Method and system for evaluating layering prognosis survival risk rate of glioma patient

Abstract

The invention provides a method and a system for evaluating stratified prognosis survival risk rate of a glioma patient. The invention provides a glioma patient layering prognosis survival risk rate evaluation method, which comprises the following steps: obtaining expression quantity data of 13 LncRNAs in tumor specimen transcriptome sequencing of a glioma patient to be detected, and obtaining a prognosis survival risk rate score of the glioma patient to be detected according to the expression quantity data of the 13 LncRNAs, the stratification prognosis survival risk rate of the glioma patient to be detected is obtained. According to the evaluation method for the stratified prognosis survival risk rate of the glioma patient, stratified prognosis of the glioma patient is facilitated, a treatment scheme is clinically improved, excessive treatment is avoided, the treatment efficiency is improved, and a solution idea is provided for precise treatment of glioma.

Description

Technical Field

[0001] The present invention relates to medical care informatics, and in particular to an evaluation method and system for stratified prognosis survival risk rate of glioma patients. Background Art

[0002] Glioma is a tumor originating from glial cells of the nervous system. It is the most common malignant tumor in the brain, accounting for about 40%-50% of the total number of intracranial tumors. Depending on the type of cells, gliomas can be divided into astrocytomas, glioblastomas, oligodendrogliomas and other types. The clinical treatment of gliomas includes surgical resection, radiotherapy, chemotherapy, targeted therapy and immunotherapy. Among them, radiotherapy has a significant clinical effect on improving the survival time of glioma patients, but 95% of glioma patients still relapse after receiving radiotherapy.

[0003] During the treatment of glioma, critical time is often lost due to the trial and error approach to find effective therapies. In addition, tumor heterogeneity leads to huge differences in the effects of the same drug regimen for different patients. Therefore, how to perform individual stratification and prognosis assessment is a technical problem that needs to be solved urgently by technicians in this field. Summary of the invention

[0004] The purpose of the present invention is to provide a method for evaluating the stratified prognosis survival risk rate of glioma patients.

[0005] In a first aspect, the present invention provides a method for evaluating the stratified prognosis survival risk rate of a glioma patient to be tested, comprising: The expression data of 13 LncRNAs in the transcriptome sequencing results of tumor samples of glioma patients to be tested were obtained, and the genes corresponding to the 13 LncRNAs were AC004067.1, AC012213.1, AC012213.1, AC015922.3, AC020915.3, AC020916.1, AC025171.1, AC025171.5, AC026401.3, AC064875.1, AC093673.1, AC120036.4, and AC131097.4; The expression data of the 13 LncRNAs were input into the stratified prognosis survival risk rate evaluation model for glioma patients to obtain the stratified prognosis survival risk rate scores for the glioma patients to be tested.

[0006] In the above evaluation method, the glioma patient to be tested refers to a patient clinically diagnosed with glioma, and the clinical diagnosis method may include at least one of clinical manifestations, imaging examinations, histopathology and molecular analysis, and molecular markers. In addition, the glioma patient to be tested may also undergo clinical treatment, and the clinical treatment method may include at least one of radiotherapy, chemotherapy, immunotherapy, and targeted therapy.

[0007] In the above evaluation method, LncRNA refers to long non-coding RNA (Long non-coding RNA).

[0008] In the above evaluation method, transcriptome sequencing is performed on tumor samples of glioma patients to be tested, and the expression levels of the above 13 LncRNAs are obtained based on the transcriptome sequencing results of the tumor samples.

[0009] In the above evaluation method, the transcription genes corresponding to the 13 LncRNAs were numbered according to the human reference genome GRCh38 / hg38 version.

[0010] In the above evaluation method, the method for constructing the evaluation model for the prognosis survival risk rate of glioma patients comprises the following steps: Step 1, obtain data from the TCGA database; Step 2: All LncRNAs with a P value less than 0.05 associated with the survival time of glioma patients were screened from the TCGA database as candidate genes by Kaplan-Meier survival analysis; Step 3: Use the L1 penalty method to screen multiple LncRNAs that meet the preset screening conditions from the candidate genes as the final evaluation genes, and use the candidate genes to cross-validate the final evaluation genes and determine the regression coefficient of the L1 penalty method; Step 4: Based on the final evaluation gene and the regression coefficient of the L1 penalty method, combined with the prognosis survival risk rate scoring expression of glioma patients, the prognosis survival risk rate evaluation model of glioma patients is obtained.

