CN118932051A - Reagents for predicting or evaluating the prognosis of glioma patients - Google Patents
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Abstract
本发明提供了用于预测或评估胶质瘤患者预后的试剂。本发明结合免疫相关的基因，找到一组可以稳定预测弥漫性胶质瘤预后的特征基因；采用本发明系统可以很好地预测弥漫性胶质瘤预后；本发明还进行了单因素和多因素Cox比例风险回归分析，证明免疫风险得分确实可以作为弥漫性胶质瘤的独立预后因子。
The present invention provides a reagent for predicting or evaluating the prognosis of glioma patients. The present invention combines immune-related genes to find a group of characteristic genes that can stably predict the prognosis of diffuse glioma; the system of the present invention can well predict the prognosis of diffuse glioma; the present invention also performs univariate and multivariate Cox proportional hazard regression analysis, proving that the immune risk score can indeed be used as an independent prognostic factor for diffuse glioma.
Description
The application relates to a Chinese patent application with the application date of 2021, 6-month and 18-date, the application number of 202110681350.9 and the application of an immune related gene in a kit and a system for predicting the prognosis of diffuse glioma.
Technical Field
The invention belongs to the field of biomedicine, and in particular relates to application of immune related genes in a kit and a system for predicting prognosis of diffuse glioma.
Background
Gliomas are the most common primary tumors of the brain and central nervous system, accounting for about 30% of all brain tumors and central nervous system tumors, accounting for more than 80% of all malignant primary brain tumors. The incidence rate per year is 3.55 cases per 10 tens of thousands of 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 (LGGs) and grade IV Glioblastomas (GBMs) based on histological features. GBM is the most deadly tumor in all grades. Despite significant advances in therapies including chemotherapy, radiation therapy and surgical excision, GBM has an overall median lifetime of only 15 months. At present, the histological grading of gliomas is a main clinical prognosis index of gliomas, however, the treatment response and prognosis of patients at the same grade are greatly different, so that molecular typing of gliomas is a necessary requirement for individual treatment of gliomas.
Gene molecular markers refer to models constructed by statistical and machine learning methods based on the expression of a set of genes for clinical prediction. Currently, commonly used 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 gene expression detection, there are many methods for finding a group of gene combinations for prognosis prediction of diffuse glioma, and have good prediction performance, and there is no related study.
The immune system has proven to be a key factor in tumor development. Tumor infiltrating immune cells are an important component of the tumor microenvironment, playing a vital 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, extensive studies have not been conducted to predict prognosis of diffuse gliomas using immune-related genes.
The main disadvantages of the prior art are: no organic binding immune related genes act 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 provide the application of immune related genes in a kit and a system for prognosis of diffuse glioma, and the risk of a diffuse glioma patient 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 achieve the above purpose, the following technical scheme is adopted: use of a combination of genes CDC42, PPP4C, NRG, VIM and HDAC1 in the preparation of a kit for a diffuse glioma patient or a kit for predicting prognosis of a diffuse glioma patient.
The present invention provides a method of assessing the prognosis of a glioma patient, said method comprising: extracting tumor tissues of a glioma patient, detecting expression levels of CDC42, PPP4C, NRG, VIM and HDAC1, 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 patient into different risk groups according to the sizes of the risk score values: if the risk score is more than or equal to 4.084, the high risk group is the high risk group, and the prognosis is poor; if the risk score is less than 4.084, the low risk group is the low risk group, and the prognosis is better.
In addition, the invention also provides the use of the reagent for detecting the relative expression level of the immune genes in the preparation of a kit for predicting patients with diffuse glioma or a kit for predicting prognosis of patients with diffuse glioma, wherein the reagent is used for detecting the expression levels of genes CDC42, PPP4C, NRG, VIM and HDAC 1.
In addition, the invention also provides a kit for predicting prognosis of patients with diffuse glioma, which comprises reagents for detecting expression levels of genes CDC42, PPP4C, NRG, VIM and HDAC 1.
In addition, the present invention also provides a prediction system for predicting prognosis of patients with diffuse glioma, comprising:
a data input module for inputting results of immune related gene expression levels of transcription per kilobase per million map reads (FPKM) of diffuse glioma patients into the model calculation module, the immune related genes including CDC42, PPP4C, NRG3, VIM and HDAC 1.
The model calculation module comprises LASSOCox regression models, is used for calculating patient immune risk scores according to the immune related gene expression level of the diffuse glioma patient and LASSOCox regression models, and a calculation formula of the risk scores uses a weighted phase method: f (x) =sum [ Coeffcient (weight coefficient of each gene in model) ×expression level of each gene in model ], the genes CDC42, PPP4C, NRG3, VIM and Coeffcient of HDAC1 are respectively: 0.1503546, 0.2003038, -0.0319212, 0.1307647, 0.1443997;
And the result output module is used for predicting the risk of the diffuse glioma patient according to the immune risk score of the diffuse glioma patient. When the immune risk score of diffuse glioma patient > median risk score (4.084), diffuse glioma patient is at high risk, prognosis is worse, more and more aggressive treatment is needed. When the immune risk score of the diffuse glioma patient is less than or equal to the median of the risk score (4.084), the diffuse glioma patient is low in risk and good in prognosis, and a milder treatment scheme can be used to avoid over treatment.
The invention has the advantages that: the invention provides application of immune related genes in a kit and a system for prognosis of patients with diffuse glioma, and finds a group of characteristic genes capable of stably predicting prognosis of diffuse glioma by combining the immune related genes; the system can be used for well predicting the prognosis of the diffuse glioma; the invention also carries out single-factor and multi-factor Cox proportional risk regression analysis, and proves that the immune risk score can be truly used as an independent prognosis factor of diffuse glioma.
Drawings
Figure 1 shows the construction and validation of an immune prognosis feature (risk score) model. Wherein figure A, B shows that in the CGGA training dataset, LASSOCox regression analysis determined the immune genes most relevant to overall survival of diffuse glioma patients to construct the model. Fig. C, F shows a graph of survival of the 5 immune-related gene model partitioned low-risk and high-risk groups in the CGGA training dataset and TCGA validation dataset, and Log-rank test indicates that the 5 immune-related gene partitioned high-risk and low-risk groups can effectively partition overall survival of diffuse glioma patients (P < 0.001). Fig. D, G shows a time-dependent ROC graph of two lines of diffuse glioma patients, namely a CGGA training set and a TCGA verification set, constructed by 5 immune-related genes, and AUC (area under the curve) shows that 5 immune-related genes have a good prognosis prediction effect on the diffuse glioma patients. Figure E, H shows the risk score distribution in two cohorts of CGGA training set, TCGA validation set, expression profile of 5 immune-related genes, and survival for each patient.
FIG. 2 shows that 5 is specific for IPS primers and internal reference qPCR amplification.
FIG. 3qPCR shows that the expression level of 5 IPS genes in 12 diffuse glioma samples is consistent with that in a prognosis model, and qPCR results show that CDC42, VIM, PPP4C, HDAC1 and NRG3 have significant differences in the expression in diffuse glioma and normal tissues.
Detailed Description
For a better understanding of the invention with the objects, advantages and technical solutions, the present invention will be further described with reference to the following examples and drawings.
Example 1
Construction of diffuse glioma immune prognosis model (fig. 1):
the GSE4290 dataset was downloaded from the GEO database, which contained 153 cancer tissues and 27 paracancerous tissues.
Differential expression analysis was performed on the cancer group and the paracancer group in the GSE4290 dataset using limma R package, and differential expression gene screening criteria were |log foldchange | >0.5 and p.adjust <0.05, after differential expression analysis, 3383 differential expression genes including 1459 up-regulated genes and 1924 down-regulated genes were obtained in total.
The immune-related genes were obtained from ImmPort database, and 1811 genes were combined with the differentially expressed genes to obtain 236 differentially expressed immune-related genes (DE-IRGs).
Using these 236 DE-IRGs for weighted co-expression network analysis in CGGA dataset to obtain the gene module most relevant to glioma survival, we selected genes in the turquoise gene module significantly relevant to glioma survival (r= -0.6, p < 0.001) to construct a model, which contained 120 DE-IRGs in total.
The 120 DE-IRGs were subjected to single factor Cox regression and LASSO-Cox regression analysis, and finally we obtained a diffuse glioma 5 gene immune prognosis model.
The calculation formula of the model uses weighted phase multiplication: f (x) =sum [ Coeffcient (weight coefficient of each gene in model) ×expression level of each gene in model (FPKM) ], CDC42, PPP4C, NRG3, VIM and Coeffcient of HDAC1 are respectively: 0.1503546, 0.2003038, -0.0319212, 0.1307647, 0.1443997.
Example 2
Predicting prognosis of diffuse glioma using this model;
A training queue (CGGA) and a validation queue (TCGA) were tested on the total 902 case samples. As shown in FIG. 1, wherein the test effects in CGGA (AUC values of 1, 3 and 5 are 0.795, 0.855 and 0.896, respectively), glioma patients in the TCGA test set were tested (AUC values of 1, 3 and 5 are 0.815, 0.855 and 0.813, respectively)
As shown in tables 1 and 2, the invention also performs single factor cox regression analysis and multi-factor cox regression analysis, and proves that the immune risk score model (IRGS) calculated by the model of the invention can be used for predicting the prognosis risk of glioma patients independently.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted equally without departing from the spirit and scope of the technical solution of the present invention.
TABLE 1 immune-related Gene model combining single-and Multi-factor analysis of clinical and case factors
TABLE 2 immune-related Gene model combining single-and Multi-factor analysis of clinical and case factors
Example 3
Verification of expression levels of genes used in an immune prognosis model in clinical samples
Based on GEO database and TCGA database, we collected 12 diffuse glioma tissue samples (728487g, 691253g,689599g,695948g,754905g, 71933 g,779526g,756384g,786591g, 70000 g,694592g,701763 g) and 6 normal tissue samples (709872n, 53195 n,786009n,605854n,705306n, 474898n) from hospitals. Total RNA was extracted and 5 IPS genes used in the immune prognosis model were detected by qPCR: CDC42, PPP4C, NRG3, VIM and HDAC1 expression levels, ACTB was used as an internal positive control. The evaluation significance of our model was verified at qPCR level.
TABLE 3qPCR primer Table
| Primer(s) | Sequence (5 '-3') |
| CDC42-F | GGCGATGGTGCTGTTGGTAA |
| CDC42-R | GTGGATAACTCAGCGGTCGT |
| PPP4C-F | TCACGCAGGTCTATGGCTTC |
| PPP4C-R | GCTGACAGGCTGAGGTAGTC |
| VIM-F | GGACCAGCTAACCAACGACA |
| VIM-R | AAGGTCAAGACGTGCCAGAG |
| HDAC1-F | CTATCGCCCTCACAAAGCCA |
| HDAC1-R | CTGCTTGCTGTACTCCGACA |
| NRG3-F | CCCGTTCTTCAGTAGCAGCA |
| NRG3-R | AAGGTAGGAGGAGGTAGCGT |
| ACTB-F | AGACCTGTACGCCAACACAG |
| ACTB-R | CGCTCAGGAGGAGCAATGAT |
"-F" means forward primer and "-R" means reverse primer.
Major instrumentation and reagents for qPCR validation
The main instrument is a fluorescent quantitative PCR instrument Viia of ABI, a gradient PCR instrument C1000 Touch TM of Bio-Rad, and a desktop high-speed refrigerated centrifuge 5424R of Eppendorf. Key reagents are Takara RNAiso Plus (Cat#9109), thermoFisher's FIRST STRAND CDNASYNTHESIS KIT (Cat#K1612) and PowerTrack TM SYBR GREEN MASTER Mix (Cat#A 46113).
