CN113462776B - m 6 Application of A modification-related combined genome in prediction of immunotherapy efficacy of renal clear cell carcinoma patient - Google Patents
m 6 Application of A modification-related combined genome in prediction of immunotherapy efficacy of renal clear cell carcinoma patient Download PDFInfo
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Abstract
The invention relates to the technical field of medical biological detection, and provides a new application of a combined genome of HNRNPA2B1 and ALKBH5, in particular to an application in preparation of a renal clear cell carcinoma immunotherapy curative effect prediction reagent or a kit. Also provides a kit for predicting the curative effect of the immunotherapy of the renal clear cell carcinoma and a system for predicting the curative effect of the immunotherapy of the renal clear cell carcinoma. The genome of the present invention is derived from renal clear cell carcinoma m 6 A modification pattern differential expression pattern, m 6 The discovery of the A modification related gene combination model provides a brand-new strategy for predicting the immunotherapy curative effect of the renal clear cell carcinoma patient, is beneficial to guiding a clinician to implement an individualized accurate treatment strategy, improves the survival rate of the patient, and has important guiding significance for the immunotherapy application of the renal clear cell carcinoma patient.
Description
Technical Field
The invention belongs to the technical field of medical biological detection, and relates to a probe consisting of HNRNPA2B1 and ALKBH5 m 6 The application of the A modification related combined genome as a marker, in particular to the application of the combined genome in preparing a kit and a prediction system for predicting the curative effect of the immunotherapy of renal clear cell carcinoma.
Background
Renal cell carcinoma is one of the most common malignant tumors of the urogenital system, accounting for about 5% of all adult male new cases and 3% of female new cases. According to statistics, about 73,820 new cases of kidney cancer and 14,770 death cases exist in the whole U.S. in 2019, about 6.68 ten thousand cases of new kidney cancer patients occur every year in China, and the second place of the incidence rate of the tumors in the urinary system is in China. Clear cell renal carcinoma (ccRCC) is the most common pathological type of renal carcinoma with high degree of malignancy, accounting for about 70-85% of all renal carcinoma patients, with metastasis occurring in about 25-30% of ccRCC patients at first visit, while the 5-year survival rate of metastatic ccRCC is only 32%. For the tumor in the clinical local period, the treatment means still mainly reserves nephron operation or radical nephrectomy intervention, and further cytokine or individualized accurate adjuvant therapy after the operation can reduce the recurrence and metastasis rate of the tumor and improve the long-term survival rate of the patient. At present, first-line treatment drugs for advanced kidney cancer mainly comprise Tyrosine Kinase Inhibitors (TKI) targeting Vascular Endothelial Growth Factor Receptors (VEGFRs), such as pezopanib, sunitinib, cabozantinib, and acitinib. Although the anti-angiogenesis drugs can inhibit tumor proliferation to a certain extent and can remarkably prolong the survival of low-risk ccRCC patients, the drugs have obvious side effects and poor overall curative effect, the objective response rate of treatment is only less than 30%, and the prolonged median total survival time is also less than 12 months. Furthermore, even patients who are initially therapeutically effective will develop disease progression over time, when most patients will lack a subsequently effective treatment.