[0011] Furthermore, the preset screening condition may be LncRNA whose regression coefficient of the L1 penalty method is not 0.

[0012] Furthermore, the “1-se” lambda method can be used to select the smallest lambda value in the LASSO regression; using the selected lambda value, the “cvfit” function is repeated 1000 times to determine the regression coefficients of the L1 penalty method.

[0013] Furthermore, the prognosis survival risk rate score expression of the glioma patient to be tested is: Prognostic survival risk score of glioma patients = (Coef1*AC004067.1 expression) + (Coef2*AC012213.1 expression) + (Coef3*AC012213.1 expression) + (Coef4*AC015922.3 expression) + (Coef5*AC020915.3 expression) + (Coef6*AC020916.1 expression) + (Coef7 *AC025171.1)+(Coef8*AC025171.5)+(Coef9*AC026401.3)+(Coef10*AC064875.1)+(Coef11*AC093673.1)+(Coef12*AC120036.4)+(Coef13*AC131097.4); Among them, Coef1-Coef13 are the regression coefficients corresponding to the expression level of LncRNA.

[0014] Furthermore, the values ​​of Coef1-Coef13 are shown in Table 1.

[0015]

[0016] In the above-mentioned evaluation method, the method also includes a judgment step. Specifically, when the stratified prognosis survival risk rate score of the glioma patient to be tested is greater than or equal to a preset threshold, it is judged that the prognosis of the glioma patient to be tested is poor, that is, the prognosis survival risk is high; when the prognosis survival risk rate score of the glioma patient to be tested is less than the preset threshold, it is judged that the prognosis of the glioma patient to be tested is good, that is, the survival risk is low.

[0017] Furthermore, the preset threshold can be determined according to the median of the stratified prognostic survival risk rate score of the glioma patients to be tested in the study population.

[0018] In a second aspect, the present invention provides an evaluation system for stratified prognosis survival risk rate of glioma patients to be tested, comprising: A data acquisition module is used to obtain the expression data of 13 LncRNAs in the transcriptome sequencing results of tumor samples of glioma patients to be tested, wherein the genes corresponding to the 13 LncRNAs are AC004067.1, AC012213.1, AC012213.1, AC015922.3, AC020915.3, AC020916.1, AC025171.1, AC025171.5, AC026401.3, AC064875.1, AC093673.1, AC120036.4, and AC131097.4; The data processing module is used to: input the expression data of the 13 LncRNAs into the evaluation model of the stratified prognosis survival risk rate of glioma patients to obtain the stratified prognosis survival risk rate score of the glioma patients to be tested.

[0019] The above-mentioned evaluation system also includes a data judgment module, which is used to judge the prognosis survival risk of the glioma patient to be tested according to the stratified prognosis survival risk rate score of the glioma patient to be tested; when the stratified prognosis survival risk rate score of the glioma patient to be tested is greater than or equal to a preset threshold, it is judged that the prognosis of the glioma patient to be tested is poor, that is, the survival risk is high; when the stratified prognosis survival risk rate score of the glioma patient to be tested is less than the preset threshold, it is judged that the prognosis of the glioma patient to be tested is good, that is, the survival risk is low.

[0020] In a third aspect, the present invention provides an auxiliary screening method for treating glioma patients, comprising: The expression data of 13 LncRNAs in the transcriptome sequencing results of tumor samples of glioma patients to be tested were obtained, and the genes corresponding to the 13 LncRNAs were AC004067.1, AC012213.1, AC012213.1, AC015922.3, AC020915.3, AC020916.1, AC025171.1, AC025171.5, AC026401.3, AC064875.1, AC093673.1, AC120036.4, and AC131097.4; Inputting the expression data of the 13 LncRNAs into any of the above-mentioned evaluation models for stratified prognosis survival risk rate of the glioma patient to be tested, to obtain the prognosis survival risk rate score of the glioma patient to be tested; According to the prognosis survival risk rate score of the glioma patient to be tested, therapeutic drugs that are sensitive to the glioma patient to be tested are screened.

[0021] The screening method described above can be used for drug sensitivity screening, such as using pRRophetic based on ridge regression to screen therapeutic drugs for glioma patients.

[0022] In a fourth aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for evaluating the stratified prognosis survival risk rate of the glioma patients to be tested, the method for assisting clinical decision-making for the treatment of the glioma patients, or the method for assisting screening of the treatment drugs for the glioma patients is implemented.

[0023] In a fifth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for evaluating the stratified prognosis survival risk rate of the glioma patients to be tested, or the above-mentioned method for assisting clinical decision-making for the treatment of the glioma patients, or the above-mentioned method for assisting screening of the treatment drugs for the glioma patients.

[0024] The present invention provides a method for assessing the prognosis survival risk rate of patients with glioma to be tested, which has a wide distribution pattern in different clinical characteristics, helps to improve treatment plans clinically, avoid overtreatment, omit the complex steps of various molecular diagnoses, improve treatment efficiency, and improve the responsiveness of clinical treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 The statistical results of the prognostic survival risk rate score and survival time of glioma patients and the expression intensity of 13 LncRNAs were analyzed; A is the statistical results of the prognostic survival risk rate score and survival status of glioma patients, B is the distribution of the prognostic survival risk rate score of glioma patients, and C is the expression intensity analysis of 13 LncRNAs in glioma patients; Figure 2Figure 2 is the expression analysis results of 13 LncRNAs in different glioma subtypes; A is the expression analysis results of 13 LncRNAs in four glioma subtypes, including classic glioma (CL), mesenchymal glioma (ME), neural glioma (NE), and proneural glioma (PN); B is the expression analysis results of 13 LncRNAs in patients with low-grade glioma (G2), anaplastic glioma (G3), and glioblastoma (G4); C is the expression analysis results of 13 LncRNAs in seven glioma subtypes, including Classic-like, G-CIMP-high, LGm6-GBM, PA-like, Codel, G-CIMP-low, and Mesenchymal-like. A is the expression analysis results of 13 LncRNAs in glioma methylation subtypes; D is the expression analysis results of 13 LncRNAs in four glioma subtypes, including IDH mutation 1p / 19q co-deletion (IDHmut-Codel), IDH mutation 1p / 19q non-co-deletion (IDHmut-non-Codel), and IDH wild type (IDhwt); E is the expression analysis results of 13 LncRNAs in four glioma subtypes, including astrocytoma, glioblastoma, oligoastrocytoma, and oligodendroglioma; F is the expression analysis results of 13 LncRNAs in four glioma subtypes, including C1-C4; Figure 3 The results of correlation analysis between TAMSig and survival time in patients with four subtypes of glioma; A is the result of correlation analysis between TAMSig and survival time in patients with C1 subtype, B is the result of correlation analysis between TAMSig and survival time in patients with C2 subtype, C is the result of correlation analysis between TAMSig and survival time in patients with C3 subtype, and D is the result of correlation analysis between TAMSig and survival time in patients with C4 subtype; Figure 4 These are the results of correlation analysis between TAMSig and survival time of glioma patients after receiving different treatment methods; A is the result of correlation analysis between TAMSig and survival time of glioma patients after receiving radiotherapy, B is the result of correlation analysis between TAMSig and survival time of glioma patients after receiving chemotherapy; C is the result of correlation analysis between TAMSig and survival time of glioma patients after receiving targeted therapy; Figure 5 The results of univariate and multivariate regression analysis using multiple parameters; A is the result of univariate regression analysis, and B is the result of multivariate regression analysis; Figure 6 This is an analysis of the accuracy of TAMSig in predicting the prognosis of glioma patients; A is the ROC curve constructed from 10 different clinical index parameter data, B is the correlation analysis result between disease progression and TAMSig after different glioma patients received different treatments, C is the correlation analysis result between different glioma subtypes and TAMSig, and D is the correlation analysis result between different glioma subtypes and TAMSig; Figure 7 The distribution of glioma subtypes in patients with high TAMSig group and low TAMSig group; Figure 8 The results of the correlation analysis between TAMSig and various DNA damage parameters are shown; Fig. 9 The figure is a schematic diagram of drug screening based on TAMSig; wherein, A is the effect of roscovitine on the viability of glioma cells in vitro, B is the immunoblot analysis result of glioma cells, C is the effect of roscovitine on the secretion level of IL-6, D is the effect of roscovitine on the secretion level of IFN-γ, E is the statistical result of roscovitine on tumor weight, F is the effect of roscovitine on tumor size, G is the effect of roscovitine on the survival percentage of mice, H is the flow cytometry detection result of roscovitine on CD8+ T cells and CD4+ T cells, I is the statistical result of roscovitine on CD8+ T cells, and J is the statistical result of roscovitine on CD4+ T cells.

[0026] Figure 1-Figure 9 In the table, * indicates p < 0.05; ** indicates p < 0.01; *** indicates p < 0.001; ns indicates no significant difference. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments, and they should not be understood as limitations on the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In the description of the present invention, it should be understood that the terms used are only for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0028] The experimental methods in the following examples, unless otherwise specified, are all conventional methods, and are performed according to the techniques or conditions described in the literature in the field or according to the product instructions. The materials, reagents, etc. used in the following examples, unless otherwise specified, can all be obtained from commercial channels.

[0029] Example 1 Construction of a model for assessing the sensitivity of glioma patients to treatment methods This example is used to screen lncRNAs related to the survival of glioma patients from the TCGA database and establish a corresponding evaluation model, which specifically includes the following steps: Step 1: Obtain transcriptome data, clinical treatment methods, and survival time of glioma patients from the Cancer Genome Atlas (TCGA) database; Step 2: Screen from the TCGA database using Kaplan-Meier survival analysis P All LncRNAs with a correlation of less than 0.05 with the survival time of glioma patients were selected as candidate genes.

[0030] Step 3: Use the L1 penalty method to screen multiple LncRNAs with non-zero L1 penalty regression coefficients from the candidate genes as the final evaluation genes, and perform 1000 cross-validations on the data set through LASSO regression, and use the "1-se" lambda method to select the smallest lambda value in the LASSO regression; use the selected lambda value and repeat 1000 times through the "cvfit" function to determine the regression coefficient of the L1 penalty method; Step 4: Based on the final evaluation gene and the regression coefficient of the L1 penalty method, combined with the stratified prognostic survival risk rate scoring expression of the glioma patients to be tested, an evaluation model for the stratified prognostic survival risk rate of the glioma patients to be tested is obtained. The constructed model is named TAMSig.

[0031]

[0032] In Table 2 , the transcribed genes corresponding to the lncRNAs are numbered according to the human reference genome version GRCh38 / hg38.

[0033] Example 2 Analysis of the accuracy of TAMSig in assessing the prognosis of glioma patients 2.1. Based on the data obtained from the TCGA database, the prognosis survival risk rate scores of 700 glioma patients were calculated using the TAMSig model constructed in Example 1; the patients were divided into a high TAMSig group and a low TAMSig group according to the median of the prognosis survival risk rate score of the glioma patients, and the survival time of the patients was statistically analyzed. The statistical results are as follows: Figure 1 As shown in AB, it can be seen that the low TAMSig group has more survivors and a longer survival time.

[0034] The expression intensity of 13 LncRNAs in 700 glioma patients was analyzed. Figure 1As shown in middle C, it can be seen that there are significant differences in the expression intensity of some LncRNAs related to the prognosis of patients in the low TAMSig group and the high TAMSig group.

[0035] 2.2 Analysis of the expression of 13 LncRNAs in different glioma subtypes According to the transcriptome data of different glioma subtypes in TCGA, the expression levels of 13 LncRNAs in four glioma subtypes, including classic-like (CL), mesenchymal (ME), neural (NE), and proneural (PN), were analyzed. The analysis results are as follows: Figure 2 As shown in A.

[0036] According to the World Health Organization's grading criteria for gliomas, the expression levels of 13 LncRNAs in patients with low-grade gliomas (G2), anaplastic gliomas (G3), and glioblastomas (G4) were analyzed. Figure 2 As shown in B.

[0037] According to the glioma methylation subtypes proposed by methylation data, the expression levels of 13 LncRNAs in 7 glioma subtypes including Classic-like, G-CIMP-high, LGm6-GBM, PA-like, Codel, G-CIMP-low, and Mesenchymal-like were analyzed. The analysis results are shown in the figure. Figure 2 As shown in C.

