Human glioma DNA marker and its application

Abstract

The present application belongs to the technical field of biological medicine, and particularly relates to a human glioma DNA marker and application thereof. The marker comprises three DNAs, namely PBK, SPP1 and LTA, and the corresponding DNA nucleotide sequences are shown in SEQ ID NO. 1, SEQ ID NO. 2 and SEQ ID NO. 3, respectively. The present application provides a molecular marker for glioma prognosis prediction and application thereof. The transcriptome dataset of glioma patients is obtained by TCGA, CGGA-array and CGGA-seq datasets, and three gene markers are screened. Research shows that the three gene markers can independently predict the survival of glioma patients, and can be used as a reference index for judging the survival time of glioma prognosis and predicting the survival risk. By studying the relationship between the three gene markers and the clinical prognosis of glioma patients, a new marker is provided for the prognosis of glioma, so as to improve the timely intervention treatment of glioma patients and improve the clinical prognosis of patients. It has very important significance in clinical practice.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, and in particular relates to a human glioma DNA marker and its application. Background Technology

[0002] This invention relates to the field of biomedical technology, specifically to three gene markers for glioma and their applications, particularly concerning glioma prognosis and prediction of immune cell infiltration.

[0003] Gliomas, including astrocytomas and oligodendrogliomas, are the most common malignant primary brain tumors in adults and the most common malignant tumor of the central nervous system (CNS), accounting for 75% of all malignant primary brain tumors in adults. They are characterized by high recurrence and mortality rates. In 2021, the WHO revised its glioma classification system, providing a more detailed classification based on four dimensions: histological characteristics, isocitrate dehydrogenase status, histological grade, and molecular characteristics, aiming to assign more targeted treatments to patients according to their subtype.

[0004] The conventional treatment for gliomas is surgical resection followed by postoperative radiotherapy and chemotherapy. Despite continuous advancements in glioma diagnosis and treatment in recent years, patient life expectancy has not significantly improved. The key to surgical resection is to remove as much tumor tissue as possible while ensuring safety. A retrospective study found that for newly diagnosed gliomas, the extent of surgical resection is closely related to patient prognosis; the greater the extent of tumor resection, the higher the survival rate. However, for invasive tumors, complete surgical removal is usually impossible. Chemotherapy, represented by temozolomide, has achieved good therapeutic results since its clinical application in 1999. It mainly induces apoptosis in tumor cells by attacking DNA, but the inherent or acquired drug resistance of glioma cells limits the therapeutic effect of chemotherapy. Radiotherapy is a crucial part of glioma treatment. It can further kill residual tumor cells that cannot be completely removed surgically or that are resistant to chemotherapy, and can prolong the survival of patients with low-grade gliomas. However, gliomas are not very sensitive to radiotherapy, and normal brain tissue has poor tolerance to radiotherapy; these factors limit the effectiveness of radiotherapy for gliomas. As research continues, emerging treatments such as molecular targeted therapy, therapeutic electric fields (TEFs), and immunotherapy are being combined in the treatment of some gliomas. However, the prognosis is poor, with a median survival of less than 15 months, influenced by a variety of factors. Among these factors, the quantity, type, and distribution of immune cells play a crucial role in prognosis.

[0005] Recurrent chromosomal abnormalities and genetic alterations have been reported in gliomas, with isocitrate dehydrogenase (IDH) mutations affecting the immune response to gliomas. IDH is an enzyme that catalyzes the oxidative decarboxylation of isocitrate to α-ketoglutarate (αKG). IDH1 and IDH2 mutations are common, occurring in more than 80% of glioma patients and persisting through repeated recurrences, chemotherapy, and resection. IDH mutants (IDH-MUT) lead to reduced αKG efficacy and the accumulation of 2-hydroxyglutarate (2HG), resulting in genomic hypermethylation and the silencing of thousands of genes. Furthermore, IDH-MUT cancer induces hypermethylation of genomic DNA and histones, leading to epigenetic dysfunction and transcriptional repression of various genes, including immune-related genes. Increasing evidence suggests a correlation between IDH-MUT and immunosuppression in glioma patients, potentially indicating a suppressive effect in glioma patients.

