CN116590415B - 一种基于组蛋白修饰基因特征开发的前列腺癌预后风险评估模型及应用 - Google Patents
一种基于组蛋白修饰基因特征开发的前列腺癌预后风险评估模型及应用 Download PDFInfo
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Abstract
本发明公开了一种基于组蛋白修饰基因特征开发的前列腺癌预后风险评估模型及应用，属于生物医学领域。本发明提供一种用于前列腺癌预后评估的产品，该产品包括检测生物标志物表达水平的试剂，所述生物标志物为21个组蛋白修饰调节基因；还提供一种前列腺癌预后风险评估模型，所述前列腺癌预后风险评估模型以所述生物标志物的表达水平为输入变量，计算风险评分。本发明建立了由这21个基因组成的预后模型，并经试验证实了该模型对前列腺癌风险评估的有效性和准确性，增强临床工作者预测前列腺癌患者预后风险的能力。
Description
技术领域
本发明涉及生物医学领域，特别是涉及一种基于组蛋白修饰基因特征开发的前列腺癌预后风险评估模型及应用。
背景技术
前列腺癌是一种高度异质性疾病，是全球男性中普遍发生的恶性肿瘤之一，也是男性癌症相关死亡的主要癌种之一。针对前列腺癌的管理有多种手段，其中根治性前列腺切除术、放疗和雄激素剥夺疗法是治疗器官局限性和雄激素依赖性前列腺癌的重要组成部分。尽管大多数患者可以从不同的治疗方式中获益，然而，仍有大约三分之一的患者在局部治疗后出现生化复发(biochemical recurrence，BCR)，这类患者迅速进展，并最终发展为去势抵抗性前列腺癌(castration-resistant prostate cancer，CRPC)，可能在2至4年内死亡，这也是前列腺癌患者死亡的主要原因。因此，如何准确识别局部治疗后容易发生疾病进展的高危患者，为其制定个性化治疗方案具有重要意义。既往研究表明，格里森评分(Gleason score)、前列腺特异性抗原(prostate specific antigen，PSA)和临床T分期等参数与前列腺癌患者生存率有关，并能预测局部治疗后的预后，然而它们存在一定的局限性，导致预测准确率较低。
近年来，为了更好地识别预后差的患者，学者们开展了一系列研究，探索出了一些基于基因表达数据和临床数据的多种基因特征来预测局部治疗后患者的预后。例如，Daojun Lv等人建立并验证了一个基于免疫相关基因的7基因分子特征来监测免疫状态并评估前列腺癌患者的无复发生存率；Qijie Zhang等确定了一种凋亡相关基因特征，将基因特征纳入临床参数可以进一步改善患者的BCR风险分层，从而识别出预后差的患者等等。
表观遗传学在前列腺癌进展中扮演着重要的角色。表观遗传调控可以通过DNA甲基化、组蛋白修饰和microRNA表达等来实现，从而在癌症中发挥着重要的作用。其中，组蛋白修饰是用于调节染色质结构、DNA修复和基因表达的最常见的表观遗传方法之一，充满了多样性和复杂性。越来越多的证据表明组蛋白修饰与前列腺癌的发生和进展密切相关，可以影响肿瘤细胞的某些生物学过程，如增殖、凋亡、转移等。值得注意的是，有研究表明癌细胞的组蛋白修饰的全局变化可独立于肿瘤分期、术前前列腺癌特异性抗原和包膜浸润预测前列腺癌的复发，是低级别前列腺癌患者肿瘤复发风险的指标之一。然而，当前对于组蛋白修饰在前列腺癌患者临床诊疗中的作用的研究依旧局限，且尚无研究探索组蛋白修饰因子的综合转录组表达水平是否具有区分高危预后患者的作用，表明组蛋白修饰在前列腺癌中的应用还有很大的探索与发展空间。
鉴于以上背景，本发明旨在基于组蛋白修饰因子的综合转录组表达水平建立一种新的组蛋白修饰相关的预测模型，以改善局部治疗后前列腺癌患者的风险分层，这些结果可能有助于为前列腺癌患者提供更好的治疗决策，从而改善预后。
发明内容
本发明的目的是提供一种基于组蛋白修饰基因特征开发的前列腺癌预后风险评估模型及应用，以解决上述现有技术存在的问题，本发明建立了由21个基因组成的预后模型，并证实了该模型对前列腺癌风险评估的有效性和准确性，增强临床工作者预测前列腺癌患者预后风险的能力。
为实现上述目的，本发明提供了如下方案：
本发明提供一种用于前列腺癌预后评估的产品，所述产品包括检测生物标志物表达水平的试剂，所述生物标志物为组蛋白修饰调节基因，包括MXD3、CCDC28B、COL11A2、SLC39A5、GPT、DNASE1L2、PIF1、KRTAP5.9、TTLL10、KRTAP5.1、KRTAP5.10、HAGHL、MSLNL、AMH、NKAIN4、CCDC114、SLC9A3、SULT1E1、SLC6A14、ALB和RPE65。
本发明还提供检测生物标志物的试剂在制备用于前列腺癌预后评估产品中的应用，所述生物标志物包括MXD3、CCDC28B、COL11A2、SLC39A5、GPT、DNASE1L2、PIF1、KRTAP5.9、TTLL10、KRTAP5.1、KRTAP5.10、HAGHL、MSLNL、AMH、NKAIN4、CCDC114、SLC9A3、SULT1E1、SLC6A14、ALB和RPE65。
本发明还提供一种前列腺癌预后风险评估模型，所述前列腺癌预后风险评估模型以所述生物标志物的表达水平为输入变量，按以下公式计算风险评分：
风险评分＝(MXD3表达水平+CCDC28B表达水平+COL11A2表达水平+SLC39A5表达水平+GPT表达水平+DNASE1L2表达水平+PIF1表达水平+KRTAP5.9表达水平+TTLL10表达水平+KRTAP5.1表达水平+KRTAP5.10表达水平+HAGHL表达水平+MSLNL表达水平+AMH表达水平+NKAIN4表达水平+CCDC114表达水平+SLC9A3表达水平+SULT1E1表达水平+ALB表达水平)/19-(SLC6A14表达水平+RPE65表达水平)/2。
本发明还提供一种前列腺癌预后风险评估的系统，所述系统包括计算单元，所述计算单元利用所述前列腺癌预后风险评估模型计算风险评分。
进一步地，还包括检测单元，所述检测单元用于检测所述生物标志物的表达水平。
进一步地，还包括信息获取单元，所述信息获取单元用于执行获取受试者检测信息的操作，所述检测信息包括所述生物标志物的表达水平。
进一步地，还包括评估单元，所述评估单元用于执行根据所述计算单元的计算结果判断受试者前列腺癌预后的风险概率，给出合理化预防和治疗建议。
进一步地，还包括结果显示单元，所述结果显示单元用于显示所述评估单元得出的结论。
进一步地，所述结果显示单元通过屏幕显示、声音播报或打印的方式显示结果。
本发明还提供所述的产品或所述的前列腺癌预后风险评估模型或所述的系统在筛选前列腺癌治疗药物中的应用。
本发明公开了以下技术效果：
本发明研究表明，以组蛋白修饰因子表达不同而将前列腺癌患者划分的亚型有不同的生物学行为及肿瘤微环境，据此，通过LASSOCox回归方法建立了由21个基因组成的预后模型(包括MXD3、CCDC28B、COL11A2、SLC39A5、GPT、DNASE1L2、PIF1、KRTAP5.9、TTLL10、KRTAP5.1、KRTAP5.10、HAGHL、MSLNL、AMH、NKAIN4、CCDC114、SLC9A3、SULT1E1、SLC6A14、ALB、RPE65)，之后以其预测前列腺癌患者1年期、3年期和5年期PFI的受试者工作特性曲线下面积分别为0.71、0.78、0.79，1年期、3年期和5年期BCR的受试者工作特性曲线下面积分别为0.75、0.76和0.75，说明其有效性。因此，该模型可提高现有的前列腺癌风险评估的准确性，增强临床工作者预测前列腺癌患者预后风险的能力。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为k＝4时得到的共识聚类矩阵；
图2为TCGA-PRAD队列中495例患者按4个共识聚类分型划分的67个组蛋白修饰调节因子表达的热图；
图3为基于来自TCGA-PRAD队列中249名患者4个前列腺癌亚型的Kaplan-Meier生存曲线分析结果；A：无进展间期(Progression-free interval，PFI)；B：无病间期(Disease-free interval，DFI)；
图4为各亚型及临床因素对无进展生存期(A)和无病生存期(B)影响的风险比森林图，其中列入的临床因素包括年龄、前列腺特异性抗原，T分期和组蛋白修饰模式的4个分型；
图5为4个前列腺癌亚型中患者的HIS_score值分布；
图6为TCGA-PRAD队列中高低HIS_score分组的患者以PFI(A)和DFI(B)描绘的生存曲线；
图7为HIS_score对TCGA-PRAD队列中患者1年、3年、5年生存率的预测价值；
图8为TCGA队列中多因素Cox回归模型分析的森林图；
图9为GSE70770队列中高低HIS_score分组的患者以生化复发(biochemicalrecurrence，BCR)描绘的生存曲线；
图10为HIS_score对GSE70770队列中患者1年、3年、5年生存率的预测价值；
图11为GEO队列中多因素Cox回归模型分析的森林图；
图12为构建结合HIS_score和临床特征预测PFI的列线图；
图13为HIS_score评估模型预测TCGA-PRAD队列患者的1年(A)、3年(B)和5年(C)的无进展生存时间的校准曲线；
图14为基于1年、3年和5年HIS_score风险评分的决策曲线分析；模型1：HIS_score，模型2：年龄+前列腺特异性抗原+格里森评分+T分期，模型3：年龄+前列腺特异性抗原+格里森评分+T分期+HIS_score；A：净受益率结果；B：每百名患者干预的净减少结果；
图15为临床样本中高低HIS_score分组的患者以BCR描绘的生存曲线。
具体实施方式
现详细说明本发明的多种示例性实施方式，该详细说明不应认为是对本发明的限制，而应理解为是对本发明的某些方面、特性和实施方案的更详细的描述。
应理解本发明中所述的术语仅仅是为描述特别的实施方式，并非用于限制本发明。另外，对于本发明中的数值范围，应理解为还具体公开了该范围的上限和下限之间的每个中间值。在任何陈述值或陈述范围内的中间值，以及任何其他陈述值或在所述范围内的中间值之间的每个较小的范围也包括在本发明内。这些较小范围的上限和下限可独立地包括或排除在范围内。
除非另有说明，否则本文使用的所有技术和科学术语具有本发明所述领域的常规技术人员通常理解的相同含义。虽然本发明仅描述了优选的方法和材料，但是在本发明的实施或测试中也可以使用与本文所述相似或等同的任何方法和材料。本说明书中提到的所有文献通过引用并入，用以公开和描述与所述文献相关的方法和/或材料。在与任何并入的文献冲突时，以本说明书的内容为准。
在不背离本发明的范围或精神的情况下，可对本发明说明书的具体实施方式做多种改进和变化，这对本领域技术人员而言是显而易见的。由本发明的说明书得到的其他实施方式对技术人员而言是显而易见得的。本发明说明书和实施例仅是示例性的。
关于本文中所使用的“包含”、“包括”、“具有”、“含有”等等，均为开放性的用语，即意指包含但不限于。
实施例1
一种基于组蛋白修饰基因特征开发的预测前列腺癌患者预后的评估模型的构建方法，具体步骤如下：
(1)组蛋白修饰因子的获取
在文献中检索获得67个组蛋白修饰调节基因并将其纳入分析，包括28种乙酰基转移酶(SUV39H2、SUV39H1、SETD7、SETD6、SETD5、SETD4、SETD3、SETD2、SETD1B、SETD1A、PRDM9、SETDB2、SETDB1、EHMT2、EHMT1、NSD1、ASH1L、N6AMT1、SMYD5、SMYD4、SMYD3、SMYD2、SMYD1、DOT1L、EZH2、PRMT6、PRMT5、CARM1)、11种去乙酰基酶(KDM6B、KDM5B、KDM5A、KDM4D、KDM3B、KDM2B、KDM1B、PHF8、PHF2、JMJD1C、UTY)、10种甲基转移酶(KAT5、KAT2B、KAT2A、CREBBP、EP300、ELP3、HAT1、NCOA1、CLOCK、GTF3C4)和18种去甲基酶(HDAC9、HDAC8、HDAC7、HDAC6、HDAC5、HDAC4、HDAC3、HDAC2、HDAC11、HDAC10、HDAC1、SIRT7、SIRT6、SIRT5、SIRT4、SIRT3、SIRT2、SIRT1)。分析它们对前列腺癌患者预后的影响及相关性。
(2)公共数据的获取及处理
从癌症基因组图谱TCGA和NCBIGEO数据库中检索获取了TCGA-PRAD、GEO70770这两个前列腺癌独立队列，分别包含495和203个前列腺癌样本的RNA-seq数据以及相应的临床信息。使用无进展间期(Progression-freeinterval，PFI)及无病间期(Disease-freeinterval，DFI)作为研究的临床终点。从UCSCXena数据库(https://xenabrowser.net)中获得了TCGA-PRAD的基因组突变数据。数据经过“voom”算法预处理后，使用R语言中的“ComBat”包来调整非生物技术偏差导致的批次效应。
在模型建立中，TCGA-PRAD数据作为训练集，GSE70770数据作为验证集。
表1本研究使用的数据集的患者信息
(3)组蛋白修饰因子表达模式的识别
通过无监督聚类识别不同的组蛋白修饰模式，并对TCGA-PRAD患者进行分类，共识聚类算法用于评估聚类稳定性并使用R包“ConsensusClusterPlus”选择最佳聚类数，参数设置如下：pItem＝0.8，pFeature＝0.8，maxK＝6，reps＝1000，使用基于1-Spearman相关距离(distance)的K-means聚类算法(clusterAlg)。当k＝4时可划分稳定的集群(图1)。我们将4个表达模式命名为前列腺癌亚型，分别为C1(n＝142)、C2(n＝142)、C3(n＝155)、C4(n＝56)。以它们的基因表达谱绘制的热图如图2所示。
(4)分析前列腺癌亚型的临床意义
使用Kaplan-Meier方法对前列腺癌亚型进行生存分析，如图3中A和B所示，对于PFI，C3患者的生存时间明显短于C1、C2和C4，而C1患者生存最优(p＝6.63e-06)。4个前列腺癌亚型患者的3年无进展生存率分别为92.96％、88.03％、76.77％、85.71％。在基于249名接受R0切除的患者的DFI分析结果同样显示，C1患者的生存最优，5年无病生存率高达97.32％，而C3表现出比其他亚型更高的复发风险(p＝0.015)。随后，纳入了年龄、前列腺特异性抗原、格里森评分、临床T分期的多因素Cox回归分析结果证实了前列腺癌亚型是影响TCGA-PRAD队列PFI和DFI的独立预后因素，C4较C1同时显示出显著更高的进展(p＝0.037，风险比＝8.831，95％置信区间＝1.139-68.476，图4中A)和复发风险(p＝0.006，风险比＝3.166，95％置信区间＝1.397-7.714，图4中B)。治疗方面，使用癌症药物敏感性基因组学(GDSC)数据库计算了10种化疗药物在各亚型中的半抑制浓度(IC50)，对比发现不同亚型对药物敏感性有差异。
(5)分析前列腺癌亚型的生物学行为及肿瘤微环境
使用GSVA(Gene set variation analysis，基因集变异分析)和CIBERSORT以评估各前列腺癌亚型的生物学功能和肿瘤免疫细胞浸润情况。CIBERSORT是一种基于基因表达谱表征复杂组织的细胞组成的反卷积方法。此外，计算了各组的TIDE得分，并且匹配了干性特征、肿瘤负荷、上皮间充质转化、肿瘤纯度等评分在各组的分布以分析肿瘤恶性程度及对免疫治疗的反应等。这些结果强调了前列腺癌中确实存在4个不同的组蛋白修饰因子表达模式，它们代表了不同的生物学行为和肿瘤微环境特征，导致了预后的差异。
(6)构建组蛋白评估模型HIS_score并验证
风险评分的建立上，使用“limma”R包以识别C1和C3对比的差异表达基因，显著性标准设置为p<0.05(调整后)和|logFC|>1，将单因素Cox模型筛选及Kaplan-Meier方法筛选的与预后相关的基因取交集后纳入LASSO回归分析(10倍交叉验证)，重复1000次运算以保证稳定性，产生最佳的21基因模型(包括MXD3、CCDC28B、COL11A2、SLC39A5、GPT、DNASE1L2、PIF1、KRTAP5.9、TTLL10、KRTAP5.1、KRTAP5.10、HAGHL、MSLNL、AMH、NKAIN4、CCDC114、SLC9A3、SULT1E1、SLC6A14、ALB、RPE65)，建立基于组蛋白修饰相关的预后模型的风险评分(HIS_score)。具体计算公式为：HIS_score＝(MXD3+CCDC28B+COL11A2+SLC39A5+GPT+DNASE1L2+PIF1+KRTAP5.9+TTLL10+KRTAP5.1+KRTAP5.10+HAGHL+MSLNL+AMH+NKAIN4+CCDC114+SLC9A3+SULT1E1+ALB)/19-(SLC6A14+RPE65)/2。来自TCGA数据库的495例PRAD患者的数据被用作训练集，按评分由高到低的顺序，取前后1/3的患者分为高风险和低风险组。箱线图显示C3较其他亚型有更高的HIS_score(图5)。随后，使用Kaplan-Meier法分析高风险和低风险组患者之间的生存差异，图6中A和B显示，与低评分组相比，高评分组的PFI(p＝1.48e-09，风险比＝2.996,95％置信区间＝1.904-4.715，图6中A)和DFI显著缩短(p＝0.00022,风险比＝3.415,95％置信区间＝1.340-8.706，图6中B)。1年期、3年期和5年期PFI的受试者工作特性曲线下面积分别为0.71、0.78、0.79(图7)。对来自GSE70770的203例PRAD患者的数据执行相同的过程，结果显示该预后模型在GEO数据集中也有预测能力(图9，图10)。此外通过在TCGA-PRAD和GSE70770数据集中构建多因素回归模型来构建列线图以实现模型可视化和临床应用，结果如图8和11所示，高HIS_score是前列腺癌患者预后的预测因素。基于列线图中每个因素的相应的数字之和的总分越高，3年和5年PFI率越低(图12)。并采用校正曲线(图13)和决策曲线(图14)以评估模型的可靠性和准确性。
(7)使用临床样本验证预后评估模型的准确性
为了进一步确认HIS_score模型的临床价值，申请人使用既往在南方医科大学南方医院收集的54例前列腺癌临床样本中进行BULK-RNA测序的数据集，提取HIS_score相关基因的表达计算HIS_score得分并验证HIS_score对前列腺癌患者预后的预测作用。我们运用中位值将患者分为高低评分组，使用临床数据中的无生化复发时间为重点进行生存分析。结果提示临床样本中，高HIS_score患者的预后更差(p＝0.013，图15)，表明HIS_score对于识别预后不良的前列腺癌患者的作用是可重复且稳健的。
以上所述的实施例仅是对本发明的优选方式进行描述，并非对本发明的范围进行限定，在不脱离本发明设计精神的前提下，本领域普通技术人员对本发明的技术方案做出的各种变形和改进，均应落入本发明权利要求书确定的保护范围内。
Claims (5)
1.一种用于前列腺癌预后评估的生物标志物，其特征在于，所述生物标志物为组蛋白修饰调节基因，由MXD3、CCDC28B、COL11A2、SLC39A5、GPT、DNASE1L2、PIF1、KRTAP5.9、TTLL10、KRTAP5.1、KRTAP5.10、HAGHL、MSLNL、AMH、NKAIN4、CCDC114、SLC9A3、SULT1E1、SLC6A14、ALB和RPE65组成。
2.检测生物标志物表达水平的试剂在制备用于前列腺癌预后评估产品中的应用，其特征在于，所述生物标志物由MXD3、CCDC28B、COL11A2、SLC39A5、GPT、DNASE1L2、PIF1、KRTAP5.9、TTLL10、KRTAP5.1、KRTAP5.10、HAGHL、MSLNL、AMH、NKAIN4、CCDC114、SLC9A3、SULT1E1、SLC6A14、ALB和RPE65组成。
3.一种前列腺癌预后风险评估的系统，其特征在于，所述系统包括计算单元，所述计算单元以权利要求1所述生物标志物的表达水平为输入变量，按以下公式计算风险评分：
风险评分=（MXD3表达水平+CCDC28B表达水平+COL11A2表达水平+SLC39A5表达水平+GPT表达水平+DNASE1L2表达水平+PIF1表达水平+KRTAP5.9表达水平+TTLL10表达水平+KRTAP5.1表达水平+KRTAP5.10表达水平+HAGHL表达水平+MSLNL表达水平+AMH表达水平+NKAIN4表达水平+CCDC114表达水平+SLC9A3表达水平+SULT1E1表达水平+ALB表达水平）/19-(SLC6A14表达水平+RPE65表达水平）/2；
所述系统还包括检测单元、信息获取单元和评估单元；所述检测单元用于检测权利要求1所述生物标志物的表达水平；所述信息获取单元用于执行获取受试者检测信息的操作，所述检测信息包括所述生物标志物的表达水平；所述评估单元用于执行根据所述计算单元的计算结果判断受试者前列腺癌预后的风险概率，给出合理化预防和治疗建议。
4.根据权利要求3所述的系统，其特征在于，还包括结果显示单元，所述结果显示单元用于显示所述评估单元得出的结论。
5.根据权利要求4所述的系统，其特征在于，所述结果显示单元通过屏幕显示、声音播报或打印的方式显示结果。
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