CN114990222B - Low-grade glioma patient total survival prediction model - Google Patents
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Abstract
The invention belongs to the technical field of bioengineering and tumor markers, relates to a model for predicting the total survival of a low-grade glioma patient, and particularly relates to a model for predicting the total survival of the low-grade glioma patient based on 8 necrotic apoptosis related genes. The low-level glioma patient total survival prediction model based on 8 necrotic apoptosis related genes has good applicability in experimental groups and test groups, and the prognosis model provided by the invention can well divide LGG patients into high-risk groups and low-risk groups, thereby being beneficial to the selection of clinical treatment schemes and the prognosis evaluation of patients.
Description
Technical Field
The invention belongs to the technical field of bioengineering and tumor markers, relates to a model for predicting the total survival of a low-grade glioma patient, and particularly relates to a model for predicting the total survival of the low-grade glioma patient based on 8 necrotic apoptosis related genes.
Background
Low-grade gliomas (LGGs) are the most common adult invasive brain tumors, and although LGG has a variety of treatments including surgery, chemotherapy, radiation therapy, targeted therapy, immunotherapy, etc., some LGG patients relapse and even progress to high-grade gliomas, severely affecting patient survival. There is currently no efficient LGG predictive model. Thus, in-depth research into the molecular mechanisms of LGG, the search for potential therapeutic and prognostic markers is critical for improving patient survival (OS).
Necrotic apoptosis is a form of programmed necrosis that has emerged in recent years as a complete departure from traditional apoptosis. Necrotic apoptosis can be activated under apoptosis-deficient conditions by receptor-interacting protein kinases (RIP 1/RIP 3), death receptors such as CD95, tumor Necrosis Factor (TNF) receptors 1 and 2 (TNFR 1 and 2), and TNF-related apoptosis-inducing ligand receptors 1 and 2, involved in tumor immune processes. Many studies have been performed over the last decade to demonstrate that necrotic apoptosis plays an important role in the development of a variety of human diseases. The Feng et al study demonstrated that RIP1 overexpression can significantly inhibit colorectal cancer cell proliferation in vitro. The Hockendorf et al study demonstrated that RIPK3 limits the occurrence of myelogenous leukemia by promoting cell death and differentiation of leukemia initiating cells. However, there is no mechanism for necrotic apoptosis in LGG development and progression.
Disclosure of Invention
The invention provides a novel low-grade glioma patient lifetime prediction model aiming at the problems existing in the traditional low-grade glioma diagnosis.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
the invention combines the clinical information of LGG patients in TCGA, GTEx, CGGA database and the related gene of necrotic apoptosis to carry out bioinformatics analysis. 8 differential expression genes related to prognosis are screened out through a statistical method, a risk score model is established, and patients are divided into a high risk group and a low risk group according to the risk score of the patients. The model's prognostic evaluation ability was verified in a training set and a verification set, respectively.
The 8 necrotic apoptosis-related genes were CFLAR, glad 1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1, TNFRSF21, respectively. The sequences of the 8 genes can be obtained as follows:
the https:// www.ncbi.nlm.nih.gov/, open the drop down list and select Gene. The types of genes to be searched, such as CFLAR, glad 1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1, and TNFRSF21 in the present invention, are input in the dialog, and then are searched for.
The genes expressed in the first (human) human in research results were selected.
Find genomics context column, click GenBank.
The FASTA format is selected to export the gene sequence to the local folder.
The model formula is: risk score = CFLAR 0.239+glad1 (-0.384) +mapk10 (-0.091) +pla 2G4A 0.105+sirt1 (-0.499) +stat 1 (-0.139+stub1 (-0.157) +tnfrsf 21 (-0.030).
