CN113481298A - Application of immune related gene in kit and system for predicting diffuse glioma prognosis - Google Patents

Application of immune related gene in kit and system for predicting diffuse glioma prognosis Download PDF

Info

Publication number
CN113481298A
CN113481298A CN202110681350.9A CN202110681350A CN113481298A CN 113481298 A CN113481298 A CN 113481298A CN 202110681350 A CN202110681350 A CN 202110681350A CN 113481298 A CN113481298 A CN 113481298A
Authority
CN
China
Prior art keywords
prognosis
glioma
ppp4c
nrg3
vim
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110681350.9A
Other languages
Chinese (zh)
Inventor
杜军
张金阔
马丹军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Zhongke Qingzi Medical Technology Co ltd
Original Assignee
Guangdong Zhongke Qingzi Medical Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Zhongke Qingzi Medical Technology Co ltd filed Critical Guangdong Zhongke Qingzi Medical Technology Co ltd
Priority to CN202110681350.9A priority Critical patent/CN113481298A/en
Publication of CN113481298A publication Critical patent/CN113481298A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Abstract

The invention provides application of immune related genes in a kit and a system for prognosis of a patient with diffuse glioma; the system can be used for well predicting the diffuse glioma prognosis; the invention also carries out single-factor and multi-factor Cox proportional risk regression analysis, and proves that the immune risk score can be used as an independent prognostic factor of the diffuse glioma.