In recent years, novel immunotherapies represented by PD-1/PD-L1, CTLA4 inhibitors have rapidly emerged in the field of kidney cancer therapy, showing encouraging efficacy in patients with advanced refractory stages. Since 2015, the FDA approves the application of the Nabriuyuzumab to the advanced renal cell carcinoma patient who has received anti-angiogenesis drug treatment based on the Checkmate025 research, and the ASCO GU publishes the 5-year follow-up result of the Checkmate025 research in 2020, the result shows that the 5-year survival rate of the Nabriuyuzumab in second-line treatment is as high as 26%, and the survival benefit advantage of immunotherapy is shown. Subsequently, the immune checkpoint inhibitor gradually moves from the second line to the first line, and the first-line treatment of advanced renal cancer by combining PD-1 monoclonal antibody with CTLA-4 monoclonal antibody and PD-1/PD-L1 monoclonal antibody with anti-angiogenesis drug is obtained in the FDA sequentially at present, and a new chapter is introduced for the treatment of advanced renal cancer. The immune checkpoint inhibitor and TKI play a role in various aspects of inducing normalization of anti-Tumor immunity, inhibiting a main signal pathway of occurrence and development of late-stage renal cancer and regulating Tumor Microenvironment (TME), and the success of the immune checkpoint inhibitor and TKI depends on deep understanding of interaction of Tumor cells and TME. With the progress of research, more evidences show that the curative effect of immunotherapy depends on the activation of tumor immune microenvironment, and the curative effect of traditional treatment means such as targeted therapy also depends on the strength of anti-tumor immune response of organisms. The cells and molecules in TME are in a dynamically changing process, reflecting the evolutionary nature of cancer, and together promote immune escape, growth and metastasis of tumors. Exploring the potential mechanism of TME-driven tumorigenesis and development has important significance for developing potential methods for cancer treatment, improving the effective rate of various existing treatment means and discovering new accurate targets for treating kidney cancer.
The most common chemical modification of RNA involves N6-adenylate methylation (m) 6 A) N1-adenylate methylation (m 1A), cytosine hydroxylation (m 5C), and the like. m is 6 A is methylation modification with the highest abundance of mRNAs in eukaryotic cells, and plays an important role in chemical modification of mRNAs, miRNAs and lncRNA. Recently, several studies have revealed TME-infiltrated immune cells and m 6 The specific correlation between a modifications, which cannot be explained by the RNA degradation mechanism. Dali et al report YTHDF1 and codified envelope m 6 The transcript binding of A methylation modified lysosomal protease improves the translation efficiency of lysosomal cathepsin in Dendritic Cells (DCs), and inhibition of cathepsin in DCs significantly enhances the ability to cross-express tumor antigen, thereby enhancing the anti-tumor response of tumor-infiltrating CD8+ T cells. The inhibition of YTHDF1 also improves the efficacy of anti-PD-L1 blockade. A study by Huamin et al revealed METTL3 mediated m 6 The a modification promotes activation and maturation of DCs. The specific depletion of METTL3 results in decreased expression of the co-stimulatory molecules CD80 and CD40, reducing the ability to stimulate T cell activation. m is 6 A has close relation with tumor immunity, and m is explored 6 The difference in A modification pattern is likely to provide a powerful help for the precise immunotherapy of kidney cancer.
In the last decade, high throughput techniques in combination with bioinformatic analysis have been widely used to detect comprehensive mRNA expression levels, which has helped identify Differentially Expressed Genes (DEG) and explore markers closely related to the components of the ccRCC immune microenvironment.
Disclosure of Invention
The present invention has been made in view of the above-mentioned studies, and it is an object of the present invention to provide a biomarker for predicting the efficacy of immunotherapy for patients with renal clear cell carcinoma, and it is also an object of the present invention to provide a biomarker for predicting the efficacy of immunotherapy for patients with renal clear cell carcinoma, and m of HNRNPA2B1 and ALKBH5 6 A is a new application of the modification-related combined genome, namely an application in a renal clear cell carcinoma immunotherapy curative effect prediction kit and a prediction system.
The inventors downloaded gene expression and survival data of patients with renal clear cell carcinoma from TCGA through complicated bioinformatics screening in the early stage, and then extracted 21 m from expression matrix of renal clear cell carcinoma 6 Expression of A regulatory factor and identification of potential 3 m using consensus clustering 6 A modification patterns ( Cluster 1, 2, 3) and m was found in Cluster3 6 Significant correlation exists between A modification pattern and poor prognosis of patients and higher immune checkpoint expression in tumor specimens, and the suggestion is that m is similar to m 6 The a modification pattern may be useful in the prediction of immunotherapy response.
