Applications, products, expression interference reagents and prognostic model construction methods of CKAP4 protein

Abstract

The present invention relates to the application, product, expression interference reagent and prognosis model construction method of CKAP4 protein, so as to solve the technical problems of lack of effective means to monitor the treatment, deterioration and development of glioblastoma and prognosis. The application of CKAP4 protein is as a glioblastoma-specific molecular marker. The product includes a reagent for detecting the expression level of CKAP4. The application of the expression interference reagent of CKAP4 in the preparation of a pharmaceutical composition for inhibiting the deterioration of glioblastoma. The prognosis model construction method includes: 1. Obtaining mRNA expression data; 2. Integrating mRNA sequencing data; 3. Obtaining factors related to CKAP4 expression level; 4. Obtaining adverse factors for the prognosis of glioblastoma; 5. Obtaining independent risk factors for the prognosis of glioblastoma; 6. Constructing a prediction nomogram of the survival probability of glioblastoma patients to obtain a prognosis model.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine, and in particular to a detection reagent, a product, an expression interference reagent, an expression level detection method and a prognosis model construction method of CKAP4. Background Art

[0002] Glioblastoma (GBM) is the most common malignant tumor of the central nervous system and the most malignant grade IV glioma (World Health Organization, 2007). The important biological characteristics of GBM are invasive growth, easy recurrence and cell heterogeneity within the tumor tissue. In particular, high-grade glioblastoma deteriorates rapidly and may recur 8-9 months after initial treatment. However, there is still a lack of effective means for the treatment, prognosis and monitoring of the deterioration of glioblastoma.

[0003] CKAP4 (cytoskeleton-associated protein 4), also known as p63 and CLIP-63, is a type II transmembrane protein located in the endoplasmic reticulum (ER) membrane and plays an important regulatory role in the occurrence and development of cancer. Summary of the invention

[0004] The present invention aims to solve the technical problem of lack of effective means to monitor the treatment, deterioration and development of glioblastoma and the prognosis, and provides an application, product, expression interference reagent and prognosis model construction method of CKAP4 protein.

[0005] The technical solution of the present invention is:

[0006] The present invention provides an application of CKAP4 protein, which is special in that the CKAP4 protein is used as a glioblastoma-specific molecular marker.

[0007] Furthermore, the CKAP4 protein serves as a molecular target for monitoring the development process of glioblastoma.

[0008] Furthermore, glioblastoma is diagnosed by detecting the expression level of CKAP4.

[0009] Furthermore, the CKAP4 expression level detection method includes label-free quantitative analysis based on mass spectrometry and immunoblotting.

[0010] The present invention also provides a product for diagnosing glioblastoma, which is special in that the product includes a reagent for detecting the expression level of CKAP4. Specifically, the product can be a kit or chip for diagnosing glioblastoma.

[0011] The present invention also provides the use of a CKAP4 expression interference agent in preparing a pharmaceutical composition for inhibiting the deterioration of glioblastoma.

[0012] Furthermore, the CKAP4 expression interference reagent is siRNA-CKAP4, and its sequence is: GCAGGAUUUGAAAGCCUUATT, sense; UAAGGCUUUCAAAUCCUGCTT, antisense.

[0013] The present invention also provides a method for constructing a glioblastoma prognosis model based on CKAP4, which is special in that it comprises the following steps:

[0014] S1. Obtain glioma samples from multiple glioblastoma patients, collect mRNA expression profiles of the glioma samples to obtain mRNA expression data, and match them with clinical data corresponding to each glioma sample;

[0015] S2. ComBat function is used to eliminate batch effects between different batches of glioma samples and integrate the mRNA sequencing data of glioma samples;

[0016] S3. According to the statistical differences between high or low age, gender, high or low glioma grade, treatment methods radiotherapy and / or chemotherapy, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, and MGMT promoter methylation or promoter unmethylation and CKAP4 expression levels, high or low age, high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, and MGMT promoter methylation or promoter unmethylation were obtained as factors related to CKAP4 expression levels;

[0017] S4. Analyze the statistical differences between high or low age, high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, MGMT promoter methylation or promoter unmethylation, and CKAP4 expression level and the prognosis of glioblastoma, and obtain that high or low age, high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, MGMT promoter methylation or promoter unmethylation, and CKAP4 expression level are adverse factors for the prognosis of glioblastoma;

[0018] S5. Multivariate regression analysis was performed on the adverse prognostic factors of glioblastoma, and it was found that glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, and CKAP4 expression level were independent risk factors for the prognosis of glioblastoma;

[0019] S6. Based on the independent risk factors for glioblastoma prognosis, a predictive nomogram for the survival probability of glioblastoma patients was constructed to obtain a glioblastoma prognostic model.

