CN113969318A - Application of combined tar death related gene in esophageal adenocarcinoma prognosis model - Google Patents

Application of combined tar death related gene in esophageal adenocarcinoma prognosis model Download PDF

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CN113969318A
CN113969318A CN202111326421.XA CN202111326421A CN113969318A CN 113969318 A CN113969318 A CN 113969318A CN 202111326421 A CN202111326421 A CN 202111326421A CN 113969318 A CN113969318 A CN 113969318A
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esophageal adenocarcinoma
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prognosis
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陈浩
沙卫红
曾瑞杰
卓泽伟
吴慧欢
蒋磊
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Guangdong General Hospital
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Abstract

The invention provides application of a combined focal death related gene in an esophageal adenocarcinoma prognosis model, wherein the combined focal death related gene is CASP1, GSDMB, IL1B, PYCARD and ZBP 1; the method for establishing the esophageal adenocarcinoma prognosis model comprises the steps of collecting and sorting data, screening differential expression scorch related genes, analyzing the differential expression scorch related genes, establishing a risk index evaluation model, and establishing a nomogram. The prognosis model of the invention has the advantage of high accuracy, and can provide a new method for disease diagnosis and prognosis for patients with esophageal adenocarcinoma clinically.

Description

Application of combined tar death related gene in esophageal adenocarcinoma prognosis model
Technical Field
The invention belongs to the technical field of tumor molecular biology, and particularly relates to application of a combined focal death related gene in an esophageal adenocarcinoma prognosis model.
Background
Esophageal cancer is one of the most common malignant tumors worldwide, with about 60 million cases of new onset and 54 million cases of death worldwide each year; esophageal Adenocarcinoma (EAC) and Esophageal Squamous Cell Carcinoma (ESCC) constitute the major histological subtypes of esophageal cancer, with the incidence of EAC in western countries increasing dramatically over the past decades; despite advances in surgery, radiation therapy, chemotherapy, and targeted drug therapy, the 5-year survival rate of esophageal cancer is still less than 20%. Therefore, biomarkers and effective models are urgently needed to predict the prognosis of EAC.
Apoptosis is a programmed pattern of cell death with an inflammatory response that depends on the activity of inflammatory proteases of the caspase (caspase) family. Cell apoptosis is characterized by rapid disruption of the cytoplasmic membrane followed by release of proinflammatory intracellular contents. In recent years, studies have been emerging on the role of apoptosis in neurological, infectious, autoimmune, cardiovascular, and neoplastic diseases.
However, although studies have shown the role and importance of cell apoptosis in ESCC, the role of cell apoptosis in EAC remains to be explored, and a convenient, accurate and efficient early warning model for EAC prognosis is lacking at present. Therefore, the present patent comprehensively evaluates the tar death-related genes in EAC and develops a model based on tar death genes to predict patient prognosis.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the application of the combined tar death related gene in an esophageal adenocarcinoma prognosis model, has the advantage of high accuracy, and can provide a new method for disease diagnosis and prognosis for esophageal adenocarcinoma patients clinically.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
the application of the combined focal death related genes in an esophageal adenocarcinoma prognosis model, wherein the combined focal death related genes are CASP1, GSDMB, IL1B, PYCARD and ZBP 1.
As another specific embodiment of the invention, the establishment method of the esophageal adenocarcinoma prognosis model comprises the following steps:
step one, collecting and sorting data
Acquiring clinical data and normal tissue transcriptome data of esophageal adenocarcinoma patients from a cancer genome map (TCGA) database;
step two, screening the differential expression tar death related gene
Carrying out differential expression analysis on a plurality of known cell apoptosis related genes by using a Limma algorithm, and screening out a gene combination which is obviously expressed compared with a normal tissue;
step three, re-analysis of differentially expressed tar death related genes
Aiming at the screened gene combinations, performing LASSO regression analysis by adopting a glmnet package of an R language, and removing redundant genes to obtain different gene combinations;
step four, establishing a risk index evaluation model
Establishing a prognosis risk index evaluation model of the scorch related genes by using an LASSO Cox regression method based on the difference gene combination obtained in the third step, wherein the calculation method of the evaluation model comprises the following steps:
risk index score-regression coefficient x gene expression level;
calculating a regression coefficient corresponding to each gene in the differential gene combination by adopting a glmnet function;
step five, constructing a nomogram
Based on genetic and clinical characteristics, alignment plots were constructed using rms, foreign, survival packages, to assess patient survival and to assess the efficacy of the model.
As another embodiment of the present invention, the combinations of genes selected in step two to be significantly expressed compared to normal tissues are CASP1, CASP5, GSDMB, GZMB, IL1B, NLRP6, PYCARD, TNF, TREM2 and ZBP 1.
As another embodiment of the present invention, the set of difference genes obtained in step three are CASP1, GSDMB, IL1B, PYCARD and ZBP 1.
As another specific embodiment of the present invention, the model calculation method in step four is:
risk index score ═ (0.042 × CASP1mRNA expression level) + (-0.025 × GSDMB mRNA expression level) + (0.021 × IL1B mRNA expression level) + (-0.037 × PYCARD mRNA expression level) + (-0.243 × ZBP 1mRNA expression level).
As another embodiment of the present invention, in step four, the patients are divided into a high risk group and a low risk group based on median risk score, and the overall survival rate between the two groups is compared using Kaplan-Meier analysis.
As another embodiment of the invention, principal component analysis is performed on the high-risk group and the low-risk group respectively, separability of the two groups is evaluated through a "prcomp" function, and a working characteristic curve of the subject and an area calculation under the curve are constructed by using the packages of "survival", "survivminiser", "timeROC" and "riskReguration".