Further, the inventors have constructed a transcriptome classifier in this way, and since there is a lack of available renal cancer immunotherapy cohort data, the inventors have used this classifier in the published IMvigor210 cohort of bladder cancer, and found that this classifier can predict the patient's response to immunotherapy with great strength. Using binary logistic regression, the inventors simplified the classifier to the formula: predictive score =1.889 hnrnpa2b1 expression level-0.451 alkbh5 expression level. HNRNPA2B1 and ALKBH5 are found for the first time as biomarkers for predicting the curative effect of immunotherapy of patients with renal clear cell carcinoma.
In a first aspect of the invention, there is provided the use of the combined genome of HNRNPA2B1 and ALKBH5 as a biomarker for predicting the efficacy of immunotherapy for patients with renal clear cell carcinoma.
In a second aspect of the invention, the application of the combined genome in preparing a renal clear cell carcinoma immunotherapy curative effect prediction reagent or a kit is provided, wherein the prediction reagent is a reagent combination for detecting relative expression levels of HNRNPA2B1 and ALKBH5 in a biological sample; the prediction kit comprises a reagent combination for detecting relative expression levels of HNRNPA2B1 and ALKBH5 in a biological sample.
Preferably, the reagent combination is used for detecting the mRNA expression level of the gene, the reagent combination comprises a PCR primer with detection specificity to the gene, and the sequence of the primer is shown in the following table SEQ ID NO. 1-4.
Preferably, the biological sample is a tumor specimen section surgically excised from a patient with renal clear cell carcinoma.
In a third aspect of the invention, a kit for predicting the curative effect of immunotherapy on renal clear cell carcinoma is provided, which comprises a reverse transcription system, a primer system and an amplification system, wherein the primer system comprises PCR primers shown as SEQ ID NO. 1-4.
In a fourth aspect of the invention, a renal clear cell carcinoma immunotherapy efficacy prediction system is provided, which comprises a prediction reagent kit and an immune subset classification model installed on a terminal carrier. The prediction reagent kit detects the relative expression level of the marker genes as described above, the immune subgroup classification model carries out prediction scoring according to the following formula, and determines whether the immune typing of the current sample belongs to immune rejection type or immune desert type according to the score, and the prediction scoring =1.889 expression level of HNRNPA2B1-0.451 ALKBH5 expression level.
Preferably, the immune subpopulation classification model is constructed according to binary logistic regression and uses binary logistic analysis to predict a significant correlation of the prediction score to the patient's immunotherapy.
In a fifth aspect of the present invention, there is provided a method for predicting an immune therapeutic effect of renal clear cell carcinoma using the immune therapeutic effect prediction system, comprising the steps of:
(a) Carrying out reverse transcription and amplification on a tumor sample by using a reagent in the kit to obtain the mRNA expression level of each gene;
(b) The renal clear cell carcinoma immunotherapy prediction score is calculated according to the following formula: predictive score = 1.889-0.451-ALKBH5 expression level of HNRNPA2B1, and whether the immunotype of the current sample is immune rejection type or immune desertification type is determined according to the score.
The Cluster3 regulatory subtype of the immune rejection type is significantly associated with poor survival and also with higher T stage. Compared with cluster1/2, the gene expression profile of cluster3 is significantly enriched in biological processes such as steroid metabolic process, synaptic membrane, neuroactive ligand-receptor interaction and the like, but the patients can benefit from immunotherapy.
The invention has the following beneficial guarantee and effects:
the genome of the invention is derived from a cell involved in renal clear cell carcinoma 6 The gene combination formed by the A modified transcription subtype has obvious verification results in an IMvigor210 queue and a real-world queue of FUSCC, and can effectively predict the response of a patient to immunotherapy. The expression levels of HNRNPA2B1 and ALKBH5 are proved to have higher value for predicting the curative effect of immunotherapy, and the application of immune checkpoint inhibitor therapy on renal cancer patients more accurately is possibly facilitated.
Therefore, the discovery of the gene combination model provides a brand-new strategy for predicting the immunotherapy curative effect of the renal clear cell carcinoma patient, can evaluate the possibility of the patient responding to the immunotherapy, is beneficial to guiding clinicians to implement individualized accurate treatment strategies, improves the postoperative survival rate of the patient, and has important guiding significance for postoperative follow-up monitoring and sequential therapy management of the renal clear cell carcinoma patient.
