m6Application of A modification-related combined genome in prediction of immunotherapy efficacy of renal clear cell carcinoma patient
阅读说明：本技术 m6A修饰相关联合基因组在预测肾透明细胞癌患者免疫治疗疗效中的应用 (m6Application of A modification-related combined genome in prediction of immunotherapy efficacy of renal clear cell carcinoma patient ) 是由 田熙 瞿元元 徐文浩 艾合太木江·安外尔 朱殊璇 王骏 施国海 叶定伟 于 2021-06-25 设计创作，主要内容包括：本发明涉及医学生物检测技术领域,提供了HNRNPA2B1和ALKBH5的联合基因组的新用途,具体是在制备肾透明细胞癌免疫治疗疗效预测试剂或试剂盒中的应用。同时还提供了肾透明细胞癌免疫治疗疗效预测试剂盒以及肾透明细胞癌免疫治疗疗效预测系统。本发明的基因组合来源于肾透明细胞癌m～(6)A修饰模式差异表达模式,该m～(6)A修饰相关基因组合模型的发现为预测肾透明细胞癌患者免疫治疗疗效提供了一条全新的策略,有助于指导临床医生实施个体化精准治疗策略,提高患者生存率,对于肾透明细胞癌患者的免疫治疗应用具有重要的指导意义。(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 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 curative effect of immunotherapy of renal clear cell carcinoma patients, and is helpful for guiding clinicians to implementThe precise treatment strategy is integrated, the survival rate of the patient is improved, and the method has important guiding significance for the immunotherapy application of the renal clear cell carcinoma patient.)
1.m6The application of the A modification related combined genome in preparing a renal clear cell carcinoma immunotherapy curative effect prediction agent or kit is characterized in that the combined genome is the combination of HNRNPA2B1 and ALKBH5。
2. M according to claim 16The application of the A modification related combined genome in preparing a renal clear cell carcinoma immunotherapy curative effect prediction reagent or a kit is characterized in that 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 the relative expression level of HNRNPA2B1 and ALKBH5 in a biological sample.
3. M according to claim 1 or26The application of the A modified related combined genome in preparation of a renal clear cell carcinoma immunotherapy curative effect prediction reagent or a kit is characterized in that 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. M according to claim 1 or26The application of the A modification related combined genome in preparation of a renal clear cell carcinoma immunotherapy curative effect prediction reagent or kit is characterized in that the biological sample is a tumor specimen slice excised by a renal clear cell carcinoma patient.
5. A kit for predicting the curative effect of immunotherapy of renal clear cell carcinoma is characterized in that: the kit comprises a reverse transcription system, a primer system and an amplification system, wherein the primer system comprises a PCR primer 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 value,
predictive score-1.889 HNRNPA2B1 expression level-0.451 akbh 5 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.
Technical Field
The invention belongs to the technical field of medical biological detection, and relates to a probe consisting of HNRNPA2B1 and ALKBH56The 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 and 14,770 death cases of kidney cancer exist in the whole U.S. in 2019, about 6.68 ten thousand new cases of kidney cancer patients live in China every year, 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 malignancy, accounting for about 70-85% of all renal carcinoma patients, with metastasis occurring in the initial diagnosis of about 25-30% of ccRCC patients, and 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 and CTLA4 inhibitors have rapidly risen in the field of kidney cancer therapy, and show encouraging therapeutic effects on patients refractory to advanced 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 inhibitors gradually move from the second line to the first line, and currently, 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 successively obtained by FDA, and new chapters are 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 the anti-tumor immune response of the organism. 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)6A) N1-adenylate methylation (m1A), cytosine hydroxylation (m5C) and the like. m is6A is trueMethylation modification of mRNAs with the highest abundance in nuclear cells plays an important role in chemical modification of mRNAs, miRNAs and lncrnas. Recently, several studies have revealed TME-infiltrated immune cells and m6The specific correlation between a modifications, which cannot be explained by the RNA degradation mechanism. Dali et al report YTHDF1 and coding quilt m6The transcription of the A methylation modified lysosomal protease is combined, so that the translation efficiency of lysosomal cathepsin in Dendritic Cells (DCs) is improved, and the inhibition of cathepsin in DCs remarkably enhances the capability of cross-expressing tumor antigen, thereby enhancing the anti-tumor reaction of tumor-infiltrated CD8+ T cells. The inhibitory effect of YTHDF1 also improves the therapeutic effect against PD-L1 blockade. A study by Huamin et al revealed METTL3 mediated m6The 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 is6A has close relation with tumor immunity, and m is explored6The 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 further on the basis of the above-mentioned studies, and has an object to provide a biomarker for predicting the efficacy of immunotherapy for patients with renal clear cell carcinoma, and also has an object to m of HNRNPA2B1 and ALKBH56A 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 carcinoma6Expression of A regulatory factor and identification of potential 3 m using consensus clustering6A modification pattern (Cluster1,2. 3), and found m in Cluster36Significant 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 m6The a modification pattern may be useful for 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 akbh 5 expression level. HNRNPA2B1 and ALKBH5 were first discovered as biomarkers for predicting the efficacy of immunotherapy in renal clear cell carcinoma patients.
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 prognosis agent or a kit for the curative effect of the immunotherapy of renal clear cell carcinoma is provided, wherein the prognosis agent is a reagent combination for detecting the relative expression levels of HNRNPA2B1 and ALKBH5 in a biological sample; the prediction kit comprises a reagent combination for detecting the 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 primer sequence 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 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 the immune rejection type or the immune desert type according to the score, wherein the prediction scoring is 1.889 HNRNPA2B1 expression level-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: predicting the expression level-0.451 ALKBH5 of 1.889 HNRNPA2B1, and determining whether the immune typing of the current sample belongs to the immune rejection type or the immune desert type according to the score.
