CN112011612B - Biomarker and kit for prognosis classification of glioma patient survival prediction - Google Patents
Biomarker and kit for prognosis classification of glioma patient survival prediction Download PDFInfo
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Abstract
The invention relates to a biomarker for prognosis classification of glioma patient survival prediction, wherein the biomarker is seven genes of CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX25, a kit for detecting expression values of the seven genes to predict the glioma patient survival prediction prognosis classification is established, and a forward primer and/or a reverse primer required for synthesizing cDNA are also provided, and the primer sequence is shown as SEQ ID No.1-SEQ ID No. 14. And converting the expression values of the seven genes in the glioma patient sample from the survival prediction into the nuclear transport risk score of the glioma patient, and carrying out prognosis classification on the survival prediction of the glioma patient to be detected according to the nuclear transport risk score of the glioma patient.
Description
Technical Field
The invention belongs to the technical field of biological information and biomedicine, and particularly relates to a biomarker and a kit for prognosis classification of glioma patient survival prediction.
Background
Gliomas are the most common primary malignancies of the central nervous system and are one of the most common types of brain tumors, accounting for approximately 40-60% of intracranial primary tumors. Because of high invasiveness, even if the operation is removed and the radiotherapy and chemotherapy are assisted, the recurrence rate of the tumor is still high, the prognosis of the patient is poor, and the tumor is still the tumor with the worst prognosis in the central nervous system tumor. Although gliomas have a lower incidence in systemic tumors, patients have a very high mortality rate and the study of prognostic-related molecules is more desirable. Currently, WHO grade ii and grade iii gliomas are collectively termed Low Grade Gliomas (LGGs) and suggest that clinical treatment and prognostic evaluation cannot be further guided by solely relying on histological features. Therefore, diagnostic methods combining histological features and molecular biological markers are the focus of the current glioma studies. Classification was done by histology and genomic phenotype. The gene group characteristics of IDH mutation and 1p/19q co-deletion are included, and the characteristics of transcriptome and polyoma are also included, and the characteristics can be used as marker genes for molecular classification. The domestic and foreign researches show that the expression level of some tumor molecular markers, such as PTEN gene, can be used as an independent factor to influence the prognosis survival time of glioma patients, but the number of the molecular markers which can independently influence the prognosis survival time of glioma patients is less, so that more molecular markers influencing the prognosis survival time of glioma patients need to be found. Especially the integration of prognostic classification with classical molecular biomarkers requires further investigation.
Eukaryotic cells are divided by the nuclear envelope into nucleus and cytoplasm. The movement of macromolecules between the nucleus and the cytoplasm, mainly including proteins and RNA, is carried out by the nuclear transport system. The nuclear transport system comprises three main components: nuclear Pore Complex (NPC), small molecule GTP-binding protease in the nucleus (RanGTPase) and Nuclear Transport Receptor (NTR). It has been reported that the nuclear transport system plays an essential role in the development and metastasis of tumors. Nuclear transport can serve as a therapeutic target for several cancer types. Targeting the nuclear transport system may be a promising therapeutic approach. Many genes involved in nuclear transport have been reported to be associated with the prognosis of cancer patients. These results indicate that nuclear transport may be a marker for cancer prognosis. However, single molecules do not represent the activity of the entire system and there is a lack of systematic analysis of nuclear transport and its prognostic value in cancer involving expression profiling.
Disclosure of Invention
In view of this, the present invention aims to provide a biomarker for prognosis classification of glioma patient survival prediction, and also provides an application of the relevant marker, and a kit for prognosis classification of glioma patient survival prediction.
In order to achieve the purpose, the invention provides the following technical scheme:
1. biomarkers for prognosis classification of glioma patient survival prediction, said biomarkers being seven genes of CALR, KPNA2, HDAC3, NDC1, SP100, bcccip and DDX 25.