[0006] Immunotherapy has emerged as a promising fourth alternative treatment for various cancers; however, its efficacy against brain tumors remains unproven. Previous studies have shown lymphocyte infiltration in gliomas, and lymphocytes are predictors of clinical outcomes in the brain (an immune privileged organ). Given the impact of IDH-MUT on the content of immune cells in gliomas, research using molecular markers to predict glioma prognosis is increasingly prevalent.

[0007] While numerous studies have explored the immune status and clinical significance of IDH-MUT glioma patients, research on the immune status of IDH-WT glioma patients is scarce. This study analyzed the differences in the immune system between IDH-MUT and IDH-WT gliomas, and then focused on the IDH-WT glioma population to develop a three-gene immune-related signaling model as an independent prognostic factor. This prognostic model provides a comprehensive perspective on IDH-WT glioma patients, highlighting its potential value in clinical prognostic prediction.

[0008] The rapid development of NGS technology and the widespread application of bioinformatics analysis tools have greatly aided our understanding of tumors. In exploring tumor pathogenesis and prognostic factors through NGS and bioinformatics analysis, three genes (PBK, SPP1, and LTA) have been identified as playing crucial roles in tumors. Therefore, constructing prognostic genes with high potential clinical application value is of great significance. However, in clinical practice, finding a single gene biomarker with high sensitivity and specificity for predicting glioma prognosis remains extremely challenging. Summary of the Invention

[0009] To address the aforementioned problems, this invention proposes a human glioma DNA biomarker, which effectively solves the technical issues of low accuracy and high detection cost in existing technologies.

[0010] To achieve the above objectives, the present invention adopts the following technical solution: a human glioma DNA marker, the marker comprising three DNAs, namely PBK, SPP1 and LTA, the corresponding DNA nucleotide sequences of which are shown in SEQ ID NO.1, SEQ ID NO.2 and SEQ ID NO.3, respectively.

[0011] Furthermore, the aforementioned human glioma DNA markers are used to predict immune cell infiltration in patients with human glioma.

[0012] Furthermore, the aforementioned human glioma DNA marker is used in the preparation of human glioma detection reagents.

[0013] Furthermore, the use of the aforementioned human glioma DNA marker in the preparation of a kit for predicting immune responses in human glioma patients.

[0014] Furthermore, the kit contains primers, probes, and standards specifically targeting the biomarkers PBK, SPP1, and LTA.

[0015] Furthermore, the specific sequences of the primers are as follows:

[0016] PBK upstream primer: AGGTTTGTCTCATTCTCCTTGGG;

[0017] PBK downstream primer: AGCAAGACACAGACTGCCAT;

[0018] SPP1 upstream primer: GCCGAGGTGATAGTGTGGTT;

[0019] SPP1 downstream primer: CAATCAGAAGGCGCGTTCAG;

[0020] LTA upstream primer: AGATGCATCTTGCCCACAGC;

[0021] LTA downstream primer: ACCACCTGGGAGTAGACGAA.

[0022] Furthermore, a detection method using the aforementioned marker includes the following steps: extraction of total RNA from the sample, preparation of cDNA from the sample, and amplification of the DNA, as detailed below:

[0023] Step 1: Extraction of total RNA from the sample: Total RNA was extracted from glioma tumor tissue according to the TRIZOL reagent instructions. The concentration and purity of the extracted RNA were detected, and the integrity of the extracted RNA was determined by agarose gel electrophoresis.

[0024] Step 2: Preparation of cDNA from the sample: The extracted total RNA was reverse transcribed using the TIANScript II RT Kit reagents and corresponding steps to synthesize cDNA;

[0025] Step 3: DNA amplification: using... The reagents and procedures for a real-time PCR kit, using reverse-transcribed cDNA as a template, via StepOnePlus. TM The Real-Time PCR system was used to amplify two types of DNA using PCR.

[0026] Furthermore, the PCR amplification system for a single DNA sample is as follows: 0.8 μL of 10 μmol / L upstream primer, 0.8 μL of 10 μmol / L downstream primer, 2 μL of glioma sample cDNA, 0.4 μL of 50× ROX Reference Dye, 10 μL of TBGreen Premix ExTaqII (TliRNase HPlus) (2×), and 6 μL of deionized water.

