CN116665898A - Biomarker for predicting prognosis of gastric cancer based on histone modification regulator characteristics, scoring model and application - Google Patents

Biomarker for predicting prognosis of gastric cancer based on histone modification regulator characteristics, scoring model and application Download PDF

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CN116665898A
CN116665898A CN202310640320.2A CN202310640320A CN116665898A CN 116665898 A CN116665898 A CN 116665898A CN 202310640320 A CN202310640320 A CN 202310640320A CN 116665898 A CN116665898 A CN 116665898A
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戎晓祥
黄月
周锐
黄振华
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Southern Hospital Southern Medical University
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Abstract

The invention discloses a biomarker for predicting gastric cancer prognosis based on histone modification regulator characteristics, a scoring model and application thereof, and belongs to the field of biomedicine. The invention clusters the transcriptome expression of 76 histone acetylation and methylation regulating factors in an unsupervised mode, is used for comprehensively evaluating an expression model of the histone modification regulating factors, utilizes a bioinformatics method to identify gastric cancer prognosis risk characteristics by using LASSO regression and multivariable stepwise Cox regression analysis based on a gastric cancer public database, and establishes a scoring model consisting of 10 characteristic genes: hr_score=acvr1+brd3+c11orf 95+cryab+fermt2+rae1+uckl1+vps72+ythdf1-IFNA2. The scoring model is beneficial to identifying a specific histone modification regulatory factor mode gastric cancer patient with poor prognosis and guiding an individualized treatment scheme.

Description

Biomarker for predicting prognosis of gastric cancer based on histone modification regulator characteristics, scoring model and application
Technical Field
The invention relates to the field of biomedicine, in particular to a biomarker for predicting gastric cancer prognosis based on histone modification regulating factor characteristics, a scoring model and application.
Background
Gastric cancer is the fifth most common malignancy, and is the third leading cause of cancer-related death in China. Currently, the AJCC (American Joint Committee on Cancer) -based TNM staging system is the most widely used gastric cancer staging system clinically. However, TNM staging systems miss molecular characterization information for patients, and even for patients in the same stage, their prognosis varies widely. Therefore, developing new indexes based on molecular characteristic typing of patients is helpful for promoting accurate diagnosis and treatment process of gastric cancer. In recent years, in order to better improve prognosis of gastric cancer patients, several molecular typing schemes have been proposed to characterize different biological characteristics of gastric cancer, such as TCGA genotyping and ACRG genotyping, but the high throughput technique used for typing is expensive and complex in process, and the current stage of typing also needs a lot of research and further optimization, so that we have urgent need to develop an alternative method for molecular typing that is economical, convenient and easy to clinically transform.
Studies have shown that epigenetic alterations may be an emerging clinical biomarker for tumor-related diagnosis, prognosis and treatment. As an important epigenetic modification, dysregulation of histone modification may lead to aberrant transcription processes, affecting tumor immunogenicity and the function of immune cells involved in anti-tumor responses. Among these, studies have shown that changes in histone acetylation or methylation patterns and their interactions are one of the most widely affected epigenetic pathways in tumors. Histone methylation is one of the most important post-transcriptional modifications, mainly by the addition of methyl groups at lysine residues of histones H3 and H4, and can be used as an active or inhibitory marker of gene expression. Whereas histone acetylation is mainly regulated by the balance between Histone Acetyltransferase (HAT) and Histone Deacetylase (HDAC). Based on the "charge neutralization model," acetylation of histone tail lysine residues reduces the amount of positive charge carried by the histone to reduce its electrostatic affinity with DNA, resulting in relaxation of chromatin structure, while the open chromatin conformation is more prone to binding to transcription factors and significantly increases gene expression. Thus, histone acetylation is generally considered as an active histone mark. Furthermore, acetylation and methylation in the same lysine residue can act as antagonists to inhibit each other, resulting in cross-talk between