In terms of technology, two kinds of gene expression level detection are essentially quantitative PCR detection of tissue samples, have the characteristics of simple and convenient operation, sensitive detection, good specificity, high repeatability and the like, and are increasingly applied to clinical examination technology nowadays. The technology is proved to be a high-sensitivity and high-accuracy detection method in modern experimental diagnostics, and the experimental technology is mature. Moreover, the standard curve quantitative method in the technology can accurately quantify the characteristic nucleic acid molecules in various samples.
Drawings
FIG. 1 is a graph showing the differentiation of tumor tissue from normal tissue m 6 A regulatory factor expression and recognition of renal clear cell carcinoma m 6 Process of transcription subtype a: (A) 21 m in tumor tissue and paracancerous normal tissue 6 Differential distribution box plot for A regulatory factor expression, most m 6 The A regulatory factor has obviously different distribution in tumor tissues and paracancerous normal tissues; (B) 21 m in tumor tissue 6 Correlation of the expression levels of A regulatory factors in a heatmap, a variety of m can be found 6 The expression of the A regulatory factors is positively correlated, for example, the correlation coefficient of CBLL1 and YTHDF3 is 0.58, the correlation coefficient of METTL14 and YTHDC1 is 0.66, and the correlation coefficient of METTL14 and LRPRC is 0.6; (C) Consensus clustering showed that TCGA-CCRCC was classified into 3 types m 6 A regulates the subclass to have the best effect; (D) Principal component analysis showed three classes of m 6 The a regulatory subclass gene expression patterns differ significantly.
FIG. 2 shows exploration m 6 A regulates potential clinical phenotypic differences between subclasses: (A) M with integrated clinical information 6 A modulation subclass gene expression heatmap showing significantly more fate in cluster 3; (B) Survival analysis showed that overall survival compared to cluster1 was similar to that of patients in cluster2, while overall survival of patients in cluster3 was significantly worse than that of patients in cluster1 and cluster2 (p = 0.002); (C) Single factor regression analysis showed that m is selected from HNRNPA2B1, ZC3H13, LRPRC, METLL14, YTHDC1, and the like 6 There is a significant correlation between a regulatory factor and overall survival of patients.
FIG. 3 shows exploration m 6 A regulates the subclasses between (cluster 3 vs cluster 1)&2) Potential differences in biological function, immune checkpoint expression and immune cell infiltration components: (A) Compared with cluster1 and cluster2, the cluster 3-regulated subtype kidney cancer is obviously enriched in the biological processes of steroid metabolism, synaptic membrane function, receptor ligand activity and the like; (B) Differential analysis shows that CTLA4, PDCD1, TNFSF14, LAG3 and the like show remarkably high expression (log) compared with cluster1, cluster2 and cluster3 2 FoldChange>1，p<0.05 Suggest that cluster3 may be exempt fromAn association exists between epidemic inhibitory microenvironments; (C) The CIBERSORT algorithm is used for predicting the abundance of immune cells in a renal cancer microenvironment, and the result shows that the cluster3 renal cancers have obviously increased CD8 positive T cells (p) compared with cluster1 and cluster2<0.001 And Tfh cell infiltration (p)<0.001 Whereas infiltration of M2-type macrophages was significantly reduced in cluster 3.
FIG. 4 shows that the overall survival of cluster3 patients is significantly worse than that of non-cluster 3 patients by using Kaplan-Meier method for the survival analysis of cluster1&2 and cluster 3.
Figure 5 is a nomogram of the correlation of prediction scores with patient response to immunotherapy. Inclusion of PDL1, PD1, CTLA4, LAG3 four immune checkpoint molecules into a nomogram construct, results show that predictive scores can be effectively used for prediction of a patient's response to immunotherapy.
Figure 6 is a receiver operating characteristic curve (ROC) for predicting a patient's immune therapy response in an IMvigor210 cohort using predictive scores showing an area under the curve (AUC) of 0.65, p < -0.001, demonstrating that predictive scores constructed in this project can better predict a patient's response to immune therapy.