[0038] According to the molecular classification of glioma, the expression levels of 13 LncRNAs in IDH mutant 1p / 19q co-deletion (IDHmut-Codel), IDH mutant 1p / 19q non-co-deletion (IDHmut-non-Codel), and IDH wild type (IDhwt) were analyzed. The analysis results are as follows: Figure 2 As shown in D.

[0039] According to the common pathological classification of glioma, the expression levels of 13 LncRNAs in astrocytoma, glioblastoma, oligoastrocytoma, and oligodendroglioma were analyzed. The results are as follows: Figure 2 As shown in E.

[0040] According to the study proposed by Roel Verhaak in 2010, glioblastoma is divided into four subtypes C1-C4. The expression levels of 13 LncRNAs in different glioblastoma subtypes were analyzed. The analysis results are as follows: Figure 2 As shown in F.

[0041] according to Figure 2 It can be seen that the expression levels of the above 13 LncRNAs showed significant differences in unsupervised clustering, methylation clustering, TCGA subtypes, tumor grades, molecular subtypes and pathological types.

[0042] 2.3 Correlation analysis between stratified prognostic survival risk score and survival risk rate in patients with glioblastoma C1-C4 subtypes According to the median TAMSig of glioblastoma C1-C4 subtype patients, each subtype of patients were divided into high TAMSig group and low TAMSig group, and correlation analysis was performed based on the survival time of patients in each group. The analysis results are shown in Figure 3 As shown. Figure 3 It can be seen that in patients with different subtypes of glioblastoma, the correlation p between TAMSig and patient survival risk rate was less than 0.05, indicating that the two are correlated and TAMSig can be used as a stratified prognostic standard.

[0043] 2.4 Correlation analysis of survival risk rates of glioma patients after different treatment methods As we all know, radiotherapy, chemotherapy and targeted therapy are the current mainstream treatment options for glioma. This example tests the potential of TAMSig to reflect the prognosis and survival rate of glioma patients under different treatments.

[0044] Based on the expression levels of 13 LncRNAs and treatment data of glioma patients obtained from the TCGA database, TAMSig was calculated based on the expression levels of the 13 LncRNAs, and the patients were classified according to the median of TAMSig in different treatment methods, and the survival time of the patients was counted. Specifically: According to the median of TAMSig of the patients, the patients were divided into high TAMSig group and low TAMSig group, and according to whether the patients in the high TAMSig group and the low TAMSig group received radiotherapy after tumor resection surgery, the patients were divided into the radiotherapy group and the non-radiotherapy group, that is, the patients were divided into four groups, namely, high TAMSig radiotherapy glioma patients, low TAMSig radiotherapy glioma patients, high TAMSig non-radiotherapy glioma patients and low TAMSig non-radiotherapy glioma patients. The survival rate of each group of patients within 15 years was counted and correlation analysis was performed. The results are as follows: Figure 4 As shown in A.

[0045] Using the same analysis method, the patients were divided into high TAMSig group and low TAMSig group according to the median TAMSig of the patients. According to whether the patients in the high TAMSig group and the low TAMSig group received chemotherapy after tumor resection surgery, the high TAMSig group could be divided into a group that had received radiotherapy and a group that had not received radiotherapy. All patients in the low TAMSig group received chemotherapy, that is, the patients were divided into three groups, namely, patients with gliomas who received high TAMSig chemotherapy, patients with gliomas who received low TAMSig chemotherapy, and patients with gliomas who did not receive high TAMSig chemotherapy. The survival rate of each group of patients within 15 years was calculated and correlation analysis was performed. The results are as follows: Figure 4 As shown in B.

[0046] Using the same analysis method, the patients were divided into high TAMSig group and low TAMSig group according to the median TAMSig of the patients, and divided into the group that had received targeted therapy and the group that had not received targeted therapy according to whether the patients in the high TAMSig group and the low TAMSig group received targeted therapy after tumor resection surgery. That is, the patients were divided into four groups, namely, patients with gliomas treated with high TAMSig targeted therapy, patients with gliomas treated with low TAMSig targeted therapy, patients with gliomas treated with high TAMSig but not targeted therapy, and patients with gliomas treated with low TAMSig but not targeted therapy. The survival rate of patients in each group within 15 years was counted and correlation analysis was performed. The results are as follows: Figure 4 As shown in C.

