Application of reagent for detecting RELL2 gene expression quantity in preparation of glioblastoma prognosis preparation

Abstract

The invention discloses application of a reagent for detecting RELL2 gene expression quantity in preparation of a glioblastoma prognosis preparation, and relates to the field of tumor molecular biology. Researches find that by detecting the transcription expression level of the RELL2 gene in a glioblastoma patient, the prognosis condition of the glioblastoma can be detected in a high-sensitivity and high-specificity mode, prognosis layering is independent of tumor pathological staging, the RELL2 gene can be applied to primary and recurrent glioblastoma patients, and the RELL2 gene can be used for detecting the prognosis condition of the glioblastoma in a high-sensitivity and high-specificity mode. A powerful molecular biology basis is provided for prognosis detection of glioblastoma, important guiding significance is provided for precise medical treatment of patients, and the kit has extremely high clinical application value.

Description

Technical Field

[0001] The present invention relates to the field of tumor molecular biology, and specifically to the application of a reagent for detecting the expression level of the RELL2 gene in the preparation of a prognostic agent for glioblastoma. Background Art

[0002] Glioblastoma accounts for a relatively high proportion among brain tumors. Its onset is often relatively concealed, and patients may present with various symptoms such as headache, vomiting, visual impairment, and limb weakness. Due to the rapid growth and infiltrative growth of the tumor, it often invades the surrounding normal brain tissue, resulting in severe impairment of the patient's neurological function, greatly affecting the patient's quality of life and survival time.

[0003] Currently, the standard treatment regimens for glioblastoma include surgical resection, radiotherapy, and chemotherapy (such as temozolomide). However, even with comprehensive treatment, the prognosis of patients remains poor. The recurrence rate of the tumor is extremely high, and it has strong resistance to treatment. It is difficult to completely resect the tumor surgically because the tumor boundary is unclear and difficult to distinguish from normal brain tissue. Although radiotherapy and chemotherapy can control tumor growth to a certain extent, they also bring serious side effects, and tumor cells are prone to developing drug resistance.

[0004] Prognosis research can provide important reference for doctors to help them make more informed decisions when formulating treatment plans. For example, for patients with poor prognosis, more aggressive treatment methods can be considered, such as combined treatment or participating in clinical trials; for patients with good prognosis, on the premise of ensuring the treatment effect, the side effects of treatment can be minimized as much as possible. Summary of the Invention

[0005] The purpose of the present invention is to provide the application of a reagent for detecting the expression level of the RELL2 gene in the preparation of a prognostic agent for glioblastoma to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solution: the application of a reagent for detecting the expression level of the RELL2 gene in the preparation of a prognostic agent for glioblastoma, wherein the reagent for detecting the expression level of the RELL2 gene is a reagent for detecting gene transcriptional expression level, and the prognosis of glioblastoma is judged by detecting the transcriptional expression level of the RELL2 gene.

[0007] The human RELL2 gene is located on chromosome 5q31.3, with GeneID (gene accession number) being 285613 in NCBI (National Center for Biotechnology Information, USA), and the complete transcript number in the GeneBank database being NM_173828.5. Its specific sequence is as follows:

[0008] RELL2 (NM_173828.5) gene sequence:

[0009] Atgtcggaaccacagcctgacctggaaccgccccaacatgggctatatatgctcttcctgcttgtgctggtcttcttcctcatgggcctggtaggcttcatgatctgccacgtgctcaagaagaagggctaccgctgccgcacgtcgaggggctctgagcctgacgatgcccagcttcagccccctgaggacgatgacatgaatgaggacacagtagagaggattgttcgctgcatcatccagaatgaagccaatgctgaggccttgaaggagatgctgggggacagtgaaggagaagggacagtgcagctgtccagtgtggatgccacctccagcctgcaggacggagccccctcccatcatcacacagtgcacctgggctctgcagccccttgcctccattgcagccgcagcaagaggcctccacttgtccgtcagggacgctccaaggaaggaaaaagccgcccccggacaggggagaccactgtgttctctgtgggcaggttccgggtgacacacattgagaagcgctatggactgcacgaacaccgtgatggctcccccacagacaggagctggggctctggtgggggacaggacccagggggtggtcaggggtctgggggagggcagcccaaggcagggatgcctgccatggagaggctgccccctgagaggccacagccccaggtcctagccagccccccagtacagaatggaggactcagggacagcagcctaacccctcgtgcacttgaagggaaccccagagcttctgcagagccaacactgagggccggagggaggggcccaagcccagggctgcccactcaagaggcaaatgggcagccaagcaaaccagacacttctgatcaccaggtgtctctaccacagggagcagggagtatgtga.