[0020] Furthermore, in step S5, the multivariate regression analysis is to perform a multivariate analysis on the prognosis of glioblastoma patients using a Cox proportional hazards model to determine independent risk factors.

[0021] Furthermore, in step S6, a predictive nomogram for the survival probability of glioblastoma patients is constructed based on the independent risk factors for the prognosis of glioblastoma. Specifically, based on the independent risk factors filtered from the multivariate regression analysis, an array graph model is constructed using rms in Rstudio to quantify the relationship between multiple independent risk factors and the survival probability.

[0022] Beneficial effects of the present invention:

[0023] 1. The present invention verifies that CKAP4 is highly expressed in glioblastoma through a large number of experiments, and transfects the glioblastoma cells in the experiment by designing CKAP4 expression interfering agent siRNA and CKAP4 overexpression vector, respectively knocking down and overexpressing the expression level of CKAP4 in glioblastoma cells, and then verifies that inhibiting the expression of CKAP4 in glioblastoma cells through CCK8 test, wound healing test and transwell test can inhibit the proliferation, migration and invasion of glioblastoma cells. This shows that CKAP4 protein can be used as a glioblastoma-specific molecular marker, and shows the feasibility of using CKAP4 detection reagents in the preparation of glioblastoma diagnostic products, and shows that CKAP4 expression interfering reagents have application prospects in inhibiting the deterioration and development of glioblastoma.

[0024] 2. The method for constructing a glioblastoma prognosis model provided by the present invention can evaluate the patient's prognosis based on the screened independent risk factors and prediction nomogram, and provide a reference for prognostic treatment.

[0025] 3. The present invention not only identifies CKAP4 protein as a biomarker or a promising therapeutic target in the future, but also establishes a nomogram for predicting the survival risk and probability of glioma patients, which can lay a certain theoretical foundation for understanding the complex biological process of GBM and provide more risk predictions for the treatment and prognosis of glioma. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 Schematic diagram of the quantitative proteomic analysis workflow of GBM and adjacent tissues in an embodiment of the present invention;

[0027] Figure 2It is a volcano plot of quantitative differentially expressed proteins in GBM of the present invention;

[0028] Figure 3 This is a protein-protein interaction network diagram of 99 upregulated proteins in GBM according to an embodiment of the present invention;

[0029] Figure 4 A schematic diagram of screening Hub genes from a PPI network diagram in an embodiment of the present invention;

[0030] Figure 5 The KEGG pathway relationship diagram of the 10 Hub genes in the embodiment of the present invention;

[0031] Figure 6 This is a graph comparing the mRNA expression levels of the Hub gene in the GEPIA database of an embodiment of the present invention;

[0032] Figure 7 This is a graph comparing the mRNA expression levels of Hub genes in the UALCAN database of an embodiment of the present invention;

[0033] Figure 8 This is a graph comparing the Hub gene protein expression levels in the HPA database of an embodiment of the present invention;

[0034] Fig. 9 It is a Kaplan-Meier curve analysis diagram of the CGGA database CKAP4 in an embodiment of the present invention;

[0035] Fig.10 It is a Kaplan-Meier curve analysis diagram of the CGGA database CANX in an embodiment of the present invention;

[0036] Fig.11 This is a result diagram of immunoblotting to verify CKAP4 protein expression in an embodiment of the present invention;

[0037] Fig.12 This is a result diagram of the main signaling pathways involved in CKAP4 in the embodiments of the present invention;

[0038] Fig.13 This is a statistical result diagram of the transfection efficiency of siRNA-CKAP4 and pcDNA3.1-CKAP4 detected by qPCR in the embodiment of the present invention;

[0039] Fig.14 This is a schematic diagram of the results of immunoblotting to detect the transfection efficiency of siRNA-CKAP4 and pcDNA3.1-CKAP4 in the embodiments of the present invention;

[0040] Fig.15 The CCK8 test of T98G cells in the embodiment of the present invention detects the cell proliferation rate;

[0041] Fig.16 The CCK8 test of A172 cells in the embodiment of the present invention detects the cell proliferation rate;