Total RNA extraction from tumor tissue and normal tissue
The first step: sample treatment. Taking 50-100 mg tissue blocks into a mortar precooled by liquid nitrogen, grinding the samples to be powdery, and adding 1mL RNAiso Plus. The homogenate was then transferred to a 1.5mL centrifuge tube, shaken and mixed, then allowed to stand at room temperature for 5min, and centrifuged at 12000g for 5min at 4℃and carefully aspirated the supernatant into a fresh centrifuge tube. And a second step of: the phases are separated. Adding 0.2mL of chloroform into each 1mL of homogenate, covering a centrifugal tube cover, shaking and uniformly mixing for 15s, standing for 5min at room temperature, and centrifuging for 15min at the temperature of 12000g at the temperature of 4 ℃. The homogenate is now divided into three layers, namely: a colorless supernatant layer (containing RNA), an intermediate albumin layer and a colored lower organic phase. Carefully aspirate the supernatant and transfer to a new centrifuge tube. And a third step of: RNA was precipitated. 0.5mL of isopropanol is added into the supernatant, the centrifuge tube is turned upside down, fully mixed evenly and then kept stand for 10min at room temperature. Centrifuge at 12000rpm for 10min at 4 ℃. Fourth step: washing the RNA. The supernatant was discarded, 1mL of 75% ethanol was added, and after mixing, the mixture was centrifuged at 4℃and 7500g for 5min, and the supernatant was discarded. Fifth step: RNA was solubilized. 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 RNA were then determined for use.
Preparation of cDNA by reverse transcription
FIRST STRAND CDNA SYNTHESIS KIT (Cat#K1612) of the thermal Fisher reagent required for the melting and reverse transcription reaction was mixed slightly upside down, centrifuged briefly and placed on ice for use.
Preparing an RNA-Primer Mix. The following reagents were added to a pre-chilled RNASE FREE reaction tube to a total volume of 11 μl.
TABLE 4 RNA-Primer Mix reagent
| Reagent component | 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 the RNA-Primer Mix reaction tube to a total volume of 20. Mu.l
TABLE 5 reverse transcription reaction solution
| Reagent component | 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 of | 20μl |
Reverse transcription reaction. Mix well and react Mix, after a short centrifugation at 37 ℃ for 1 hour. Heat treatment at 85 deg.c for 5min to terminate the reverse transcription reaction, diluting the reverse transcription product with 5 times water, and maintaining at-20 deg.c for further use.
Quantitative PCR detection
SYBR GREEN MASTER Mix was thawed at 4℃and gently mixed upside down and centrifuged briefly to prepare the reaction solutions in the following table on ice.
TABLE 6 quantitative PCR
TABLE 7 qPCR reaction procedure set-up
| Cycle number | Step (a) | Temperature (temperature) | Time of | Fluorescence acquisition |
| 1 | Pre-denaturation | 95℃ | 2min | No |
| 40 | Denaturation (denaturation) | 95℃ | 5s | No |
| Annealing and extending | 60℃ | 30s | Yes |
After the PCR reaction, melting curve analysis was performed using the following procedure:
| Temperature (temperature) | Fluorescence acquisition |
| 60℃～95℃，0.05℃/S | Yes |
Results one 5 was specific for IPS primers and internal qPCR amplification (fig. 2);
Results two qPCR detected that the expression level of 5 IPS genes in 12 diffuse glioma samples was consistent with that in the prognosis model (FIG. 3).
QPCR results show that CDC42, VIM, PPP4C, HDAC1 and NRG3 have significant differences in diffuse glioma and normal tissue expression. CDC42 is expressed at about 2.2 times higher than normal tissue in diffuse glioma, VIM is expressed at about 1.6 times higher than normal tissue in diffuse glioma, PPP4C is expressed at about 1.9 times higher than normal tissue in diffuse glioma, HDAC1 is expressed at about 2.3 times higher than normal tissue in diffuse glioma. NRG3 is low expressed in diffuse gliomas, around 27% of normal tissues. qPCR results showed that the expression basis of the 5 IPS genes used in our modeling was consistent with that in clinical samples.
Table 8.Qpcr CT value data table
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