Description

Application of immune related gene in kit and system for predicting diffuse glioma prognosis
Technical Field
The invention belongs to the field of biomedicine, and particularly relates to application of immune related genes in a kit and a system for predicting diffuse glioma prognosis.
Background
Gliomas are the most common primary tumors of the brain and central nervous system, accounting for about 30% of all brain and central nervous system tumors, and accounting for more than 80% of all malignant primary brain tumors. The annual incidence is 3.55 cases per 10 million people. According to the classification of central nervous system tumors by the World Health Organization (WHO) in 2007, diffuse gliomas are classified into grade II and grade III low-grade gliomas (LGG) and grade IV Glioblastoma (GBM) based on histological features. GBM is the most lethal tumor of all grades. Despite major advances in therapy, including chemotherapy, radiation therapy and surgical resection, the median overall survival of GBM is only 15 months. At present, the histological grade of glioma is the main clinical prognostic index of glioma, however, the treatment response and prognosis of patients in the same grade are greatly different, so that the molecular typing of glioma is an inevitable requirement for individualized treatment of glioma.
The gene molecular marker is a model constructed by a statistical and machine learning method based on the expression of a group of genes and used for clinical prediction. Currently, common methods for gene expression detection include high-throughput RNA-seq technology, chip technology, and relatively low-throughput real-time quantitative polymerase chain reaction (RT-qPCR). Although there are many methods for detecting gene expression, how to find a group of gene combinations for prognosis prediction of diffuse glioma, and how to have good prediction performance, there is no relevant research.
The immune system has been shown to be a key factor in tumor development. Tumor-infiltrating immune cells are an important component of the tumor microenvironment and play a crucial role in tumor progression, metastasis and immune escape. In recent years, immune checkpoint proteins such as cytotoxic T lymphocyte antigen 4(CTLA-4) or programmed cell death ligand 1/protein 1(PD-L1/PD-1) have been used as key targets for cancer immunotherapy. However, large-scale studies have not been conducted on the prognosis of diffuse glioma using immune-related genes.
The main disadvantages of the prior art are: there was no effect of organically binding immune-related genes on diffuse gliomas and no large-scale validation was performed.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides the application of immune related genes in a kit and a system for diffuse glioma prognosis, and the risk of a patient with diffuse glioma can be accurately predicted by detecting the expression levels of 5 immune related genes screened by the invention and substituting the expression levels into an immune characteristic gene model.
In order to realize the purpose, the technical scheme is as follows: use of the combination of genes CDC42, PPP4C, NRG3, VIM and HDAC1 for the preparation of a kit for a patient with diffuse glioma or a kit for predicting the prognosis of a patient with diffuse glioma.
The present invention provides a method of assessing the prognosis of a patient with glioma, said method comprising: extracting tumor tissues of glioma patients, detecting the expression levels of CDC42, PPP4C, NRG3, VIM and HDAC1 in the tumor tissues, carrying out linear transformation on the expression levels of the 5 genes and variable coefficients in a corresponding immune prognosis model, calculating risk scores, and dividing the glioma patients into different risk groups according to the magnitude of the risk score values: if the risk score is greater than or equal to 4.084, the group is a high risk group, and the prognosis is poor; if the risk score is less than 4.084, the group is a low risk group and the prognosis is better.
In addition, the present invention also provides use of an agent for detecting relative expression levels of immune genes for detecting expression levels of genes CDC42, PPP4C, NRG3, VIM and HDAC1 in the preparation of a kit for predicting a patient with diffuse glioma or a kit for predicting the prognosis of a patient with diffuse glioma.
In addition, the present invention also provides a kit for predicting the prognosis of a patient with diffuse glioma, comprising reagents for testing the expression levels of the genes CDC42, PPP4C, NRG3, VIM and HDAC 1.
In addition, the present invention provides a prediction system for predicting the prognosis of a patient with diffuse glioma, comprising:
a data entry module for entering results of immune-related gene expression levels of the diffuse glioma patient including CDC42, PPP4C, NRG3, VIM and HDAC1 into the model calculation module, the gene expression levels being the number of fragments per million map reads per kilobase of transcription (FPKM).
A model calculation module comprising a LASSO Cox regression model for calculating a patient immune risk score based on the immune-related gene expression level of the patient with the diffuse glioma and the LASSO Cox regression model, the calculation formula of the risk score being multiplied by a weighting: f (x) Sum [ Coeffcient (weight coefficient of each gene in the model) × expression level of each gene in the model ], coeffcients of the genes CDC42, PPP4C, NRG3, VIM, and HDAC1 are: 0.1503546, 0.2003038, -0.0319212, 0.1307647, 0.1443997;
and the result output module is used for predicting the risk of the patient with the diffuse glioma according to the immune risk score of the patient with the diffuse glioma. Diffuse glioma patients are at high risk, have a poorer prognosis and require more and more aggressive treatment when the diffuse glioma patient immune risk score > median risk score (4.084). When the immune risk score of the patient with the diffuse glioma is less than or equal to the median risk score (4.084), the patient with the diffuse glioma is low in risk and good in prognosis, a milder treatment scheme can be used, and over-treatment is avoided.