different histone marks. It has been found that abnormal histone modification patterns can become a driving factor for tumors, but that histone methylation and acetylation modification patterns and interactions thereof have been studied in gastric cancer only to a limited extent. Moreover, the current research is mainly focused on the importance of histone modification regulators on tumor progression and Tumor Microenvironment (TME), their potential roles and interactions in TME cell infiltration, drug sensitivity and immunotherapy have not been explored yet, and due to technical limitations, most research is limited to a few histone modification regulators, whereas the highly integrated interactions exist between them, exploring the influence of histone modification regulators' regulatory networks on tumor heterogeneity and biological functions of TME might contribute to the development of personalized therapeutic strategies. Currently, in gastric cancer, some scoring models based on epigenetic modifications such as DNA methylation profile, m6A methylation modification and the like have been proposed, for example Qi Meng et al construct DNA Methylation Scores (DMS) by identifying different DNA methylation modification patterns and different tumor microenvironment characteristics to enhance the recognition of gastric cancer immune microenvironment, bo Zhang et al construct m6Ascore to quantify m6A modification patterns of individual tumors using principal component analysis algorithms by recognizing m6A modification patterns and correlating with TME cell infiltration characterization systems, but the effectiveness and clinical utility of these scoring models remain to be explored. In view of the importance of histone modification, we hope to explore further more efficient molecular typing in gastric cancer from the point of view of histone modification pattern to complement existing typing systems.
Disclosure of Invention
The invention aims to provide a biomarker for predicting gastric cancer prognosis based on histone modification regulator characteristics, a scoring model and application thereof, so as to solve the problems of the prior art, and the scoring model constructed by using 10 characteristic genes is beneficial to identifying gastric cancer patients with a specific histone modification regulator mode with poor prognosis and guiding an individualized treatment scheme.
In order to achieve the above object, the present invention provides the following solutions:
the invention provides a biomarker for predicting gastric cancer prognosis based on histone modification regulator characteristics, which comprises genes ACVR1, BRD3, C11orf95, CRYAB, FERM 2, RAE1, UCKL1, VPS72, YTHDF1 and IFNA2.
The invention also provides a kit for predicting prognosis of gastric cancer based on the characteristics of the histone modification regulator, which comprises a reagent for detecting the transcriptional expression level of genes contained in the biomarker.
The invention also provides application of the reagent for detecting the biomarker in preparation of a kit for predicting gastric cancer prognosis.
The invention also provides a construction method of a scoring model for predicting gastric cancer prognosis based on histone modification regulator characteristics, which comprises the following steps:
s1: determining four different histone modification regulator expression patterns by consensus clustering and analysis of transcriptome data of 76 histone acetylation and methylation regulators;
s2: screening out 10 characteristic genes based on the expression mode of the histone modification regulator determined by S1 by using LASSO regression and multivariable stepwise Cox regression analysis, and constructing a quantitative scoring model for quantifying the histone modification state and distinguishing the HMR-C1 subtype;
wherein the characteristic genes comprise ACVR1, BRD3, C11orf95, CRYAB, FERM 2, RAE1, UCKL1, VPS72, YTHDF1 and IFNA2;
the formula of the scoring model is: hr_score=acvr1+brd3+c11orf 95+cryab+fermt2+rae1+uckl1+vps72+ythdf1-IFNA2.
The invention also provides a grading model for predicting gastric cancer prognosis based on histone modification regulator characteristics, which is formed by the construction method.
The invention also provides application of the scoring model in construction of a gastric cancer prognosis system or device, and gastric cancer patients are grouped according to scoring results calculated by the scoring model so as to predict prognosis of the gastric cancer patients.
Preferably, the criteria for predicting prognosis of gastric cancer patient based on the scoring result are: HR score scores were ranked from large to small, with the top 1/3 and bottom 1/3 samples being defined as high scoring and low scoring groups, respectively, where the high scoring group had poor patient prognosis.