The application of seven genes, namely CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX25, as biomarkers in preparing prognosis products for survival prediction of glioma patients.
Further, the product comprises reagents for detecting expression values of seven genes, namely CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX 25.
Further, the detection is an RNA-seq detection method.
Furthermore, the detection reagent comprises forward and/or reverse primers which are needed for synthesizing cDNA by taking sample mRNA as a template, and the sequences of the primers are shown as SEQ ID No.1-SEQ ID No. 14.
3. A kit for prognosis classification of glioma patient survival prediction, which comprises reagents for detecting expression values of seven genes of CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX 25.
Furthermore, the detection method of the gene expression value is an RNA-SEQ detection method, the mRNA of a patient sample is used as a template in the detection, a forward primer and/or a reverse primer required by the cDNA are synthesized, and the sequences of the primers are shown as SEQ ID No.1-SEQ ID No. 14.
Further, a cDNA library was constructed using primer sequences SEQ ID No.1 to SEQ ID No.14, seven gene expression values of CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP, and DDX25 of the patient were measured using RNA-SEQ, FPKM values were calculated from the FPKM values, and NTRS ═ for each sample was summed up by multiplying the FPKM expression value of each gene by the corresponding correlation coefficient.
Further, the kit also comprises a terminal repair enzyme mixture, a terminal repair reaction buffer solution, DNA ligase, a ligation reaction buffer solution, a linker containing a molecular label, a PCR (polymerase chain reaction) premix solution, a linker blocking agent, a DNA blocking agent, a hybridization buffer solution, a hybridization enhancer, a magnetic bead washing solution and a hybridization washing solution.
4. Application of primer sequences SEQ ID No.1-SEQ ID No.14 in building a cDNA library of glioma patients based on a second-generation sequencing technology.
The invention has the beneficial effects that: the method comprises the steps of determining 7 marker genes and related coefficients thereof through TCGA (TCGA) data set screening analysis, then carrying out Nuclear Transport Risk Scoring (NTRS) by using FPKM (fragments per kinase Million) expression values and related coefficients of the 7 genes, establishing a prognosis classification system for glioma patient survival prediction based on the nuclear transport risk scoring, providing a detectable kit, accurately reflecting the influence of glioma patient prognosis, classifying patients with high survival risk, and guiding clinical treatment or prognosis more accurately by the result.
Drawings
In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a graph of TCGA data sets analyzed by LASSO regression and 7 gene Nuclear Transport Risk Scores (NTRS) determined. Where a, Gene Set Enrichment Analysis (GSEA) of nuclear transport between LGG and GBM in training and validation data sets. NES normalized enrichment fraction. B,. NTRS. And C, performing cross validation on parameter selection in the proportional risk model by using a TCGA data set. D, Risk coefficient (Coeff) values for the 7 genes selected for LASSO.
FIG. 2 is the relationship between NTRS and clinical pathology. Where a, heatmap display training set shows the distribution and association of NTRS with clinical or genetic features (n 660). B is patient NTRS profile P <0.05 for validation set using WHO stratification, age, IDH status and 1P/19q status; p < 0.01; p < 0.001.
FIG. 3 is a graph of the prognostic significance of NTRS in glioma patients. Where A, cutoff values were determined by ROC analysis. Patients with higher NTRS values (<0.078) were assigned to the NTRS-High group, and patients with lower NTRS values (<0.078) were assigned to the NTRS-Low group. B, Kaplan-Meier survival curves show the overall survival of glioma patients in the NTRS-High and NTRS-Low groups of the training and validation sets. The risk ratio was determined by the Mantel-Haenszel method and the P-value was determined by the two interclass chi-square test. C and D, prognostic effect of NTRS on different grades and subset of indications. E, ROC curves show the sensitivity and specificity of NTRS and other markers in the training and validation sets to predict 2-year survival.