The invention has the advantages that: the invention provides application of immune related genes in a kit and a system for prognosis of a patient with diffuse glioma; the system can be used for well predicting the diffuse glioma prognosis; the invention also carries out single-factor and multi-factor Cox proportional risk regression analysis, and proves that the immune risk score can be used as an independent prognostic factor of the diffuse glioma.
Drawings
FIG. 1 shows construction and validation of an immune prognostic signature (risk score) model. Where FIG. A, B shows that in the CGGA training dataset, LASSO Cox regression analysis established the most relevant immune genes to overall survival in patients with diffuse glioma to construct the model. Fig. C, F shows survival plots for low-risk and high-risk groups of 5 immune-related genetic model partitions in the CGGA training dataset and TCGA validation dataset, and Log-rank test indicates that high-risk and low-risk groups of 5 immune genetic partitions can effectively partition the overall survival of patients with diffuse glioma (P < 0.001). Fig. D, G shows that the diffuse glioma prognosis model constructed by 5 immune-related genes combines the time-dependent ROC graphs of 1 year, 3 years and 5 years of two cohort diffuse glioma patients of the CGGA training set and the TCGA validation set, and AUC (area under the curve) shows that 5 immune-related genes have good diffuse glioma patient prognosis prediction effect. FIG. E, H shows the risk score distribution in two cohorts of the CGGA training set, TCGA validation set, the expression profile of 5 immune-related genes, and the survival status of each patient.
Figure 2 shows that 5 pairs of IPS primers and internal reference qPCR amplification are specific.
FIG. 3 shows that the expression levels of 5 IPS genes in 12 diffuse glioma samples are consistent with those in a prognosis model through qPCR detection, and the qPCR results show that the expressions of CDC42, VIM, PPP4C, HDAC1 and NRG3 in diffuse glioma and normal tissues are significantly different.
Detailed Description
To better illustrate the objects, advantages and solutions of the present invention, the present invention will be further described with reference to the following detailed description and accompanying drawings.
Example 1
Construction of a diffuse glioma immune prognosis model (fig. 1):
the GSE4290 data set, which contained 153 cancer tissues and 27 paracarcinoma tissues, was downloaded from the GEO database.
Differential expression analysis is carried out on a cancer group and a paracancer group in a GSE4290 data set by using a limma R packet, the screening standards of differential expression genes are | log2foldchange | >0.5 and p.adjust <0.05, and after the differential expression analysis, 3383 differential expression genes are obtained in total, wherein the differential expression genes comprise 1459 up-regulated genes and 1924 down-regulated genes.
The immune-related genes were obtained from ImmPort database, 1811 genes were summed up, and these genes were intersected with differentially expressed genes to obtain 236 differentially expressed immune-related genes (DE-IRGs).
Using these 236 DE-IRGs to perform a weighted co-expression network analysis in the CGGA dataset to obtain the gene modules most correlated with glioma survival, we selected genes from the agapultite gene module that were significantly correlated with glioma survival (r ═ 0.6, P <0.001) to construct a model, which contains a total of 120 DE-IRGs.
And performing single-factor Cox regression and LASSO-Cox regression analysis on the 120 DE-IRGs, and finally obtaining a diffuse glioma 5 gene immune prognosis model.
The calculation formula of the model uses a weighted phase multiplication: f (x) Sum [ Coeffcient (weight coefficient of each gene in the model) × expression level (FPKM) of each gene in the model ], and coeffcients of the genes CDC42, PPP4C, NRG3, VIM, and HDAC1 are: 0.1503546, 0.2003038, -0.0319212, 0.1307647, 0.1443997.
Example 2
Using the model to predict the prognosis of diffuse glioma;
the test was performed on a training Cohort (CGGA), a validation cohort (TCGA) totaling 902 case samples. As shown in the graph I, in which the test effect in CGGA (AUC values at 1 year, 3 years and 5 years of 0.795, 0.855 and 0.896, respectively) was observed in glioma patients in the TCGA test set (AUC values at 1 year, 3 years and 5 years of 0.815, 0.855 and 0.813, respectively)
As shown in tables 1 and 2, the single-factor cox regression analysis and the multi-factor cox regression analysis are also performed in the invention, so that the immune risk score model (IRGS) calculated by the model can be used for independently predicting the prognosis risk of glioma patients.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
TABLE 1 Single-and Multi-factor analysis of immune-related Gene models in combination with clinical and case factors
Figure BDA0003122693590000041
Figure BDA0003122693590000051
TABLE 2 Single-and Multi-factor analysis of immune-related Gene models in combination with clinical and case factors
Figure BDA0003122693590000052
Figure BDA0003122693590000061
Figure BDA0003122693590000071
Figure BDA0003122693590000081
Figure BDA0003122693590000091
Figure BDA0003122693590000101
Figure BDA0003122693590000111
Figure BDA0003122693590000121
Figure BDA0003122693590000131
Figure BDA0003122693590000141
Figure BDA0003122693590000151
Figure BDA0003122693590000161
Figure BDA0003122693590000171
Figure BDA0003122693590000181
Figure BDA0003122693590000191
Figure BDA0003122693590000201
Figure BDA0003122693590000211
Figure BDA0003122693590000221
Figure BDA0003122693590000231
Figure BDA0003122693590000241
Example 3
Validation of expression levels of genes used in immune prognostic models in clinical samples