FIG. 7 is an immune microenvironment of a primary focal specimen of renal clear cell carcinoma with a high predictive score in a patient who received immunotherapy;
FIG. 8 is an immune microenvironment of a primary renal clear cell carcinoma specimen with a low predictive score in a patient who received immunotherapy;
fig. 9 is a ROC curve for the assessment of 98 patients' immunotherapy response in a FUSCC cohort using predictive scores.
Detailed Description
The following embodiments are implemented on the premise of the technical scheme of the present invention, and give detailed implementation modes and specific operation procedures, but the protection scope of the present invention is not limited to the following embodiments.
The reagents and starting materials used in the present invention are commercially available or can be prepared according to literature procedures. The experimental procedures, in which specific conditions are not noted in the following examples, are generally carried out according to conventional conditions or according to conditions recommended by the manufacturers.
In the following examples, tumor tissue samples from renal clear cell carcinoma patients were obtained from the subsidiary tumor hospital of the university of Compound Dane, and were clearly diagnosed as renal clear cell carcinoma by a pathologist.
The invention aims to explore and investigate the multi-queue-based immunotherapy efficacy prediction gene expression profile. RNA-seq data for 602 clear cell renal carcinoma tumors and paracancerous normal tissues (72 paracancerous normal tissues and 530 tumor tissues) were downloaded from The Cancer Genome Atlas (TCGA). Then 21 m were extracted from RNA-seq 6 The expression data of the A regulatory factor further explores m in cancer and paracancer 6 The expression difference of the A regulatory factor and the interaction relationship between the A regulatory factor and the A regulatory factor. Three types of m are identified by using consensus clustering method 6 A transcriptional regulation subtype Cluster1\ 2\3.
21 m were evaluated using a one-way regression method 6 The prognosis value of the A regulatory factor, single-factor regression analysis shows that IGF2BP1, HNRNPA2B1 and the like are significantly related to poor prognosis, while the expression of CBLL1, FMR1 and the like are significantly related to good prognosis. The analysis shows that the survival of cluster3 is far worse than that of cluster1\ 2. All immune checkpoint related genes are expressed and extracted, and PDCD1, CTLA4, LAG3 and the like are found to be remarkably highly expressed in cluster 3. Prompting us to kidney cancer m 6 The a regulatory subtype may be associated with the immune microenvironment. Further, the composition of tumor infiltrating lymphocytes is evaluated by using a CIBERSORT algorithm, and the content difference of immune cells such as tumor infiltrating CD8 positive T cells of cluster3 and cluster1/2 is found to be obvious.
Next, the reporter constructs m using binary logistic regression 6 A, adjusting a subtype classifier to obtain a formula: m6Ascore =1.889 hnrnpa2b1-0.451 alkkh5. The predicted score for each patient in the IMvigor210 cohort was calculated using a formula, and binary logistic analysis found that the predicted score was significantly correlated with the patient's response to immunotherapy (p < 0.0001) and the area under the model ROC curve (AUC) was 0.65. And the relationship of the prediction score to the patient immunotherapy response was verified in the FUSCC cohort using RT-qPCR techniques.
The research of the invention comprises three stages: first, we identified probable renal cell carcinoma m in the TCGA renal carcinoma cohort using consensus clustering 6 A modified subtype; in the second stage, renal cell carcinoma m was explored 6 Clinical phenotypic differences and possible biological differences between the a modified subtypes and differences in immune checkpoint expression and immune cell infiltration were explored; in the third stage, based on m 6 The a modified subtype constructed a classifier and then explored, validated the predictive role of the predictive score on patient immunotherapy response using the IMvigor210 cohort and real world data. The following detailed description is made in conjunction with the accompanying drawings.