[0047] according to Figure 4 It can be seen that patients in the low TAMSig group had a higher survival rate regardless of the treatment they received, indicating that TAMSig can be used to predict the prognosis and survival rate of glioma patients.

[0048] 2.5. Using histology, grade, gender, ID status, 1p19q coding (co-deletion of 1p and 19q chromosome arms), MGMT status, ATRX status, DAXX status, transcriptome subtype, and methylation cluster as parameters, univariate and multivariate regression analysis was performed to obtain the hazard ratio (Hazard radio). The results are as follows: Figure 5 As shown, according to Figure 5 It can be seen that in the univariate and multivariate regression analysis, the P value of TAMSig was less than 0.001, indicating that TAMSig is an independent prognostic factor for glioma patients.

[0049] Example 3 TAMSig as an independent factor of prognostic sensitivity in glioma patients 3.1. In order to quantify the prediction ability of TAMSig, this embodiment uses the ROC curve to measure AUC to reflect the accuracy of the prediction. Specifically: The sensitivity score expression of glioma patients to treatment methods was constructed based on 10 clinical indicator parameters. The 10 clinical molecular parameters included histology, grade, gender, ID status, 1p19q coding (1p and 19q chromosome arm co-deletion), MGMT status, ATRX status, DAXX status, transcriptome subtype, and methylation cluster. The 1-, 3-, and 5-year ROC curves were constructed based on the above data. The results are shown in Figure 2. Figure 6 As shown in A.

[0050] according to Figure 6 As shown in Figure A, in the 1-year, 3-year, and 5-year survival predictions, compared with other clinical molecular parameters, TAMSig always maintained the largest AUC value, with AUCs of 0.843, 0.914, and 0.821, respectively, indicating that the predictive ability of TAMSig is better than that of the other 10 clinical indicator parameters.

[0051] 3.2. Follow-up analysis was conducted on glioma patients who had undergone radiochemotherapy and targeted therapy, and the TAMSig of patients in the complete response group (Complete Response, CR), partial response group (Partial Response, PR), stable disease group (Stable Disease, SD), and progressive disease group (Progressive Disease, PD) after treatment were counted. The statistical results are as follows: Figure 6 As shown in Figure B, the TAMSig in the response group (CR and PR) was significantly lower than that in the disease group (PD and SD). However, there was no statistical difference between the complete response group (CR) and the partial response group (PR).

[0052] The TAMSig of four glioma subtypes, including classic-like (CL), mesenchymal (ME), neural (NE), and proneural (PN), was analyzed. Figure 6 As shown in C.

[0053] The TAMSig of patients with three glioma subtypes were analyzed. The three glioma subtypes were IDHmut-codel (IDH gene mutation, and co-deletion of 1p and 19q chromosome arms), IDHmut-non-codel (IDH gene mutation, and no co-deletion of 1p and 19q chromosome arms), and IDHwt (IDH gene not mutated). The analysis results are as follows Figure 6 As shown in D.

[0054] according to Figure 6 It can be seen from the BD that there is a correlation between TAMSig and clinical parameters.

[0055] 3.3. According to the median TAMSig of glioma patients, the patients were divided into high TAMSig group and low TAMSig group. The occurrence of co-deletion of 1p and 19q chromosome arms (1p19q codel / non-codel), IDH mutation (IDH mutant / WT), grade (G2-G4), subtype (CL / ME / NE / PN), glioblastoma subtype (C1-C4), ATRX mutation (ATRX mutant / WT), methylation type (Classic-like / Codel / G-CIMP-high / G-CIMP-low / LGm6-GBM / Mesenchymal-like / PA-like) and glioma type (Astrocytoma / Glioblastoma / Oligoastrocytoma / Oligodendroglioma) were analyzed in different groups. The results are shown in the figure. Figure 7 shown.

[0056] It can be seen that TAMSig is related to the clinical characteristic parameters of glioma patients, which also indirectly confirms the clinical significance of TAMSig.

[0057] 3.4. The degree of DNA damage and immune cell receptor density have been shown to be influencing factors of chemotherapy resistance and immune escape. This example tests the correlation between TAMSig and various DNA damage parameters, including tumor ploidy and homologous recombination defect, tumor purity, ITH, LOH, CNV load, aneuploidy score, white blood cell ratio, mutation load and immune cell receptor diversity. The test results are as follows: Figure 8 As shown in Figure 3, except for tumor ploidy and homologous recombination defect, all other parameters, including tumor purity, ITH, LOH, CNV load, aneuploidy score, leukocyte ratio, mutation load, and immune cell receptor diversity, were significantly positively correlated with TAMSig.