The invention also provides a system or device for predicting gastric cancer prognosis, which comprises a scoring model for predicting gastric cancer prognosis based on the characteristics of the histone modification regulator, wherein the scoring model takes the expression level of the biomarker as an input variable for evaluating the predicted gastric cancer prognosis; wherein the scoring model calculates a score for gastric cancer prognosis using the following formula: hr_score=acvr1+brd3+c11orf 95+cryab+fermt2+rae1+uckl1+vps72+ythdf1-IFNA2.
Preferably, the scoring criteria are: HR score scores were ranked from large to small, with the top 1/3 and bottom 1/3 samples being defined as high scoring and low scoring groups, respectively, where the high scoring group had poor patient prognosis.
The invention discloses the following technical effects:
the invention clusters the transcriptome expression of 76 histone acetylation and methylation regulating factors in an unsupervised mode, is used for comprehensively evaluating an expression model of the histone modification regulating factors, utilizes a bioinformatics method to identify gastric cancer prognosis risk characteristics by using LASSO regression and multivariable stepwise Cox regression analysis based on a gastric cancer public database, establishes a scoring model consisting of 10 genes, guides the personalized treatment of gastric cancer patients by analyzing clinical characteristics and tumor microenvironment of gastric cancer, and names the scoring model as HR_score.
The scoring model constructed by the invention is beneficial to identifying a gastric cancer patient with a specific histone modification regulatory factor mode with poor prognosis and guiding an individual accurate treatment scheme. Further analysis showed that hr_score is an independent poor prognosis biomarker useful for predicting patient outcome, with a significant negative correlation to total survival time, high hr_score patients also being predictive of higher risk of mortality.
The developed scoring tool HR_score can be developed into diagnostic products such as test kits and the like based on the transcriptional expression level of 10 genes only; and the calculation of the HR_score only needs to subtract the average expression values of the 2 groups of genes, so that the method is convenient and easy for clinical transformation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a Circo diagram representing the expression and prognosis of histone methyltransferase (A), histone demethylase (B), histone acetyltransferase (C) and histone deacetylase (D) modulators in gastric cancer patients; the outer circles in the A-D figures are orange, green, blue, and dark grey represent histone methyltransferase (A), histone demethylase (B), histone acetyltransferase (C), and histone deacetylase (D), respectively;
FIG. 2 is a graph of an optimal histone modification typing matrix of gastric cancer patients;
FIG. 3 is a heat map of histone modulator expression patterns in gastric cancer patients;
FIG. 4 is a Kaplan-Meier plot of total survival (OS) plotted in meta-GEO queue according to histone modification regulator pattern;
FIG. 5 is a Kaplan-Meier plot of total survival (OS) for different HMR clusters of meta-GEO cohorts, based on whether adjuvant chemotherapy is performed;
FIG. 6 is a forest map of a multi-factor COX regression analysis based on clinical characteristics of patients in GSE62245 cohorts;
FIG. 7 is a forest graph based on the benefits of adjuvant chemotherapy for different HMR subtypes in the GSE62245 cohort;
FIG. 8 is a box plot of the distribution of HR_score over HMR subtypes;
FIG. 9 is a heat map of the gene expression pattern of histone modification regulator typing after dimension reduction by the Borata algorithm; yellow represents high expression, blue represents low expression;
FIG. 10 is a Kaplan-Meier survival curve based on meta-GEO (A) and TCGA-STAD (B) datasets;
FIG. 11 is a forest graph showing a multi-factor COX risk regression model based on GSE62254 (A) and TCGA-STAD cohorts (B) with patient gender, age, lanren typing, TNM typing, ACRG typing as covariates;
FIG. 12 is an alignment chart based on meta-GEO (A) and TCGA-STAD (B) queue multifactor Cox regression model results shown in a visual graph;
FIG. 13 is a graph showing three modes of clinical decision curves (DCA decision curves) of model1 (clinical features: TNM phase + Lauren type + molecular type), model2 (HR_score), and model3 (clinical features combined with HR_score), respectively; the benefit score is displayed on the vertical axis, and the probability threshold is displayed on the horizontal axis;
FIG. 14 is a graph of the consistency between actual and predicted occurrence compared by a calibration curve (Calibration curve) for 1,3,5 years.