Example 1: construction of immunotherapy prediction scores
1. m is 6 Recognition of A transcriptional regulatory subtype
RNA-seq data for 602 clear cell renal carcinoma tumors and paracancerous normal tissues (72 paracancerous normal tissues and 530 tumor tissues) were downloaded from The Cancer Genome Atlas (TCGA). Then, the applicant extracted 21 m from the RNA-seq 6 Expression data of A regulatory factor, including 8 writers (METTL 3, RBM15, METTL14, RBM15B, KIAA1429, WTAP, CBLL1, ZC3H 13), 2 erasers (FTO, ALKBH 5) and 11 readers (YTHDF 1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, HNRNPC, IGF2BP1, HNRNPA2B1, LRPRC, FMR1, ELAVL 1).
Further explore m in cancer and paracarcinoma 6 Expression difference of A regulatory factor, 21 m in tumor tissue and paracancerous normal tissue 6 The A regulatory factor expression is different, and most m 6 The A regulatory factor has a significantly different distribution in tumor tissues and paracancerous normal tissues (FIG. 1A); at the same time, the interaction relationship between them is explored, 21 m in the tumor tissue 6 Correlation of the expression levels of A regulatory factors in a heatmap, a variety of m can be found 6 The A regulators have positive correlation, such as correlation coefficient of CBLL1 and YTHDF3 of 0.58, correlation coefficient of METTL14 and YTHDC1 of 0.66, and correlation coefficient of METTL14 and LRPRC of 0.6 (FIG. 1B).
Consensus clustering is a method of providing quantitative evidence for determining the number and membership of possible clusters in a dataset. The method is used for treating cancerThe method is widely applied to genomics, and a plurality of authoritative omics researches identify a plurality of novel disease molecule subclasses through the clustering method. In the invention, three types of m are identified by using a consensus clustering method 6 A transcription regulation subtype Cluster1\ 2\, 3, showing the classification of TCGA-CCRCC as this 3 class m 6 A best effect of modulation subclass (fig. 1C); principal component analysis showed three classes of m 6 The a regulatory subclass gene expression patterns were significantly different (fig. 1D).
2. m is 6 Clinical phenotypic differential exploration of A transcriptional regulatory subtypes
The sex, age, tumor stage and grade of the patient were taken together, and m was searched 6 Clinical differences between the a regulatory subtypes showed significantly more fatality outcome in cluster3 (fig. 2A). Further analysis showed that cluster3 regulatory subtype was significantly associated with poor survival and also with higher T-staging. And using the Kaplan-Meier method to treat three types of m 6 Comparing the overall survival rates of the A regulation subtypes, and finding that the survival of cluster3 is far worse than that of cluster1\ 2; overall survival was significantly worse in cluster3 than in cluster1 and cluster2 (p = 0.002) compared to similar overall survival in cluster1 and cluster2 patients (fig. 2B). 21 m were evaluated using a one-way regression method 6 The analysis of the prognostic value of the a regulatory factor shows that IGF2BP1, HNRNPA2B1, etc. are significantly associated with poor prognosis, while expression of CBLL1, FMR1, etc. are significantly associated with good prognosis (fig. 2C).
3. m is 6 Exploration of potential biological differences in A transcriptional regulatory subtypes
Differential gene analysis was performed using limma algorithm for cluster3 and cluster1/2 (fold difference greater than 2, p less than 0.05 considered significant). Then, functional enrichment analysis is carried out on the differential genes, and compared with cluster1/2, the gene expression profile of cluster3 is obviously enriched in the biological processes such as steroid metabolic process, synaptic membrane, neuroactive ligand-receptor interaction and the like (figure 3A); to explore m 6 The relationship between the A modification mode and the tumor immune microenvironment extracts the expression of all the relevant genes of the immune check points, and compared with cluster1, cluster2 and cluster3, CTLA4, PDCD1 and TNFSF14 and LAG3, etc. all showed significantly high expression (log) 2 FoldChange>1，p<0.05 Suggesting that cluster3 may be associated with an immunosuppressive microenvironment, suggesting kidney cancer m 6 The a regulatory subtype may be associated with the immune microenvironment (fig. 3B); further using the CIBERSORT algorithm to predict the abundance of immune cells in the renal cancer microenvironment, the results show that there are significantly increased CD8 positive T cells (p) in cluster3 renal cancers compared to cluster1 and cluster2 (p)<0.001 And Tfh cell infiltration (p)<0.001 Whereas infiltration of M2-type macrophages was significantly reduced in cluster3 (fig. 3C).