[0042] Fig.17 This is a schematic diagram of the results of detecting cell migration rate in a wound healing assay of T98G cells in an embodiment of the present invention;

[0043] Fig.18 This is a schematic diagram of the statistical results of the cell migration rate detected by the wound healing assay of T98G cells in an embodiment of the present invention;

[0044] Fig.19 This is a schematic diagram of the results of detecting cell migration rate in a wound healing assay of A172 cells in an embodiment of the present invention;

[0045] Fig. 20 This is a schematic diagram of the statistical results of the cell migration rate detected by the wound healing assay of A172 cells in an embodiment of the present invention;

[0046] Fig.21 This is a schematic diagram of the results of detecting the migration ability of T98G cells and A172 cells by transwell assay in an embodiment of the present invention;

[0047] Fig. 22 This is a schematic diagram of the results of detecting the invasive ability of T98G cells and A172 cells by transwell assay in an embodiment of the present invention;

[0048] Fig.23 This is a schematic diagram of the statistical results of the transwell assay for detecting the migration ability of T98G cells and A172 cells in an embodiment of the present invention;

[0049] Fig.24 This is a schematic diagram of the statistical results of the invasive ability of T98G cells and A172 cells detected by the transwell assay in an embodiment of the present invention;

[0050] Fig.25 The correlation analysis between CKAP4 expression level and clinical pathological characteristics in the embodiment of the present invention;

[0051] Fig.26 The relationship between patient age, grade, IDH1, X1p19q and MGMT promoter methylation and prognosis in the embodiments of the present invention;

[0052] Fig. 27 This is a multivariate regression analysis result diagram in an embodiment of the present invention;

[0053] Fig.28 It is a prognostic model array diagram in an embodiment of the present invention;

[0054] Fig.29This is a graph showing the accuracy of the ROC curve evaluation model in an embodiment of the present invention;

[0055] Fig.30 This is a result diagram of the prediction ability of the calibration curve evaluation model in an embodiment of the present invention;

[0056] Fig.31 This is a graph showing the actual value of the area under the ROC curve (AUC) in the internal verification of the embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0058] This example verifies the high expression and function of CKAP4 in glioblastoma through the following experimental steps, and also verifies through a large number of experiments that when the expression of CKAP4 in glioblastoma cells is inhibited, the proliferation, migration and invasion of glioblastoma cells can be inhibited, and vice versa; at the same time, by retrospectively analyzing 442 glioma specimens in the Chinese Glioma Genome Atlas Project CGGA (Chinese Glioma Genome Atlas), the prognostic model developed based on multiple independent risk factors of multivariate regression analysis has good predictive ability and accuracy.

[0059] 1. Experimental Subjects

[0060] The experimental subjects of this example were 18 patients with pathological diagnosis of primary glioblastoma multiforme, and the samples of cancerous tissue and adjacent tissue were obtained. The tissue samples were provided by Tangdu Hospital, the Second Affiliated Hospital of Air Force Medical University. This study was approved by the Ethics Committee of the Second Affiliated Hospital of the Fourth Military Medical University, and all patients signed informed consent.

[0061] 2. Screening of differentially expressed proteins and Hub genes based on label-free quantitative proteomics analysis

[0062] Cancerous tissue samples and paracancerous tissue samples were collected from 6 experimental subjects. Proteins were extracted from the samples and enzymatically hydrolyzed into peptides after reduction and alkylation. The peptides were desalted and entered the mass spectrometer. PD software was used for data analysis. Protein data analysis was filtered with a 1% FDR threshold. The filtering criteria were: 1. High protein FDR confidence; 2. SequestHT score greater than zero; 3. Unique peptides greater than zero. The proteins screened according to the above criteria were quantitative proteins, totaling 5737. The quantitative protein acquisition process can be found in Figure 1 .

[0063] like Figure 2As shown, region Ⅲ represents 99 up-regulated proteins screened based on log2(fold change) ≥ 1 and p-value < 0.05, region Ⅰ represents 466 down-regulated proteins screened based on log2(fold change) ≤ -1 and p-value < 0.05, and region Ⅱ represents unchanged proteins with p-value < 0.05 and -1 < log2(fold change) < 1; region Ⅳ represents proteins with no significant difference (p > 0.05). By comparison, it is found that the vast majority of proteins among the quantifiable proteins are proteins with no differential expression, and only a small number of proteins are differentially expressed.