[0058] According to the above experiments, TAMSig can reflect the sensitivity of glioma patients to therapy and help clinicians choose appropriate treatment methods.

[0059] Example 4 Biological studies verify the pharmacological prediction accuracy of TAMSig In order to verify the biological prediction accuracy of TAMSig, this example uses pRRophetic based on ridge regression to explore potential regulatory compounds. Specifically, pRRophetic based on ridge regression is used to predict potential drugs for glioma treatment, and Roscovitine with the smallest P value is finally selected for subsequent biological experiments.

[0060] like Fig. 9 As shown in A, GL261 cells were cultured in a conventional cell culture medium (DMEM + 20% FBS), and 30 μM roscovitine was added to the culture medium. At the same time, a control group including only cell culture medium was set up, and the cells were cultured under PBS conditions (1, 3, 5 days), and the cell survival rate was observed by MTT method. It can be seen that roscovitine can significantly reduce the survival rate of glioma cells in vitro.

[0061] Using a stereotactic apparatus, glioma cells were injected into the intracranial parietal cortex of mice to construct glioma model mice; then, the glioma model mice were divided into two groups, with 5 mice in each group. The first group of mice were injected with roscovitine solution (the solvent was roscovitine, the dosage was 50 mg / kg) every day, and the second group of mice were injected with an equal volume of PBS solution every day. During the injection period, the mice were free to eat and drink, and the survival status and survival rate of the mice were observed.

[0062] TAMs are associated with the PI3K / AKT and NF-κB pathways. Proteins from mouse glioma cells were extracted and Western Blot experiments were performed, such as Fig. 9 As shown in Figure B, it can be seen that roscovitine can significantly reduce the phosphorylation of PI3K / AKT and NF-κB in glioma cells.

[0063] IL-6 is an important component and representative marker of TAMs self-maintenance. ELISA was performed on the supernatant of mouse spleen cells, such as Fig. 9 As shown in Figures 3 and 4, it can be seen that rosovitamin B can significantly reduce the secretion level of IL-6 and increase the level of interferon-γ (IFN-γ), an anti-tumor immune marker, which also means that rosovitamin B can significantly reduce the phenotype and aggregation of TAMs.

[0064] The weight of mouse tumor tissue was counted, and the results were as follows: Fig. 9As shown in Figure EF, it can be seen that roscovitine treatment can significantly inhibit the weight of glioma.

[0065] The survival rate of mice was statistically analyzed. Fig. 9 As shown in middle G, it can be seen that roscovitine can significantly improve the survival time of mice.

[0066] Mouse spleen cells were tested by flow cytometry. Fig. 9 As shown in Figure 3HJ, it can be seen that roscovitine significantly increased the proportion of CD4+ and CD8+ cells. Roscovitine treatment can significantly inhibit the progression of glioma and prolong the survival time of glioma mice. These results show that roscovitine can significantly reduce immunosuppression and stemness, further inhibit the progression of glioma, and also indirectly confirm the prediction accuracy of TAMSig.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the stratified prognosis survival risk rate of glioma patients, characterized in that: include: The expression data of 13 LncRNAs in the transcriptome sequencing results of tumor samples of glioma patients to be tested were obtained, and the genes corresponding to the 13 LncRNAs were AC004067.1, AC012213.1, AC012213.1, AC015922.3, AC020915.3, AC020916.1, AC025171.1, AC025171.5, AC026401.3, AC064875.1, AC093673.1, AC120036.4, and AC131097.4; The expression data of the 13 LncRNAs were input into the stratified prognosis survival risk assessment model for glioma patients to obtain the stratified prognosis survival risk rate scores for the glioma patients to be tested.

