CN116356022A

CN116356022A - Integrated stress related kit for prognosis evaluation prediction in glioma

Info

Publication number: CN116356022A
Application number: CN202310189455.1A
Authority: CN
Inventors: 朱晨; 耿子昂
Original assignee: First Hospital of China Medical University
Current assignee: First Hospital of China Medical University
Priority date: 2023-03-02
Filing date: 2023-03-02
Publication date: 2023-06-30

Abstract

The invention discloses an integrated stress related kit for prognosis evaluation prediction in glioma, and belongs to the field of biomedical professions. In particular to an integrated stress related kit for prognosis evaluation and chemotherapy effect prediction in glioma, which comprises oxidative stress, autophagy and iron death. The invention firstly depicts the integrated stress state in glioma, establishes the first integrated stress related model in glioma, converts the model into a kit, is used for distinguishing the integrated stress state, guides the diagnosis of glioma, predicts the application of poor prognosis and other aspects, provides a constructive guiding strategy for searching the patient subgroup which is best suitable for targeted treatment, and has great clinical significance.

Description

Integrated stress related kit for prognosis evaluation prediction in glioma

Technical Field

The invention belongs to the field of biomedical professions, and particularly relates to a dry integrated stress related kit for prognosis evaluation prediction in glioma.

Background

Gliomas are the most common primary intracranial malignant tumors in adults, and due to their highly invasive and malignant proliferative capacity, the recurrence rate and mortality rate are high, especially Gliobastoma (GBM), and even with the standard treatment regimen of surgical maximum excision combined with chemoradiotherapy, the median survival time is only 14.6 months. One important reason that the traditional glioma targeting therapy has poor effect is that the interactive transformation of tumor cells and micro-environment components is ignored. In recent years, more and more research has begun focusing on the mutual regulation and adaptation of malignant cells to the components of the microenvironment in which they are located.

Stimulation of the microenvironment factors and external therapeutic signals often lead to programmed death of tumor cells (Programmed Cell Death, PCD). Programmed death is intentionally induced cell death, accompanied by a series of regulatory steps, resulting in programmed self-destruction during development, such as apoptosis, autophagy, iron death, necrotic apoptosis, and apoptosis of the coke. Dysregulation of programmed death is often associated with malignant tumor characteristics such as metastasis and the like, and is an important influencing factor of tumor death rate and recurrence. Studies have shown that tumor cells in the process of programmed cell death release a large number of inflammatory mediators, chemokines and intracellular components, thereby engineering the neighboring immune microenvironment.

The prognosis of patients is often closely related to the programmed death of tumor cells, which is the case for gliomas. The intervention of the therapeutic means in the initial stage can often lead the tumor cells to be in a highly stressed state by various signals outside the tumor cells, so that autophagy or programmed death of the tumor cells is caused, and the effect of killing the tumor is achieved, but with the duration of treatment, the high plasticity of the tumor cells can lead the residual tumor cells to generate drug resistance or treatment resistance, and the tumor cells survive under the stressed state. The release of a large number of signals by tumor cells undergoing programmed death to the microenvironment to modify the microenvironment to maintain residual tumor cell self-renewal and energy supply is an important contributor to this process.

At present, no report is yet made on a kit for a prognosis evaluation model of an integrated stress pathway in glioma. The invention provides an integrated stress state in glioma, and establishes a first integrated stress related model, and prepares a kit for evaluating the effect of poor prognosis.

Disclosure of Invention

In view of the above problems, the present invention provides an integrated stress-related kit for prognostic evaluation in gliomas. The invention discloses an integrated stress state in glioma for the first time, establishes a first integrated stress related model in glioma, converts the model into a kit for distinguishing the integrated stress state, guiding the diagnosis of glioma, predicting bad prognosis and other multi-aspect applications, and simultaneously provides a constructive guiding strategy for searching the patient subgroup which is best suitable for targeted treatment to a certain extent.

In order to achieve the above purpose, the present invention provides the following technical solutions.

