CN113555121A - Screening and classifying method of gastric cancer prognosis marker, reagent for detecting gastric cancer prognosis and application - Google Patents
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Abstract
The invention provides a method for screening and classifying gastric cancer prognosis markers, a gastric cancer prognosis marker, a reagent for detecting gastric cancer prognosis and application, and relates to the technical field of gastric cancer prognosis. The screening and classifying method comprises the following steps: (1) introducing multigroup prognosis marker information related to the gastric cancer into a trained Support Vector Machine (SVM) model, and classifying the information into an A subtype, a B subtype and a C subtype; (2) screening genes with specific expression in the A subtype, the B subtype and the C subtype to obtain the gastric cancer prognosis markers. The gastric cancer prognosis marker is related to the multiomics and survival information, can improve the accuracy of prognosis prediction and treatment selection, and has high consistency between the judgment result and the real prognosis data result in TCGA.
Description
Technical Field
The invention belongs to the technical field of gastric cancer prognosis, and particularly relates to a screening and classifying method of gastric cancer prognosis markers, a gastric cancer prognosis marker, a reagent for detecting gastric cancer prognosis and application.
Background
The incidence of Gastric Cancer (Gastric Cancer) is ranked in the front worldwide, and different treatment regimens also produce clinically diverse results due to the very high heterogeneity of histology and etiology of Gastric Cancer. Therefore, it is of great clinical significance how to accurately predict the survival and prognosis risk of gastric cancer patients.
There are different methods for classifying gastric cancer, and the specific methods include a TCGA classification system and an ACRG classification system, but the above classification systems are not related to survival information of patients or only related to information of a single omic during molecular classification of gastric cancer, so a classification method based on correlation of multiomic and survival information is urgently needed to supplement the current classification system to improve accuracy of prognosis prediction and treatment selection.
Disclosure of Invention
In view of the above, the present invention aims to provide a method for screening and classifying gastric cancer prognostic markers, a gastric cancer prognostic marker, a reagent for detecting gastric cancer prognosis, and an application thereof, wherein the judgment result of the gastric cancer prognostic markers obtained by screening and classifying has high consistency with the real prognosis data result in TCGA.
In order to achieve the above object, the present invention provides the following technical solutions:
the invention provides a method for screening and classifying gastric cancer prognosis markers, which comprises the following steps:
(1) introducing multigroup prognosis marker information related to the gastric cancer into a trained Support Vector Machine (SVM) model, and classifying the information into an A subtype, a B subtype and a C subtype;
(2) screening genes with specific expression in the A subtype, the B subtype and the C subtype to obtain the gastric cancer prognosis markers.
Preferably, the multiple group prognosis markers related to gastric cancer in step (1) comprise: mRNA, miRNA, lncRNA, and DNA methylation sites.
Preferably, the multiple group prognosis markers related to gastric cancer in step (1) comprise: 100 mRNA genes, 50 mirnas, 50 lncRNA, and 50 DNA methylation sites.
Preferably, the subtype A and the subtype B in the step (1) have good prognosis effect, and the subtype C has poor prognosis effect.
The invention also provides a gastric cancer prognostic marker obtained by the screening and classifying method.
Preferably, 12 high expression B subtype genes and 12 high expression C subtype genes are included;
the 12 high-expression B subtype genes comprise: LILRA2, ALOX5AP, CREB5, PSTPIP1, KIAA1949, IFFO1, P2RX7, BBS2, CCDC109B, PARP11, UTRN, and TRIM 22;
the 12 high-expression C subtype genes comprise: PAGE2, MAGEC2, ZNF716, C18orf2, COX7B2, MAGEA9B, DSCR4, CT45A5, MAGEB2, GAGE2D, MAGEA4 and CLEC 2L.
The invention also provides a reagent for detecting gastric cancer prognosis, which comprises the specific primer pair of the gastric cancer prognosis marker.
The invention also provides the application of the gastric cancer prognostic marker or the reagent in preparing a tool for accurately predicting survival and prognostic risks of gastric cancer patients.
Has the advantages that: the invention provides a screening and classifying method of gastric cancer prognosis markers, which is based on the correlation of multiomics and survival information and can improve the accuracy of prognosis prediction and treatment selection. The gastric cancer prognosis markers obtained by screening by the screening and classifying method have high consistency of the judgment result with the real prognosis data result in TCGA.
Drawings
FIG. 1 shows the classification results between different subtypes;
FIG. 2 is a heat map of gene expression between different subtypes;
FIG. 3 is a graph showing the results of different subtypes of prognosis.
Detailed Description
The invention provides a method for screening and classifying gastric cancer prognosis markers, which comprises the following steps: (1) introducing multigroup prognosis marker information related to the gastric cancer into a trained Support Vector Machine (SVM) model, and classifying the information into an A subtype, a B subtype and a C subtype;
(2) screening genes with specific expression in the A subtype, the B subtype and the C subtype to obtain the gastric cancer prognosis markers.
The omics of the present invention preferably comprise: mRNA, miRNA, lncRNA, and DNA methylation sites. In the present invention, it is preferable to use 100 mRNA genes (table 1), 50 mirnas (table 2), 50 lncRNA (table 3) and 50 DNA methylation sites (table 4) for screening and classification of gastric cancer prognosis markers. The mRNA, miRNA, lncRNA and DNA methylation sites of the invention are preferably obtained by TCGA query.
Table 1 shows the information of 100 mRNA genes
50 miRNA information obtained by table 2 query
Table 3 shows the obtained 50 lncRNA
Table 4 queries the resulting 50 DNA methylation sites
The invention preferably introduces the information of the four omics into a trained Support Vector Machine (SVM) model, the model defines different features as different neurons by using a neural network algorithm, the subtype classification is carried out by using the neural network algorithm based on input features, and each sample can be divided into an A subtype, a B subtype and a C subtype by the model. The SVM model of the support vector machine is preferably constructed according to literature data of Zhang F and the like (Zhang F, Kaufman HL, Deng Y, Drabier R.Cursive SVM biomar selection for early detection of cleavage in vivo blood block BMC Medium genomics.2013; 6Suppl 1(Suppl 1): S4.doi: 10.1186/1755-.
In the invention, the A subtype and the B subtype have good prognosis effects, and the C subtype has poor prognosis effect (the A subtype and the B subtype have the prognosis results which are obviously higher than those of the C subtype, and the A subtype has the best prognosis effect).
The invention also provides a gastric cancer prognostic marker obtained by the screening and classifying method.
In the embodiment of the invention, 12 high-expression B subtype genes and 12 high-expression C subtype genes are obtained by co-screening; the 12 high-expression subtype B genes preferably comprise: LILRA2, ALOX5AP, CREB5, PSTPIP1, KIAA1949, IFFO1, P2RX7, BBS2, CCDC109B, PARP11, UTRN, and TRIM 22;
the 12 high-expression C subtype genes preferably include: PAGE2, MAGEC2, ZNF716, C18orf2, COX7B2, MAGEA9B, DSCR4, CT45A5, MAGEB2, GAGE2D, MAGEA4 and CLEC 2L.
The gastric cancer prognosis markers obtained by screening and classifying correlate multiple groups of scientific information with survival information of patients, and can be used for improving accuracy of prognosis prediction and treatment selection.
The invention also provides a reagent for detecting gastric cancer prognosis, which comprises the specific primer pair of the gastric cancer prognosis marker.
The method for designing the specific primer pair is not particularly limited, and the specific primer pair can be designed by using the conventional primer design method and software in the field.
The invention also provides the application of the gastric cancer prognostic marker or the reagent in preparing a tool for accurately predicting survival and prognostic risks of gastric cancer patients.
The following examples are provided to describe in detail the method for screening and classifying gastric cancer prognostic markers, and gastric cancer prognosis detection reagents and applications provided by the present invention, but they should not be construed as limiting the scope of the present invention.
Example 1
Providing multiple sets of mathematical data shown in tables 1 to 4, wherein the multiple sets of mathematical data comprise 100 mRNA genes, 50 miRNA, 50 lncRNA and 50 DNA methylation sites, and the multiple sets of mathematical data are used as evaluation markers for evaluating gastric cancer prognosis;
the information of the four omics is imported into a trained SVM (Zhang F, Kaufman HL, Deng Y, Drabier R.Cursive SVM biorarker selection for early detection of noise cancer in peripheral blood group. BMC Medium genomics.2013; 6Suppl 1(Suppl 1): S4.doi: 10.1186/1755-.
The types classified into subtype a and subtype B showed good prognosis, and the type classified into subtype C showed poor prognosis, and 12 genes in the sample classified into subtype B showed high expression, and the high expression genes are shown in table 5, and 12 genes in the sample classified into subtype C showed high expression, and the high expression genes are shown in table 6 (fig. 2 to fig. 3).
TABLE 5 subtype B highly expressed genes
| LILRA2 | ALOX5AP | CREB5 | PSTPIP1 | KIAA1949 | IFFO1 |
| P2RX7 | BBS2 | CCDC109B | PARP11 | UTRN | TRIM22 |
TABLE 6 subtype C highly expressed genes
| PAGE2 | MAGEC2 | ZNF716 | C18orf2 | COX7B2 | MAGEA9B |
| DSCR4 | CT45A5 | MAGEB2 | GAGE2D | MAGEA4 | CLEC2L |
The molecular markers obtained by the screening are compared with the real prognosis data in TCGA, the results are shown in Table 7, the judgment result is highly consistent with the real prognosis data in TCGA, and the life states in Table 7 are as follows: 0 represents survival and 1 represents death.
TABLE 7 data between prognosis times and molecular subtypes
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A method for screening and classifying gastric cancer prognostic markers, which comprises the following steps: (1) introducing multigroup prognosis marker information related to the gastric cancer into a trained Support Vector Machine (SVM) model, and classifying the information into an A subtype, a B subtype and a C subtype;
(2) screening genes with specific expression in the A subtype, the B subtype and the C subtype to obtain the gastric cancer prognosis markers.
2. The screening and classification method according to claim 1, wherein the multiple sets of prognostic markers associated with gastric cancer of step (1) comprise: mRNA, miRNA, lncRNA, and DNA methylation sites.
3. The screening and classification method according to claim 1 or 2, wherein the multiple group prognostic markers associated with gastric cancer of step (1) include: 100 mRNA genes, 50 mirnas, 50 lncRNA, and 50 DNA methylation sites.
4. The screening and classifying method according to claim 3, wherein the subtype A and the subtype B in step (1) have a good prognostic effect and the subtype C has a poor prognostic effect.
5. A gastric cancer prognostic marker obtained by the screening and classifying method according to any one of claims 1 to 4.
6. The gastric cancer prognosis marker according to claim 5, comprising 12 high-expression subtype B genes and 12 high-expression subtype C genes;
the 12 high-expression B subtype genes comprise: LILRA2, ALOX5AP, CREB5, PSTPIP1, KIAA1949, IFFO1, P2RX7, BBS2, CCDC109B, PARP11, UTRN, and TRIM 22;
the 12 high-expression C subtype genes comprise: PAGE2, MAGEC2, ZNF716, C18orf2, COX7B2, MAGEA9B, DSCR4, CT45A5, MAGEB2, GAGE2D, MAGEA4 and CLEC 2L.
7. A reagent for detecting the prognosis of gastric cancer, which comprises a primer set specific to the gastric cancer prognostic marker of claim 5 or 6.
8. Use of the gastric cancer prognostic marker according to claim 5 or 6 or the reagent according to claim 7 for the preparation of a tool for accurately predicting the survival and prognostic risk of gastric cancer patients.
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| WO2023234485A1 (en) * | 2022-05-31 | 2023-12-07 | 서울대학교병원 | Biomarker for predicting likelihood of developing gastric cancer and use thereof |
| CN115424666A (en) * | 2022-09-13 | 2022-12-02 | 江苏先声医学诊断有限公司 | Method and system for screening pan-cancer early-screening molecular marker based on whole genome bisulfite sequencing data |
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