This study analyzed 529 LGG patients and 105 normal human data in the cancer genomic map (TCGA) to identify 5432 differentially expressed genes. 204 genes associated with necrotic apoptosis were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https:// www.genome.jp/entry/map 04217). The two are crossed to obtain 61 low-grade glioma necrotic apoptosis related genes. K-M survival analysis was performed on each of the 61 genes to obtain 23 differentially expressed genes related to the OS of the patient. And performing LASSO regression analysis on the 23 differential expression genes related to prognosis, and finally obtaining 8 differential genes for constructing a prognosis model. Calculating risk scores according to the model, and dividing the TCGA database LGG patients into high risk groups and low risk groups as experimental groups; 693 LGG patient data were downloaded from the chinese glioma database (CGGA) and classified into high and low expression groups according to risk score as validation groups. The risk factors, the K-M survival curves and the ROC curves of the two groups of patients are respectively researched, and the results show that the risk scoring model has good prognosis prediction capability in the two groups of data. Next, single and multi-factor Cox regression analysis was performed on the risk score and clinical features such as patient age, sex, IDH mutation, 1p19q, and the results showed that the risk score, WHO stratification, and patient age group (60 years or > 60 years) were independent prognostic factors for LGG patients. Combining the above three factors, a noman map was created for clinical prediction of survival probability of LGG patients. ROC curve results show that AUCs of experimental groups 1 year, 3 years and 5 years are 0.934,0.904,0.783 respectively, AUCs of experimental groups 1 year, 3 years and 5 years are 0.732,0.770,0.721 respectively, and the results show that the prediction model has good prediction capability. Meanwhile, decision Curve Analysis (DCA) is carried out, and the consistency between the predicted and actual observations of the Norman diagram is good. Therefore, the prognosis model established based on 8 necrotic apoptosis related genes can well divide LGG patients into high-risk groups and low-risk groups, and is beneficial to the selection of clinical treatment schemes and the prognosis evaluation of patients.
Compared with the prior art, the invention has the advantages and positive effects that:
the invention establishes a low-level glioma patient total survival prediction model based on 8 necrotic apoptosis related genes, has good applicability in experimental groups and test groups, and the prognosis model provided by the invention can well divide LGG patients into high-risk and low-risk groups, thereby being beneficial to the selection of clinical treatment schemes and the prognosis evaluation of patients.
Drawings
FIG. 1 shows LGG and necrotic apoptosis-related differential genes. Wherein, the graph A is a thermal graph of the TCGA database for screening out the differential gene between LGG and normal patients, the graph B is the intersection wien of the differential gene in TCGA and the necrotic apoptosis related gene, and the graph C is the cell path involved in the differential gene.
Fig. 2 is a diagram of construction of a LASSO regression model. Wherein A is LASSO regression coefficient diagram. B is LASSO regression model cross-validation.
Figure 3 training set and test set model effect verification. Wherein A is the survival score distribution B of the training group and B is a K-M curve. C is the ROC curve. D is the validation set survival score distribution. E is a K-M curve. F is the ROC curve result.
Detailed Description
In order that the above objects, features and advantages of the invention will be more clearly understood, a further description of the invention will be provided with reference to specific examples. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
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 otherwise than as described herein, and therefore the present invention is not limited to the specific embodiments of the disclosure that follow.
Example 1
As shown in the drawing, the embodiment provides the result of the construction process and effect verification of the total survival prediction model of the low-grade glioma patient.
1.1 data download and processing
After differential expression analysis was performed on 529 LGG samples downloaded from TCGA database and 105 samples obtained from GTEx using R language limma package, 5432 differential expression genes (Log 2|fc| >1 and FDR < 0.05) were obtained, and a heat map was created (fig. 1A).
1.2 Gene processing and screening
From the KEGG database, 204 necroptosis-related genes were downloaded, and by further crossing with the necroptosis-related genes (fig. 1B), we finally obtained a total of 61 necroptosis-related genes, 21 of which were down-regulated and 40 of which were up-regulated. We performed GO and KEGG enrichment fractionation on these 61 genes, which showed that these genes were mainly enriched in Necrotopsides and Influenza A KEGG channels, as well as GO regulation of protein secretion and ubiquitin-like protein ligase binding (FIG. 1C).
1.3 establishment and evaluation of a risk score model.
We performed single factor K-M survival curve analysis on the determined 61 necrotic apoptosis-related genes, respectively, and the results showed that 23 genes were significantly related to the overall survival of the patient. To avoid overfitting the prognostic signature, we further performed LASSO regression analysis on 23 genes (fig. 2), refining the gene set by calculating regression coefficients. After excluding the genes with regression coefficients of 0, 8 genes were finally selected. The sequences of the 8 genes are all known and can be obtained from the network, the invention does not make any change to the sequences, and one of the obtaining modes is provided in the embodiment.
The sequences of the 8 genes can be obtained as follows:
(1) The https:// www.ncbi.nlm.nih.gov/, open the drop down list and select Gene. The types of genes to be searched, such as CFLAR, glad 1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1, and TNFRSF21 in the present invention, are input in the dialog, and then are searched for.
(2) The genes expressed in the first (human) human in research results were selected.
(3) Find genomics context column, click GenBank.
(4) And selecting FASTA format to export the gene sequence to a local folder to obtain the specific sequence of the gene.
Risk score = CFLAR 0.239+glad1 (-0.384) +mapk10 (-0.091) +pla 2G4A 0.105+sirt1 (-0.499) +stat 1 (-0.139+stub1 (-0.157) +tnfrsf 21 (-0.030).
The risk score for each patient in the training set was calculated from these 8 genes and the associated regression coefficients. To further evaluate the correlation of risk scores with patient OS, we split the data into high risk groups and low risk groups according to the median of risk scores for patients, K-M survival curve results show that the high risk groups have shorter patient OS and worse prognosis. To verify the accuracy of this model in the training set, we performed ROC survival curve analyses of 1 year, 3 years, and 5 years, with area under the curve (AUC) of 0.864,0.872,0.732, respectively.
2. Verification group effect verification
In order to verify whether the risk score model has universality, a Chinese glioma database (CGGA) is selected to verify the accuracy of the risk score model, the risk score of each LGG patient in the CGGA database is calculated according to the scoring model, and the patients are divided into a high risk group and a low risk group according to the median of the risk scores. K-M survival curve results showed significant differences in patient OS between the high risk group and the low risk group in CGGA, with the high risk group patient OS being worse and significantly different. The areas under the ROC curves of 3 years and 5 years are 0.721,0.729,0.675 respectively, and the scoring model is proved to have good prediction effect. The above results indicate that this risk score model is equally applicable in the validation set.
2.1 clinical relevance studies and independence studies of risk score models
To further determine whether this risk score model is an independent risk factor for LGG patient OS, we performed single factor Cox regression analysis on patient age, gender, WHO classification, IDH, 1P19q, MGMT, and risk score, respectively, in the training set, with the relevant factors of P <0.05 included in the multi-factor Cox regression analysis. The multi-factor results show that risk score, patient age, and WHO stratification are independent risk factors for patient prognosis (table 1).
Table 1 single-and multifactor analysis of prognostic factors in TCGA database
2.2 Norman Chart predicting LGG patient prognosis
The risk score, patient age, WHO rating are further included to build an nomogram. And further evaluate the predictive power of this alignment. The area under the ROC curves for 1 year, 3 years and 5 years was 0.934,0.904,0.783, respectively. DCA results also showed good agreement between the average error rates of 1 year, 3 years, and 5 years with the error rates of the ideal model. The area under the ROC curves for 1 year 3 and 5 years in the validation set was 0.732,0.770,0.721, respectively. DCA results also showed good agreement between the average error rates of 1 year, 3 years, and 5 years with the error rates of the ideal model. The results indicate that nomograms built in combination with sub-risk scores, patient age and WHO classification also have good prognostic predictive power.
FIG. 3 shows model effect verification for training and test sets. Wherein, A is the survival score distribution of the training group, which shows that the mortality rate of the patients in the high risk group is higher. B is a K-M curve, and shows that the survival time difference of the patients in the high-risk group and the low-risk group of the training group is obvious. C is an ROC curve, and the result shows that the training group prediction model has good prediction effect in 1 year, 3 years and 5 years. D is a validated group survival score distribution, showing higher mortality in the high risk group of patients. E is a K-M curve, and shows that the survival time difference of the patients in the high-risk group and the low-risk group of the verification group is obvious. F is ROC curve result, shows that the prediction model of the verification group has good prediction effect in 1 year, 3 years and 5 years.
The present invention is not limited to the above-mentioned embodiments, and any equivalent embodiments which can be changed or modified by the technical content disclosed above can be applied to other fields, but any simple modification, equivalent changes and modification made to the above-mentioned embodiments according to the technical substance of the present invention without departing from the technical content of the present invention still belong to the protection scope of the technical solution of the present invention.
Claims (1)
1. Application of substances for detecting 8 necrotic apoptosis-related genes in preparing a low-grade glioma patient total survival prediction kit, wherein the 8 necrotic apoptosis-related genes are CFLAR, GLUD1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1 and TNFRSF21 respectively;
the kit further comprises a diagnostic model, wherein the diagnostic model is as follows: risk score = CFLAR 0.239+glad1 (-0.384) +mapk10 (-0.091) +pla 2G4A 0.105+sirt1 (-0.499) +stat 1 (-0.139+stub1 (-0.157) +tnfrsf 21 (-0.030).
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