The invention provides an integrated stress related kit for prognosis evaluation prediction in glioma, which is characterized by comprising related reagents capable of measuring the expression levels of integrated stress related model 6 genes IL6, MPO, VKORC1L1, ZC3H12A, SREBF2 and NCOA4 in glioma tissues.

Further, the kit comprises: the upstream and downstream primers for IL6, MPO, VKORC1L1, ZC3H12A, SREBF and NCOA4, the upstream and downstream primers for GAPDH as an internal reference, and RNA extraction reagents: trizol, isopropanol, chloroform, absolute ethanol, reverse transcription reagents, SYBR Green polymerase chain reaction System: PCR buffer, fluorescent dye, dNTPs, RNase-free and water.

Further, the method for establishing the integrated stress model specifically comprises the following steps:

1) Selecting 421 integrated stress related genes including oxidative stress pathway, autophagy pathway and iron death pathway, screening out 6 genes related to prognosis by single-factor and multi-factor COX analysis, and establishing a risk model according to the expression value and regression coefficient of each gene, wherein the scoring formula is as follows: risk value = (-0.3243 ×il6 expression level) + (1.2845 ×mpo expression level) + (1.2139 ×vkorc1L1 expression level) + (1.1373 ×zc3H12A expression level) +(-0.6550 ×srebf2 expression level) +(-1.0469 ×ncoa4 expression level);

2) Selecting 2 glioblastoma patients in a public database, and dividing the patients into a high risk group and a low risk group according to the median of the risk values, wherein the result shows that the model has stable prognosis guiding value, namely the survival time of the high risk group is obviously shorter than that of the low risk group, and the risk model is also suitable for the patients with low-grade glioblastoma and the patients with different sub-groups of different levels in specific refinement;

3) The prognosis accuracy is further improved by comparing the model with the conventional glioma independent prognosis factors including age and IDH mutation;

4) The model was converted to a kit.

Further, the use method of the kit is as follows:

1) Selecting a fresh patient tissue specimen with a proper size, fully grinding and extracting RNA;

2) Reverse transcription into cDNA template;

3) Screening 6 genes forming an integrated stress model and specific primers of GAPDH, and performing real-time quantitative PCR;

4) Taking reference GAPDH as a reference, recording the Ct value of each reaction, and expressing the detection result as delta Ct, wherein delta Ct=Ct target genes-CtGAPDH, and finally calculating the expression multiple 1/2 delta Ct of 6 target genes relative to the reference GAPDH;

5) Substituting the expression, defining the cut-off point in glioblastoma as a high risk group with the score being more than-2.065; less than-2.065 is defined as a low risk group; for low grade glioma patients, cutoff values of-4.095, scores greater than-4.095 are defined as high risk groups; less than-4.095 is defined as a low risk group.

The present invention also provides a system for predicting glioma prognosis, characterized by comprising: the acquisition module is used for acquiring 6 gene expression level altogether of IL6, MPO, VKORC1L1, ZC3H12A, SREBF2 and NCOA 4; and the prediction module is used for predicting the risk score of the glioma according to the expression level obtained by the acquisition module and outputting the risk score, wherein the acquisition module is connected with the prediction module in a wireless and/or wired mode.

Further, the prediction module comprises a glioma prognosis score model comprising a mathematical formula: risk value = (-0.3243 ×il6 expression level) + (1.2845 ×mpo expression level) + (1.2139 ×vkorc1L1 expression level) + (1.1373 ×zc3H12A expression level) +(-0.6550 ×srebf2 expression level) +(-1.0469 ×ncoa4 expression level).

Further, the use method of the system is as follows:

2) Reverse transcription into cDNA template;

Compared with the prior art, the invention has the beneficial effects.

(1) The invention establishes a first integrated stress prognosis evaluation model in glioma based on expression profile data, characterizes the integrated stress state in glioma of different levels, reveals important roles of oxidative stress, autophagy and iron death process in glioma malignant progress, clarifies that the related genes of the pathway can become molecular markers for diagnosing malignant GBM phenotype, and provides a new thought for researching molecular mechanism of glioma disease progress.