FIG. 4 is a prediction of the prognosis by NTRS in cohorts divided according to WHO ranking, IDH mutation and 1p/19q co-deletion status. (A) Distribution of NTRS height in glioma patients in the subset indicated by WHO grade, IDH mutation, 1p/19q co-deletion status. (B) Survival analysis was performed on (a) glioma patients.
FIG. 5 prognostic analysis of validation set.
FIG. 6 is a prognostic classification system for prediction of glioma patient survival.
Figure 7 is patient NTRS profiles P <0.05 for training set using WHO stratification, age, IDH status and 1P/19q status partitioning; p < 0.01; p < 0.001.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The experimental procedures, in which specific conditions are not specified in the examples, are generally carried out under conventional conditions or under conditions recommended by the manufacturers.
The abbreviation list related by the invention:
NTRS: nuclear transport risk scoring
LASSO: minimum absolute shrinkage and selection operator
NPC: nuclear pore complex
WHO: world health organization
GSEA: gene set enrichment analysis
TCGA: genomic map of cancer
CGGA: chinese glioma genome map
GBMs: glioblastoma multiforme
LGGs: low grade glioma
IDH: isocitrate dehydrogenase
ROC: working curve of subject
AUC: area under curve, area enclosed by ROC curve and coordinate axis
GO: ontology of genes
BP: biological processes
And OS: overall life cycle
And (3) NGS: second Generation sequencing
Example 1
The first data using the TCGA training set included RNA-seq data from LGG and GBM patients (n 660) and clinical data from cbioport (http:// www.cbioportal.org) (comprehensive diagnosis according to world health organization WHO classification (2016)). The expression of the nuclear transport gene set was analyzed by performing analysis using the FPKM value as an expression value. Fig. 1 is a graph of GBM showing a clear nuclear transport phenotype compared to LGG by LASSO regression analysis of the TCGA dataset and determination of 7 gene Nuclear Transport Risk Score (NTRS), as shown in a in fig. 1, and Gene Set Enrichment Analysis (GSEA) based on the TCGA and CGGA datasets also confirmed that the GBM group was enriched in the nuclear transport-associated transcriptional program. To develop a gene signature based on the nuclear transport pathway, glioma samples and nuclear transport-associated genes, which are all nuclear transport-associated genes in GO, were first screened in a training set. The nuclear transport gene set (n 338) was from the molecular signature database v7.0(http:// software. broadinstruction. org/gsea/msigdb). The screening method comprises the following steps:
1. firstly, downloading FPKM value data of RNA-seq of LGG and GBM from TCGA;
2. sample screening: removing normal tissues, part of recurrent tumors, samples without clinical data and survival information;
3. gene screening: firstly, converting an ensemble number of a data gene name into a symbol number of a gene; and taking intersection of the gene in the downloaded data and the nuclear transport gene.
From 660 glioma samples and 336 genes, 251 OS-associated genes (p <0.01) were selected by univariate Cox regression analysis (shown in fig. 1, B). Then 7 marker genes and related coefficients thereof are obtained through LASSO analysis of a minimum absolute contraction selection operator: CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX25, and obtain the Nuclear Transport Risk Score (NTRS) of the training set (shown in C, D in fig. 1). CALR is Calreticulin, Calreticulin; KPNA2 is a nuclear transport protein, Karyopherin alpha 2; HDAC3 is Histone deacetylase 3, Histone deacetylase 3 And NDC1 are transmembrane porin (Nuclear Division Cycle 1Homolog), SP100 (a Protein coding Gene related to diseases such as herpes simplex And primary biliary cirrhosis, Entrez Gene:6672) And BCCIP (BRCA2 And CDKN1A Interacting Protein); DDX25 is an intolerant RNA Helicase (DEAD-Box heliconase 25).