Based on the construction and preliminary validation of the GEO database and TCGA database, a diffuse glioma immune prognosis model was constructed and we collected 12 diffuse glioma tissue samples (728487G, 691253G, 689599G, 695948G, 754905G, 719303G, 779526G, 756384G, 786591G, 700690G, 694592G, 701763G) and 6 normal tissue samples (709872N, 531955N, 786009N, 605854N, 705306N, 474898N) from the hospital. Total RNA was extracted and 5 IPS genes used by the immune prognosis model were detected by qPCR: expression levels of CDC42, PPP4C, NRG3, VIM and HDAC1, with ACTB as an internal control positive control. The evaluation significance of our model was verified at the qPCR level.
TABLE 3 qPCR primer Table
Figure BDA0003122693590000242
Figure BDA0003122693590000251
"-F" denotes the forward primer and "-R" denotes the reverse primer.
Major instruments and reagents for qPCR validation
The main instruments are ABI fluorescent quantitative PCR instrument Viia7, Bio-Rad gradient PCR instrument C1000 TouchTMEppendorf desk top high speed refrigerated centrifuge 5424R. Among the key reagents are Takara's RNAisso Plus (Cat #9109), ThermoFisher's First Strand cDNA Synthesis Kit (Cat # K1612) and PowerTrack SYBR Green Master Mix (Cat # A46113).
Extraction of total RNA from tumor tissue and normal tissue
The first step is as follows: and (4) processing a sample. 50-100 mg of the tissue block is taken into a mortar precooled by liquid nitrogen, the sample is ground into powder, and 1mL of RNAasso Plus is added. The homogenate was then transferred to a 1.5mL centrifuge tube, shaken, mixed, allowed to stand at room temperature for 5min, centrifuged at 12000g at 4 ℃ for 5min, and the supernatant carefully aspirated into a fresh centrifuge tube. The second step is that: the phases were separated. Adding 0.2mL of chloroform into 1mL of homogenate, tightly covering a centrifugal tube cover, shaking and uniformly mixing for 15s, standing at room temperature for 5min, and centrifuging at 12000g at 4 ℃ for 15 min. The homogenate is now divided into three layers, namely: a colorless supernatant layer (containing RNA), an intermediate white protein layer, and a colored lower organic phase. Carefully aspirate the supernatant and transfer to a new centrifuge tube. The third step: RNA was precipitated. 0.5mL of isopropanol was added to the supernatant, the tubes were inverted upside down and mixed well, and then left to stand at room temperature for 10 min. Centrifuge at 12000rpm for 10min at 4 ℃. The fourth step: the RNA is washed. Discarding the supernatant, adding 1mL of 75% ethanol, mixing well, centrifuging at 4 deg.C for 5min, and discarding the supernatant. The fifth step: the RNA is dissolved. The RNA precipitate was dried at room temperature for 5min, and an appropriate amount of RNase-free water was added to dissolve the precipitated RNA. The concentration and purity of the RNA is then determined for use.
Preparation of cDNA by reverse transcription
The First Strand cDNA Synthesis Kit (Cat # K1612) of ThermoFisher, a reagent required for reverse transcription reaction, was thawed, mixed by gentle inversion from top to bottom, centrifuged briefly, and then placed on ice for use.
Prepare RNA-Primer Mix. The following reagents were added to the reaction tube of the pre-cooled RNase free to a total volume of 11. mu.l.
TABLE 4 RNA-Primer Mix reagents
Reagent composition Volume of Final concentration
total RNA 1μg
250μM Random primer 1μl 10μM
RNase-free Water To a total volume of 11. mu.l
Preparing reverse transcription reaction liquid. The following reagents were added to a total volume of 20. mu.l in an RNA-Primer Mix reaction tube
TABLE 5 reverse transcription reaction solution
Reagent composition Volume of Final concentration
RNA-Primer Mix 11μl
5×Reaction Buffer 4μl 1×
10mM dNTP Mix 2μl 1mM
20U/μl RNase Inhibitor 1 μl 1U/μl
20U/μl M-MLV RTase 2 μl 8U/μl
Total volume 20μl
And (5) reverse transcription reaction. Mix reaction Mix well, centrifuge briefly and react for 1 hour at 37 ℃. Heat treating at 85 deg.C for 5min to terminate reverse transcription reaction, diluting reverse transcription product with 5 times of water, and storing at-20 deg.C.
Quantitative PCR detection
The SYBR Green Master Mix was thawed at 4 ℃, gently mixed upside down and briefly centrifuged, and the reaction solutions in the following table were prepared on ice.
TABLE 6 quantitative PCR
Composition (I) Amount of addition Final concentration
SYBR Green Master Mix 5μL 1×
Forward Primer(2μM) 1μL 0.2μM
Reverse Primer(2μM) 1μL 0.2μM
cDNA 2μL
RNase-free Water 1μL
Total volume 10μL
TABLE 7 qPCR reaction Programming set-ups
Number of cycles Step (ii) of Temperature of Time Fluorescence collection
1 Pre-denaturation 95℃ 2min No
40 Denaturation of the material 95℃ 5s No
Annealing and stretching 60℃ 30s Yes
After the PCR reaction, melting curve analysis was performed using the following procedure:
temperature of Fluorescence collection
60℃～95℃，0.05℃/S Yes
Results one 5 pairs of IPS primers and internal reference qPCR amplification were specific (FIG. 2);
results two, the expression level of 5 IPS genes in 12 diffuse glioma samples detected by qPCR is consistent with that in the prognosis model (FIG. 3).
qPCR results indicate that there are significant differences in expression of CDC42, VIM, PPP4C, HDAC1, and NRG3 in diffuse glioma and normal tissues. CDC42 is expressed in diffuse glioma at a level about 2.2 times that of normal tissue, VIM is expressed in diffuse glioma at a level about 1.6 times that of normal tissue, PPP4C is expressed in diffuse glioma at a level about 1.9 times that of normal tissue, and HDAC1 is expressed in diffuse glioma at a level about 2.3 times that of normal tissue. NRG3 was low expressed in diffuse gliomas, around 27% of normal tissues. The qPCR results show that the expression basis of the 5 IPS genes we used for modeling is consistent with that in clinical samples.
TABLE 8 qPCR CT value data sheet
Figure BDA0003122693590000271
Figure BDA0003122693590000281
Figure BDA0003122693590000291
Figure BDA0003122693590000301