Detailed Description
Various exemplary embodiments of the invention will now be described in detail, which should not be considered as limiting the invention, but rather as more detailed descriptions of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In addition, for numerical ranges in this disclosure, it is understood that each intermediate value between the upper and lower limits of the ranges is also specifically disclosed. Every smaller range between any stated value or stated range, and any other stated value or intermediate value within the stated range, is also encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference for the purpose of disclosing and describing the methods and/or materials associated with the documents. In case of conflict with any incorporated document, the present specification will control.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the invention described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from consideration of the specification of the present invention. The specification and examples of the present invention are exemplary only.
As used herein, the terms "comprising," "including," "having," "containing," and the like are intended to be inclusive and mean an inclusion, but not limited to.
In a comprehensive queue consisting of 838 gastric cancer patients in a gene expression comprehensive (GEO) database, 76 transcriptome expressions of histone acetylation and methylation regulating factors are clustered in an unsupervised mode to comprehensively evaluate an expression model of the histone modification regulating factors, and a multifactor Cox risk model capable of predicting gastric cancer prognosis is constructed by utilizing a bioinformatics method based on a gastric cancer public database. By analyzing the clinical characteristics of gastric cancer and tumor microenvironment, the personalized adjuvant chemotherapy of gastric cancer patients is guided, and the scoring model is named HR_ score (Histone modification regulator Riskscore). Further description will be made by way of specific examples.
EXAMPLE 1 determination of transcriptional expression of histone modification regulatory molecules from gastric cancer patients and correlation with clinical prognosis
(1) Collection and pretreatment of gastric cancer public data sets
Common transcriptome data and clinical data for GC samples were retrospectively collected from NCBI GEO database (https:// www.ncbi.nlm.nih.gov/GEO /) including GSE62254/ACRG queue (n=300), GSE57303 (n=70), GSE29272 (n=268), GSE15459 (n=200), and the above data integration was named meta-GEO queue. The latest clinical data and sample information for TCGA-STAD samples were obtained from a database (https:// portal. Gdc. Cancer. Gov /) using the TCGAbiolinks R package. The raw data of the Affymetrix dataset was background adjusted using the RMA algorithm in the Affy software package, quantile normalized, and the oligonucleotides for each transcript were finally summarized using a median optimization algorithm. Corresponding clinical data is collected from the supplemental files of the related articles.
(2) The expression and prognosis characteristics of the histone modification regulator of the gastric cancer patient are clear.
By reference to reviews on histone acetylation and methylation modifications, we summarize a total of 76 known putative histone modification modulators, including: the 14 acetyl transferases are specifically: NCOA1, KAT8, KAT7, KAT6B, KAT6A, KAT, KAT2B, KAT2A, CREBBP, EP300, ELP3, HAT1, CLOCK, GTF3C4; the 16 deacetylases are specifically: HDAC5, HDAC4, SIRT7, SIRT6, SIRT3, HDAC9, HDAC7, HDAC6, HDAC3, HDAC2, HDAC11, HDAC1, SIRT5, SIRT4, SIRT2, SIRT1; the 26 methyltransferases are specifically: KMT2B, SETD5, SETD3, SETD1B, SMYD3, SETD4, PRDM9, DOT1L, EZH2, KMT2D, KMT2A, SUV H2, SUV39H1, SETD6, SETD2, SETD1A, SETDB1, EHMT2, EHMT1, NSD1, ASH1L, N6AMT1, SMYD5, SMYD2, PRMT5, carmm 1; and 20 demethylases, specifically: KDM5B, KDM5A, KDM3A, PHF, KDM8, KDM7A, KDM4C, KDM4A, UTY, KDM6B, KDM6A, KDM5D, KDM5C, KDM4D, KDM4B, KDM3B, KDM2A, KDM1A, PHF, JMJD1C. Applicants extracted the transcriptional profiles of these 76 genes in the TCGA-STAD cohort. The expression differences of the above genes in tumor and normal tissues were analyzed in R software using a "limma" R package, and a univariate COX regression analysis was used to assess whether the histone modification genes were associated with prognosis.
