因的单细胞表达谱绘制20个细胞亚群的分布图(图4C),使用DDRTree方法对这些基因进一步降维,对20个细胞亚群绘制细胞轨迹图(图4D,细胞发育轨迹)。
1.2细胞亚群细胞组成分析
为了对这20个clust进行细胞定义,我们使用在线数据库http://bis.zju.edu.cn/HCL/对16880个细胞4121个基因中进行分析,通过选择细胞相关性最高的细胞类型,结果发现,16880个细胞总共可以分为72个细胞类型,如表hcl_cell_types.txt所示。进一步,我们分析了20个clust细胞亚群中细胞类型的种类以及比例,如table1所示。从统计结果来看,如clust1细胞亚群中总共是由三种细胞组成的,其中将近97%的都由Hepatocyte.Adult.Liver4.细胞组成的。
表1. 20个clust细胞组成
1.3细胞亚群的特征基因的识别
为了更好的识别每一个clust细胞亚群中mark基因,我们分别对20个clust与2038个基因进行相关性分析,然后挑选相关性大于0.4的基因作为该clust的特征基因。结果见表marker.xls。我们对每一个clust中包含的基因进行统计,如table2所示。除了clust4和clust5细胞亚群中不存在特有基因,其他18个clust中均含有特有基因。表table2为20个clust特征基因的筛选。
表2clust细胞亚群中相关性基因和独有基因的统计
1.4细胞亚型细胞来源的统计
首先,我们统计了10个病人采集的细胞在20个clust细胞亚群统计结果(table3),从S1中可以看出,cluster7、13、15、16、18细胞来源非常复杂。cluster1细胞组成中最重要就是来源于HCC03T病人,而cluster2细胞来源于HCC08T,cluster3细胞来源于HCC04T,cluster4细胞来源于HCC03T,cluster5细胞来源于HCC05T,cluster6细胞主要来源于HCC02T,Cluster8细胞来源于HCC010T,cluster9细胞来源于HCC01T,cluster10细胞来源于HCC09T,cluster11细胞来源于HCC04T,cluster12细胞来源于HCC08T,cluster14细胞来源于HCC09T,cluster17细胞来源于HCC04T,cluster19细胞主要来源于HCC02T,cluster20细胞主要来源于HCC05T。大部分的细胞亚群细胞来源是比较单一的。
表3. 20个细胞亚群细胞来源统计
1.5TCGA数据的联合分析
我们提取TCGA数据库中,关于2038个基因的表达谱矩阵(总共371个肿瘤样本,1930个基因)。我们对这1930个基因,371个肿瘤样本使用无监督聚类,可以将371个样本大致分为2个亚型,即cluster1和cluster2两个亚型(图5-A)。进一步,我们基于OS时间做两个亚型进行生存分析,结果发现(图5B)cluster1的亚型预后较差,而cluster2亚型预后较好。下一步,我们通过GSVA包分析了两个亚型的功能。结果发现(图5C),cluster1主要富集在细胞周期,DNA复制等基质相关的通路。Cluster2主要富集在代谢,免疫相关的通路。表tcga.1930.gene.exp.txt为1930个基因371个肿瘤样本的表达谱数据。表group.txt为无监督聚类得到的两个基因的group。表subtype.txt为无监督聚类分析得到的两个亚型样本。表subtype.os.txt为绘制km曲线的文件。
为了研究两个亚型的免疫细胞的组成,我们基于CIBERSORT方法对计算两个亚型在22个免疫细胞中打分。结果如图6A所示,结果发现,cluster1主要是由T.cells.CD8、T.cells.CD4.memory.activated、T.cells.follicular.helper、T.cells.regulatory..Tregs.、Macrophages.M0、免疫细胞组成,cluster2主要是由B.cells.naive、T.cells.CD4.memory.resting、NK.cells.resting、Monocytes、Macrophages.M1、Macrophages.M2、Mast.cells.resting免疫细胞组成。