Claims (7)

1. A method of assessing the prognosis of a glioma patient, said method comprising:
a) extracting tumor tissue of glioma patient
b) Detecting the expression level of CDC42, PPP4C, NRG3, VIM and HDAC1 therein
c) The expression levels of the above 5 genes and the variable coefficients in the corresponding immune prognosis models are subjected to linear transformation, the risk score is calculated,
d) glioma patients were divided into different risk groups according to the size of the risk score value: if the risk score is greater than or equal to 4.084, the group is a high risk group, and the prognosis is poor; if the risk score is less than 4.084, the group is a low risk group and the prognosis is better.
2. The method of claim 1, wherein the tissue is a glioma tissue.
3. The method of claim 1 or 2, wherein the expression level is detected by:
a) the mRNAs encoding CDC42, PPP4C, NRG3, VIM and HDAC1 were detected.
4. The method of any one of claims 1-3, wherein the amount of CDC42, PPP4C, NRG3, VIM, and HDAC1 is determined by using a targeted RNA-seq assay or an antibody against CDC42, PPP4C, NRG3, VIM, and HDAC 1.
5. The method of any one of claims 1-4, wherein the antibodies against CDC42, PPP4C, NRG3, VIM, and HDAC1 are monoclonal or polyclonal antibodies.
6. Use of an agent that detects the expression levels of CDC42, PPP4C, NRG3, VIM and HDAC1 in the preparation of a kit for predicting prognosis in a glioma patient.
7. A kit for predicting prognosis in a patient with glioma, said kit comprising:
a) reagents for determining expression levels of CDC42, PPP4C, NRG3, VIM and HDAC1, and
b) instructions for use.
CN202110681350.9A 2021-06-18 2021-06-18 Application of immune related gene in kit and system for predicting diffuse glioma prognosis Pending CN113481298A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110681350.9A CN113481298A (en) 2021-06-18 2021-06-18 Application of immune related gene in kit and system for predicting diffuse glioma prognosis

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110681350.9A CN113481298A (en) 2021-06-18 2021-06-18 Application of immune related gene in kit and system for predicting diffuse glioma prognosis

Publications (1)

Publication Number Publication Date
CN113481298A true CN113481298A (en) 2021-10-08

Family

ID=77934109

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110681350.9A Pending CN113481298A (en) 2021-06-18 2021-06-18 Application of immune related gene in kit and system for predicting diffuse glioma prognosis

Country Status (1)

Country Link
CN (1) CN113481298A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115747329A (en) * 2022-09-03 2023-03-07 昂凯生命科技(苏州)有限公司 Gene marker combination, kit and system for predicting tumor progression and prognosis

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010131006A (en) * 2008-10-31 2010-06-17 Dna Chip Research Inc Neuroglioma prognosis prediction method and kit usable therefore
US20150038357A1 (en) * 2011-10-07 2015-02-05 Universite Montpellier 2 Sciences Et Techniques Prognosis for glioma
CN107574247A (en) * 2017-09-28 2018-01-12 郑州大学第附属医院 A kind of the glioblastoma auxiliary diagnosis based on CLCF1 genes, prognostic evaluation kit and its application method
KR20180102328A (en) * 2017-03-07 2018-09-17 사회복지법인 삼성생명공익재단 Biomarker for Diagnosis or Prognosis of Glioblastoma and the Use Thereof
EP3428647A1 (en) * 2017-07-12 2019-01-16 Consejo Superior de Investigaciones Científicas (CSIC) Expression signature for glioma diagnosis and/or prognosis in a subject

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010131006A (en) * 2008-10-31 2010-06-17 Dna Chip Research Inc Neuroglioma prognosis prediction method and kit usable therefore
US20150038357A1 (en) * 2011-10-07 2015-02-05 Universite Montpellier 2 Sciences Et Techniques Prognosis for glioma
KR20180102328A (en) * 2017-03-07 2018-09-17 사회복지법인 삼성생명공익재단 Biomarker for Diagnosis or Prognosis of Glioblastoma and the Use Thereof
EP3428647A1 (en) * 2017-07-12 2019-01-16 Consejo Superior de Investigaciones Científicas (CSIC) Expression signature for glioma diagnosis and/or prognosis in a subject
CN107574247A (en) * 2017-09-28 2018-01-12 郑州大学第附属医院 A kind of the glioblastoma auxiliary diagnosis based on CLCF1 genes, prognostic evaluation kit and its application method