(2) The integrated stress related kit for prognosis evaluation and prediction in glioma provided by the invention has stable prognosis and chemotherapy effect evaluation value, and the model has stronger and more stable prediction capability and can be used for data of a plurality of large samples

The database is verified, can be used as a reliable index for predicting prognosis clinically, and has important clinical guiding significance.

(3) The integrated stress related kit has the advantages of simplicity, easiness in operation, low cost, short time, high sensitivity and the like, has a plurality of large database evidence, is stable in evaluation efficiency, has a strong clinical conversion value in the aspect of guiding treatment, is sensitive to target treatment in a high-risk prompt of stress scoring, and has a wide clinical application prospect.

(4) Studies have demonstrated that iron death plays a key role in malignant proliferative invasion of various cancers, such as hepatocellular carcinoma, glioma, all reported. However, current research into iron death has focused mainly on tumor cells, and the impact of iron death on tumor microenvironment has been ignored. The method aims at the problem, analyzes the influence of the iron death state of glioma cells on the tumor microenvironment and a series of cascade reactions brought by the iron death state, provides new insights for understanding mutual adaptation and mutual transformation of the microenvironment components in glioma, is hopeful to identify a brand-new regulation target point, establishes an optimal combined treatment scheme of combined ICB immunotherapy, and has important clinical value and scientific value.

Drawings

FIG. 1 is an integrated stress-related pathway analysis of GBM and LGG status. Wherein A is the integrated stress state in GBM; b is the stress state of the high and low risk group integration.

Figure 2 shows the differences in integrated stress risk scores for different subgroups of patients in GBM at different levels. Wherein A is the correlation between the risk value and the glioma grade; b is the relation between the integrated stress model and the IDH wild type; c is the association of the integrated stress model with the age phenotype; d is the association of an integrated stress model with an interstitial (Mesenchymal) malignant phenotype.

FIG. 3 shows that the integrated stress model has stable prognostic value in the discovery library and for low grade gliomas. Wherein A is the comparison of survival time of two groups with high risk and low risk; b is the survival analysis result of the LGG group.

Fig. 4 is a validation risk score in validating library gliomas. Wherein, A is the comparison of the prognosis differences of the high-low risk group of the verification library CGGA325 GBM; b is the comparison of the prognosis differences of the high-low risk group of the verification library CGGA325 LGG; c is the comparison of the prognosis differences of the high-low risk groups of the verification library CGGA693 GBM; d is a comparison of the prognostic differences in the high and low risk groups of the verification library CGGA693 LGG.

FIG. 5 is a hierarchical survival analysis suggesting prognostic evaluation of the inventive model within different subgroups. Wherein, A is the comparison of survival differences of high-low risk groups with GBM ages less than 50 years in the discovery library; b is a comparison of survival differences of high-low risk groups with GBM age higher than 50 years in the discovery library; c is the comparison of survival differences of high-low risk groups of GBM IDH mutant patients in the discovery library; d, finding out survival difference comparison of GBM IDH wild high-low risk groups in the library; e is the comparison of survival differences of high-low risk groups of GBM MGMT methylation in the discovery library; f is a comparison of the difference in survival of the high and low risk groups found to be unmethylated by GBM MGMT in the pool.

FIG. 6 is a comparison of the prognosis of survival for the model of the invention with that of conventional index combination. Wherein A is a model prognosis evaluation established by independent prognosis indexes; b is a 3-year survival rate prediction curve of the model in a discovery library; c is a 5-year survival rate prediction curve of the model in a discovery library; d is a 3-year lifetime prediction ROC curve comparison of the model and the single index in the discovery library; e is a model predictive ROC curve comparison with 5 year survival for individual indicators at the discovery library.

FIG. 7 is a comparison of the superiority of the evaluation prognosis of the integrated stress model. Wherein A is the comparison of the integrated stress path model with the 3-year survival prediction ROC curve of the known independent path prediction model; b is a predicted ROC curve comparison of the integrated stress path model and a known single path prediction model for 5-year survival; c is that in a specimen library of a first hospital affiliated to China university of medical science, the risk score of GBM is obviously higher than that of LGG.