2. The evaluation method according to claim 1, characterized in that: The method for constructing the stratified prognosis survival risk assessment model for glioma patients comprises the following steps: Step 1, obtain data from the TCGA database; Step 2: All LncRNAs with a P value less than 0.05 associated with the survival time of glioma patients were screened from the TCGA database as candidate genes by Kaplan-Meier survival analysis; Step 3: Use the L1 penalty method to screen multiple LncRNAs that meet the preset screening conditions from the candidate genes as the final evaluation genes, and use the candidate genes to cross-validate the final evaluation genes and determine the regression coefficient of the L1 penalty method; Step 4: Based on the final evaluation gene and the regression coefficient of the L1 penalty method, combined with the stratified prognostic survival risk score expression of glioma patients, a stratified prognostic survival risk assessment model for glioma patients is obtained.

3. The method according to claim 2, characterized in that The stratified prognostic survival risk rate score expression for glioma patients is: Stratified prognostic survival risk score for glioma patients = (Coef1*AC004067.1 expression) + (Coef2*AC012213.1 expression) + (Coef3*AC012213.1 expression) + (Coef4*AC015922.3 expression) + (Coef5*AC020915.3 expression) + (Coef6*AC020916.1 expression) + (Coef 7*AC025171.1)+(Coef8*AC025171.5)+(Coef9*AC026401.3)+(Coef10*AC064875.1)+(Coef11*AC093673.1)+(Coef12*AC120036.4)+(Coef13*AC131097.4); Among them, Coef1-Coef13 are the regression coefficients corresponding to the expression level of LncRNA.

4. The evaluation method according to any one of claims 1 to 3, characterized in that: The method also includes: when the stratified prognosis survival risk rate score of the glioma patient to be tested is greater than or equal to a preset threshold, it is determined that the prognosis of the glioma patient to be tested is poor; when the stratified prognosis survival risk rate score of the glioma patient to be tested is less than the preset threshold, it is determined that the prognosis of the glioma patient to be tested is good.

5. A system for evaluating the stratified prognosis survival risk rate of glioma patients, characterized in that: include: A data acquisition module is used to obtain the expression data of 13 LncRNAs in the transcriptome sequencing results of tumor samples of glioma patients to be tested, wherein the genes corresponding to the 13 LncRNAs are AC004067.1, AC012213.1, AC012213.1, AC015922.3, AC020915.3, AC020916.1, AC025171.1, AC025171.5, AC026401.3, AC064875.1, AC093673.1, AC120036.4, and AC131097.4; The data processing module is used to: input the expression data of the 13 LncRNAs into the stratified prognosis survival risk assessment model for glioma patients to obtain the stratified prognosis survival risk rate score for the glioma patients to be tested.

6. The evaluation system according to claim 5, characterized in that Also includes: The judgment module is used to: judge the prognosis survival risk of the glioma patient to be tested, and when the stratified prognosis survival risk rate score of the glioma patient to be tested is greater than or equal to a preset threshold, it is judged that the prognosis of the glioma patient to be tested is poor; When the stratified prognostic survival risk rate score of the glioma patient to be tested is less than a preset threshold, it is determined that the prognosis of the glioma patient to be tested is good.

7. A method for auxiliary screening of therapeutic drugs for glioma patients, characterized in that: include: The expression data of 13 LncRNAs in the transcriptome sequencing results of tumor samples of glioma patients to be tested were obtained, and the genes corresponding to the 13 LncRNAs were AC004067.1, AC012213.1, AC012213.1, AC015922.3, AC020915.3, AC020916.1, AC025171.1, AC025171.5, AC026401.3, AC064875.1, AC093673.1, AC120036.4, and AC131097.4; Inputting the expression data of the 13 LncRNAs into the stratified prognosis survival risk rate assessment model for glioma patients according to any one of claims 1 to 4 to obtain the prognosis survival risk rate score of the glioma patient to be tested; According to the prognosis survival risk rate score of the glioma patient to be tested, therapeutic drugs that are sensitive to the glioma patient to be tested are screened.

8. The method according to claim 7, characterized in that Ridge regression-based pRRophetic screening was used to identify therapeutic drugs that are sensitive to glioma patients under test.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the method for evaluating the stratified prognosis survival risk rate of the glioma patient to be tested as described in any one of claims 1-4 or the auxiliary screening method for the treatment of glioma patients as described in any one of claims 7-8.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method for evaluating the stratified prognosis survival risk rate of a glioma patient to be tested as described in any one of claims 1-4 or the auxiliary screening method for therapeutic drugs for glioma patients as described in any one of claims 7-8.

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