Evaluation Method and System for Stratified Prognostic Survival Risk Rate of Glioma Patients

Abstract

The present invention provides a method and system for evaluating the stratified prognostic survival risk rate of glioma patients. The present invention provides a method for evaluating the stratified prognostic survival risk rate of glioma patients, including obtaining the expression data of 13 LncRNAs in the transcriptome sequencing of the tumor specimens of the glioma patients to be tested, and obtaining the prognostic survival risk rate score of the glioma patients to be tested based on the expression data of the 13 LncRNAs, so as to obtain the stratified prognostic survival risk rate of the glioma patients to be tested. The method for evaluating the stratified prognostic survival risk rate of glioma patients provided by the present invention helps to stratify the prognosis of glioma patients, improve the treatment plan clinically, avoid over-treatment, improve the treatment efficiency, and provide a solution idea for the precision medicine of glioma.

Description

Technical Field

[0001] The present invention relates to healthcare informatics, and particularly to a method and system for evaluating the stratified prognostic survival risk rate of glioma patients. Background Art

[0002] Glioma is a tumor originating from glial cells in the nervous system and is the most common malignant tumor in the intracranial cavity, accounting for about 40%-50% of the total intracranial tumors. According to different cell types, gliomas can be divided into various types such as astrocytoma, glioblastoma, and oligodendroglioma. The clinical treatment methods for glioma include 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 radiotherapy.

[0003] During the treatment of glioma, due to the trial-and-error method to find effective therapies, critical time is often lost. In addition, tumor heterogeneity leads to great differences in the treatment effects of different patients using the same drug regimen. Therefore, how to perform individual stratification and prognostic evaluation is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

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

[0005] In a first aspect, the present invention provides a method for evaluating the stratified prognostic survival risk rate of a glioma patient to be tested, including:

[0006] Obtaining the expression level data of 13 LncRNAs in the transcriptome sequencing result of the tumor sample of the glioma patient to be tested, and 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 respectively;

[0007] Inputting the expression level data of the 13 LncRNAs into a glioma patient stratified prognostic survival risk rate evaluation model to obtain a stratified prognostic survival risk rate score of the glioma patient to be tested.

[0008] In the above evaluation method, the glioma patients to be tested refer to patients clinically diagnosed with glioma. The clinical diagnosis methods may include, for example, at least one of clinical manifestations, imaging examinations, histopathology and molecular analysis, and molecular markers. In addition, the glioma patients to be tested may also have undergone clinical treatment. The clinical treatment methods may include, for example, at least one of radiotherapy, chemotherapy, immunotherapy, and targeted therapy.

[0009] In the above evaluation method, LncRNA refers to long non-coding RNA.

[0010] In the above evaluation method, transcriptome sequencing is performed on the tumor samples of the glioma patients to be tested, and based on the transcriptome sequencing results of the tumor samples, the expression levels of the above 13 LncRNAs are obtained.

[0011] In the above evaluation method, the transcription genes corresponding to the 13 LncRNAs are numbered according to the human reference genome GRCh38 / hg38 version.

[0012] In the above evaluation method, the method for constructing the evaluation model of the prognostic survival risk rate of glioma patients includes the following steps:

[0013] Step 1: Obtain the data in the TCGA database;

[0014] Step 2: Through Kaplan-Meier survival analysis, screen out all LncRNAs with P less than 0.05 and related to the survival time of glioma patients from the TCGA database as candidate genes;

[0015] Step 3: Use the L1 penalty method to screen out 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 coefficients of the L1 penalty method;

[0016] Step 4: According to the final evaluation genes and the regression coefficients of the L1 penalty method, combined with the prognostic survival risk rate scoring expression formula of glioma patients, obtain the prognostic survival risk rate evaluation model of glioma patients.

[0017] Further, the preset screening conditions may be LncRNAs with non-zero regression coefficients of the L1 penalty method.

[0018] Further, the "1-se" lambda method can be used to select the smallest λ value in the LASSO regression; using the selected λ value, repeat 1000 times through the "cvfit" function to determine the regression coefficients of the L1 penalty method.