4. m is 6 Construction of A Regulation subtype classifier
We classified cluster1/2 as a class and Kaplan-Meier analysis showed significantly poorer cluster3 prognosis (FIG. 4). Construction of m Using binary logistic regression 6 A, adjusting a subtype classifier to obtain a formula: predictive score =1.889 hnrnpa2b1-0.451 alkbh5. The classifier successfully divides the matrix into cluster1/2 and cluster3, the fitness with the original classification is high, and the AUC value matched with the original classification reaches 0.985.
5. Exploration of correlation between predictive scores and immunotherapeutic efficacy
Cluster3 is significantly different from cluster1\ 2 in immune checkpoint expression, and the published data of an immunotherapy cohort (IMvigor 210) is used for predicting the curative effect of immunotherapy. The predictive score of each patient in the IMvigor210 cohort was calculated using a formula, and then a nomogram was drawn (FIG. 5), and the utility value of the predictive score was explored, and binary logistic analysis found that the predictive score was significantly correlated with the corresponding immunotherapy of the patient (p < 0.0001), and the area under the model ROC curve (AUC) was 0.65 (FIG. 6).
Example 2 external authentication
From 6 months to 9 months of 2020 in 20018, 98 surgical specimens of ccRCC patients treated with immune checkpoint inhibitors from urology surgery in the affiliated shanghai tumor center of the university of fudan were selected. Tissue samples including ccRCC and normal tissue were collected during surgery and were obtained from the FUSCC tissue bank.
1. Real-time quantitative PCR (RT-qPCR) analysis
Total RNA was isolated from the harvested cells by Trizol (Invitrogen, carlsbad, CA). It was reverse transcribed into cDNA using PrimeScript RT kit (Termo Fisher, USA). The primers were diluted and mixed in dnase-free dH2O using SYBR Green qPCR method (takara biotechnology co.) using the primer sequences shown in table 1. GAPDH RNA expression was measured for normalization. According to
Green qPCR premix (Applied Biosystems) manufacturer's protocol, specific operating cycle conditions for mRNA and GAPDH were determined and passed 2 -ΔΔCt The relative expression level of the target mRNA was calculated.
TABLE 1 primer sequences summary of three genes in qRT-PCR
2. Multi-marker immunohistochemical staining identification of immune microenvironment difference of high and low prediction score groups
Primary specimens of renal clear cell carcinoma of 98 patients who received immunotherapy were collected from the subsidiary tumor hospital (FUSCC) at the university of fudan, and the relative amounts of HNRNPA2B1 and ALKBH5 in the specimens were evaluated using RT-qPCR technique. And the prediction scores of each sample are evaluated by using a formula pair, then the samples are divided into a high-low score group according to the prediction scores, and the CD3, CD4, CD8, CK, FOXP3 and PD-L1 in the samples of the high-low score group are stained by using a multi-marker staining technology to evaluate the immune microenvironment change of the high-low score group. We found preliminarily that in the high prediction scoring group, molecules such as PD-L1, CD8, FOXP3, etc. were stained clearly, and the expression of PD-L1 molecules was significantly increased, consistent with the trend of the renal clear cell carcinoma cohort of TCGA in the early work (fig. 7). Whereas in the low prediction scoring group, the molecular staining was weak for PD-L1, CD8, CD4, etc. (fig. 8). Suggesting that the high predictive score group of renal cancers may be a functional inhibitory immune microenvironment.
3. Statistical analysis of Artificial sequence in FUSCC queue
Different mRNA expression of 2 marker genes was analyzed in ccRCC samples and a predictive score was determined for each patient, the predictive score being determined as the sum of the weights of each important oncogenic center gene. Correlations between prediction scores and immunotherapy responses were evaluated. Receiver Operating Characteristic (ROC) curves were constructed to verify specificity and sensitivity of the diagnosis, and area under the curve (AUC) analysis was performed to determine diagnostic capability.
4. Analysis of results
After integrating RT-qPCR and clinical follow-up data for 98 ccRCC patients, we validated HNRNPA2B1 and ALKBH5 mRNA expression in the FUSCC cohort and yielded predictive scores. Using a selected 2 m 6 The a modification related gene constructs an integrated genome which can be used as an independent method for predicting the immunotherapy response of ccRCC patients. ROC curves were generated to identify the ability of gene models to predict the efficacy of immunotherapy. The AUC index of the integrated model was 0.752 (FIG. 9), p for patient immunotherapy efficacy<0.001, the stability and effectiveness of the prediction score for the immunotherapy efficacy prediction is verified.
In conclusion, the invention utilizes the microarray data analysis of the well-characterized and complete ccRCC primary tumor system to reveal the tumor m 6 A modifies the associated unique gene expression subtype. And find m in one class 6 The A transcription subtype has obviously poor prognosis, the molecular expression of the immune check point is obviously improved, and the infiltration components of immune cells are obviously different. And constructing a classifier and a prediction score based on the classifier and the prediction score, wherein the prediction score is a combined genome of HNRNPA2B1 and ALKBH5.
The combined genome of the invention has obvious results of IMvigor210 queue and real-world queue verification of FUSCC, and can effectively predict the response of a patient to immunotherapy. Thus, in this study, systemic analysis of the ccRCC primary tissue can screen and identify promising biomarker expression profiles to predict immunotherapeutic efficacy.
In summary, the present study identifies m likely to be involved in the formation of ccRCC inhibitory immune microenvironment 6 And (B) modified subtype A. The expression levels of HNRNPA2B1 and ALKBH5 have higher value for predicting the curative effect of immunotherapyIt may be helpful to apply immune checkpoint inhibitor therapy more accurately in renal cancer patients.
While the preferred embodiments of the present invention have been described in detail, it will be understood by those skilled in the art that the invention is not limited thereto, and that various changes and modifications may be made without departing from the spirit of the invention, and the scope of the appended claims is to be accorded the full scope of the invention.
Sequence listing
<110> affiliated tumor hospital of double-denier university
Application of <120> m6A modification-related combined genome in predicting immunotherapy efficacy of renal clear cell carcinoma patients
<130> specification of claims
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Claims (7)
1. Detecting m in a biological sample 6 The application of the reagent for modifying the relative expression level of each gene of the related combined genome A in preparing a pretreatment reagent or a kit for the curative effect of the immunotherapy of the renal clear cell carcinoma is characterized in that the combined genome is the combination of HNRNPA2B1 and ALKBH5.
2. The use according to claim 1, wherein the prediction kit comprises a combination of reagents for detecting the relative expression levels of HNRNPA2B1 and ALKBH5 in a biological sample.
3. The use according to claim 1 or2, wherein the reagent combination comprises a PCR primer with detection specificity for the gene, and the sequence of the PCR primer is shown as SEQ ID No. 1-4.
4. The use of claim 1 or2, wherein the biological sample is a section of a tumor specimen surgically removed from a patient with renal clear cell carcinoma.
5. A kit for predicting the curative effect of immunotherapy of renal clear cell carcinoma is characterized in that: the kit consists of a reverse transcription system, a primer system and an amplification system, wherein the primer system comprises PCR primers shown as SEQ ID NO. 1-4.
6. A kidney clear cell carcinoma immunotherapy curative effect prediction system is characterized by comprising a prediction kit and an immune subset classification model arranged on a terminal carrier,
the prediction kit is the kit of claim 5, the immune subgroup classification model carries out prediction scoring according to the following formula and determines whether the immune type of the current sample belongs to immune rejection type or immune desert type according to the score,
predictive score =1.889 hnrnpa2b1 expression level-0.451 alkbh5 expression level.
7. The renal clear cell carcinoma immunotherapy efficacy prediction system according to claim 6,
wherein the immune subpopulation classification model is constructed according to binary logistic regression and predicts a significant correlation of the prediction score to the immunotherapy in the patient using binary logistic analysis.
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