The FPKM (fragments per Kilobase Million) expression values and the correlation coefficients of the 7 genes are used for calculating the Nuclear Transport Risk Score (NTRS), and the calculation method comprises the following steps: NTRS for each sample is the sum of the products of the FPKM expression value for each gene and the corresponding correlation coefficient. The NTRS calculation for each sample is as follows:
TABLE 1 partial sample data of TCGA training set
Example 2
Continuing to analyze the relationship between NTRS and clinical characteristics, the inventors selected 660 patients from the training set and 668 glioma patients with clinical information from CGGA (http:// www.cgga.org.cn/index. jsp) as validation set (the same below). The clinical, genetic and expression profiles of 7 genes of the patients are listed separately, fig. 2 is the relationship of NTRS to clinical pathology, where a is the profile and association of NTRS to clinical or genetic profile shown in the heatmap display training set (n 660). B patient NTRS profiles using WHO stratification, age, IDH status and 1P/19q status division for validation set P < 0.05; p < 0.01; p < 0.001. Figure 7 is patient NTRS profiles P <0.05 for training set using WHO stratification, age, IDH status and 1P/19q status partitioning; p < 0.01; p < 0.001. As analyzed in example 1, NTRS increased with increasing glioma grade (a in fig. 7), and was higher in patients over 50 years of age without IDH mutations or 1p/19q co-deletions (B-D in fig. 7). In addition, the survival time of the NTRS-elevated group was short based on histological subtype classification or molecular markers, e.g., patients with glioblastoma subtype or subtype did not have IDH mutation and 1p/19q co-deletion (E and F in fig. 7). It is fully shown that the provided NTRS is significantly associated with clinical and genetic characteristics of glioma, and can be used as a glioma prognostic marker.
Further demonstrating the effectiveness of NTRS as an independent prognostic marker for glioma, for the study of the prognostic value of NTRS, fig. 3 shows the prognostic significance of NTRS in glioma patients. As shown in FIG. 3A, patients were first divided into NTRS-High groups (NTRS is NTRS-High at 0.078 or more in the figure) by calculating cutoff values by maximizing the Johnson index through ROC analysisHighAlso indicated as such) and the NTRS-Low group (NTRS less than 0.078 is NTRS-Low, in the figure NTRSLowAlso meaning this). Subsequently, the correlations between the NTRS groups and the clinical pathology factors in the TCGA dataset and the CGGA dataset were independently verified, as shown in table 1.
TABLE 1 correlation between NTRS groups and clinical pathology factors in TCGA and CGGA datasets
These data all indicate that the data set in NTRS may be a potential prognostic marker for glioma. To further validate this hypothesis, survival analyses were performed in different cohorts and subgroups. Patients with high NTRS values had lower Overall Survival (OS) than patients with low NTRS values (risk ratio 12.2, 95% confidence interval 9.2-16.1; P <0.001, B in fig. 3, left). The prognostic role of NTRS was also confirmed in the validation cohort (hazard ratio 2.4, 95% confidence interval 2.0-3.0; P <0.001, B in FIG. 3, right). In addition, there was a significant difference in OS between the NTRS-High and NTRS-Low groups in glioma patients of different grades, gender, age, IDH status and 1p/19q co-deletion status (shown in C and D in FIG. 3). Through ROC analysis, the inventors compared NTRS with traditional factors such as age, grade, IDH state, 1p/19q co-deletion state and the like, predicted the sensitivity and specificity of 2-year survival rate, and found that NTRS has a better prediction value (E in FIG. 3). These data indicate that NTRS is a promising prognostic marker for glioma. NTRS was further tested for an independent prognostic biomarker and Cox regression analysis continued using the training set. NTRS, age, histology, grade, IDH mutation, chromosome 1p/19q co-deletion, MGMT promoter methylation, chromosome 9/10 status, ATRX mutation, and chromosome 19/20 status all correlated significantly with overall survival in a single factor analysis (p < 0.001). In the multifactorial analysis, NTRS (risk ratio of 2.9, 95% confidence interval 1.74-4.82), age (risk ratio of 2.39, 95% confidence interval 1.66-3.45), grade (risk ratio of 1.99, 95% confidence interval 1.51-2.62), IDH status (risk ratio of 0.48, 95% confidence interval 0.29-0.80) and chromosome 19/20 status were associated with overall survival, respectively (table 2). Therefore, NTRS was validated as an independent prognostic marker in the CGGA cohort (table 3). Taken together, these data indicate that NTRS can serve as a potent independent prognostic marker for glioma.