Detailed Description

The invention is described in further detail below with reference to specific examples and figures. The following examples are merely illustrative of the present invention and should not be construed as limiting the invention.

Example-description of integrated stress states in glioma and establishment of integrated oxidative stress related models in GBM.

To explore the differences in the integrated stress state in LGG and GBM, the inventors performed Gene Set Variation Analysis (GSVA) analysis according to a series of integrated stress-related pathways. The results show that oxidative stress-related, cell autophagy-related and iron death-related pathways are significantly enriched in GBM, suggesting that the integrated stress state is more active in GBM (fig. 1A).

In order to explore the prognostic value of integrated stress-related genes in GBM patients, 421/45 genes related to oxidative stress-related pathway, cell autophagy-related pathway and iron death-related pathway were selected. Through single-factor and multi-factor COX regression analysis, 6 potential prognostic genes (IL 6, MPO, VKORC1L1, ZC3H12A, SREBF, NCOA 4) are screened out. Among them, MPO, VKORC1L1, ZC3H12A are risk genes (HR > 1), while IL6, SREBF2, NCOA4 are considered protective genes (HR < 1).

According to the expression values and regression coefficients of the 6 genes, a risk model is established, and a scoring formula is as follows: risk values = (-0.3243 ×il6 expression level) + (1.2845 ×mpo expression level) + (1.2139 ×vkorc1L1 expression level) + (1.1373 ×zc3H12A expression level) +(-0.6550 ×srebf2 expression level) +(-1.0469 ×ncoa4 expression level), patients are divided into low risk (n=80) and high risk groups (n=80) according to the median value of the risk score.

Based on the principal component analysis of PCA using the integrated stress related genes as the background, the high and low risk groups were found to be distributed in different directions, suggesting that the two integrated stress states are significantly different (FIG. 1B).

The invention further compares the differences in integrated stress risk scores for different grades and different subgroups of patients in GBM, finds that the risk value increases with increasing glioma grade (fig. 2A), and is closely related to some malignant clinical phenotypes, and finds that the risk value is higher in IDH1 wild type (2B), aged population (2C) and interstitial GBM (2D).

Example two stable prognostic evaluation indicators.

To evaluate the prognostic value of the model, the inventors applied this formula to calculate risk scores in both the discovery and validation groups, and then divided the patients of each database into two groups of high risk and low risk based on median. In the discovery group, the high risk group survived significantly shorter than the low risk group (fig. 3A). At the same time, survival analysis of the LGG group also confirmed this result (FIG. 3B)

Meanwhile, the invention applies the integrated stress model of the invention to survival analysis in GBM (fig. 4A-B) and LGG (fig. 4C-D) of each verification group, so that the invention is also applicable, namely, the prognosis of patients in a high risk group is obviously worse than that in a low risk group.

And further evaluating whether the prognostic value of the integrated stress model is stable or not through layering survival analysis. The invention divides the patients into two groups according to ages (fig. 5 a-b), IDH1 mutation states (fig. 5 c-d) and MGMT methylation states (fig. 5 e-f) in GBM patients, respectively, and in the discovery groups, survival time of high risk groups is obviously shorter than that of low risk groups.

Example three integration stress model the accuracy of prognosis was assessed.

Through multi-factor COX regression analysis, the invention discovers that the integrated stress model can be used as an independent prognosis index when the combined analysis of the mutation conditions of the same age and IDH is performed. And in most validation groups, the model is an independent risk prognostic factor. To assess the accuracy of the integrated stress model prediction prognosis, a nomogram model was built in the discovery group by integrating several independent prognostic indicators to more accurately assess prognosis and translate to clinic (fig. 6A). The NOMOgram had a C-INDEX value of 0.840. Calibration analysis suggests that the fit of predicted and actual survival is also good (fig. 6B-C), and similar results are obtained for the validation set. Next, the present invention compares the predictive power of several clinical parameters, including the model, on 3-year and 5-year survival by subject work curves. The results show that the predictive accuracy of the integrated stress model is significantly higher than most conventional clinical features (fig. 6D-E).