[0019] Further, the prognostic survival risk rate scoring expression formula of the glioma patients to be tested is:

[0020] The prognostic survival risk rate score of glioma patients = (Coef1 * expression level of AC004067.1) + (Coef2 * expression level of AC012213.1) + (Coef3 * expression level of AC012213.1) + (Coef4 * expression level of AC015922.3) + (Coef5 * expression level of AC020915.3) + (Coef6 * expression level of AC020916.1) + (Coef7 * expression level of AC025171.1) + (Coef8 * expression level of AC025171.5) + (Coef9 * expression level of AC026401.3) + (Coef10 * expression level of AC064875.1) + (Coef11 * expression level of AC093673.1) + (Coef12 * expression level of AC120036.4) + (Coef13 * expression level of AC131097.4);

[0021] Among them, Coef1 - Coef13 are the regression coefficients corresponding to the expression levels of the LncRNAs.

[0022] Furthermore, the values of Coef1 - Coef13 are shown in Table 1.

[0023]

[0024] In the above evaluation method, the method further includes a judgment step. Specifically, when the stratified prognostic survival risk rate score of the glioma patient to be tested is greater than or equal to the preset threshold, it is determined that the prognosis of the glioma patient to be tested is poor, that is, the prognostic survival risk is high; when the prognostic 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, that is, the survival risk is low.

[0025] Furthermore, the preset threshold can be determined according to the median of the stratified prognostic survival risk rate scores of the glioma patients to be tested in the study population.

[0026] In a second aspect, the present invention provides an evaluation system for the stratified prognostic survival risk rate of glioma patients to be tested, including:

[0027] A data acquisition module, configured to: acquire the expression data of 13 LncRNAs in the transcriptome sequencing results of the tumor samples of glioma patients to be tested, and 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, AC131097.4;

[0028] A data processing module, configured to: input the expression data of the 13 LncRNAs into an evaluation model for the stratified prognostic survival risk rate of glioma patients to obtain the stratified prognostic survival risk rate score of the glioma patients to be tested.

[0029] In the above evaluation system, a data judgment module is further included, configured to judge the prognostic survival risk of the glioma patients to be tested according to the stratified prognostic survival risk rate score of the glioma patients to be tested; when the stratified prognostic survival risk rate score of the glioma patients to be tested is greater than or equal to a preset threshold, it is determined that the prognostic survival of the glioma patients to be tested is poor, that is, the survival risk is high; when the stratified prognostic survival risk rate score of the glioma patients to be tested is less than the preset threshold, it is determined that the prognosis of the glioma patients to be tested is good, that is, the survival risk is low.

[0030] In a third aspect, the present invention provides an auxiliary screening method for glioma patient treatment drugs, including:

[0031] Acquire the expression data of 13 LncRNAs in the transcriptome sequencing results of the tumor samples of glioma patients to be tested, and 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, AC131097.4;

[0032] Input the expression data of the 13 LncRNAs into the evaluation model for the stratified prognostic survival risk rate of the glioma patients to be tested as described above to obtain the prognostic survival risk rate score of the glioma patients to be tested;

[0033] According to the prognostic survival risk rate score of the glioma patients to be tested, screen the treatment drugs sensitive to the glioma patients to be tested.

[0034] For the screening method described above, drug sensitivity screening can be used. For example, pRRophetic based on ridge regression can be used to screen the therapeutic drugs for glioma patients.

[0035] In a fourth aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned method for evaluating the stratified prognostic survival risk rate of the glioma patients to be tested, or the above-mentioned clinical decision-making assistance method for the treatment methods of glioma patients, or the above-mentioned auxiliary screening method for the therapeutic drugs of glioma patients.

[0036] In a fifth aspect, the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the above-mentioned method for evaluating the stratified prognostic survival risk rate of the glioma patients to be tested, or the above-mentioned clinical decision-making assistance method for the treatment methods of glioma patients, or the above-mentioned auxiliary screening method for the therapeutic drugs of glioma patients.