The invention has the following beneficial effects:
the invention utilizes the TCGA database to analyze the whole genome of the esophageal adenocarcinoma, establishes a prognosis model related to esophageal adenocarcinoma prognosis based on five pyro-death related genes, has the advantage of high accuracy, and can provide a new method for disease diagnosis and prognosis for esophageal adenocarcinoma patients clinically.
The present invention will be described in further detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram of the primary screening of 10 differentially expressed genes associated with apoptosis in cells according to the present invention;
FIG. 2 is a schematic diagram showing the construction of risk features of apoptosis-related genes in a TCGA cohort according to the present invention;
FIG. 3 is a schematic representation of the prediction of prognosis using the five gene signatures associated with apoptosis in the TCGA cohort in accordance with the present invention;
FIG. 4 is a schematic representation of the validation of the five gene signatures associated with apoptosis in the GSE13898 cohort of the present invention;
FIG. 5 is a nomogram constructed based on the characteristics of five genes associated with pyro-death in accordance with the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Method
1) Data set
On 20/5/2021, RNA sequencing (RNA-seq) data and corresponding clinical information were retrieved from the TCGA database for 87 patients (78 with EAC; 9 normal samples). Validation cohort RNA-seq data and clinical information were obtained from a gene expression integration (GEO) database. (accession number: GSE 13898).
2) Identification of Differentially Expressed Genes (DEGs) in a apoptosis-related genome
58 genes associated with tar death were obtained from the review published earlier and the GO term "tar death" (ID: GO 0070269). DEG with P <0.05 was verified using the "limma" packet.
3) Development and verification of prediction model of tar death related gene
Cox regression analysis was used to further assess the value of tar death-related DEG for prognosis. A prognostic model was constructed using R package "glmnet" and LASSO Cox regression analysis, and the risk score was calculated using the following formula:
risk score ═ (Coef i denotes coefficient, Xi denotes normalized gene expression level). EAC patients were divided into low risk and high risk groups according to median risk score and the Overall Survival (OS) between the two groups was compared using Kaplan-Meier analysis. Principal Component Analysis (PCA) was used to evaluate the separability of the two groups by the "prcomp" function. The R packages "survival", "survivmini", "timeROC" and "riskReguration" were used for 1, 2 and 5 years of Receiver Operating Characteristic (ROC) graph and Area Under Curve (AUC) calculation
Figure BDA0003347087620000051
A histogram model with clinical features (including staging and risk scoring) was constructed from R-packs "rms", "foreign" and "survival". The EAC cohort from the GEO database (GSE13898) was used for validation and risk scores were calculated by the same method as described above, dividing the cohort into two subgroups (low risk group and high risk group).
4) Prognostic analysis of variables
Clinical data (gender and stage) of the patients were extracted from the TCGA cohort and GSE13898 cohort. Variables including gender, stage, and risk score were analyzed in the regression model by univariate and multivariate Cox regression analysis.
Results
1) Identification of DEG between EAC and Normal tissue
The expression levels of 58 apoptosis-related genes were verified in 78 EACs and 9 normal tissues of the TCGA cohort, and 10 DEGs were identified (| log2FC | ≧ 1 and P-value < 0.05; CASP1, CASP5, GSDMB, GZMB, IL1B, NLRP6, PYCARD, TNF, TREM2, ZBP1), which were all upregulated in the tumor group. The expression profile of DEG is shown in FIG. 1 (red for higher expression levels; blue for lower expression levels).
2) DEGs-based prognosis model construction
Univariate Cox regression analysis was used to assess the prognostic value of DEG (fig. 2A). Of these, 6 genes (CASP1, CASP5, GSDMB, IL1B, PYCARD and ZBP1) had P values <0.2, high expression of CASP1, CASP5, IL1B was associated with increased risk (HR >1), while GSDMB, PYCARD, ZBP1 were associated with lower risk (HR < 1). Subsequently, LASSO Cox regression analysis yielded 5 genes for prognostic model construction based on the best λ values (fig. 2B, 2C).
The risk score is calculated as follows: risk score ═ 0.042 x expCASP1) + (-0.025 x exppgsdmb) + (0.021 x exppil 1B) + (-0.037 x expPYCARD) + (-0.243 + expZBP 1). Based on the calculated median risk scores, 65 patients were divided into two groups (32 in the high risk group and 33 in the low risk group), and the clinical data are shown in fig. 3A. Patients were better differentiated into two subgroups (fig. 3B). The distribution of risk scores and survival times is shown in fig. 3C, 3D. The OS in the high risk group was significantly lower than in the low risk group (P0.0012, FIG. 3E). ROC analysis of the risk model showed that the AUCs for 1 year, 2 years and 5 years survival were 0.708, 0.815 and 0.952, respectively (fig. 3F, 3G, 3H). Both univariate and multivariate Cox regression analyses showed that characteristics of the apoptosis-associated genes independently predicted prognosis in EAC patients (fig. 3I, 3J).
3) Verification of focal death gene combinations by external data sets
Information from 60 EAC patients in the GEO GSE13898 dataset was used to validate the tar-death-related gene combinations described above. As described above, patients were subdivided into low risk and high risk groups, respectively. Patients were better differentiated into two subgroups (fig. 4A). The distribution of risk scores and survival times is shown in fig. 4B, 4C. Patients in the low risk group had significantly higher survival rates than those in the high risk group (P0.003; fig. 4D). According to the ROC curve, the AUC of the 1-year and 2-year survival prediction models were 0.678 and 0.663, respectively (fig. 4E, 4F). The risk score can also serve as an independent prognostic factor in the validation cohort (fig. 4G, 4H).
4) Nomogram construction based on genetic features and clinical data
To predict the prognosis of EAC patients more accurately, we constructed a histogram model in conjunction with TNM staging, as shown in fig. 5A. The AUC of the nomograms used to predict 1-year, 2-year, and 5-year survival were 0.722, 0.884, and 1.000, respectively (fig. 5B, 5C, and 5D), indicating that the nomograms are ideal for predicting patient survival.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that changes may be made without departing from the scope of the invention, and it is intended that all matter contained in the above description or shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.