Example four integrates the superiority of the stress model in assessing prognosis.

The invention integrates three related paths of oxidative stress in glioma cells, autophagy and iron death, selects target genes, and judges the prognosis of patients. At present, a small number of documents report the risk score related genes of the three pathways, and in order to clearly define the superiority of the invention, namely the integration stress pathway, we select 1 gene set model of the three pathways. By comparing the area under the ROC curve for the discovery set, we found that the integrated stress pathway was more sensitive and specific than the 3 simple pathways on predictions of survival for 3 years and 5 years (fig. 7A-B). Therefore, it is reasonable to consider that the integration of stress pathways is superior to the use of pure pathway prediction in predicting the survival of glioma patients.

Example five preparation and clinical transformation of relevant kits.

In order to better convert the integrated stress related prognosis model into clinical application, a corresponding integrated stress related prognosis evaluation kit is specially formulated, wherein the kit contains an upstream primer and a downstream primer of 6 genes forming the integrated stress model, an upstream primer and a downstream primer of GAPDH serving as internal references, and an RNA extraction reagent, a reverse transcription reagent, a SYBR Green polymerase chain reaction system (PCR buffer, fluorescent dye, dNTPs, RNase-free water and the like). For a clinical glioma patient, selecting a tissue sample of a fresh patient with a proper size in the operation, and fully grinding and extracting RNA; reverse transcription into cDNA template; performing real-time quantitative PCR, wherein the primers are five genes composing an interferon model and GAPDH; calculating the relative expression quantity of each gene of the other 6 genes by taking reference GAPDH; substituting the expression, defining the cut-off point in glioblastoma as a high risk group with the score being more than-2.065; less than-2.065 is defined as a low risk group; for low grade glioma patients, cutoff values of-4.095, scores greater than-4.095 are defined as high risk groups; less than-4.095 is defined as a low risk group.

The specific experimental procedure is as follows.

1) Collecting glioma samples, processing and extracting RNA.

The method comprises the steps of collecting 50 cases of tumor tissues of a patient with low-grade glioma (LGG) and Glioblastoma (GBM) which are determined by pathological diagnosis in a first hospital affiliated to China university of medical science in operation, and storing the 50 cases of tumor tissues in liquid nitrogen; a small amount of each sample is cut and ground, and RNA is extracted by a Trizol, chloroform and isopropanol method and the concentration is measured.

2) Preparation of cDNA templates.

The extracted RNA was inverted into cDNA according to the following reverse transcription system and conditions.

The reverse transcription system is as follows:

1ug RNA volume: 8. mu L;

5 X RT master mix： 4μL；

rnase-free water: 8. mu L;

the total system is as follows: 20. Mu.L;

the reverse transcription conditions were: 15min at 37 ℃, 5S at 85 ℃ and 1min at 4 ℃.

3) And (5) designing and screening primers.

And (3) designing primers of the genes formed by the kit through a Primer Bank website, carrying out specificity comparison through Pubmed, selecting three pairs of target primers for each target gene, carrying out agarose gel electrophoresis and real-time quantitative PCR, and finally determining the sequence of the specific primers of the genes formed by the kit through observing specific strips and dissolution curves.

GAPDH upstream primer ACAACTTTGGTATCGTGGAAGG;

GAPDH downstream primer GCCATCACGCCACAGTTTC;

IL6 upstream primer ACTCACCTCTTCAGAACGAATTG;

IL6 downstream primer CCATCTTTGGAAGGTTCAGGTTG;

an MPO upstream primer TGCTGCCCTTTGACAACCTG;

MPO downstream primer TGCTCCCGAAGTAAGAGGGT;

VKORC1L1 upstream primer GCTCCCGTCCTGCTAAGAG;

VKORC1L1 downstream primer AAAGACCAAATCCTCGACCCC;

ZC3H12A upstream primer GGCAGTGAACTGGTTTCTGGA;

ZC3H12A downstream primer GATCCCGTCAGACTCGTAGG;

an SREBF2 upstream primer CCTGGGAGACATCGACGAGAT;

a SREBF2 downstream primer TGAATGACCGTTGCACTGAAG;

NCOA4 upstream primer GAGGTGTAGTGATGCACGGAG;

NCOA4 downstream primer GACGGCTTATGCAACTGTGAA.