[0037] The present invention provides a method for evaluating the prognostic survival risk rate of glioma patients to be tested, which has a wide distribution pattern among different clinical features, helps to improve the treatment plan clinically, avoid over-treatment, omit the complex steps of various molecular diagnoses, improve the treatment efficiency, and improve the reactivity of clinical treatment. Description of the Drawings

[0038] Figure 1 It is the statistical result of the prognostic survival risk rate score and survival time of glioma patients and the analysis result of the expression intensity of 13 LncRNAs; among them, A is the statistical result 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 analysis of the expression intensity of 13 LncRNAs of glioma patients.

[0039] Figure 2Analysis results of the expression levels of 13 lncRNAs in different glioma subtypes; among them, A shows the analysis results of the expression levels of 13 lncRNAs in four glioma subtypes, namely Classic-like (CL), Mesenchymal (ME), Neural (NE), and Proneural (PN); B shows the analysis results of the expression levels of 13 lncRNAs in patients with low-grade glioma (G2), anaplastic glioma (G3), and glioblastoma (G4); C shows the analysis results of the expression levels of 13 lncRNAs in seven glioma methylation subtypes, namely Classic-like, G-CIMP-high, LGm6-GBM, PA-like, Codel, G-CIMP-low, and Mesenchymal-like; D shows the analysis results of the expression levels of 13 lncRNAs in four glioma subtypes, namely IDH mutant 1p / 19q co-deleted (IDHmut-Codel), IDH mutant 1p / 19q non-co-deleted (IDHmut-non-Codel), and IDH wild-type (IDhwt); E shows the analysis results of the expression levels of 13 lncRNAs in four glioma subtypes, namely astrocytoma, glioblastoma, oligoastrocytoma, and oligodendroglioma; F shows the analysis results of the expression levels of 13 lncRNAs in four glioma subtypes, C1-C4;

[0040] Figure 3 Analysis results of the correlation between TAMSig and survival time in patients with four glioma subtypes; among them, A shows the analysis results of the correlation between TAMSig and survival time in patients with C1 subtype, B shows the analysis results of the correlation between TAMSig and survival time in patients with C2 subtype, C shows the analysis results of the correlation between TAMSig and survival time in patients with C3 subtype, and D shows the analysis results of the correlation between TAMSig and survival time in patients with C4 subtype;

[0041] Figure 4 Analysis results of the correlation between TAMSig and survival time after different treatment methods for glioma patients; among them, A shows the analysis results of the correlation between TAMSig and survival time after radiotherapy for glioma patients, B shows the analysis results of the correlation between TAMSig and survival time after chemotherapy for glioma patients; C shows the analysis results of the correlation between TAMSig and survival time after targeted therapy for glioma patients;

[0042] Figure 5Analysis results of single-factor and multi-factor regression analysis using multiple parameters; among them, A is the result of single-factor regression analysis, and B is the result of multi-factor regression analysis;

[0043] Figure 6 Analysis of the prediction accuracy of TAMSig for the prognosis of glioma patients; among them, A is the ROC curve constructed from 10 different clinical index parameter data, B is the analysis result of the correlation between disease progression and TAMSig after different glioma patients receive different treatments, C is the analysis result of the correlation between different glioma subtypes and TAMSig, and D is the analysis result of the correlation between different glioma subtypes and TAMSig;

[0044] Figure 7 Distribution of glioma subtypes in patients with high TAMSig group and low TAMSig group;

[0045] Figure 8 Analysis result of the correlation between TAMSig and multiple DNA damage parameters;

[0046] Figure 9 Schematic diagram of drug screening based on TAMSig; among them, A is the result of the effect of roscovitine on the viability of glioma cells in vitro, B is the immunoblot analysis result of glioma cells, C is the result of the effect of roscovitine on the secretion level of IL-6, D is the result of the effect of roscovitine on the secretion level of IFN-γ, E is the statistical result of the effect of roscovitine on tumor weight, F is the result of the effect of roscovitine on tumor size, G is the result of 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.

[0047] Figures 1-9 In it, * indicates p < 0.05; ** indicates p < 0.01; *** indicates p < 0.001; ns indicates no significant difference. Detailed implementation manners

[0048] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments, and they should not be construed as limiting the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall 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 the purpose of description and cannot be construed as indicating or implying relative importance.