从结果可以看出,cluster1主要与二次免疫相关,而cluster2则是与一次免疫相关。从图5B中发现,cluster1亚型预后差,而cluster2亚型预后较好。接下来,我们基于estimate包计算两个亚型之间的免疫评分(图6B),结果发现cluster1亚型的免疫评分比cluster2亚型的免疫评分高。接着,我们,计算了两组免疫治疗靶点的评分,分析了免疫治疗靶标PD-L1(PDCD1:程序性细胞死亡蛋白1)和CTLA-4(细胞毒性T淋巴细胞相关蛋白4)在两个亚型中分布情况。PD-L1和CTLA-4的C1评分高于C2,说明Cluster1中PDCD1和CTLA-4的表达比cluster2的高,而且具有统计学意义(图6C)。结果提示,鉴于C1的高免疫浸润与患者预后不良相关,这一特殊的患者群体应接受免疫治疗,特别是抗pd-l1和CTLA-4抑制剂。
1.6与免疫相关亚型的比较
通过查阅文献,发现TCGA的样本可以分为6个免疫亚型,分别为C1(伤口愈合),C2(INF-r占优势),C3(炎症),C4(淋巴细胞耗竭),C5(免疫沉默)和C6(TGF-beta占优势),其中C1,C2和C6为与预后不良有关。通过分析,我们发现,在肝癌中主要以C3和C4两个亚型为主(图7A),基于单细胞分析得到了mark gene构建的两个亚型分布情况如图7B,7C所示,cluster2中主要以C3和C4为主,而cluster1中C3和C4两个亚型所包含的样本也占了一半多。进一步,我们对免疫相关的6个亚型进行生存分析(图7D)。
1.7亚型的比较
1.7.1两个亚型的GSEA富集分析
我们对TCGA数据集中使用了GSEA分析了cluster1和cluster2分组中显著富集的通路,选取的基因集为c2.cp.kegg.v7.0.symbols.gmt,里面包含了KEGG的通路。GSEA输入文件包含了表达谱数据,分子亚型标记的样本标签,样本标签是将样本标记为cluster1组和cluster2组。富集的通路选取的阈值为p<0.05,然后我们得到了显著富集的通路总共有20个,发现在TCGA数据集中的cluster1DNA_REPLICATION、PATHOGENIC_ESCHERICHIA_COLI_INFECTION、PURINE_METABOLISM、ALZHEIMERS_DISEASE、PYRIMIDINE_METABOLISM、SYSTEMIC_LUPUS_ERYTHEMATOSUS、NUCLEOTIDE_EXCISION_REPAIR、HOMOLOGOUS_RECOMBINATION、VIBRIO_CHOLERAE_INFECTION和GAP_JUNCTION通路相关(图8A),而cluster2分组中主要与LINOLEIC_ACID_METABOLISM、PRIMARY_BILE_ACID_BIOSYNTHESIS、PPAR_SIGNALING_PATHWAY、HISTIDINE_METABOLISM、BETA_ALANINE_METABOLISM、PEROXISOME、GLYCINE_SERINE_AND_THREONINE_METABOLISMFATTY_ACID_METABOLISM、DRUG_METABOLISM_CYTOCHROME_P450和RETINOL_METABOLISM通路相关(图8B)。这一结果与cluster1和cluster2亚型之间的差异基因富集结果一致。GSEA分析结果如表GSEA.result.txt所示。
1.7.2亚型间的临床特征的比较
进一步,我们分析了两个亚型间的临床特征(如TMN分期,性别等)的差异,结果如图9所示,我们发现基于单细胞分析筛选得到的marker基因构建的两个亚型在性别和M分期(远端转移)是没有差异的。但是在T分期和N分期(淋巴结转移),stage分期,grade分级都存在差异。造成两个亚型差异的主要原因可能是stage分期,TN分型,grade分级。
1.7.3亚型中关键基因的筛选