[0064] As Figure 3 shown, the depth of the color of the circle represents the level of protein expression, and from light to dark, the protein expression level gradually increases. The size of the circle represents the number of protein connection lines, and the thickness of the line connecting two circles represents the credibility of protein interaction. It can be intuitively found from the figure that CKAP4 is up-regulated and interacts with more proteins.

[0065] As Figure 4 shown, the top 10 genes ranked by the MCC method according to the score level are: P4HB, PDIA6, DNAJC3, LGALS1, CKAP4, F5, FGG, FGA, CALR, and CANX. Specifically, as shown in Table 1.

[0066] As Figure 5 shown, among the KEGG pathway relationships of the 10 Hub genes, for the pathway containing CKAP4, the difference in protein processing in the endoplasmic reticulum is relatively significant (p = 4.45×10 -7 ).

[0067] III. Database alignment verification of the expression profiles of the screened Hub genes and survival correlation analysis.

[0068] To verify the expression profiles of the screened Hub genes, the databases GEPIA, UALCAN, and the Human Protein Atlas (HPA) were used to observe whether there is consistency in the gene expression levels, and CGGA was used to determine whether the genes are related to prognosis.

[0069] As Figure 6 shown, for the comparison results in the GEPIA database, the critical value is |log2(fold change)| = 1 (p < 0.01), T represents the gene expression in glioma, and N represents the gene expression in normal controls; by comparison, it is found that the mRNA levels of seven potential key genes (P4HB, PDIA6, DNAJC3, LGALS1, CKAP4, CALR, and CANX) are higher in glioma than in normal controls.

[0070] As Figure 7As shown in the figure, the comparison results of UALCAN database, where T is glioma tissue and N is normal tissue. By comparison, it was found that except for the expression level of DNAJC3 (p>0.05), the mRNA levels of the other six Hub genes (P4HB, PDIA6, LGALS1, CKAP4, CALR and CANX) were higher in glioblastoma (P<0.001).

[0071] like Figure 8 As shown, the protein expression levels of Hub genes were compared in the HPA database, and the numbers above the bar graph indicate the percentage of samples with medium and high expression. The results showed that four genes, PDIA6, LGALS1, CKAP4 and CANX, were highly expressed in more than 50% of glioma samples.

[0072] To explore whether the above four genes have clinical relevance, the effect of gene expression on the overall survival of GBM patients was analyzed using data from the CGGA database. Fig. 9 and Fig.10 As shown, based on the Kaplan-Meier curve analysis of the CGGA database, the expression level of CANX was not related to the survival probability of the patients (p>0.05), while the expression level of CKAP4 was inversely proportional to the survival time of the patients, that is, patients with high expression of CKAP4 had a shorter survival time.

[0073] 4. Verification of CKAP4 expression level at tissue level

[0074] In order to confirm the reliability of the data obtained from the proteomic analysis, the expression of CKAP4 in cancerous tissue samples and paracancerous tissue samples obtained from another 12 experimental subjects was detected by Western blot.

[0075] Immunoblotting: Total proteins in the tissue were extracted using RIPA buffer containing 1% PMSF and 1% inhibitor. The supernatant was collected after low-temperature centrifugation, and the protein concentration was determined using a BCA protein assay kit. Equal amounts of total protein were separated on a 10% SDS-PAGE gradient gel and then transferred to a PVDF membrane. The membrane was blocked in 5% skim milk for 1 hour at room temperature and incubated with the primary antibody, shaking at 4°C overnight. Subsequently, the membrane washed with phosphate-buffered saline (PBS) or PBST was incubated with the secondary antibody at room temperature for 1 hour. Images were captured on an Image System (Tanon-4600), and band intensity was calculated using Image J software. Protein expression was detected in eight random samples and four groups of paired tissue samples, and the results are shown in the figure. Fig.11As shown, N: normal tissue, T: glioblastoma, β-actin: protein expression correction standard, each number represents a different group. Pair represents paired samples from the same patient's adjacent tumor and glioblastoma. By comparison, it was found that CKAP4 had a higher gray value and higher protein expression in tumor samples, and the proteomic data was credible.

[0076] like Fig.12 As shown, CKAP4 is associated with post-translational protein phosphorylation and the regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs).

[0077] 5. Effect of CKAP4 expression level on the proliferation, migration and invasion of glioblastoma cells.

[0078] 1. Glioblastoma cells were transfected with siRNA-CKAP4 and pcDNA3.1-CKAP4.

[0079] siRNA-CKAP4 is an expression interference reagent for CKAP4, and pcDNA3.1-CKAP4 is a plasmid for overexpression of CKAP4. The siRNA-CKAP4 and pcDNA3.1-CKAP4 are synthesized by GenePharma.

[0080] GBM cell line A172 (purchased from Procell, catalog number CL-0012) was cultured in high glucose DMEM medium containing 10% fetal bovine serum (FBS). At the same time, T98G cells (purchased from Procell, catalog number CL-0583) were grown in MEM medium containing 10% FBS. Both cell lines have been authenticated using STR analysis. All cells were cultured at 37°C, 5% CO 2 cultured in an incubator.

[0081] The cells were plated at 2 × 10 5 The cells were seeded at a density of 100 μg / mL in a six-well plate. In transient expression experiments, when the cell density was close to 80% (the area of ​​the entire well was 100% as the standard), control siRNA (any siRNA unrelated to siRNA-CKAP4), siRNA-CKAP4, pcDNA3.1 or pcDNA3.1-CKAP4 were transfected into A172 and T98G glioblastoma cells using Opti-MEM and Lipofectamine 2000 (purchased from Invitrogen). pcDNA3.1 was a control empty vector for pcDNA3.1-CKAP4.

[0082] 2. The transfection efficiency of siRNA-CKAP4 and pcDNA3.1-CKAP4 was detected by qPCR and Western blot.

[0083] Real-time quantitative PCR assay: According to the manufacturer's instructions, total RNA was extracted from cells using Trizol reagent (purchased from Invitrogen), and about 1 μg RNA was reverse transcribed using TransScript All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (using kit AT341, TRANS). TM Green qPCRSuperMix (using kit AQ601, TRANS) was used to detect the mRNA level of CKAP4, and GAPDH was used as an internal control for normalization. The sequences of the primers are as follows: CKAP4, upstream primer: ACAGACCTCAGCCAAGTCCAGAG; downstream primer: ACCTCGGTGTAGATGTCAGACTCC. GAPDH, upstream primer: GTCTCCTCTGACTTCAACAGCG, downstream primer: ACCACCCTGTTGCTGTAGCCAA. 2 -ΔΔCt Methods All mRNA expression levels were calculated. qPCR and Western blot results are shown in Fig.13 and 14 As shown, in T98G and A172 cells, the mRNA and protein levels of CKAP4 were significantly decreased after transfection with siRNA-CKAP4, while the mRNA and protein levels of CKAP4 were significantly increased after transfection with pcDNA3.1-CKAP4.

[0084] 3. Use transfected glioblastoma cells to perform CCK8 assay, wound healing assay, and transwell assay to detect cell proliferation, migration, and invasion abilities.

[0085] CCK8 assay: After glioblastoma cells T98G and A172 were transfected with control siRNA (any siRNA unrelated to siRNA-CKAP4) or siRNA-CKAP4 and pcDNA3.1 or pcDNA3.1-CKAP4, the cells were plated at 1×10 4 The cells were seeded at different densities in 96-well plates for 24, 48, and 72 hours. After adding 10 μL CCK8 to each well, the absorbance was measured at 490 nm. Fig.15 and Fig.16As shown in the figure, after CKAP4 expression was reduced, the proliferation rate of T98G and A172 cells decreased; after CKAP4 overexpression, the proliferation rate of cells was enhanced. CKAP4 can enhance the proliferation ability of cells. As a receptor for Dickkopf1 (DKK1) ligand, CKAP4 binds to DKK1 and activates potential downstream molecules of AKT and PI3K-AKT pathways to promote cell proliferation.

[0086] Wound healing assay: After transfection of glioblastoma cells T98G and A172 with control siRNA (any siRNA unrelated to siRNA-CKAP4) or siRNA-CKAP4 and pcDNA3.1 or pcDNA3.1-CKAP4, respectively, the cells were seeded in six-well plates. Subsequently, the monolayer of cells was wound in a cross shape with a 200 μL micropipette tip and washed with PBS. Then, medium without FBS was added to the cells. Images of the scratch area were captured at 0, 24, and 48 hours using a microscope (Nikon) and analyzed by Image J software. See Figures 17 to 20 , Expression analysis of CKAP4 in 0, 24 and 48 h wound healing migration assay (amplification: 40×). After transfection of cells with siRNA-CKAP4, a slower wound healing area was presented, which was reversed by overexpressed CKAP4. The effect of altered CKAP4 levels on migration and invasion assessed by 24 or 48 h transwell assay; the relative ratio of migrating cells and invasive cells per field is also shown (amplification: 40×). When CKAP4 expression was attenuated, a lower number of cells migrating through the extracellular matrix and a reduction in cells were observed, which was reversed by CKAP4 overexpression. The experiment was performed three times and all data are shown as mean ± SD. *P < 0.05, **P < 0.001.

[0087] Transwell assay: This experimental kit was purchased from Corning Costa, USA. The matrix gel was pre-coated on the upper chamber, and the bottom surface of the upper chamber had a permeable membrane with a micropore size of 8 μm. After transfection of glioblastoma cells T98G and A172 with control siRNA (any segment of siRNA unrelated to siRNA-CKAP4) or siRNA-CKAP4 and pcDNA3.1 or pcDNA3.1-CKAP4, respectively, the cells contained in the culture medium without FBS were inoculated into the upper chamber, while the lower chamber was filled with culture medium with 10% FBS. After 48 hours, images of five random fields of view were captured using a microscope and calculated by Image J. The results are shown in Figure 21 to Figure 24All data are shown as mean ± SD, *p < 0.05. By comparison, it was found that the number of T98G and A172 cells that passed through the permeable membrane on the bottom of the upper chamber of the chamber decreased when the CKAP4 expression level decreased; CKAP4 overexpression can lead to an increase in the number of cells that passed through the permeable membrane on the bottom of the upper chamber of the chamber; CKAP4 can enhance the migration and invasion ability of glioblastoma cells.

[0088] The above experiments show that the expression level of CKAP4 is positively regulated with the proliferation, migration and invasion ability of glioblastoma cells, that is, knocking down the expression of CKAP4 can inhibit the proliferation, migration and invasion of glioblastoma cells.

[0089] VI. Establishment and validation of a prognostic model for glioma patients

[0090] The Chinese Glioma Genome Atlas (CGGA) was used to obtain mRNA expression profiles and matching clinical data of 1018 glioma patients and 20 non-glioma patients. The ComBat function of the sva package was used to eliminate the batch effect of the two batches of glioma samples. At the same time, insufficient or erroneous pathological data were eliminated, and finally 442 patients with pathological diagnosis of glioma were included in the study.

[0091] 1. Correlation analysis between CKAP4 elevation and clinical pathological characteristics

[0092] Using the medical records of 442 glioma patients, we analyzed the effects of clinical parameters such as patient age, gender, glioma grade, treatment, glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, and MGMT promoter methylation or promoter unmethylation on CKAP4 expression levels. Fig.25 As shown in the results, high or low patient age, high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, and MGMT promoter methylation or promoter unmethylation all affect the expression level of CKAP4, while patient gender and treatment methods have no effect on the expression level of CKAP4. Factors such as advanced age, high grade, IDH1 wild type, X1p19q non-co-deletion, and MGMT promoter unmethylation are all unfavorable factors that induce upregulation of CKAP4 expression.

[0093] 2. CKAP4 overexpression is an independent risk factor affecting the prognosis of glioma patients

[0094] According to the median CKAP4 expression, 442 glioma patients were divided into low expression group and high expression group. The above clinical factors affecting CKAP4 expression, such as high or low age, high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion and MGMT promoter methylation or promoter unmethylation, as well as the relationship between CKAP4 expression level and survival rate or prognosis were analyzed, as shown in Table 1. Fig.26 As shown in the results, high or low age, high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, MGMT promoter methylation or promoter unmethylation, and high or low CKAP4 expression level can affect the overall survival of glioma patients. Old age, high grade, X1p19q non-co-deletion, MGMT promoter unmethylation, and IDH1 wild type are all adverse factors affecting the overall survival of patients. At the same time, glioma patients with high levels of CKAP4 have a shorter overall survival. The above six variables were included in the multivariate regression analysis, and the results are shown in Table 2. Fig. 27 As shown in the results, the results of multivariate analysis showed that high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, and high or low CKAP4 expression levels were independent risk factors affecting the survival of glioma patients.

[0095] 3. Prognostic model establishment and validation The four independent risk factors obtained by the multivariate regression analysis were high or low glioma grade, IDH1 mutant or wild type, X1p19q co-deletion or non-co-deletion, and high or low CKAP4 expression level. The samples were randomly divided into a training set (n = 310) and a validation set (n = 132). The R packages rms and survival were used to construct a predictive nomogram for the survival probability of glioblastoma patients based on the four independent risk factors to predict the 1-year, 3-year, and 5-year survival probabilities of glioma patients. Fig.28 As shown in Figure 1, each risk factor under different conditions corresponds to a specific point, and the sum of the four elements represents the total number of points that can be used to predict the survival rate of each glioma patient. The c index is 0.783, indicating that the nomogram has excellent predictive ability. Fig.29 As shown in Figure 1, the receiver operating characteristic curve (ROC) was used to evaluate the accuracy of the model; the area under the ROC curve (AUC) for 1-year survival was 0.795, the AUC for 3-year survival was 0.896, and the AUC for 5-year survival was 0.888, indicating good accuracy. In addition, the calibration curve and ROC curve were used to detect whether the theoretical value was close to the actual value, as shown in Figure 1. Fig.30 As shown in Figure 2, the calibration curve shows that the theoretical value is close to the actual value. Fig.31As shown in the figure, the actual AUC value of 0.779 for 1-year survival was close to the theoretical value of 0.795; the actual AUC value of 0.885 for 3-year survival was close to the theoretical value of 0.896; the actual AUC value of 0.868 for 5-year survival was close to the theoretical value of 0.888; the calibration curve of internal validation showed that the theoretical value was close to the actual value.

[0096] 4. Data Analysis

[0097] In this embodiment, the quantitative data between the groups were compared using a t-test. The survival rate was assessed using the Kaplan-Meier method and compared by the log-rank test. SPSS 19.0 software was used for statistical analysis. The prognosis of patients with glioma was analyzed multivariately using the Cox proportional hazard model to determine independent risk factors. Based on the independent risk factors filtered from the multivariate logistic regression model, an array graph model was constructed using "rms" in R studio to quantify the relationship between CKAP4 levels and clinical parameters and survival probability (1 year, 3 years and 5 years). The performance of the nomogram was evaluated by the area under the calibration curve and the ROC curve AUC, and then verified. P<0.05 was considered to be significantly different between each group, *P<0.05, **P<0.01, ***P<0.001.

[0098] This embodiment retrospectively analyzed the mRNA sequencing data of 442 gliomas in the Chinese Glioma Genome Atlas (CGGA), and unfavorable variables such as advanced age, high grade, IDH1 wild type, X1p19q non-common deletion and MGMT promoter unmethylation contributed to the increase in CKAP4 expression levels. In addition, individuals with high expression of CKAP4 and other unfavorable factors have a lower overall survival. IDH1 mutations are associated with prolonged overall survival as the first event of glioma formation and have important predictive value for the prognosis of high-grade gliomas. The combined deletion of X1p19q is a diagnostic molecular marker for oligodendrogliomas. Among glioma types, tumors tend to proliferate at a slower rate and are sensitive to chemotherapy. MGMT promoter methylation fails to demethylate O6 methylated guanine formed under alkylating agents and leads to DNA damage repair dysfunction, which is associated with overall survival. In combination with the alkylating agent of temozolomide, patients with MGMT promoter methylation have better long-term prognosis. Subsequently, multivariate analysis showed that CKAP4 overexpression, IDH1 wild-type, X1p19q non-common deletion, and MGMT promoter unmethylation were all independent risk factors associated with poor prognosis in glioma.

[0099] Based on independent risk factors, a nomogram was established to predict the risk and survival probability of glioma patients. The favorable AUC value and calibration curve showed that the prediction model had excellent discrimination ability and general clinical applicability in terms of survival probability, and the integration of quantitative and visual prediction scoring system could provide some guidance or reference for clinicians to make more objective treatment plans.

[0100] Table 1 Analysis of CKAP4 expression and clinical parameters

[0101]

[0102] Table 2 Univariate analysis of potential risk factors for glioma

[0103]

Claims

1. Use of a reagent interfering with the expression of CKAP4 protein in the preparation of a pharmaceutical composition for inhibiting the deterioration of glioblastoma; the reagent interfering with the expression of CKAP4 is siRNA-CKAP4, and its sequence is: GCAGGAUUUGAAAGCCUUATT,sense; UAAGGCUUUCAAAUCCUGCTT,antisense.

2. Application of CKAP4 expression level detection reagent in the preparation of glioblastoma prognosis assessment reagent.

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