As a result, as shown in FIG. 1, the outer circles (A-D) of FIG. 1 are orange, green, blue, and dark gray, respectively represent histone methyltransferase (A), histone demethylase (B), histone acetyltransferase (C), and histone deacetylase (D). Orange indicates that the gene expression in gastric cancer tissue is higher than in normal tissue. Yellow indicates that the gene expression in gastric cancer tissue is lower than in normal tissue. The blue color in the circle represents the favorable factor of total survival (HR > 1, P < 0.05), and the red color represents the dangerous factor of total survival (HR < 1, P < 0.05); gray represents a factor that has no statistical significance for the overall survival impact (P > 0.05). The inner circle represents the P value of HR value.
Example 2 clear the clinical characteristics of histone modification typing and histone modification typing of gastric cancer patients
(1) Determination of histone regulatory factor characteristic typing of gastric cancer patients
To assess whether transcriptional analysis of 76 acetylation and methylation modulators contributes to classification of gastric cancer patients. A total of 838 cases of stage I-IV gastric cancer patient samples from four independent gastric cancer cohorts (GSE 15459, GSE29272, GSE57303, GSE 62254) were included in the study, and unsupervised K-means cluster analysis was performed on meta-GEO cohorts (838 cases) to identify different gastric cancer histone modification expression models. The best cluster number was selected using the R package "consissusclusteriplus", 4 different histone modification regulator expression patterns were determined (see fig. 2), and these expression patterns were referred to as HMR-C1 (n=172), HMR-C2 (n=262), HMR-C3 (n=107), HMR-C4 (n=297). The expression of histone modification regulators in HMR subtype in meta-GEO queue is shown in FIG. 3, wherein the expression of most of histone modification regulators is enriched in HMR-C1, HMR-C2 and HMR-C3, indicating that the activities and changes of histone modification of these three types are stronger.
(2) Explore clinical significance of tumor tissue histone modification typing of gastric cancer patients
Based on the Meta-GEO queue, we generated a survival curve using R-packet "survivinal" and Kaplan-Meier analysis and tested Log-Rank against group-to-group differences using Log-Rank sums, which showed that the total time to live (OS) for HMR-C1 was significantly shorter than HMR-C2, C3 and C4. (HMR-C1 vs HMR-C2, 3, 4) Hazard Ratio (HR) 2.419, 95% confidence interval (1.684-3.475) (see FIG. 4). Multi-factor COX regression analysis of clinical features of age, sex, TNM cohort, lanren typing, ACRG typing of patients incorporating GSE62254 cohorts using IBM SPSS Statistics 26.0.0 software showed HMR-C1 to be an independent prognosis artifact (fig. 6,HR:2.743, 95%CI:1.820-4.135).
In terms of chemotherapy, we found that HMR-C1 was significantly increased compared to other types three with negative correlation to total survival (OS) in patients without adjuvant chemotherapy, whereas in patients with adjuvant chemotherapy, the differences in types four were not statistically significant (see fig. 5), suggesting that post-operative adjuvant chemotherapy could significantly improve prognosis in patients with type C1. We found that patients of type four benefited from adjuvant chemotherapy (see figure 7) but had different sensitivity to adjuvant chemotherapy drugs than the non-adjuvant chemotherapy group.