[0027] Furthermore, the PCR amplification program is as follows: 95℃ for 30s pre-denaturation; followed by 40 cycles: 95℃ for 5s, 60℃ for 30s; the PCR melting curve generation program is as follows: 95℃ for 15s, 60℃ for 60s, 95℃ for 15s.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] This invention provides molecular biomarkers for predicting the prognosis of glioma and their applications. The invention is based on a large-sample transcriptome dataset of glioma patients from three independent databases: Cancer Genome Atlas (TCGA), Chinese glioma Genome Atlas (CGGA)-array, and CGGA-seq. Whole-exome sequencing or pyrosequencing was used to determine IDH status, and further, through various statistical and machine learning methods, three gene biomarker combinations (PBK, SPP1, LTA) were identified as accurately predicting the prognosis of glioma patients, demonstrating significant clinical application value.

[0030] This invention clarifies the relationship between IDH-WT and the clinical characteristics and prognosis of Glioma patients, and confirms that IDH-WT is positively correlated with the expression of immune-related genes.

[0031] This invention demonstrates that three gene biomarkers can independently predict the survival of glioma patients, and these biomarkers significantly improve the accuracy of prognostic assessments for glioma patients at different levels. These three gene biomarkers can serve as reference indicators for judging prognostic survival time and for predicting prognostic survival risk in glioma patients.

[0032] This invention combines large-sample high-throughput sequencing data and quantitative real-time PCR data to demonstrate, through a unique model, that three gene biomarkers are closely related to the clinical prognosis of Glioma patients. By studying the relationship between risk indices and the clinical prognosis of Glioma patients, it provides new biomarkers for predicting the prognosis of Glioma patients, facilitating timely intervention and treatment, and improving clinical outcomes.

[0033] The three gene markers in glioma tissue of the present invention can be used to prepare detection agents and kits for predicting the prognosis of glioma patients.

[0034] In summary, the three-gene biomarkers of this invention have great significance in clinical practice, representing a new generation of tumor biodiagnostic biomarkers and a model for tumor prognostic assessment. Attached Figure Description

[0035] Figure 1 Expression characteristics of immune-related genes in IDH-WT and IDH-MUT patients in a cohort of Glioma patients.

[0036] Figure 2 Screening for genes related to the prognosis of Glioma patients.

[0037] Figure 3 Identification of three significant gene markers.

[0038] Figure 4 Application of three gene biomarkers in overall survival of patients with IDH-WT glioma.

[0039] Figure 5 A graph showing the prognostic value and survival predictive ability of three gene biomarkers for predicting glioma multiforme (GBM) and low-grade glioma (LGG).

[0040] Figure 6 The prognostic value of four different glioma subtypes, including the classic subtype, mesenchymal subtype, neural subtype, and preneural subtype.

[0041] Figure 7 The important role and pathway enrichment analysis of three gene markers in glioma.

[0042] Figure 8 The relationship between three gene markers and immune cell infiltration.

[0043] Figure 9 : Validate the correlation between three gene markers and immune cells using different databases. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0045] Example 1: Determining the expression characteristics of immune-related genes in IDH-WT and IDH-MUT cohorts of Glioma patients.

[0046] TCGA obtained data from 661 glioma patients with available IDH status information, including 232 IDH-WT and 429 IDH-MUT cases. Figure 1 As shown, PCA revealed significant differences in the expression profiles of immune-related genes in IDH-WT and IDH-MUT glioma tissue samples.