TABLE 2 Single and multifactor Cox regression analysis of overall survival-related factors for glioma patients in training cohort
NTRS Group (high or low); gender (male or female); histology (astrocytoma, oligodendroglioma, oligodendroastrocytoma or glioblastoma); grade (II, III or IV); IDH status (wild type or mutant); 1p/19q (deletion and non-deletion); MGMT promoter status (methylated and unmethylated); chromosome 7 amplification and chromosome 10 deletion (yes or no); chromosome 19 and chromosome 20 are both amplified (yes or no); ATRX status (wild type or mutant).
Table 3 validation of single and multifactor Cox regression analysis of overall survival-related factors for glioma patients in cohort
Example 3
The combination of NTRS with IDH mutation and 1p/19q co-deletion is a potential prognostic classification marker
Further examining the value of NTRS in glioma classification, the distribution of WHO subtypes, IDH mutation and 1p/19q co-deletion status in the NTRS group were also analyzed. FIG. 4 is a prediction of the prognosis by NTRS in cohorts divided according to WHO ranking, IDH mutation and 1p/19q co-deletion status. Among the gliomas with IDH mutations and 1p/19q co-deletions, all gliomas diagnosed as WHO grade II (100%, 92/92) had lower NTRS, while only 4% had lower NTRS diagnosed as WHO grade III (3/74). IDH mutation and no 1p/19q co-deletion, NTRS high-value rate was increased by WHO grade (7%, II grade 9/129; 22%, 25/112 grade III; 66%, 4/6 grade IV). Of the gliomas without IDH mutations, 56% WHO grade II gliomas (10/18), 94% WHO grade III gliomas (68/72) and 100% WHO grade IV gliomas (143/143) exhibited higher NTRS (a in fig. 4). Subsequently, survival analyses of the different subgroups were performed. The NTRS-High group had a shorter survival in WHO grade III glioma patients with IDH mutation and 1p/19q co-deletion (B in FIG. 4). These results indicate that NTRS may be more effective when used in combination with other glioma prognostic markers. To test this hypothesis, the prognostic value of the IDH mutation and 1p/19q co-deleted subgroups was first analyzed. In the IDH mutant group without 1p/19q co-deletion and the IDH mutant-free group, the Overall Survival (OS) of the patients with high NTRS was reduced (A in FIG. 5). Combining all experimental data, with data of IDH mutation, 1p/19q co-deletion and NTRS, a prognostic classification system for glioma patient survival prediction was established (FIG. 6). The classification method used by the prognosis classification system for glioma patient survival prediction specifically comprises the following steps:
1. classifying patients into IDH wild type (IDHwt) and IDH mutant type (IDHmut) according to IDH mutation status of glioma patients;
2. in IDH mutant, 1p/19q co-deletion (1p/19q deletion) and 1p/19q Non-co-deletion (1p/19q Non-deletion) are classified according to the deletion state of 1p/19 q;
3. calculating NTRS of other patients except the patients with 1p/19q deletion in IDH mutation, wherein NTRS is less than 0.078 and is NTRS Low; NTRS is greater than or equal to 0.078 and is NTRS High;
4. and (3) prognosis classification: 1p/19q co-deletion in IDH mutation patients has good prognosis;
1p/19q non-co-deletion in IDH mutation and NTRS is Low, and the prognosis is better;
the 1p/19q non-co-deletion in IDH mutation and NTRS is High with general prognosis;
IDH is wild type, NTRS is Low prognosis general;
IDH is wild type, NTRS is High, and prognosis is poor.
After statistics of data of the training set and the verification set, the survival rate of the patient with good prognosis is about 79% in 2 years or more, the survival rate of the patient with good prognosis is about 68% in 2 years or more, the survival rate of the patient with general prognosis is about 52% in 2 years or more, and the survival rate of the patient with poor prognosis is about 27% in 2 years or more.
The data from the validation set is used for validation using the established classification method and these results are further validated in the validation set as shown in fig. 5B.
Example 4
In order to better provide a prognostic classification system for the survival prediction of glioma patients, the invention also implements a construction method and reagents for detecting cDNA libraries required by RNA-seq of patients.
RNA extraction:
centrifuging a reserved tissue sample, removing Trizol reagent, combining into 1 tube, adding 600 mu l Trizol reagent, adding 16 steel balls baked at 180 ℃ for 4 hours and having the diameter of 0.2cm, then putting the mixture into a high-throughput tissue grinder, grinding for 6min (taking out the sample every 2min, putting the sample into a refrigerator at-20 ℃ for freezing and cooling), adding 400 mu l Trizol reagent, and standing for 5min at room temperature;
② centrifuging for 20min at 12000 Xg at 4 ℃;
③ transferring the supernatant into a new 1.5ml centrifuge tube, adding 200 mul of chloroform, violently shaking and mixing evenly, and standing for 5min at room temperature;
fourthly, centrifuging the mixture for 20min at 4 ℃ at 12000 Xg;
fifthly, transferring the supernatant into a new 1.5ml centrifuge tube, adding isopropanol with the same volume, slightly reversing and uniformly mixing, and standing for 10 minutes at room temperature;
sixthly, centrifuging for 20min at 4 ℃ at 12000 Xg;
seventhly, removing the supernatant, adding 1ml of precooled 75% ethanol solution, and centrifuging for 5min at 4 ℃ at 12000 Xg;
repeating washing for 1 time;
ninthly, abandoning the supernatant, drying at room temperature, adding a proper amount of RNase-free water for dissolution, and immediately using for total RNA quality detection or placing in a refrigerator at minus 80 ℃ for storage for standby.
And (3) detecting the total RNA quality:
quantifying by using a Nanodrop spectrophotometer (ND-1000) and evaluating the purity of total RNA;
secondly, the total RNA is detected by 1.0 percent agarose gel electrophoresis;
thirdly, detecting the integrity of the total RNA by using Agilent2100, and if the RIN value obtained by detection is more than or equal to 7, the sample is qualified.
construction of cDNA library:
first strand cDNA Synthesis:
enriching mRNA of a sample by using magnetic beads with oligo (dT);
adding fragmentation buffer to break each sample mRNA into short segments;
synthesis of second strand cDNA: first strand cDNA was synthesized using CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX25 gene primers designed as shown in Table 4, using each sample mRNA as a template.
Second strand cDNA Synthesis: adding buffer solution, dNTPs and DNA polymerase I into the first strand cDNA solution to synthesize second strand cDNA.
Purifying: double-stranded cDNA was purified using AMPure XP beads.
Selecting: and (3) carrying out end repair on the double-stranded cDNA, adding an A tail, connecting a sequencing joint, and carrying out fragment size selection by using AMPure XP beads.
Sixthly, enrichment: PCR was enriched to obtain the final cDNA library.
Detection of cDNA library:
firstly, preliminarily quantifying a cDNA library by using the Qubit2.0, and diluting the cDNA library to 1 ng/. mu.L;
detecting the length of an insert (insert size) of the cDNA library by using Agilent 2100;
thirdly, the effective concentration of the cDNA library is accurately quantified by using a Q-PCR method, the effective concentration of the cDNA library is more than 2nM, and the construction of the cDNA library is successful.
And fourthly, after the library is qualified, sequencing different sample libraries according to the effective concentration and the requirement of the target off-machine data volume.
And finally, obtaining the FPKM expression value of each gene by a computer.
TABLE 4 primer sequence Listing for construction of cDNA library of 7 genes
The gene data of 14 samples are shown in table 5, and then the clinical information of table 6 is combined, and the prognosis classification system for glioma patient survival prediction is used for making corresponding classification for later clinical reference.
TABLE 5
TABLE 6
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
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<212> DNA
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<213> Artificial Sequence (Artificial Sequence)
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<212> DNA
<213> Artificial Sequence (Artificial Sequence)
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Claims (10)
1. Biomarker for the prognostic classification of survival prediction in glioma patients, characterized in that said biomarker is a combination of seven genes CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX 25.
The application of the combination of seven genes, namely CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX25, as a biomarker in the preparation of a prognosis product for the survival prediction of glioma patients.
3. The use of claim 2, wherein the product comprises reagents for detecting the expression values of seven genes CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX 25.
4. The use of claim 3, wherein the assay is an RNA-seq assay.
5. The use of claim 4, wherein the detection reagent comprises forward and/or reverse primers for cDNA synthesis using sample mRNA as a template, and the primer sequences are shown in SEQ ID No.1-SEQ ID No. 14.
6. A kit for prognosis classification of glioma patient survival prediction, which comprises reagents for detecting expression values of seven genes, namely CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX 25.
7. The kit for prognosis classification of glioma patient survival prediction according to claim 6, wherein the detection method of gene expression value is RNA-SEQ detection method, mRNA of patient sample is used as template in the detection, the kit comprises forward and/or reverse primers required for cDNA synthesis, and the primer sequences are shown as SEQ ID No.1-SEQ ID No. 14.
8. The kit for prognosis classification of glioma patients with survival prediction according to claim 7, wherein the primer sequences SEQ ID No.1-SEQ ID No.14 are used to create a cDNA library, RNA-SEQ is used to detect the expression values FPKM values of seven genes of CALR, KPNA2, HDAC3, NDC1, SP100, BCCIP and DDX25 of the patients, the nuclear transport risk score is calculated from the FPKM values, the nuclear transport risk score of each sample = the product of the FPKM expression value of each gene and the corresponding correlation coefficient, each gene corresponds to the correlation coefficient: the CALR correlation coefficient was 0.000105, the KPNA2 correlation coefficient was 0.01038, the HDAC3 correlation coefficient was 0.027311, the NDC1 correlation coefficient was 0.032613, the SP100 correlation coefficient was 0.112187, the BCCIP correlation coefficient was-0.07376, and the DDX25 correlation coefficient was-0.00588.
9. The kit for prognostic classification of glioma patient survival prediction according to claim 7, wherein the kit further comprises a mixture of end-repair enzymes, end-repair reaction buffer, DNA ligase, ligation buffer, linker containing molecular tag, PCR pre-mix, linker blocker, DNA blocker, hybridization buffer, hybridization enhancer, magnetic bead wash, hybridization wash.
10. The use of primer sequences SEQ ID No.1-SEQ ID No.14 for the construction of a cDNA library for secondary sequencing of glioma patients.
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| CN109913420A (en) * | 2019-03-07 | 2019-06-21 | 北京师范大学 | Application of the CDC20 co-expression gene network as Treatment for Glioma target spot |
| CN109913549A (en) * | 2019-03-07 | 2019-06-21 | 北京师范大学 | Glioma molecule parting and application based on CDC20 gene co-expressing network |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109913420A (en) * | 2019-03-07 | 2019-06-21 | 北京师范大学 | Application of the CDC20 co-expression gene network as Treatment for Glioma target spot |
| CN109913549A (en) * | 2019-03-07 | 2019-06-21 | 北京师范大学 | Glioma molecule parting and application based on CDC20 gene co-expressing network |
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