4) And (5) real-time quantitative PCR detection.

Real-time quantitative PCR detection is carried out according to the following system and reaction conditions, 4 compound holes are arranged for each target gene corresponding to each sample, ct values of each reaction are recorded, detection results are expressed as delta Ct, wherein delta Ct=Ct target gene-CtGAPDH, and finally the expression multiple (1/2 delta Ct) of the target gene relative to the internal reference GAPDH is calculated.

5) And (5) calculating a risk value.

The expression fold of the gene of interest relative to GAPDH in each of the above tissues was substituted into the risk scoring formula: risk value = (-0.3243 ×il6 expression level) + (1.2845 ×mpo expression level) + (1.2139 ×vkorc1L1 expression level) + (1.1373 ×zc3H12A expression level) +(-0.6550 ×srebf2 expression level) +(-1.0469 ×ncoa4 expression level); the results showed that overall risk scores were significantly higher in glioblastoma patients than in low grade glioma patients (fig. 7C); determining that the cut-off point in glioblastoma is defined as a high risk group with the score being more than-2.065 according to the median risk value and the dispersion; less than-2.065 is defined as a low risk group; for low grade glioma patients, cutoff values of-4.095, scores greater than-4.095 are defined as high risk groups; less than-4.095 is defined as a low risk group, with poor patient prognosis in the high risk group.

Claims

1. An integrated stress related kit for prognosis evaluation prediction in glioma, characterized in that the kit comprises related reagents capable of determining the expression levels of integrated stress related model 6 genes IL6, MPO, VKORC1L1, ZC3H12A, SREBF2 and NCOA4 in glioma tissue.

2. The kit of claim 1, wherein the kit comprises: the upstream and downstream primers for IL6, MPO, VKORC1L1, ZC3H12A, SREBF and NCOA4, the upstream and downstream primers for GAPDH as an internal reference, and RNA extraction reagents: trizol, isopropanol, chloroform, absolute ethanol, reverse transcription reagents, SYBR Green polymerase chain reaction System: PCR buffer, fluorescent dye, dNTPs, RNase-free and water.

3. The kit according to claim 1, wherein the method for establishing the integrated stress model comprises the following specific steps:

4) The model was converted to a kit.

4. The kit according to claim 1, wherein the kit is used as follows:

2) Reverse transcription into cDNA template;

3) Screening five genes composing an interferon model and a specific primer of GAPDH, and carrying out real-time quantitative PCR;

4) Taking reference GAPDH as a reference, recording the Ct value of each reaction, and finally calculating the expression multiple 1/2 delta Ct of five target genes relative to the reference GAPDH, wherein the detection result is expressed as delta Ct, wherein delta Ct=Ct target gene-CtGAPDH;

5. A system for predicting glioma prognosis, comprising: the acquisition module is used for acquiring 6 gene expression level altogether of IL6, MPO, VKORC1L1, ZC3H12A, SREBF2 and NCOA 4; and the prediction module is used for predicting the risk score of the glioma according to the expression level obtained by the acquisition module and outputting the risk score, wherein the acquisition module is connected with the prediction module in a wireless and/or wired mode.

6. The system of claim 5, wherein the prediction module comprises a glioma prognosis score model comprising a mathematical formula: risk value = (-0.3243 ×il6 expression level) + (1.2845 ×mpo expression level) + (1.2139 ×vkorc1L1 expression level) + (1.1373 ×zc3H12A expression level) +(-0.6550 ×srebf2 expression level) +(-1.0469 ×ncoa4 expression level).

7. The system of claim 5, wherein the system is used as follows:

2) Reverse transcription into cDNA template;