技术领域Technical Field
本发明属于胃癌预后技术领域，具体涉及一种胃癌预后标志物的筛选和分类方法、胃癌预后标志物和检测胃癌预后的试剂及应用。The present invention belongs to the technical field of gastric cancer prognosis, and specifically relates to a method for screening and classifying gastric cancer prognosis markers, gastric cancer prognosis markers, and reagents for detecting gastric cancer prognosis and applications thereof.
背景技术Background Art
胃癌(Gastric Cancer)的发病率在全世界范围内排在前列，由于胃癌在组织学和病因学上具有非常高的异质性，不同的治疗方案在临床上也产生各种各样的结果。因此如何准确的预测胃癌病人的生存和预后风险具有非常重要的临床意义。The incidence of gastric cancer ranks among the highest in the world. Due to the high heterogeneity of gastric cancer in histology and etiology, different treatment plans also produce various clinical results. Therefore, how to accurately predict the survival and prognosis risk of gastric cancer patients is of great clinical significance.
目前有不同的方法来对胃癌进行分类，具体的方法有TCGA的分类体系和ACRG分类体系，但是，上述分类体系在进行胃癌分子分型的过程中并没有和患者的生存信息相关联或者只和单一组学的信息相关联，因此，急需一种基于多组学和生存信息相关的分类方法，补充当前的分类系统以提高预后预测和治疗选择的准确性。There are currently different methods to classify gastric cancer, including the TCGA classification system and the ACRG classification system. However, the above classification systems are not associated with the patient's survival information or are only associated with a single group of information during the molecular typing of gastric cancer. Therefore, there is an urgent need for a classification method based on multi-omics and survival information to supplement the current classification system to improve the accuracy of prognosis prediction and treatment selection.
发明内容Summary of the invention
有鉴于此，本发明的目的在于提供一种胃癌预后标志物的筛选和分类方法、胃癌预后标志物和检测胃癌预后的试剂及应用，将筛选和分类得到的胃癌预后标志物，其判定结果与TCGA中真实预后数据结果具有高度的一致性。In view of this, the purpose of the present invention is to provide a method for screening and classifying gastric cancer prognostic markers, gastric cancer prognostic markers, and reagents and applications for detecting gastric cancer prognosis, so that the gastric cancer prognostic markers obtained by screening and classification have a high degree of consistency in the judgment results with the real prognostic data results in TCGA.
为了实现上述发明目的，本发明提供以下技术方案：In order to achieve the above-mentioned invention object, the present invention provides the following technical solutions:
本发明提供了一种胃癌预后标志物的筛选和分类方法，包括以下步骤：The present invention provides a method for screening and classifying gastric cancer prognostic markers, comprising the following steps:
(1)将与胃癌相关的多组学预后标志物信息导入训练好的支持向量机SVM模型中，分类成A亚型、B亚型和C亚型；(1) The multi-omics prognostic marker information related to gastric cancer was imported into the trained support vector machine (SVM) model and classified into subtype A, subtype B, and subtype C;
(2)筛选A亚型、B亚型和C亚型中具有特异性表达的基因，得胃癌预后标志物。(2) Screening genes with specific expression in subtypes A, B, and C to obtain prognostic markers for gastric cancer.
优选的，步骤(1)所述与胃癌相关的多组学预后标志物包括：mRNA、miRNA、lncRNA和DNA甲基化位点。Preferably, the multi-omics prognostic markers associated with gastric cancer in step (1) include: mRNA, miRNA, lncRNA and DNA methylation sites.
优选的，步骤(1)所述与胃癌相关的多组学预后标志物包括：100个mRNA基因、50个miRNA、50个lncRNA和50个DNA甲基化位点。Preferably, the multi-omic prognostic markers associated with gastric cancer in step (1) include: 100 mRNA genes, 50 miRNAs, 50 lncRNAs and 50 DNA methylation sites.
优选的，步骤(1)所述A亚型和B亚型的预后效果好，C亚型的预后效果差。Preferably, in step (1), the prognostic effect of subtype A and subtype B is good, and the prognostic effect of subtype C is poor.