Example 3 establishment of a histone modification scoring model based on gastric cancer histone modification modifier typing
(1) Selecting different genes of different histone modification types
Because of the worst prognosis for patients in the HMR-C1 group, it is believed that developing a scoring model that can independently quantify the histone modulator modification status to identify patients in the HMR-C1 group may have potential clinical utility. Thus, HMR-C1 patients were differentially analyzed with all non-HMR-C1 patients in meta-GEO cohorts using the "limma" package to obtain genes specifically expressed in HMR-C1, wherein the screening criteria for differential genes was p <0.001. In combination with the screening criteria, 8020 differential genes were selected (3188 up-regulated in HMR-C1 and 4832 down-regulated in HMR-C1).
(2) Dimension reduction of differential genes using the Boruta algorithm
In the meta-GEO dataset (training set), the "Borata" package of R software is used for respectively reducing the dimension of the high-expression and low-expression differential genes in the HMR-C1, and the operation parameters are specifically set as follows: dotrace=2, maxrunos=100, ntree=500. Through dimension reduction, 126 genes are screened out from 4832 genes which are low in expression in HMR-C1 and are named as B genes; the remaining 100 genes after the dimensionality reduction of 3188 genes highly expressed in HMR-C1 were designated as class a genes.
(3) Construction of HR_score by further selection of differential genes with prognostic value by LASSO-COX regression analysis
Further, a gene with prognostic significance is selected from the genes after dimension reduction. First, 226 differential genes were analyzed for correlation with total survival by single factor Cox regression and 104 of these genes were selected for p < 0.05. These genes were subjected to LASSO-COX regression screening. Then, the LASSO-COX algorithm operation is carried out by ten times of cross validation by using the "glmnet" package, and the variable combination corresponding to the minimum value of the "Partifikelihood device" is the modeling gene. Finally, 10 genes were determined to construct a prognostic scoring model, respectively: ACVR1, BRD3, C11orf95, CRYAB, FERMT2, IFNA2, RAE1, UCKL1, VPS72, YTHDF1. The final score is obtained by subtracting the average expression level of the finally determined high expression genes from the average expression level of the finally determined low expression genes, and the specific prediction formula is as follows: hr_score=acvr1+brd3+c11orf 95+cryab+fermt2+rae1+uckl1+vps72+ythdf1-IFNA2.
The HR_score scores are arranged from large to small, and the first 1/3 sample and the lowest 1/3 sample are respectively defined as a high-scoring group and a low-scoring group. The box plot is shown in FIG. 8, which shows that the median HR_score value of HMR-C1 is highest in the meta-GEO queue. FIG. 9 is a heat map showing the pattern of gene expression after the histone modification regulator typing by the Borata algorithm for dimension reduction.
Example 4 prediction of prognosis, predictive value and adjuvant chemotherapy benefit of gastric cancer patients in meta-GEO dataset using histone modification model hr_score.
Based on the Meta-GEO and TCGA-STAD queues, we generated survival curves using R-packet "survivinal" and Kaplan-Meier analysis, and tested Log-Rank against group-to-group differences using Log-Rank sums, which indicated that the low hr_score group had significantly high overall survival time in the Meta-GEO queue (fig. 11,OS,HR:2.462, 95%CI:1.952-3.106, p < 0.0001). Similar results were observed with the TCGA-STAD queue as the validation set, with the OS of the low scoring group being longer than that of the high scoring group (FIGS. 10,OS,HR:1.708, 95%CI:1.170-2.494, P: 0.005); indicating a statistical difference between the overall survival of the high and low hr_score group samples. Using IBM SPSS Statistics 26.0.0 software, this hr_score score was confirmed to be an independent poor prognosis biomarker useful for assessing patient outcome, considering multivariate Cox regression model analysis of GSE62254 and TCGA-STAD cohorts for patient gender, age, TNM staging, lanren typing, ACRG typing (fig. 11, meta-GEO: HR 2.847, 95% ci 1.919-4.224, TCGA-STAD: HR:1.627, 95% ci: 1.160-2.284).