[0047] To obtain the combination with the best prognostic potential, GSEA further confirmed that IDH-WT gliomas were closely associated with adaptive immune responses (NES = 2.25, P < 0.001), B cell-mediated immune responses (NES = 2.20, P < 0.001), cell chemotaxis (NES = 1.97, P < 0.001), cytokine secretion (NES = 2.00, P < 0.001), and humoral immune responses (NES = 2.15, P < 0.001). CGGA-array (as shown in C in Figure 1) and CGGA-seq (as shown in Figure 1) were used. Figure 1 Heatmap analysis of immune-related genes in the dataset (shown in D) also showed a positive correlation between IDH-WT and the expression of immune-related genes, consistent with TCGA data. This study used immunohistochemistry to detect the expression of CD8+ T cells in IDH-WT and IDH-MUT glioma patients, finding that the infiltration rate of CD8+ T cells in IDH-WT patients (n=9) was higher than that in IDH-MUT patients (n=14). Figure 1 F (P < 0.01) indicates that IDHMUT inhibits the immune response of gliomas, especially the infiltration of CD8+ T cells, and the distribution of immune-related genes in the IDH-WT and IDH-MUT cohorts. Figure 1 In Figure A, the gene expression profiles of IDH-WT and mutant glioma patients were analyzed using principal component analysis. Figure 1Enrichment analysis confirmed that IDH-WT gliomas were closely associated with adaptive immune response (NES = 2.25, P < 0.001), B cell-mediated immunity (NES = 2.20, P < 0.001), cell chemotaxis (NES = 1.97, P < 0.001), cytokine secretion (NES = 2.00, P < 0.001), and humoral immune response (NES = 2.15, P < 0.001). Figure 1 In the middle, C and D are heatmaps of abundant expressed immune genes in the CGGA array data and CGGA sequence dataset cohorts, respectively. Figure 1 E and F in the image show that immunohistochemistry revealed higher CD8+ T cell levels in IDH-WT patients (n=9) compared to IDH-MUT patients (n=14). The image scale bar is 200 μm.

[0048] like Figure 2 As shown, to investigate the survival status of patients with IDH-WT glioma, univariate Cox regression analysis was used to identify 220 immune-related genes that were significantly associated with survival (P < 0.01). The heatmap illustrates the relationship between genes and their classifications and survival status (e.g., ...). Figure 2 (Part A). Furthermore, analysis was conducted using the KEGG pathway and GeneOntology (GO) methods.

[0049] KEGG pathway analysis results showed that these genes were significantly enriched in specific pathways, such as Figure 2 As shown in Figure B, this includes cytokine-cytokine receptor interactions, T-like receptor signaling pathways, and cell adhesion molecules. Furthermore, graphene oxide analysis revealed enrichment in immune-related biological processes, including immune responses, defense responses, inflammatory responses, and injury responses, indicating their crucial role in the overall immune response, such as... Figure 2 As shown in section C. Subsequently, the LASSOCox regression model was used to select nine genes with the most significant prognostic value: BST1, CD276, CD44, CD99, PDZ-binding kinase (PBK), secreted phosphorylated protein 1 (SP1), TNFRSF1A, CD274, and lipid phosphocholic acid (LTA), as follows... Figure 2 As shown in Figures D and E. To ensure that biomarker genes are more applicable to clinical practice, we performed multivariate Cox regression analysis to further refine the genes, such as... Figure 3 As shown, three significant genes, PBK, SPP1, and LTA, were identified (P < 0.01).

[0050] Finally, using the multivariate Cox regression coefficients of these three genes, a risk model for IDH-WT glioma patients was established, expressed by the three-gene risk model formula: Risk score = (0.2154 × PBK expression) + (0.2382 × SPP1 expression) + (0.2224 × LTA expression).

[0051] Example 2: Application of three gene markers in overall survival of IDH-WT glioma patients and their expression in glioma patients.

[0052] KEGG pathway analysis revealed that these genes were significantly enriched in specific pathways, including cytokine-cytokine receptor interactions, T-like receptor signaling pathways, and cell adhesion molecules. GO analysis showed enrichment in immune-related biological processes, including immune responses, defense responses, inflammatory responses, and injury responses, indicating their crucial role in the overall immune response (e.g., Figure 2 (As shown in Figure C). Subsequently, the LASSOCox regression model was used to select nine genes with the most significant prognostic value: BST1, CD276, CD44, CD99, PDZ-binding kinase (PBK), secreted phosphorylated protein 1 (SPP1), TNFRSF1A, CD274, and lipid phosphocholic acid (LTA) (as shown in Figures 2D and C). Figure 2 (As shown in Figure E). To ensure that biomarker genes are more applicable to clinical practice, we performed multivariate Cox regression analysis, in which... Figure 2 In Figure A, univariate Cox regression analysis was performed on 232 identified immune system-related genes associated with overall survival. Figure 2 B represents KEGG enrichment analysis. Figure 2 C represents the enrichment degree of G0. Figure 2 D represents five cross-validations performed on the selection of tuning parameters in the lasso model. Figure 2 E represents the minimum standard for the lasso coefficient spectrum of nine immune genes, such as... Figure 3 As shown, the genes were further refined, and three significant genes, PBK, SPP1 and LTA, were identified (P < 0.01), providing a theoretical basis and experimental evidence for these four circular RNAs to serve as prognostic indicators for GLIOMA patients.

[0053] Example 3: Determination of the predictive ability of three gene biomarkers for the prognosis of GLIOMA patients

[0054] like Figure 4As shown, in the sequencing dataset, risk scores for GLIOMA patients were calculated based on the expression levels of these three genes in GLIOMA tumor tissue. In the TCGA dataset, 232 IDH-WT glioma patients were selected for calculation. Based on the median risk score, GLIOMA patients were divided into high-risk and low-risk groups. It was found that the mortality rate of patients in the high-risk group was higher than that of patients in the low-risk group (e.g., ...). Figure 4 As shown in Figure A), Kaplan-Meier survival curve analysis indicates that the median survival time in the low-risk group was 648 days, and the median survival time in the high-risk group was 323 days (as shown in Figure A). Figure 4 As shown in B, p < 0.001). CGGA-array (as shown in B). Figure 4 C and Figure 4 (as shown in D) and CGGA-seq (as shown in D) Figure 4 China E and Figure 4 The same results were also verified (as shown in Figure F). Furthermore, meta-analysis using the TCGA, CGGA-array, and CGGA-seq datasets validated the prognostic value of this gene marker.

[0055] In the PCR validation dataset, risk scores for GLIOMA patients were calculated based on the expression levels of these three genes in the cancer tissues of GLIOMA patients. Based on the median risk score, GLIOMA patients were divided into high-risk and low-risk groups. Kaplan-Meier survival curve analysis also indicated the same phenomenon: the overall survival of GLIOMA patients in the high-risk group was significantly lower than that in the low-risk group (e.g., ...). Figure 4 As shown in GH, p < 0.001.

[0056] A combined comparative analysis of GLIOMA patient risk scores with survival time and survival status revealed that GLIOMA patients with low-risk scores had longer survival times and higher survival rates, while GLIOMA patients with high-risk scores had relatively shorter survival times and higher mortality rates. These results suggest that three gene biomarkers can predict the survival time of IDH-WT glioma patients.

[0057] Example 4: Determination of the ability of three gene markers to predict glioblastoma multiforme (GBM) and low-grade glioma (LGG).

[0058] like Figure 5 As shown, in the TCGA dataset, in the GBM cohort, patients with high-risk scores had lower survival rates than patients with low-risk scores (e.g., ...). Figure 5 As shown in Figure A, n = 146, p < 0.001). A similar trend was also observed in the LGG queue of the TCGA dataset (e.g. Figure 5As shown in D, n = 86, p = 0.023). Furthermore, data from the CGGA-array, CGGA-seq queue was used for verification, and similar results were obtained from the CGGA-array LGG queue (e.g.). Figure 5 As shown in Figure E, n=56, p<0.001) and the datasets of the CGGA-seq GBM queue (e.g. Figure 5 As shown in C, n = 102, p = 0.0039), while no statistically significant difference was observed in the GBM CGGA-array cohort (e.g., ...). Figure 5 As shown in Figure B, n = 106, p = 0.3700) and LGG CGGA-seq queue (as shown in Figure B). Figure 5 As shown in Figure F, n = 44, p < 0.0830.

[0059] Therefore, these data suggest that the three gene biomarkers can predict the prognostic value of survival in glioma multiforme (GBM) and low-grade glioma (LGG).

[0060] like Figure 6 As shown, the prognostic value of four different glioma subtypes (classical, mesenchymal, neurogenic, and premembranous gliomas) was analyzed, revealing different risk scores for these subtypes across the TCGA, CGGA-array, and CGGA-seq datasets. The mesenchymal subtype exhibited a higher risk score in the TCGA, CGGA-array, and CGGA-seq cohorts compared to the other three subtypes. Furthermore, ROC analysis was performed to further examine the diagnostic value of this feature. The areas under the curves for the three gene features were 73.8%, 75.3%, and 79.6% in the TCGA, CGGA-array, and CGGA-seq datasets, respectively. This suggests that these three gene features may serve as potential biomarkers for mesenchymal glioma subtypes.

[0061] The relationship between risk score and different glioma grades was investigated in the TCGA, CGGA-array, and CGGA-seq datasets. In the TCGA dataset, high-level expression of risk score was higher than low-level expression, with patients with grade 4 gliomas showing the highest risk score. This trend was also observed in the CGGA-seq and CGGA-array datasets. To further validate these results, ROC analysis was applied to examine the potential diagnostic value of this feature. Figure 7 As shown, the areas under the feature curves of the TCGA, CGGA-array, and CGGA-seq datasets are 90.4%, 80.8%, and 78.9%, respectively.

[0062] like Figure 8As shown, according to the multivariate Cox proportional hazards regression model, after adjusting for age, sex, and grade, this feature was associated with overall survival on the TCGA, CGGA-array, and CGGA-seq datasets. Therefore, these data suggest that these genetic markers could serve as potential biomarkers for identifying patients with mesenchymal and high-grade gliomas.

[0063] Example 5: Important functions and pathway enrichment analysis of three gene features.

[0064] like Figure 7 As shown, Pearson correlation analysis performed in TCGA revealed that 306 genes were significantly associated with the risk score (Pearson|R>0.3, P<0.05). G0 analysis showed that risk score-related genes were highly enriched in immune-related functions, particularly immune responses. Similar results were obtained in the CGGA-array and CGGA-seq datasets. Furthermore, the risk score was correlated with seven gene clusters (metagenes) representing different types of inflammation and immune responses: hematopoietic cell kinase (HCK), lymphocyte-specific protein tyrosine kinase (LCK), MHC-I, MHC-II, signal transduction and transcription activator 1 (STAT1), interferon, and IgG. Except for IgG, which is mainly associated with B lymphocytes, the other six metagenes were positively correlated with the risk score. To validate these findings, seven metagenes were generated using gene set variation analysis based on the corresponding gene clusters.

[0065] Pearson correlation coefficients between risk scores and seven meta-genes were calculated and visualized using "Corr grams" in R. The results confirmed a positive correlation between the risk score and HCK, LCK, MHC-I, MHC-II, STAT1, and interferon, and a negative correlation with IgG. Similar results were observed in the CGGA-array and CGGA-seq datasets. Figure 9 As shown, these results indicate that the three gene biomarkers are associated with the immune response in glioma patients.

[0066] Example 6: Kits and detection methods used for predicting PBK, SPP1, and LTA genes in glioma patients

[0067] 1. Components of the kit:

[0068] (1) RNA extraction kit: Ambion's TRIZOL kit was used;

[0069] (2) Reverse transcription kit: TIANScript II RTdit kit from Tiangen Biotech (Beijing) Co., Ltd. was used;

[0070] (3) Real-time PCR kit: using TaKaRa company Real-time PCR kit.

[0071] 2. Detection method:

[0072] (1) Extraction of total RNA from the sample: Refer to the instructions for the Ambion TRIZOL kit.

[0073] The steps are as follows:

[0074] ① After tumor samples from glioma patients are removed from the body, they are rapidly cooled with liquid nitrogen and stored for later use;

[0075] ②The mortar is cooled with liquid nitrogen, and the tissue sample weighing about 20 milligrams is ground with liquid nitrogen while grinding;

[0076] ③ Add about 1 ml of TRIZOL reagent to the powdered tissue sample, continue grinding, and transfer the powder to a 1.5 ml centrifuge tube;

[0077] ④ After the contents of the centrifuge tube are in a liquid state, use a 1ml pipette to blow up and down 20-30 times, add 0.2ml of chloroform to each tube, cover the centrifuge tube, shake the tube up and down by hand 15-20 times, incubate at room temperature for 5 minutes, and centrifuge at 4℃ for 10 minutes (centrifugal force 12000g).

[0078] ⑤ After centrifugation, aspirate the supernatant aqueous phase and transfer it to a new centrifuge tube. Use 0.5 ml of pre-cooled isopropanol to precipitate the RNA, and centrifuge at 4°C for 10 minutes (centrifugal force 12000g).

[0079] ⑥ Discard the supernatant, wash the RNA with 1 ml of 75% ethanol, and centrifuge at 4°C for 5 minutes (centrifugal force 12000g);

[0080] ⑦ Discard the supernatant, dry in a vacuum dryer for 5 minutes, dissolve the RNA in 50 μL of DEPC water, and after the RNA is completely dissolved, use NanoDropOne to detect the concentration and purity of the RNA. Then, use agarose gel electrophoresis to check the integrity of the extracted RNA.

[0081] (2) Preparation of cDNA from the sample: The preparation was carried out in accordance with the instructions of the TIANScript II RTKit kit from Tiangen Biotech (Beijing) Co., Ltd. The specific steps are as follows:

[0082] cDNA was prepared using the TIANScriptlI cDNA first-strand synthesis kit, and the system is as follows:

[0083] 1 ng to 2 μg Total RNA, 2 μL Random Primer (10 μM), 1 μL Superpured NTPs (10 mM), and RNase-free H2O were added to a final volume of 14.5 μL. The mixture was incubated at 65 °C for 5 min, and then placed on ice for 2 min to obtain the reaction solution.

[0084] Add the following reverse transcription components to the above reaction solution: 14.5 μL of the above reaction solution; 4 μL of 5×TIANScriptIIRTase Buffer, 0.5 μL of Nasin (40 U / μL), and 1 μL of TIANScriptIIRTase (200 U / μL); incubate at 25 °C for 10 min, incubate at 42 °C for 60 min, and terminate the reaction by heating at 85 °C for 5 min. Place the product on ice for subsequent experiments or freeze for storage.

[0085] (3) Amplification of PBK, SPP1, and LTA gene markers: using TaKaRa technology. The real-time PCR kit uses reverse-transcribed cDNA as a template, via StepOnePlus... TM The NASP and ANXA2 genes were amplified by PCR using the t epOnePlusxE system.

[0086] The quantitative real-time PCR system consists of: 0.8 μL of upstream primer (10 μmol / L) and 0.8 μL of downstream primer (10 μmol / L) for a single gene; 2 μL of GBM sample cDNA; 0.4 μL of 50× ROX Reference Dye; 10 μL of TBGreen Premix ExTaqII (TliRNase HPlus) (2×); and 6 μL of deionized water.

[0087] PCR amplification program: 95℃ for 30s pre-denaturation; 40 cycles: 95℃ for 5s, 60℃ for 30s;

[0088] The reaction procedure for generating the PCR dissociation curve is: 95℃ for 15s, 60℃ for 60s, and 95℃ for 15s.

[0089] The primer sequence information for NASP and ANXA2 is shown in Table 1.

[0090] Table 1: Gene sequences of PBK, SPP1, and LTA genes and primer sequence information for the internal reference gene GAPDH.

[0091]

[0092] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. The use of detection reagents for the three genes PBK, SPP1, and LTA in the preparation of a prognostic kit for IDH-WT glioma patients. A risk model for IDH-WT glioma patients was established using multivariate Cox regression analysis coefficients of these three genes. The formula for the three-gene risk model is as follows: Risk score = (0.2154 × PBK expression) + (0.2382 × SPP1 expression) + (0.2224 × LTA expression).

2. The use according to claim 1, characterized in that: The kit contains primers, probes, and standards specifically targeting the biomarkers PBK, SPP1, and LTA. The specific sequences of the primers are as follows: PBK upstream primer: AGGTTTGTCTCATTCTCCTTGGG; PBK downstream primer: AGCAAGACACAGACTGCCAT; SPP1 upstream primer: GCCGAGGTGATAGTGTGGTT; SPP1 downstream primer: CAATCAGAAGGCGCGTTCAG; LTA upstream primer: AGATGCATCTTGCCCACAGC; LTA downstream primer: ACCACCTGGGAGTAGACGAA.

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