[0049] In the following examples, the experimental methods are conventional methods unless otherwise specified, and are carried out according to the techniques or conditions described in the literature in this field or according to the product specifications. The materials, reagents, etc. used in the following examples can be obtained from commercial sources unless otherwise specified.

[0050] Example 1 Construction of an evaluation model for the sensitivity of glioma patients to treatment methods

[0051] 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:

[0052] Step 1: Obtain the transcriptome data, clinical treatment methods, and survival time of glioma patients from the The Cancer Genome Atlas (TCGA) database.

[0053] Step 2: Screen out all LncRNAs with a value less than 0.05 and related to the survival time of glioma patients from the TCGA database through Kaplan-Meier survival analysis as candidate genes. P All LncRNAs less than 0.05 and related to the survival time of glioma patients are used as candidate genes.

[0054] Step 3: Use the L1 penalty method to screen out multiple LncRNAs with non-zero L1 penalty regression coefficients from the candidate genes as the final evaluation genes, and perform 1000-fold cross-validation on the dataset through LASSO regression, and use the "1-se" lambda method to select the smallest λ value in LASSO regression; use the selected λ value and repeat 1000 times through the "cvfit" function to determine the regression coefficients of the L1 penalty method.

[0055] Step 4: According to the final evaluation genes and the regression coefficients of the L1 penalty method, combined with the expression formula for the stratified prognostic survival risk rate score of the glioma patients to be tested, obtain the evaluation model for the stratified prognostic survival risk rate of the glioma patients to be tested, and the constructed model is named TAMSig.

[0056]

[0057] In Table 2, the transcription genes corresponding to LncRNAs are numbered according to the human reference genome GRCh38 / hg38 version.

[0058] Example 2 Analysis of the accuracy of TAMSig in evaluating the prognosis sensitivity of glioma patients

[0059] 2.1. Based on the data obtained from the TCGA database, the prognostic survival risk rate scores of 700 glioma patients were calculated using the model TAMSig constructed in Example 1. According to the median of the prognostic survival risk rate scores of glioma patients, the patients were divided into a high TAMSig group and a low TAMSig group, and the survival time of the patients was statistically analyzed. The statistical results are shown in Figure 1 A - B below. It can be seen that the number of survivors in the low TAMSig group is larger and the survival time is longer.

[0060] The expression intensities of 13 LncRNAs in 700 glioma patients were analyzed. The analysis results are shown in Figure 1 C below. It can be seen that there are significant differences in the expression intensities of some LncRNAs in the prognosis of patients in the low TAMSig group and the high TAMSig group.

[0061] 2.2. Analysis of the expression of 13 LncRNAs in different subtypes of glioma

[0062] Based on the transcriptome data of different glioma subtypes in TCGA, the expression levels of 13 LncRNAs in four glioma subtypes, namely classic - like glioma (CL), mesenchymal glioma (ME), neural glioma (NE), and proneural glioma (PN), were analyzed. The analysis results are shown in Figure 2 A below.

[0063] According to the grading criteria of glioma by the World Health Organization, the expression levels of 13 LncRNAs in patients with low - grade glioma (G2), anaplastic glioma (G3), and glioblastoma (G4) were analyzed. The analysis results are shown in Figure 2 B below.

[0064] Based on the glioma methylation subtypes proposed according to methylation data, the expression levels of 13 LncRNAs in 7 glioma subtypes, namely Classic - like, G - CIMP - high, LGm6 - GBM, PA - like, Codel, G - CIMP - low, and Mesenchymal - like, were analyzed. The analysis results are shown in Figure 2 C below.

[0065] According to the molecular classification of glioma, the expression levels of 13 LncRNAs in IDH - mutated 1p / 19q co - deleted (IDHmut - Codel), IDH - mutated 1p / 19q non - co - deleted (IDHmut - non - Codel), and IDH - wild - type (IDhwt) were analyzed. The analysis results are shown in Figure 2 D below.

[0066] According to the collusive pathological classification of gliomas, the expression levels of 13 LncRNAs in astrocytoma, glioblastoma, oligoastrocytoma, and oligodendroglioma were analyzed, and the analysis results are as Figure 2 shown in E below.

[0067] According to the classification proposed by Roel Verhaak in his 2010 study, glioblastomas were divided into four subtypes C1 - C4, and the expression levels of 13 LncRNAs in different glioblastoma subtypes were analyzed. The analysis results are as Figure 2 shown in F below.

[0068] According to Figure 2 it can be seen that the expression levels of the above - mentioned 13 LncRNAs have significant differences in unsupervised clustering, methylation clustering, TCGA subtypes, tumor grades, molecular subtypes, and pathological types.

[0069] 2.3. Correlation analysis of the stratified prognostic survival risk rate score and survival risk rate in patients with glioblastoma subtypes C1 - C4

[0070] According to the median TAMSig of patients with glioblastoma subtypes C1 - C4, each subtype of patients was divided into a high - TAMSig group and a low - TAMSig group, and a correlation analysis was performed based on the survival time of patients in each group. The analysis results are as Figure 3 shown below. According to Figure 3 it can be seen that in patients with different subtypes of glioblastoma, the correlation p between TAMSig and the patient survival risk rate is less than 0.05, indicating that there is a correlation between the two, and TAMSig can be used as a stratified prognostic criterion.

[0071] 2.4. Correlation analysis of the survival risk rate in glioma patients after different treatment methods

[0072] As is well known, radiotherapy, chemotherapy, and targeted therapy are the current mainstream treatment options for gliomas. In this example, the potential of TAMSig to reflect the prognostic survival rate of glioma patients for different treatment methods was tested.

[0073] Based on the expression levels of 13 LncRNAs and treatment data of glioma patients obtained from the TCGA database, the TAMSig was calculated according to the expression levels of the 13 LncRNAs. According to the median of TAMSig in different treatment methods, the patients were classified, and the survival time of the patients was statistically analyzed. Specifically: according to the median of TAMSig of the patients, the patients were divided into a high TAMSig group and a low TAMSig group. According to whether the patients in the high TAMSig group and the low TAMSig group received radiotherapy after tumor resection, the patients were divided into a radiotherapy-received group and a non-radiotherapy-received 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 rates of the patients in each group within 15 years were statistically analyzed and correlation analysis was performed. The results are as Figure 4 shown in A

[0074] Using the same analysis method, according to the median of TAMSig of the patients, the patients were divided into a high TAMSig group and a low TAMSig group. According to whether the patients in the high TAMSig group and the low TAMSig group received chemotherapy after tumor resection, the high TAMSig group could be divided into a radiotherapy-received group and a non-radiotherapy-received group, and the patients in the low TAMSig group all received chemotherapy. That is, the patients were divided into three groups, namely high TAMSig chemotherapy glioma patients, low TAMSig chemotherapy glioma patients, and high TAMSig non-chemotherapy glioma patients. The survival rates of the patients in each group within 15 years were statistically analyzed and correlation analysis was performed. The results are as Figure 4 shown in B

[0075] Using the same analysis method, according to the median of TAMSig of the patients, the patients were divided into a high TAMSig group and a low TAMSig group. According to whether the patients in the high TAMSig group and the low TAMSig group received targeted therapy after tumor resection, the patients were divided into a targeted-therapy-received group and a non-targeted-therapy-received group. That is, the patients were divided into four groups, namely high TAMSig targeted-therapy glioma patients, low TAMSig targeted-therapy glioma patients, high TAMSig non-targeted-therapy glioma patients, and low TAMSig non-targeted-therapy glioma patients. The survival rates of the patients in each group within 15 years were statistically analyzed and correlation analysis was performed. The results are as Figure 4 shown in C

[0076] According to Figure 4 it can be seen that the patients in the low TAMSig group have a relatively high survival rate regardless of the treatment method, indicating that TAMSig can be used to predict the prognostic survival rate of glioma patients.