[0010] Further, the test sample targeted by the detection reagent is selected from primary and recurrent glioblastoma tissues and / or primary and recurrent glioblastoma cells.

[0011] Further, the detection reagent includes a preparation for detecting the transcriptional expression level of gene RELL2 in a sample by RT-PCR (reverse transcription polymerase chain reaction reagent), gene expression profiling chip, or RNA sequencing.

[0012] Further, taking RNA sequencing as an example, the sample is measured for gene expression by next-generation sequencing, and the TPM value (transcripts per million reads) of the gene marker expression is obtained after normalization.

[0013] Further, in the next-generation sequencing of the present invention, multiple transcripts of a gene can be targeted simultaneously, and the gene expression is obtained by adding up the sequencing values of all transcripts. The primers provided by the present invention can simultaneously amplify the mRNA (messenger ribonucleic acid) transcribed from these multiple transcripts.

[0014] Further, taking the median of TPM_RELL2 as the standard, each sample is grouped, and the prognostic risk level is determined according to each cohort; when TPM_RELL2 ≤ 1.2, the sample is classified into the low-risk group, and when TPM_RELL2 > 1.2, the sample is classified into the high-risk group.

[0015] Further, taking RT-PCR as an example, the preparation includes specific primers for real-time fluorescence quantitative PCR (polymerase chain reaction) of the RELL2 gene:

[0016] Forward primer (5’→3’): GCTCAAGAAGAAGGGCTACCG;

[0017] Reverse primer (5’→3’): TGGATGATGCAGCGAACAAT.

[0018] Further, taking the gene expression profiling chip as an example, the detection reagent includes a gene probe that specifically recognizes the RELL2 gene.

[0019] The present invention also provides a glioblastoma prognosis preparation, which is the above-mentioned detection reagent.

[0020] The present invention establishes a gene marker model by the combined application of gene detection markers and data mining algorithms, and uses the single-gene expression level stratification to evaluate the prognostic level of primary glioblastoma, specifically including the following steps:

[0021] (1) Collect clinical samples of glioblastoma in situ, perform next-generation sequencing on the samples, and construct a glioblastoma gene expression profile dataset;

[0022] (2) Normalize the sequencing data to calculate the TPM value of the RELL2 gene in the sample, TPM_RELL2. The prognostic level of this glioblastoma multiforme in situ sample will be predicted based on the calculated TPM_RELL2 level. When TPM ≤ 1.2, the sample will be classified into the low-risk group; when TPM > 1.2, the sample will be classified into the high-risk group.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0024] The present invention predicts the prognosis of glioblastoma patients by detecting the expression level of the gene marker RELL2 gene, helps clinicians with medication guidance, realizes precision medicine, and improves the survival rate of glioblastoma patients. It has been verified that the kit of the present invention can accurately predict the prognosis of glioblastoma, has a wide application range, high accuracy, a short experimental period, and has important clinical significance for the precise treatment of patients. Description of the Drawings

[0025] Figure 1 It is a schematic diagram showing the difference in RELL2 expression between 33 types of tumor tissues and corresponding normal tissues;

[0026] In the figure, ACC is adrenocortical carcinoma; BLCA is bladder tumor; BRCA is breast-ovarian primary cancer; CESC is cervical squamous cell carcinoma; CHOL is cholangiocarcinoma; COAD is colorectal cancer; DLBC is diffuse large B-cell lymphoma; ESCA is esophageal cancer; GBM is glioblastoma multiforme; HNSC is head and neck squamous cell carcinoma; KICH is soft tissue tumor; KIRC is metastatic renal clear cell carcinoma; KIRP is renal papillary cell carcinoma; LAML is acute myeloid leukemia-like tumor; LGG is multiple myeloma; LIHC is hepatocellular carcinoma; LUAD is lung adenocarcinoma; LUSC is non-small cell lung cancer; MESO is malignant mesothelioma; OV is oncolytic virus; PAAD is pancreatic cancer; PCPG is pheochromocytoma and paraganglioma; PRAD is prostate cancer; READ is rectal adenocarcinoma; SARC is pulmonary sarcomatoid carcinoma; SKCM is cutaneous melanoma; STAD is gastric adenocarcinoma; TGCT is giant cell tumor of tendon sheath; THCA is papillary thyroid carcinoma; THYM is thymoma; UCEC is endometrial cancer; UCS is uterine carcinosarcoma; UVM is uveal melanoma.

[0027] Figure 2 It is a schematic diagram of the ROC curve (Receiver Operating Characteristic Curve) of the RELL2 gene involved in the present invention in the TCGA-GBM dataset with overall survival as the evaluation index;

[0028] Figure 3Schematic diagram of the Kaplan-Meier survival curve of the RELL2 gene involved in the present invention in the TCGA-GBM dataset with overall survival as the evaluation index;

[0029] Figure 4 Schematic diagram of the ROC curve of the RELL2 gene involved in the present invention in glioblastoma of the CGGA_325 dataset with overall survival as the evaluation index;

[0030] Figure 5 Schematic diagram of the Kaplan-Meier survival curve of the RELL2 gene involved in the present invention in glioblastoma of the CGGA_325 dataset with overall survival as the evaluation index;

[0031] Figure 6 Schematic diagram of the ROC curve of the RELL2 gene involved in the present invention in glioblastoma of the CGGA_693 dataset with overall survival as the evaluation index;

[0032] Figure 7 Schematic diagram of the Kaplan-Meier survival curve of the RELL2 gene involved in the present invention in glioblastoma of the CGGA_693 dataset with overall survival as the evaluation index;

[0033] Figure 8 Schematic diagram of the ROC curve of the RELL2 gene involved in the present invention in glioblastoma of the GSE108474 dataset with overall survival as the evaluation index;

[0034] Figure 9 Schematic diagram of the Kaplan-Meier survival curve of the RELL2 gene involved in the present invention in glioblastoma of the GSE108474 dataset with overall survival as the evaluation index;

[0035] Figure 10 Schematic diagram of the ROC curve of the RELL2 gene involved in the present invention in glioblastoma of the GSE16011 dataset with overall survival as the evaluation index;

[0036] Figure 11 Schematic diagram of the Kaplan-Meier survival curve of the RELL2 gene involved in the present invention in glioblastoma of the GSE16011 dataset with overall survival as the evaluation index;

[0037] Figure 12 Schematic diagram of the ROC curve of the RELL2 gene involved in the present invention in primary glioblastoma of the GLASS dataset with overall survival as the evaluation index;

[0038] Figure 13Schematic diagram of the Kaplan-Meier survival curve of the RELL2 gene involved in the present invention in primary glioblastoma in the GLASS dataset, with overall survival as the evaluation index;

[0039] Figure 14 Schematic diagram of the ROC curve of the RELL2 gene involved in the present invention in recurrent glioblastoma in the GLASS dataset, with overall survival as the evaluation index;

[0040] Figure 15 Schematic diagram of the Kaplan-Meier survival curve of the RELL2 gene involved in the present invention in recurrent glioblastoma in the GLASS dataset, with overall survival as the evaluation index.

[0041] In the figure, the ordinate in the ROC curve is sensitivity, and the abscissa is specificity; in the Kaplan-Meier survival curve, the ordinate is survival rate, and the abscissa is survival time.

[0042] Figure 16 Schematic diagram of the differential comparison of the transcriptional expression of the RELL2 gene involved in the present invention in primary and recurrent glioblastoma in the GLASS dataset.

[0043] In the figure, initial represents primary glioblastoma, and recurrence represents recurrent glioblastoma.

[0044] Figure 17 Physical map for evaluating the overexpression level of the RELL2 gene involved in the present invention in glioblastoma cell line (U87) after overexpression by using real-time fluorescence quantitative PCR technology;

[0045] Figure 18 Statistical chart for evaluating the overexpression level of the RELL2 gene involved in the present invention in glioblastoma cell line (U87) after overexpression by using real-time fluorescence quantitative PCR technology;

[0046] Figure 19 Schematic diagram for evaluating the cell proliferation of the RELL2 gene involved in the present invention in glioblastoma cell line U87 after overexpression by using CCK8 technology.

[0047] In the figure, the ordinate represents the optical density measurement value at a wavelength of 450 nm, and the abscissa is time. Detailed implementation mode

[0048] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0049] In the following specific embodiments,

[0050] The "training set" is a set of sample data of all glioblastoma patients from the TCGA-GBM (glioblastoma multiforme dataset), including relevant information such as clinical diagnosis, tumor grade, survival data, etc.;

[0051] The "test set" is a set of samples of all glioblastoma patients from the CGGA (Chinese Glioma Genome Atlas Project), GEO (Gene Expression Omnibus) database, and GLASS (Glioma Longitudinal Analysis Consortium) cohort, with the same data types as those included in the training set. The validation set in the present invention is a set of data independent of the training set, mainly used to evaluate the effect of the model during the training process and determine the performance of biomarkers for auxiliary diagnosis.

[0052] Example 1: Evaluate the expression specificity of RELL2, and collect, process, and construct a model for the training set samples;

[0053] Step 1: Evaluate the expression specificity of RELL2

[0054] Based on the GEPIA database (an interactive analysis platform for gene expression level values) (http: / / gepia.cancer-pku.cn / ), compare and visualize the expression levels of the RELL2 gene in the transcriptome data of 33 types of tumor tissues and their corresponding normal tissues (such as Figure 1 ), in the corresponding normal tissue transcriptome data, it can be found that the RELL2 gene has a specific high-expression state in the normal tissues corresponding to LGG and GBM, and there are significant differences in the expression levels between the corresponding tumor tissues and normal tissues;

[0055] Step 2: Collect, process, and construct a model for the training set samples

[0056] (1) Download the transcriptome expression profile data and corresponding clinical feature data of glioblastoma in the TCGA-GBM dataset from the GDC data repository (https: / / portal.gdc.cancer.gov / ); (2) Annotate the gene names in the transcriptome expression profile file according to the sequencing platform; (3) Merge the mRNA expression values with the same gene names; (4) Perform preprocessing of normalization and standardization on the gene expression of each sample; (5) Divide the samples into high-risk and low-risk groups according to the median of gene expression values; (6) Perform ROC curve plotting and Kaplan-Meier survival analysis on the high- and low-risk groups; According to the linear fitting between the expression value of the RELL2 gene and the overall survival (OS), when TPM_RELL2 ≤ 1.2, the sample is classified into the low-risk group, and when TPM_RELL2 > 1.2, the sample is classified into the high-risk group;

[0057] In the training set, with the overall survival as the evaluation index, the predicted AUC of this model for the 1-year survival was 56.43%; the predicted AUC for the 2-year survival was 53.67%; the predicted AUC for the 3-year survival was 57.83%, as Figure 2 ; In the training set, with the overall survival as the evaluation index, the Kaplan-Meier survival curve showed that the overall survival of the high-risk group in the training set cohort was significantly lower than that of the low-risk group (P < 0.056), as Figure 3 .

[0058] Example 2: Validate the prognostic risk model based on an external dataset;

[0059] (1) Extract the glioblastoma transcriptome data of the CGGA_325, CGGA_693, GSE108474, GSE16011, and GLASS datasets; Annotate the gene names in the transcriptome expression profile file according to the sequencing platform; (3) Merge the mRNA expression values with the same gene names; (4) Perform preprocessing of normalization and standardization on the gene expression of each sample; (5) Divide the samples into high-risk and low-risk groups according to the median of gene expression values; (6) Perform ROC curve plotting and Kaplan-Meier survival analysis on the high- and low-risk groups; According to the linear fitting between the expression value of the RELL2 gene and the overall survival (OS), when TPM_RELL2 ≤ 1.2, the sample is classified into the low-risk group, and when TPM_RELL2 > 1.2, the sample is classified into the high-risk group;

[0060] In the validation set, with the overall survival as the evaluation index, in the CGGA_325 dataset, the predicted AUC of this model for the 1-year survival was 54.78%; the predicted AUC for the 2-year survival was 55.47%; the predicted AUC for the 3-year survival was 74.86%, asFigure 4 ; In the validation set, with overall survival as the evaluation index, in the CGGA_325 dataset, the Kaplan-Meier survival curve shows that the overall survival of the high-risk group in the training set cohort is significantly lower than that of the low-risk group (P < 0.056), as Figure 5 ;

[0061] In the validation set, with overall survival as the evaluation index, in the CGGA_693 dataset, the predicted AUC of this model for 1-year survival is 55.41%; the predicted AUC for 2-year survival is 54.95%; the predicted AUC for 3-year survival is 62.93%, as Figure 6 ; In the validation set, with overall survival as the evaluation index, in the CGGA_693 dataset, the Kaplan-Meier survival curve shows that the overall survival of the high-risk group in the training set cohort is significantly lower than that of the low-risk group (P < 0.056), as Figure 7 ;

[0062] In the validation set, with overall survival as the evaluation index, in the GSE108474 dataset, the predicted AUC of this model for 1-year survival is 54.74%; the predicted AUC for 2-year survival is 64.93%; the predicted AUC for 3-year survival is 64.93%, as Figure 8 ; In the validation set, with overall survival as the evaluation index, in the GSE108474 dataset, the Kaplan-Meier survival curve shows that there is no significant difference in overall survival between the high-risk group and the low-risk group in the training set cohort (P = 0.056), but there is a trend that the high-risk group has a worse prognosis, as Figure 9 ;

[0063] In the validation set, with overall survival as the evaluation index, in the GSE16011 dataset, the predicted AUC of this model for 1-year survival is 54.23%; the predicted AUC for 2-year survival is 55.76%; the predicted AUC for 3-year survival is 52.47%, as Figure 10 ; In the validation set, with overall survival as the evaluation index, in the GSE16011 dataset, the Kaplan-Meier survival curve shows that the overall survival of the high-risk group in the training set cohort is significantly lower than that of the low-risk group (P < 0.056), as Figure 11 ;

[0064] In the validation set, according to the different grades of primary glioblastoma and recurrent glioblastoma, in the GLASS dataset, the box plot of the differential comparison of the transcriptional expression of the RELL2 gene shows that the RELL2 gene expression level in each group of recurrent glioblastoma in the validation set cohort is significantly higher than that of the RELL2 gene in primary glioblastoma; as Figure 16;

[0065] In the validation set, with the overall survival as the evaluation index, in the primary glioblastoma of the GLASS dataset, the predicted AUC of this model for 1-year survival was 56.23%; the predicted AUC for 2-year survival was 57.28%; the predicted AUC for 3-year survival was 54.65%, as Figure 12 ; In the validation set, with the overall survival as the evaluation index, in the primary glioblastoma of the GLASS dataset, the Kaplan-Meier survival curve showed that the overall survival of the high-risk group in the training set cohort was significantly lower than that of the low-risk group (P < 0.056), as Figure 13 ;

[0066] In the validation set, with the overall survival as the evaluation index, in the recurrent glioblastoma of the GLASS dataset, the predicted AUC of this model for 1-year survival was 59.53%; the predicted AUC for 2-year survival was 50.60%; the predicted AUC for 3-year survival was 52.66%, as Figure 14 ; In the validation set, with the overall survival as the evaluation index, in the recurrent glioblastoma of the GLASS dataset, the Kaplan-Meier survival curve showed that the overall survival of the high-risk group in the training set cohort was significantly lower than that of the low-risk group (P < 0.056), as Figure 15 .

[0067] Example 3: Validate the function of the RELL2 gene based on glioblastoma cell lines;

[0068] (1) Culture the glioblastoma cell line U87 (OE-NC); (2) Using the lentiviral plasmid pLV-CMV-MCS-EF1-ZsGreen1-T2A-Puro as the backbone vector, insert the target gene RELL2 (NM_173828.5) to construct the RELL2 (NM_173828.5) overexpression lentiviral vector RELL2 (NM_173828.5)-pLV-CMV-MCS-EF1-ZsGreen1-T2A-Puro; (3) Transfect the glioblastoma cell line U87 with the constructed lentiviral vector to obtain the U87 cell line with stable overexpression of RELL2 (OE-RELL2); (4) Quantitatively detect the relative mRNA expression level of the RELL2 gene by RT-qPCR (real-time quantitative reverse transcription polymerase chain reaction) technology, and select GAPDH (glyceraldehyde-3-phosphate dehydrogenase) as the internal reference; (5) Evaluate the cell proliferation by CCK8 (Cell Counting Kit-8) technology; (6) Measure the OD values of the OE-NC and OE-RELL2 groups at 450 nm at 24 h, 48 h, and 72 h respectively, and plot the change relationship between the OD values of the two groups and time;

[0069] According to the absorbance values of the OE-NC group and the OE-RELL2 group at 24 h, 48 h, and 72 h, statistical tests were performed. As Figure 17 , 18 , as shown in Figure 19, the OD value of the OE-RELL2 group at 450 nm was significantly higher than that of the OE-NC group at 72 h (P < 0.01).

[0070] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claims involved.

Claims

1. A reagent for detecting the expression level of the RELL2 gene, characterized in that: A detection reagent for detecting the transcriptional expression level of gene RELL2 in a sample by RT-PCR, gene expression profiling chip or RNA sequencing.

2. The reagent for detecting the expression level of the RELL2 gene according to claim 1, wherein: The detection reagent is selected from: specific primers for real-time fluorescence quantitative PCR of RELL2 gene; or a probe specifically recognizing RELL2; or reagents required for next-generation sequencing technology.

3. The reagent for detecting the expression level of the RELL2 gene according to claim 2, characterized in that: The specific primers for real-time fluorescence quantitative PCR of RELL2 gene are as follows: Forward primer (5’→3’): GCTCAAGAAGAAGGGCTACCG; Reverse primer (5’→3’): TGGATGATGCAGCGAACAAT.

4. The reagent for detecting the expression level of the RELL2 gene according to claim 1, wherein: The sample to be detected by the detection reagent is primary glioblastoma tissue and / or primary glioblastoma cells.

5. The reagent for detecting the expression level of the RELL2 gene according to claim 4, characterized in that: Measure the gene expression level of the sample by next-generation sequencing, perform normalization to obtain the TPM value of gene expression level, group each sample according to the median of TPM_RELL2, and determine the prognosis risk level according to each cohort.

6. The reagent for detecting the expression level of the RELL2 gene according to claim 5, characterized in that: When TPM_RELL2 ≤ 1.2, the sample is classified into the low-risk group; when TPM_RELL2 > 1.2, the sample is classified into the high-risk group.

7. Use of a reagent for detecting the expression level of the RELL2 gene, characterized in that: Use of the detection reagent according to any one of claims 1-6 in the preparation of a glioblastoma prognosis preparation.

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