Non-Patent Citations (5)

* Cited by examiner, † Cited by third party
Title
JING‑JING WANG 等: "Development of a prognostic model of glioma based on immune-related genes", ONCOLOGY LETTERS, vol. 21, no. 2, pages 2 *
XIAOBO CUI 等: "Elevated IQGAP1 and CDC42 levels correlate with tumor malignancy of human glioma", ONCOLOGY R 768 EPORTS, vol. 37, no. 2 *
XIAO-QIANG WANG 等: "Knockdown of HDAC1 expression suppresses invasion and induces apoptosis in glioma cells", ONCOTARGET, vol. 8, no. 29 *
李孟友: "PP4C在人脑胶质瘤中的表达、功能及其相关调控机制研究", 中国博士学位论文全文数据库 医药卫生科技辑, no. 03 *
王影 等: "miR-29a对胶质瘤细胞CDC42表达及迁移和侵袭的影响", 中国肿瘤临床, vol. 40, no. 11, pages 629 - 633 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115747329A (en) * 2022-09-03 2023-03-07 昂凯生命科技(苏州)有限公司 Gene marker combination, kit and system for predicting tumor progression and prognosis
CN115747329B (en) * 2022-09-03 2023-10-17 昂凯生命科技(苏州)有限公司 Gene marker combination, kit and system for predicting tumor progression and prognosis

Similar Documents

Publication Publication Date Title
Brannon et al. Meta-analysis of clear cell renal cell carcinoma gene expression defines a variant subgroup and identifies gender influences on tumor biology
Skibsted et al. Bench-to-bedside review: future novel diagnostics for sepsis-a systems biology approach
Lang et al. Expression profiling of circulating tumor cells in metastatic breast cancer
CN105874079A (en) Molecular diagnostic test for lung cancer
KR101950717B1 (en) Methods for predicting effectiveness of chemotherapy for breast cancer patients
CN105874080A (en) Molecular diagnostic test for oesophageal cancer
CN111139300B (en) Application of group of colon cancer prognosis related genes
CN113066585A (en) Method for efficiently and quickly evaluating prognosis of stage II colorectal cancer patient based on immune gene expression profile
Yeganeh et al. Dysregulation of AKT3 along with a small panel of mRNAs stratifies high-grade serous ovarian cancer from both normal epithelia and benign tumor tissues
Zhang et al. The predictive value of selected serum microRNAs for acute GVHD by TaqMan MicroRNA arrays
CN109929934B (en) Application of immune related gene in kit and system for colorectal cancer prognosis
Shi et al. LncRNA CADM1-AS1 serves as a new prognostic biomarker for gastric cancer.
Zeng et al. A narrative review of five multigenetic assays in breast cancer
CN113481298A (en) Application of immune related gene in kit and system for predicting diffuse glioma prognosis
Liang et al. An immune-related prognostic classifier is associated with diffuse large B cell lymphoma microenvironment
Qin et al. Systematic construction and validation of a novel ferroptosis-related gene model for predicting prognosis in cervical cancer
Zhi et al. Long non-coding RNA RP11-342L8. 2, derived from RNA sequencing and validated via RT-qPCR, is upregulated and correlates with disease severity in psoriasis patients
CN116863990A (en) Hepatocellular carcinoma prognosis layering construction method based on exosome ceRNA network-vascular infiltration characteristics
CN106755309A (en) Application of the molecular marked compound in cancer of pancreas prognosis evaluation product is prepared
Li et al. GBP2 as a potential prognostic predictor with immune-related characteristics in glioma
Wang et al. Identification of CD20, ECM, and ITGA as biomarkers for osteosarcoma by integrating transcriptome analysis
CN107299129A (en) Circle nucleic acid as breast cancer biomarker application
Xing et al. Identification of the different gene expression characteristics from liver cirrhosis to hepatocellular carcinoma using single-cell sequencing analyses
CN113528509A (en) Construction method of tumor microenvironment scoring system for predicting gastric cancer immunotherapy and molecular probe
CN111748626A (en) System for predicting treatment effect and prognosis of neoadjuvant radiotherapy and chemotherapy of esophageal squamous carcinoma patient and application of system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination