CN114990222A - Low-grade glioma patient overall survival period 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 overall survival time of low-grade glioma patients, and particularly relates to a model for predicting the overall survival time of low-grade glioma patients based on 8 necrotizing apoptosis-related genes. The low-grade glioma patient overall survival prediction model based on 8 necrotizing apoptosis-related genes, which is established by the invention, has good applicability in both experimental groups and test groups.
Description
Technical Field
The invention belongs to the technical field of bioengineering and tumor markers, relates to a model for predicting the overall survival time of low-grade glioma patients, and particularly relates to a model for predicting the overall survival time of low-grade glioma patients based on 8 necrotizing apoptosis-related genes.
Background
Low-grade glioma (LGG) is the most common adult invasive brain tumor, and although there are various treatments for LGG including surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy, some patients relapse and even progress to high-grade glioma, which severely affects patient survival. However, there is currently no effective LGG prediction model. Therefore, the molecular mechanism of LGG is deeply studied, and the search for potential therapeutic and prognostic markers is crucial to improving patient survival (OS).
Necrotic apoptosis is a programmed form of necrosis that has emerged in recent years that is completely different from traditional apoptosis. Necrotic apoptosis can be activated by receptor interacting protein kinase (RIP1/RIP3), death receptors such as CD95, Tumor Necrosis Factor (TNF) receptors 1 and 2 (TNFR1 and 2), and TNF-related apoptosis-inducing ligand receptors 1 and 2 under conditions deficient in apoptosis, and participate in tumor immune processes. Much research has been carried out over the last decade to demonstrate that necrotic apoptosis plays an important role in the development of a variety of human diseases. Feng et al demonstrated that over-expression of RIP1 significantly inhibited colorectal cancer cell proliferation in vitro. The Hockendorf et al study demonstrated that RIPK3 limits the development of myeloid leukemia by promoting cell death and differentiation of leukemia initiating cells. However, there is currently no study of the mechanisms of necrotic apoptosis in the development and progression of LGG.
Disclosure of Invention
The invention provides a novel low-grade glioma patient life cycle prediction model aiming at the problems in the traditional low-grade glioma diagnosis.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
the invention combines the clinical information of LGG patients in TCGA, GTEx and CGGA databases and the necrotic apoptosis related genes to carry out bioinformatics analysis. 8 differential expression genes relevant to prognosis are screened out by a statistical method to establish a risk score model, and patients are divided into a high risk group and a low risk group according to the risk scores of the patients. The prognostic evaluation capability of the model is verified in the training set and the verification set, respectively.
The 8 necroptosis related genes are CFLAR, GLUD1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1 and TNFRSF21 respectively. The sequences of 8 genes can be obtained as follows:
the https:// www.ncbi.nlm.nih.gov/, the drop down list is opened, and Gene is selected. The type of gene to be searched, such as CFLAR, GLUD1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1 and TNFRSF21, is input into the dialog box, and the input is respectively searched.
Genes expressed in the first (human) human in research results were selected.
Find the Genomiccontext column, click GenBank.
The FASTA format export gene sequences are selected to the local folder.
The model formula is as follows: risk score = CFLAR 0.239 + GLUD1 (-0.384) + MAPK10 (-0.091) + PLA2G4A + 0.105 + SIRT1 (-0.499) + STAT1 + 0.139 + STUB1 (-0.157) + TNFRSF21 (-0.030).
This study analyzed 529 patients with LGG and 105 normal human data in a cancer genomic map (TCGA), and identified 5432 differentially expressed genes. 204 Genes associated with necroptosis were downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) (https:// www.genome.jp/entry/map 04217). The two genes are intersected to obtain 61 low-grade glioma necroptosis related genes. K-M survival analysis was performed on the 61 genes to obtain 23 differentially expressed genes associated with patient OS. And performing LASSO regression analysis on the 23 prognosis-related differential expression genes to finally obtain 8 differential genes for constructing a prognosis model. Calculating a risk score according to the model, and dividing TCGA database LGG patients into high and low risk groups as experimental groups; 693 LGG patient data were downloaded from the China glioma database (CGGA) and divided into high and low expression groups based on risk scores as validation groups. The risk factors, the K-M survival curves and the ROC curves of two groups of patients are researched, and the results show that the risk scoring model has good prognosis prediction capability in two groups of data. The risk score and clinical characteristics of the patients' age, gender, IDH mutation, 1p19q were then subjected to single-and multifactorial Cox regression analysis, and the results showed that the risk score, WHO stratification and patient age group (60 years or 60) were independent prognostic factors for LGG patients. Combining the three factors, a nomann map is established for clinical prediction of the survival probability of LGG patients. The results of the ROC curve show that the AUCs of the experimental group in 1 year, 3 years and 5 years are 0.934, 0.904 and 0.783 respectively, and the AUCs of the test group in 1 year, 3 years and 5 years are 0.732, 0.770 and 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 prediction and actual observation of the Nomaman graph is good. Therefore, the prognosis model established based on 8 necroptosis related genes provided by the invention can well divide LGG patients into high-risk and low-risk groups, and is beneficial to selection of clinical treatment schemes and prognosis evaluation of patients.
Compared with the prior art, the invention has the advantages and positive effects that:
the invention establishes a low-grade glioma patient overall survival prediction model based on 8 necroptosis related genes, has good applicability in both experimental groups and test groups, can well divide LGG patients into high-risk and low-risk groups by the prognosis model, and is beneficial to selection of clinical treatment schemes and patient prognosis evaluation.
Drawings
FIG. 1 shows LGG and necrotic apoptosis-related differential genes. Wherein, the graph A is a heat map of differential genes screened out by a TCGA database between LGG and a normal patient, the graph B is an intersection waine of the differential genes and necrotizing apoptosis related genes in the TCGA, and the graph C is a cell pathway in which the differential genes participate.
FIG. 2 is a diagram of the construction of a LASSO regression model. Wherein A is a LASSO regression coefficient map. And B is LASSO regression model cross validation.
Fig. 3 training set and test set model effect verification. Wherein A is the survival fraction distribution of the training set, and B is a K-M curve. C is an ROC curve. D is the validation set survival score distribution. E is a K-M curve. F is the result of ROC curve.
Detailed Description
In order that the above objects, features and advantages of the present invention may be more clearly understood, the present invention will be further described with reference to specific embodiments. 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 otherwise than as specifically described herein and, therefore, the present invention is not limited to the specific embodiments disclosed in the following description.
Example 1
As shown in the figure, the embodiment provides a process for constructing a prediction model of the overall survival time of a low-grade glioma patient and a result of verifying the effect.
1.1 data download and processing
After 529 LGG samples downloaded from TCGA database and 105 samples obtained from GTEx were subjected to differential expression analysis using R language limma package, 5432 differentially expressed genes (Log 2| FC | >1 and FDR < 0.05) were obtained, and heatmap was made (fig. 1A).
1.2 Gene treatment and screening
From the KEGG database 204 necroptosis related genes were downloaded and finally we obtained a total of 61 necroptosis related genes by further intersection with necroptosis related genes (fig. 1B), of which 21 were down-regulated and 40 were up-regulated. We performed GO and KEGG enrichment ranking on these 61 genes, and the results showed that these genes were mainly enriched in Necroptosis and Influnza A KEGG channels and GO alignment of protein secretion and ubiquitin-like protein ligation binding (see FIG. 1C).
1.3 establishment and evaluation of risk score model.
We respectively perform single-factor K-M survival curve analysis on 61 identified necrotic apoptosis-related genes, and the results show that 23 genes are obviously related to the overall survival time of patients. To avoid over-fitting 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 whose regression coefficients were 0, 8 genes were finally selected. The sequences of 8 genes are known and can be obtained from the internet, the invention does not make any changes to the sequences, and one of the obtaining modes is given in the embodiment.
The sequences of 8 genes can be obtained as follows:
(1) the https:// www.ncbi.nlm.nih.gov/, the drop down list is opened, and Gene is selected. The type of gene to be searched, such as CFLAR, GLUD1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1 and TNFRSF21 in the dialog box is input, and the search is performed after the input.
(2) Genes expressed in the first (human) human in research results were selected.
(3) Find the Genomiccontext column, click GenBank.
(4) The specific sequence of the gene can be obtained by selecting FASTA format derived gene sequence to local folder.
Risk score = CFLAR 0.239 + GLUD1 (-0.384) + MAPK10 (-0.091) + PLA2G4A + 0.105 + SIRT1 (-0.499) + STAT1 + 0.139 + STUB1 (-0.157) + TNFRSF21 (-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 assess the relevance of the risk scores to the patient's OS, we divided the data into high risk groups and low risk groups according to the median risk score of the patients, and the K-M survival curve results showed that the patients in the high risk group had shorter OS and poorer prognosis. To verify the accuracy of this model in the training set, we performed ROC survival curve analysis for 1 year, 3 years and 5 years, and the area under the curve (AUC) was 0.864,0.872 and 0.732.