我们对前面分析得到两个亚型之间的差异基因,筛选差异变化倍数较大的基因(以|log2FC|>2)。共筛选出121个重要基因,而在121个基因中,有17个基因表达上调,104个基因表达下调(图10A)。我们使用在线数据库string(https://string-db.org/),并设置minimum required interaction score为400,查看这些重要基因相互作用关系,关系文件为string_interactions.tsv,通过cytoscape绘制相互关系网络(图10B,1.cys),表node.degree.csv为所有点的连接度。从表中可以看出,CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP基因的degree不下于20个,而这7个基因中AFP表达上调,其他6个基因均为表达下调的基因。通过文献查找可以发现,CYP3A4基因的多态性与乳腺癌的发生和发展是有关的(PMID:30218411)。NR1I2基因通过激活外源受体PXR参与外源性反应,如解毒、代谢和炎症反应。也有证据证明PXR信号在凋亡、细胞周期阻滞、增殖、血管生成和氧化应激过程中起作用,它们与癌症密切相关(PMID:32082968)。已有分析表明CYP2C9基因可能可以作为肝癌的预后靶标(PMID:29974848)。CYP1A2在膀胱癌、肺癌等均有研究(PMID:27173252、PMID:27942533)。血清中AFP的检测对于AFPGC和HAS亚型病人的肝转移具有非常重要的意义(PMID:2915128)。
进一步,我们分析了这7个基因的相关性(图10C),结果发现这7个基因中,可以分为三个个部分,第一部分为CYP3A4、CYP2C9和CYP1A2三个基因内部呈现强正相关性,且与其他四个基因表现出负相关。这三个基因属于Cytochrome P450家族成员,主要是代谢体内异源性物质,在已发现的57个CYP450同工酶中,有7种亚型酶负责了90%以上临床常用药物的代谢清除,即:CYP1A2、CYP2C9、CYP2C18、CYP2C19、CYP2D6、CYP2E1和CYP3A4。第二部分为NR1I2、TTR和APOC3内部呈现正相关性(相关性弱于第一部分),且与其他四个基因表现出负相关。NR1I2基因产物属于核受体超家族,具有配体结合结构域和dna结合结构域的转录因子。该编码蛋白是细胞色素P450基因CYP3A4的转录调节因子,以异源二聚体结合到CYP3A4启动子的响应元件上。诱导CYP3A4的化合物激活。TTR主要参与先天免疫系统和与视觉转导相关的疾病。APOC3基因主要参与脂蛋白代谢途径和他汀类药物途径。第三部分为AFP基因,该基因与其余6个基因都呈现出负相关。从图10D中可以发现,除了AFP基因在cluster1中表达高于cluster2外,其他6个基因都表现出一致性,在cluster2中表达高于cluster1。
接着,我们分析在TN分型、stage分期和grade分级中这7个基因表达水平的分布情况,证明了基于单细胞数据分析得到的分子分类的准确性,如图11所示。由于两个分子分型之间存在显著的临床差异,我们进一步研究了这7个基因在两个分子分型之间的临床差异,包括肿瘤的stage分期、T、N和M。其中,CYP3A4、NR1I2、CYP2C9、TTR和APOC3在T分期中的表达具有显著性。CYP3A4、NR1I2和TTR在N分期中的表达具有显著性。CYP3A4、NR1I2、CYP2C9、TTR和APOC3在stage分期中的表达具有显著性。CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP在grade分期中的表达具有显著性。AFP与临床病理进展呈正相关,而其他6个基因呈负相关。
2、肝癌预后预测模型的构建和验证
KM分析对7个关键基因进行单细胞RNA测序筛选分析,分析结果见图12。