[0077] 2.5. Using Histology, Grade, Gender, ID_status, 1p19q_codetion (co-deletion of chromosome arms 1p and 19q), MGMT_status, ATRX_status, DAXX_status, Transcriptome_Subtype, and Methylation_Cluster as parameters, univariate and multivariate regression analyses were performed separately to obtain the Hazard ratio. The results are as Figure 5 shown. According to Figure 5 it can be seen that in both univariate and multivariate regression analyses, the P-value of TAMSig was less than 0.001, indicating that TAMSig is an independent prognostic factor for glioma patients.

[0078] Example 3 TAMSig as an independent factor for the prognostic sensitivity of glioma patients

[0079] 3.1. To quantify the predictive ability of TAMSig, the ROC curve was used in this example to measure the AUC, which reflects the accuracy of the prediction. Specifically:

[0080] Based on 10 clinical indicator parameters, a sensitivity scoring expression for the treatment method of glioma patients was constructed. The 10 clinical molecular parameters include Histology, Grade, Gender, ID_status, 1p19q_codetion (co-deletion of chromosome arms 1p and 19q), MGMT_status, ATRX_status, DAXX_status, Transcriptome_Subtype, and Methylation_Cluster. According to the above data, 1-year, 3-year, and 5-year ROC curves were constructed. The results are as Figure 6 shown in A.

[0081] According to Figure 6 shown in A, in the 1-year, 3-year, and 5-year survival predictions, compared with other clinical molecular parameters, TAMSig always maintained the largest AUC values, with AUC values of 0.843, 0.914, and 0.821 respectively, indicating that the predictive ability of TAMSig is superior to the other 10 clinical indicator parameters.

[0082] 3.2. Follow-up analysis was performed on glioma patients who had undergone radiotherapy, chemotherapy, and targeted therapy. The TAMSig of patients in the complete response group (CR), partial response group (PR), stable disease group (SD), and progressive disease group (PD) after treatment was statistically analyzed. The statistical results are shown as Figure 6 shown in B below. It can be seen that the TAMSig of the response group (CR and PR) was significantly lower than that of the disease group (PD and SD). However, there was no statistical difference between the complete response group (CR) and the partial response group (PR).

[0083] The TAMSig in four glioma subtypes, namely classic-like (CL), mesenchymal (ME), neural (NE), and proneural (PN), was analyzed. The analysis results are shown as Figure 6 shown in C below.

[0084] The TAMSig of patients with three glioma subtypes was analyzed. The three glioma subtypes were IDHmut-codel (the IDH gene had a mutation and there was a co-deletion of chromosome arms 1p and 19q), IDHmut-non-codel (the IDH gene had a mutation and there was no co-deletion of chromosome arms 1p and 19q), and IDHwt (the IDH gene had no mutation). The analysis results are shown as Figure 6 shown in D below.

[0085] According to Figure 6 B-D below, there was a correlation between TAMSig and clinical parameters.

[0086] 3.3. According to the median of TAMSig in glioma patients, the patients were divided into a high TAMSig group and a low TAMSig group, and it was statistically analyzed whether the patients in different groups had co-deletion of chromosome arms 1p and 19q (1p19q codel / non-codel), whether IDH had a mutation (IDH Mutant / WT), grade (G2-G4), subtype (CL / ME / NE / PN), glioblastoma subtype (C1-C4), whether ATRX had a 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). The analysis results are as Figure 7 shown.

[0087] 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.

[0088] 3.4. The degree of DNA damage and the density of immune cell receptors have been proven to be influencing factors for chemotherapy resistance and immune escape. In this example, the correlation between TAMSig and various DNA damage parameters was tested. The DNA damage parameters include tumor ploidy and homologous recombination deficiency, tumor purity, ITH, LOH, CNV load, aneuploidy score, white blood cell ratio, mutation load, and immune cell receptor diversity. The test results are as Figure 8 shown. It can be seen that except for tumor ploidy and homologous recombination deficiency, all other parameters, including tumor purity, ITH, LOH, CNV load, aneuploidy score, white blood cell ratio, mutation load, and immune cell receptor diversity, are significantly positively correlated with TAMSig.