2. Validation group effect validation
In order to verify whether the risk score model has universality or not, a China 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 a 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 show that there is a clear difference in patient OS between the high risk group and the low risk group in CGGA, with the patients in the high risk group having worse and significantly different OS. The areas under ROC curves of 3 years and 5 years in 1 year are 0.721,0.729 and 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 and independence of Risk score model
To further clarify whether this risk score model is an independent risk factor for LGG patients OS, we performed single-factor Cox regression analysis on patient age, gender, WHO rating, IDH, 1P19q, MGMT, and risk score in the training set, respectively, and included the relevant factors with P <0.05 in the results into the multi-factor Cox regression analysis. The multifactorial results show that risk score, patient age, and WHO stratification are independent risk factors for patient prognosis (table 1).
TABLE 1 Single-and Multi-factor analysis of prognostic factors in TCGA databases
2.2 prediction of LGG patient prognosis by Nomaman maps
Risk scores, patient age, and WHO ratings were further included to establish nomograms. And further evaluate the predictive power of this nomogram. The areas under the ROC curves for 1 year, 3 years and 5 years were 0.934, 0.904 and 0.783, respectively. The DCA results also show that the average error rates for 1 year, 3 years and 5 years are in good agreement with the error rate of the ideal model. The areas under the ROC curves for 1 year, 3 years and 5 years in the validation group were 0.732, 0.770 and 0.721, respectively. The DCA results also show that the average error rates for 1 year, 3 years and 5 years are in good agreement with the error rate for the ideal model. The results show that the nomograms established in combination with the sub-risk score, patient age and WHO stratification also have good prognostic predictive capabilities.
Fig. 3 shows the model effect verification of the training set and the test set. Wherein A is the survival score distribution of the training group, and shows that the mortality rate of the patients in the high-risk group is higher. And B is a K-M curve, which shows that the life cycle difference of patients in the high-risk group and the low-risk group of the training group is obvious. And C is an ROC curve, and results show that the prediction effect of the training set prediction model is good in 1 year, 3 years and 5 years. And D is the survival score distribution of the verification group, and shows that the mortality rate of the patients in the high-risk group is higher. And 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. And F is an ROC curve result, and shows that the verification group prediction model has good prediction effect in 1 year, 3 years and 5 years.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention in other forms, and any person skilled in the art may apply the above modifications or changes to the equivalent embodiments with equivalent changes, without departing from the technical spirit of the present invention, and any simple modification, equivalent change and change made to the above embodiments according to the technical spirit of the present invention still belong to the protection scope of the technical spirit of the present invention.
Claims (5)
1. The application of substances for detecting 8 necroptosis related genes in a prediction kit for the overall survival time of low-grade glioma patients is characterized in that the 8 necroptosis related genes are CFLAR, GLUD1, MAPK10, PLA2G4A, SIRT1, STAT1, STUB1 and TNFRSF21 respectively.
2. The use of claim 1, wherein the kit further comprises a diagnostic model comprising: risk score = CFLAR 0.239 + GLUD1 (-0.384) + MAPK10 (-0.091) + PLA2G4A + 0.105 + SIRT1 (-0.499) + STAT1 + 0.139 + STUB1 (-0.157) + TNFRSF21 (-0.030).
3. The application of the substance for detecting 8 necroptosis related genes in the selection of clinical treatment schemes of low-grade glioma patients and the application of a patient prognosis evaluation and diagnosis kit.
4. The application of the substance for detecting 8 necroptosis related genes in a diagnosis kit for low-grade glioma patients with relapse or progression to high-grade glioma.
5. The construction method of the overall survival period prediction model of the low-grade glioma patient is characterized by comprising the following steps:
(1) selecting 529 LGG patients and 105 normal people from TCGA of cancer genome map to identify differential expression gene; downloading genes related to necroptosis from a KEGG website; obtaining low-grade glioma necrotizing apoptosis related genes after intersection of the two genes;
(2) performing K-M survival analysis on the low-level glioma necroptosis related genes respectively to obtain 23 differential expression genes related to the OS of the patient;
(3) LASSO regression analysis was performed on the 23 differentially expressed genes associated with patient OS to obtain 8 differential genes for constructing a prognostic model.
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| CN110993104A (en) * | 2019-12-03 | 2020-04-10 | 中国医科大学附属第一医院 | Tumor patient life prediction system |
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| CN112941184A (en) * | 2018-06-13 | 2021-06-11 | 深圳市颐康生物科技有限公司 | Biomarker for detecting cancer recurrence risk |
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