用R包Limma分析分子组间的差异表达基因(DEGs)。使用经验贝叶斯方法计算分子群之间的折叠变化,并通过使用调节t检验的共识聚类方法识别。采用Benjamini Hochberg校正对多次检验的p值进行调整。在分子组间鉴定出假发现率(FDR)<0.05和折叠变化>2的DEGs。我们使用在线数据库STRING(https://string-db.org/)确定了关键基因之间的交互作用,并设置了最低要求的交互作用分数为400。利用Cytoscape生成重要基因的蛋白-蛋白相互作用网络。此外,利用R函数“cor.test”来计算和检验相关系数。然后,利用UniCox和LASSO-Cox算法进行降维,并利用cox模型对筛选出的关键基因建立预测预后模型。
根据每个样本中关键基因的表达水平计算出每个样本的风险得分,并绘制出风险得分分布图。根据中位风险评分将HCC样本分为高危组和低危组,生存分析采用KM法,生存时间比较采用log-rank检验。通过受试者工作特征(ROC)曲线评价预后模型的敏感性和特异性。使用R软件包pkgsearch来绘制ROC曲线(见图13)。
通过单因素和多因素分析预后模型与临床特征之间的关系。我们构建列线图来比较使用R包RMS的预后模型和临床特征之间的风险评分。校准曲线分析用于评估预后模型的性能(见图14)。
此外,采用Cox比例风险回归进行单因素和多因素logistic分析,以评估预后模型的风险评分与临床特征之间的关系。同时计算预后模型和其他临床特征的危险比(HR)和95%置信区间(CI)(图15)。采用卡方分析评价分子分型与临床特征的关系。用unpairedStudent’st检验和Mann-Whitney u检验比较变量为正态分布和非正态分布的两组。比较三组数据时,参数数据和非参数数据均采用单向方差分析和Kruskal-Wallis检验。本研究的所有分析均采用R软件(3.5.1版)和SPSS软件(24版)进行。P值均为双侧,P<0.05时差异有统计学意义(图15)。
图14C的校准曲线显示,实际和预测生存率一致性较高,表明该预测模型准确性高。校准曲线显示,预测结果在1年、3年和5年与实际结果之间的一致性良好。实际的结果就是虚线的斜率,校准曲线可以看出来1年、3年和5年的斜率与实际情况斜率差不多,说明符合性很好。
图15采用单因素和多因素生存分析森林图验证临床特征和预后模型的独立性和有效性,从森林图可以看出,riskscore的P值<0.05,且riskscore的HR值相比其他值大,所以具有很好的独立性和有效性。
以上仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员来说,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 (9)
1.预测肝癌预后的标志物组合,包括CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP中的至少两种基因。
2.权利要求1所述的标志物组合在制备预测肝癌患者预后的产品中的应用。
3.根据权利要求2所述的应用,其特征在于,所述预后包括对肝癌进行分类,确定治疗方案并预测治疗方案的有效性,预测患者生存率,和/或评估患者的免疫细胞的浸润率。
4.根据权利要求2所述的应用,其特征在于,AFP基因与预后不良相关,其余6个基因与预后良好呈正相关。
5.根据其权利要求2所述的应用,其特征在于,所述治疗方案包括免疫治疗;所述免疫治疗的药物包括PD-L1抑制剂和CTLA-4抑制剂。
6.预测肝癌患者预后的产品,其特征在于,包括检测如下基因表达水平的产品,或检测由如下基因所编码的蛋白表达水平的产品,或检测由如下基因所转录的mRNA表达水平的产品:
所述基因包括CYP3A4、NR1I2、CYP2C9、TTR、APOC3、CYP1A2和AFP中的至少两种。
7.根据权利要求6所述的产品,其特征在于,所述产品包括试剂、试剂盒、芯片或其他诊断工具。
8.权利要求1所述的标志物组合在构建预测肝癌预后的模型中的应用。
9.一种肝癌预后模型的构建方法,其特征在于,包括如下步骤:
结合单细胞RNA测序和大数据分析筛选与HCC密切相关的细胞和基因;对肝癌关键基因进行生物信息学分析、分子分类和多维相关分析,确定对肝癌预后影响关键的标志物,根据所述标志物构建肝癌预后模型;
所述的标志物为权利要求1所述的标志物组合。
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