Claims (7)

1. The application of the combined focal death related gene in an esophageal adenocarcinoma prognosis model is characterized in that the combined focal death related gene is CASP1, GSDMB, IL1B, PYCARD and ZBP 1.
2. The use of the combination of apoptosis-related genes of claim 1 in esophageal adenocarcinoma prognosis models, wherein the esophageal adenocarcinoma prognosis models are established by a method comprising the steps of:
step one, collecting and sorting data
Acquiring clinical data and normal tissue transcriptome data of esophageal adenocarcinoma patients from a cancer genome map (TCGA) database;
step two, screening the differential expression tar death related gene
Carrying out differential expression analysis on a plurality of known cell apoptosis related genes by using a Limma algorithm, and screening out a gene combination which is obviously expressed compared with a normal tissue;
step three, re-analysis of differentially expressed tar death related genes
Aiming at the screened gene combinations, performing LASSO regression analysis by adopting a glmnet package of an R language, and removing redundant genes to obtain different gene combinations;
step four, establishing a risk index evaluation model
Establishing a prognosis risk index evaluation model of the scorch related genes by using an LASSO Cox regression method based on the difference gene combination obtained in the third step, wherein the calculation method of the evaluation model comprises the following steps:
risk index score-regression coefficient x gene expression level;
calculating a regression coefficient corresponding to each gene in the differential gene combination by adopting a glmnet function;
step five, constructing a nomogram
Based on genetic and clinical characteristics, alignment plots were constructed using rms, foreign, survival packages, to assess patient survival and to assess the efficacy of the model.
3. The use of the combined apoptosis-related genes in the esophageal adenocarcinoma prognosis model of claim 2, wherein the genes selected in step two that are significantly expressed compared to normal tissues are CASP1, CASP5, GSDMB, GZMB, IL1B, NLRP6, PYCARD, TNF, TREM2 and ZBP 1.
4. Use of the combined apoptosis-related genes of claim 3 in the esophageal adenocarcinoma prognosis model, wherein the set of difference genes obtained in step three is CASP1, GSDMB, IL1B, PYCARD and ZBP 1.
5. The use of the combined apoptosis-related genes of claim 4 in esophageal adenocarcinoma prognosis models, wherein the model calculation method in step four is as follows:
risk index score ═ (0.042 × CASP1mRNA expression level) + (-0.025 × GSDMB mRNA expression level) + (0.021 × IL1B mRNA expression level) + (-0.037 × PYCARD mRNA expression level) + (-0.243 × ZBP 1mRNA expression level).
6. Use of the combined apoptosis-related genes in esophageal adenocarcinoma prognosis model according to claim 2, wherein in step four, patients are divided into high risk group and low risk group according to median risk score, and the overall survival rate between the two groups is compared using Kaplan-Meier analysis.
7. Use of the combined apoptosis-related genes in esophageal adenocarcinoma prognosis model according to claim 6, wherein the principal component analysis is performed on the high risk group and the low risk group separately, and the subject working characteristic curve and the area under the curve are calculated using the "survivval", "survivminister", "timeROC" and "riskReguration" packages.
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