本发明还提供了一种利用上述筛选和分类方法得到的胃癌预后标志物。The present invention also provides a gastric cancer prognosis marker obtained by using the above screening and classification method.
优选的，包括12个高表达B亚型基因和12个高表达C亚型基因；Preferably, it includes 12 highly expressed B subtype genes and 12 highly expressed C subtype genes;
所述12个高表达B亚型基因包括：LILRA2、ALOX5AP、CREB5、PSTPIP1、KIAA1949、IFFO1、P2RX7、BBS2、CCDC109B、PARP11、UTRN和TRIM22；The 12 highly expressed B subtype genes include: LILRA2, ALOX5AP, CREB5, PSTPIP1, KIAA1949, IFFO1, P2RX7, BBS2, CCDC109B, PARP11, UTRN and TRIM22;
所述12个高表达C亚型基因包括：PAGE2、MAGEC2、ZNF716、C18orf2、COX7B2、MAGEA9B、DSCR4、CT45A5、MAGEB2、GAGE2D、MAGEA4和CLEC2L。The 12 highly expressed C subtype genes include: PAGE2, MAGEC2, ZNF716, C18orf2, COX7B2, MAGEA9B, DSCR4, CT45A5, MAGEB2, GAGE2D, MAGEA4 and CLEC2L.
本发明还提供了一种检测胃癌预后的试剂，所述试剂包括上述胃癌预后标志物的特异性引物对。The present invention also provides a reagent for detecting gastric cancer prognosis, wherein the reagent comprises a specific primer pair of the gastric cancer prognosis marker.
本发明还提供了上述胃癌预后标志物或上述试剂在制备准确预测胃癌病人的生存和预后风险的工具中的应用。The present invention also provides the use of the above-mentioned gastric cancer prognostic marker or the above-mentioned reagent in preparing a tool for accurately predicting the survival and prognostic risk of gastric cancer patients.
有益效果：本发明提供了一种胃癌预后标志物的筛选和分类方法，所述筛选和分类方法基于多组学和生存信息相关，可提高预后预测和治疗选择的准确性。利用本发明所述筛选和分类方法筛选得到的胃癌预后标志物，其判定结果与TCGA中真实预后数据结果具有高度的一致性。Beneficial effects: The present invention provides a method for screening and classifying gastric cancer prognostic markers, which is based on multi-omics and survival information correlation and can improve the accuracy of prognosis prediction and treatment selection. The gastric cancer prognostic markers screened by the screening and classification method of the present invention have a high degree of consistency with the real prognostic data results in TCGA.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为不同亚型之间的分类结果；Figure 1 shows the classification results between different subtypes;
图2为不同亚型之间的基因表达热图；Figure 2 is a heat map of gene expression between different subtypes;
图3为不同亚型的预后差异结果图。Figure 3 shows the prognostic differences among different subtypes.
具体实施方式DETAILED DESCRIPTION
本发明提供了一种胃癌预后标志物的筛选和分类方法，包括以下步骤：(1)将与胃癌相关的多组学预后标志物信息导入训练好的支持向量机SVM模型中，分类成A亚型、B亚型和C亚型；The present invention provides a method for screening and classifying gastric cancer prognostic markers, comprising the following steps: (1) importing multi-omics prognostic marker information related to gastric cancer into a trained support vector machine (SVM) model and classifying the information into subtype A, subtype B and subtype C;
(2)筛选A亚型、B亚型和C亚型中具有特异性表达的基因，得胃癌预后标志物。(2) Screening genes with specific expression in subtypes A, B, and C to obtain prognostic markers for gastric cancer.
本发明所述多组学优选包括：mRNA、miRNA、lncRNA和DNA甲基化位点。在本发明中，优选利用100个mRNA基因(表1)、50个miRNA(表2)、50个lncRNA(表3)和50个DNA甲基化位点(表4)进行胃癌预后标志物的筛选和分类。本发明所述mRNA、miRNA、lncRNA和DNA甲基化位点优选均通过TCGA查询得到。The multi-omics of the present invention preferably includes: mRNA, miRNA, lncRNA and DNA methylation sites. In the present invention, 100 mRNA genes (Table 1), 50 miRNAs (Table 2), 50 lncRNAs (Table 3) and 50 DNA methylation sites (Table 4) are preferably used to screen and classify gastric cancer prognostic markers. The mRNA, miRNA, lncRNA and DNA methylation sites of the present invention are preferably obtained by TCGA query.
表1查询得到的100个mRNA基因信息Table 1 Information of 100 mRNA genes obtained by query
表2查询得到的50个miRNA信息Table 2 Information of 50 miRNAs obtained by query
表3查询得到的50个lncRNATable 3 50 lncRNAs obtained by query
表4查询得到的50个DNA甲基化位点Table 4 50 DNA methylation sites obtained by query
本发明优选将上述四种组学的信息导入到训练好的支持向量机SVM模型中，模型利用神经网络的算法，将不同的feature定义为不同的神经元，利用神经网络算法基于input features进行亚型分类，模型会将每个样本划分为A亚型、B亚型和C亚型。本发明所述支持向量机SVM模型优选根据Zhang F等的文献资料进行构建(Zhang F,Kaufman HL,Deng Y,Drabier R.Recursive SVM biomarker selection for early detection ofbreast cancer in peripheral blood.BMC Med Genomics.2013；6 Suppl 1(Suppl 1):S4.doi:10.1186/1755-8794-6-S1-S4.Epub 2013 Jan 23.PMID:23369435；PMCID:PMC3552693.)。The present invention preferably imports the information of the above four omics into a trained support vector machine SVM model, and the model uses a neural network algorithm to define different features as different neurons, and uses the neural network algorithm to perform subtype classification based on input features. The model will divide each sample into subtype A, subtype B and subtype C. The support vector machine SVM model of the present invention is preferably constructed according to the literature of Zhang F et al. (Zhang F, Kaufman HL, Deng Y, Drabier R. Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood. BMC Med Genomics. 2013; 6 Suppl 1 (Suppl 1): S4. doi: 10.1186/1755-8794-6-S1-S4. Epub 2013 Jan 23. PMID: 23369435; PMCID: PMC3552693.).
在本发明中，所述A亚型和B亚型的预后效果好，C亚型的预后效果差(A亚型和B亚型的预后结果显著高于C亚型，A亚型预后效果最好)。In the present invention, the prognostic effects of subtypes A and B are good, and the prognostic effect of subtype C is poor (the prognostic results of subtypes A and B are significantly higher than that of subtype C, and subtype A has the best prognostic effect).
本发明还提供了一种利用上述筛选和分类方法得到的胃癌预后标志物。The present invention also provides a gastric cancer prognosis marker obtained by using the above screening and classification method.
在本发明实施例中，共筛选得到12个高表达B亚型基因和12个高表达C亚型基因；所述12个高表达B亚型基因优选包括：LILRA2、ALOX5AP、CREB5、PSTPIP1、KIAA1949、IFFO1、P2RX7、BBS2、CCDC109B、PARP11、UTRN和TRIM22；In the embodiment of the present invention, a total of 12 highly expressed B subtype genes and 12 highly expressed C subtype genes were screened; the 12 highly expressed B subtype genes preferably include: LILRA2, ALOX5AP, CREB5, PSTPIP1, KIAA1949, IFFO1, P2RX7, BBS2, CCDC109B, PARP11, UTRN and TRIM22;
所述12个高表达C亚型基因优选包括：PAGE2、MAGEC2、ZNF716、C18orf2、COX7B2、MAGEA9B、DSCR4、CT45A5、MAGEB2、GAGE2D、MAGEA4和CLEC2L。The 12 highly expressed C subtype genes preferably include: PAGE2, MAGEC2, ZNF716, C18orf2, COX7B2, MAGEA9B, DSCR4, CT45A5, MAGEB2, GAGE2D, MAGEA4 and CLEC2L.
本发明筛选和分类得到的胃癌预后标志物，将多组学信息和患者的生存信息相关联，可用于提高预后预测和治疗选择的准确性。The gastric cancer prognostic markers screened and classified by the present invention associate multi-omics information with the patient's survival information and can be used to improve the accuracy of prognosis prediction and treatment selection.
本发明还提供了一种检测胃癌预后的试剂，所述试剂包括上述胃癌预后标志物的特异性引物对。The present invention also provides a reagent for detecting gastric cancer prognosis, wherein the reagent comprises a specific primer pair of the gastric cancer prognosis marker.
本发明对所述特异性引物对的设计方法并没有特殊限定，利用本领域的常规引物设计方法和软件进行设计即可。The present invention does not particularly limit the design method of the specific primer pair, and the design can be performed using conventional primer design methods and software in the art.
本发明还提供了上述胃癌预后标志物或上述试剂在制备准确预测胃癌病人的生存和预后风险的工具中的应用。The present invention also provides the use of the above-mentioned gastric cancer prognostic marker or the above-mentioned reagent in preparing a tool for accurately predicting the survival and prognostic risk of gastric cancer patients.
下面结合实施例对本发明提供的一种胃癌预后标志物的筛选和分类方法、胃癌预后标志物和检测胃癌预后的试剂及应用进行详细的说明，但是不能把它们理解为对本发明保护范围的限定。The following is a detailed description of a method for screening and classifying a gastric cancer prognosis marker, a gastric cancer prognosis marker, and a reagent for detecting gastric cancer prognosis and their applications provided by the present invention in conjunction with the embodiments, but they should not be construed as limiting the scope of protection of the present invention.
实施例1Example 1
提供表1～表4所示的多组学数据，包括100个mRNA基因，50个miRNA，50个lncRNA和50个DNA甲基化位点，作为评估胃癌预后的评价标志物；The multi-omics data shown in Tables 1 to 4 are provided, including 100 mRNA genes, 50 miRNAs, 50 lncRNAs, and 50 DNA methylation sites, as evaluation markers for assessing the prognosis of gastric cancer;
上述四种组学的信息进行导入到训练好的支持向量机SVM模型(Zhang F,KaufmanHL,Deng Y,Drabier R.Recursive SVM biomarker selection for early detection ofbreast cancer in peripheral blood.BMC Med Genomics.2013；6Suppl 1(Suppl 1):S4.doi:10.1186/1755-8794-6-S1-S4.Epub 2013 Jan 23.PMID:23369435；PMCID:PMC3552693)中，模型会将每个样本划分为A、B和C三种亚型中的一种(图1)。The information of the above four omics is imported into the trained support vector machine SVM model (Zhang F, Kaufman HL, Deng Y, Drabier R. Recursive SVM biomarker selection for early detection of breast cancer in peripheral blood. BMC Med Genomics. 2013; 6 Suppl 1 (Suppl 1): S4. doi: 10.1186/1755-8794-6-S1-S4. Epub 2013 Jan 23. PMID: 23369435; PMCID: PMC3552693), and the model will divide each sample into one of the three subtypes A, B and C (Figure 1).
分类为类亚型A和亚型B的类型预后效果好，分类为亚型C的预后效果差，并且分类为亚型B的样本中有12个基因出现高表达，高表达基因如表5所示，亚型C的样本中12个基因出现高表达，高表达基因如表6所示(图2～图3)。The types classified as subtype A and subtype B had a good prognosis, while those classified as subtype C had a poor prognosis. Twelve genes were highly expressed in the samples classified as subtype B, as shown in Table 5, and 12 genes were highly expressed in the samples of subtype C, as shown in Table 6 (Figures 2 to 3).
表5亚型B高表达基因Table 5 Highly expressed genes in subtype B
表6亚型C高表达基因Table 6 Highly expressed genes in subtype C
利用上述筛选得到的分子标志物，与TCGA中真实预后数据进行对比，结果如表7所示，其判定结果与TCGA中真实预后数据结果具有高度的一致性，表7中生命状态：0代表存活，1代表死亡。The molecular markers obtained by the above screening were compared with the real prognosis data in TCGA. The results are shown in Table 7. The judgment results are highly consistent with the real prognosis data in TCGA. In Table 7, the life status: 0 represents survival and 1 represents death.
表7预后时间和分子亚型之间的数据Table 7 Data between prognostic time and molecular subtypes
以上所述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。The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.
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