[0089] According to the above experiments, TAMSig can reflect the sensitivity of glioma patients to therapies and help clinicians select appropriate treatment methods.

[0090] Example 4 Biological Research to Verify the Pharmacological Prediction Accuracy of TAMSig

[0091] To verify the biological prediction accuracy of TAMSig, in this example, pRRophetic based on ridge regression was used to explore potential regulatory compounds. Specifically, the pRRophetic method based on ridge regression was used to predict potential drugs for glioma treatment, and finally, Roscovitine with the smallest P value was selected for subsequent biological experiments.

[0092] As Figure 9 shown in A, GL261 cells were cultured in a conventional (DMEM + 20% FBS) cell culture medium, and 30 μM of Roscovitine was added to the culture medium. At the same time, a control group containing only the cell culture medium was set up, and the cells were cultured under PBS conditions (1, 3, 5 days), and the cell viability was observed by the MTT method. It can be seen that Roscovitine can significantly reduce the viability of glioma cells in vitro.

[0093] Using a stereotaxic apparatus, glioma cells were injected into the parietal cerebral cortex of mice intracranially to construct glioma model mice; subsequently, the glioma model mice were divided into two groups, with 5 mice in each group. The first group of mice was injected with Roscovitine solution (the solvent was Roscovitine, and the dosage was 50 mg / kg) every day, and the second group of mice was injected with an equal volume of PBS solution every day. During the injection period, the mice were allowed to eat and drink freely, and the survival status and survival rate of the mice were observed.

[0094] TAMs are related to the PI3K / AKT and NF-κB pathways. Proteins in mouse glioma cells were extracted and Western Blot experiments were performed. As Figure 9 shown in B, it can be seen that Roscovitine can significantly reduce the phosphorylation of PI3K / AKT and NF-κB in glioma cells.

[0095] IL-6 is an important component and representative marker for TAM self-maintenance. By performing ELISA detection on the supernatant of mouse spleen cells, as Figure 9 shown in C-D, it can be seen that Roscovitine can significantly reduce the secretion level of IL-6 and increase the level of the anti-tumor immune marker interferon-γ (IFN-γ), which also means that Roscovitine can significantly reduce the phenotype and aggregation of TAMs.

[0096] The weights of the mouse tumor tissues were statistically analyzed, and the results are as Figure 9 shown in E-F. It can be seen that Roscovitine treatment can significantly inhibit the weight of gliomas.

[0097] The survival rates of the mice were statistically analyzed, and the results are as Figure 9 shown in G. It can be seen that Roscovitine can significantly increase the survival time of mice.

[0098] Flow cytometry was performed on mouse spleen cells, and the detection results are as shown in Figure 9 H-J. 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-bearing mice. These results indicate that roscovitine can significantly reduce immunosuppression and stemness, further inhibit the progression of glioma, and also confirm the prediction accuracy of TAMSig from the side.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. 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; A data processing module is used to: input the expression data of the 13 LncRNAs into a stratified prognosis survival risk rate assessment model for glioma patients to obtain a stratified prognosis survival risk rate score for the glioma patients to be tested; 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; The stratified prognosis survival risk rate evaluation model for glioma patients is the following stratified prognosis survival risk rate scoring expression for glioma patients: The stratified 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) + (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.

2. 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; The expression data of the 13 LncRNAs are input into the stratified prognosis survival risk rate evaluation model for glioma patients to obtain the prognosis survival risk rate score of the glioma patients to be tested; Screening therapeutic drugs that are sensitive to the glioma patients to be tested according to the prognosis survival risk rate score of the glioma patients to be tested; The stratified prognosis survival risk rate evaluation model for glioma patients is the following stratified prognosis survival risk rate scoring expression for glioma patients: The stratified 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) + (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.

3. The method according to claim 2, characterized in that Ridge regression-based pRRophetic screening was used to identify therapeutic drugs that are sensitive to glioma patients under test.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the auxiliary screening method for treating glioma patients as described in any one of claims 2-3 is implemented.

5. 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, the auxiliary screening method for treating glioma patients as described in any one of claims 2-3 is implemented.

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