Regarding the predictive value of hr_score, we constructed an alignment graph using multivariate Cox regression based on the clinical features of GSE62254 and TCGA-STAD cohorts, presented Cox regression model results in a visual graph, and predicted 1 year, 5 year total survival (OS) probabilities for gastric cancer patients (fig. 12), to evaluate the clinical value of alignment graph we plotted model1 (clinical features: TNM staging + Lauren typing + molecular typing), model2 (hr_score), model3 (clinical features combined hr_score) clinical decision curves, showing that hr_score provides better net benefit than "full" or "none" and model3 (clinical features combined hr_score) shows greater value, respectively (fig. 13). To further verify the validity of nomogram, calibration curves were also drawn to compare the predicted 1 year, 3 years and 5 years total survival (OS) probabilities with the actual observations with good agreement (fig. 14).
The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the design spirit of the present invention.

Claims (9)

1. A biomarker for predicting gastric cancer prognosis based on histone modification regulator characteristics, characterized in that the biomarker comprises the genes ACVR1, BRD3, C11orf95, CRYAB, FERMT2, RAE1, UCKL1, VPS72, YTHDF1 and IFNA2.
2. A kit for predicting prognosis of gastric cancer based on the characteristics of a histone modification regulator, characterized in that the kit comprises a reagent for detecting the transcriptional expression level of a gene contained in the biomarker of claim 1.
3. Use of a reagent for detecting the biomarker of claim 1 in the preparation of a kit for predicting prognosis of gastric cancer.
4. The method for constructing the scoring model for predicting the prognosis of the gastric cancer based on the characteristics of the histone modification regulating factor is characterized by comprising the following steps:
s1: determining four different histone modification regulator expression patterns by consensus clustering and analysis of transcriptome data of 76 histone acetylation and methylation regulators;
s2: screening out 10 characteristic genes based on the expression mode of the histone modification regulator determined by S1 by using LASSO regression and multivariable stepwise Cox regression analysis, and constructing a quantitative scoring model for quantifying the histone modification state and distinguishing the HMR-C1 subtype;
wherein the characteristic genes comprise ACVR1, BRD3, C11orf95, CRYAB, FERM 2, RAE1, UCKL1, VPS72, YTHDF1 and IFNA2;
the formula of the scoring model is: hr_score=acvr1+brd3+c11orf 95+cryab+fermt2+rae1+uckl1+vps72+ythdf1-IFNA2.
5. A scoring model for predicting gastric cancer prognosis based on histone modification regulator features, which is characterized by being constructed by the construction method according to claim 4.
6. Use of a scoring model according to claim 5 for constructing a gastric cancer prognosis system or device, wherein gastric cancer patients are grouped according to scoring results calculated by the scoring model, thereby predicting prognosis of gastric cancer patients.
7. The use according to claim 6, wherein the criteria for predicting prognosis of a gastric cancer patient based on the scoring result are: HR score scores were ranked from large to small, with the top 1/3 and bottom 1/3 samples being defined as high scoring and low scoring groups, respectively, where the high scoring group had poor patient prognosis.
8. A system or device for predicting gastric cancer prognosis, comprising a scoring model for predicting gastric cancer prognosis based on characteristics of histone modification regulator, the scoring model taking the expression level of the biomarker of claim 1 as an input variable for evaluating the predicted gastric cancer prognosis; wherein the scoring model calculates a score for gastric cancer prognosis using the following formula: hr_score=acvr1+brd3+c11orf 95+cryab+fermt2+rae1+uckl1+vps72+ythdf1-IFNA2.
9. The system or device for predicting prognosis of gastric cancer according to claim 8, wherein the scoring criteria is: HR score scores were ranked from large to small, with the top 1/3 and bottom 1/3 samples being defined as high scoring and low scoring groups, respectively, where the high scoring group had poor patient prognosis.
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