The Cluster 3-regulated subtype of the immune rejection type was 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 processes, synaptic membranes, neuroactive ligand-receptor interactions and the like, but such patients can benefit from immunotherapy much more.
The invention has the following beneficial guarantee and effects:
the genome of the invention is derived from the cells involved in renal clear cell carcinoma m6The 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 result shows that the expression levels of HNRNPA2B1 and ALKBH5 have higher predicted immunityThe therapeutic efficacy value of the drug is likely to be helpful for more accurately applying the immune checkpoint inhibitor to the treatment of renal cancer patients.
Therefore, the discovery of the gene combination model provides a brand-new strategy for predicting the curative effect of immunotherapy of the renal clear cell carcinoma patient, can evaluate the possibility of response of the patient to the immunotherapy, is helpful for guiding a clinician to implement an individualized accurate therapy strategy, improves the survival rate of the patient after operation, and has important guiding significance for the follow-up monitoring and the sequential therapy management of the renal clear cell carcinoma patient after operation.
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 m6A regulatory factor expression and recognition of renal clear cell carcinoma m6Process of transcription subtype a: (A) 21 m in tumor tissue and paracancerous normal tissue6Differential distribution box plot for A regulatory factor expression, most m6The A regulatory factor has obviously different distribution in tumor tissues and paracancerous normal tissues; (B) 21 m in tumor tissue6Correlation of the expression levels of A regulatory factors in a heatmap, a variety of m can be found6The 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 classes m6A regulates the subclass to have the best effect; (D) principal component analysis showed three classes of m6The a regulatory subclass gene expression patterns differ significantly.
FIG. 2 shows exploration m6A regulates potential clinical phenotypic differences between subclasses: (A) integrate withM of bed information6A regulatory subclass gene expression heatmap showing significantly more fatality in cluster 3; (B) survival analysis showed similar overall survival to patients in cluster2 compared to cluster1, whereas overall survival was significantly worse in patients in cluster3 than in cluster1 and cluster2 (p 0.002); (C) single factor regression analysis showed m for HNRNPA2B1, ZC3H13, LRPRC, METLL14, YTHDC1, and the like6There is a significant correlation between a regulatory factor and overall survival of patients.
FIG. 3 shows exploration m6A regulates the subclasses between (cluster3 vs cluster 1)&2) Potential differences in biological function, immune checkpoint expression and immune cell infiltration components: (A) compared with cluster1 and cluster2, the kidney cancer of cluster3 regulated subtype is significantly 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 cluster32FoldChange>1，p<0.05), suggesting that cluster3 may be associated with an immunosuppressive microenvironment; (C) the CIBERSORT algorithm is used for predicting the abundance of immune cells in the microenvironment of the kidney cancer, and the result shows that the kidney cancer of cluster3 class has obviously increased CD8 positive T cells (p) compared with cluster1 and cluster2<0.001) and Tfh cell infiltration (p)<0.001) and infiltration of macrophages type M2 was significantly reduced in cluster 3.
FIG. 4 results of survival analysis of cluster1&2 and cluster3 using Kaplan-Meier method, showing that the overall survival of cluster3 patients is significantly worse than that of non-cluster 3 patients.
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 the nomogram construction, results show that the predicted scores can be effectively used for prediction of patient response to immunotherapy.
Fig. 6 is a receiver operating characteristic curve (ROC) for predicting a patient's immune therapy response in an IMvigor210 cohort using a prediction score, showing an area under the curve (AUC) of 0.65 and p <0.001, illustrating that the prediction score constructed in this project may 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 patient immunotherapy responses in the FUSCC cohort using the prediction 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-seq6The expression data of the A regulatory factor further explores m in cancer and paracancer6The 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 a consensus clustering method6A transcription regulation subtype cluster1\2\ 3.
21 m were evaluated using a one-way regression method6The prognosis value of the A regulatory factor, and single-factor regression analysis shows that IGF2BP1, HNRNPA2B1 and the like are remarkably related to poor prognosisHowever, the expression of CBLL1, FMR1, etc. is 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 m6The 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 difference of the contents of immune cells such as tumor infiltrating CD8 positive T cells of cluster3 and cluster1/2 is found to be remarkable.
Next, the reporter constructs m using binary logistic regression6A, adjusting a subtype classifier to obtain a formula: m6Ascore 1.889 HNRNPA2B1-0.451 akbh 5. The predicted score for each patient in the IMvigor210 cohort was calculated using a formula, and binary logistic analysis found that the predicted score correlates significantly with the patient 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 study of the invention comprises three stages: first, we identified probable renal cell carcinoma m in the TCGA renal carcinoma cohort using consensus clustering6A modified subtype; in the second stage, renal cell carcinoma m was explored6Clinical 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 m6The 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
One, m6Recognition 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-seq6Expression data of A regulatory factor, including 8writers (METTL3, RBM15, METTL14, RBM15B, KIAA1429, WTAP, CBLL1, ZC3H13), 2 erasers (FTO, ALKBH5) and 11 readers (YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, HNRNPC, IGF2BP1, HNRNPA2B1, LRPRC, FMR1, AVELL 1).
Further explore m in cancer and paracarcinoma6Expression difference of A regulatory factor, 21 m in tumor tissue and paracancerous normal tissue6The A regulatory factor expression is different, and most m6The 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 tissue6Correlation of the expression levels of A regulatory factors in a heatmap, a variety of m can be found6The A regulators are positively correlated, for example, the correlation coefficient between CBLL1 and YTHDF3 is 0.58, the correlation coefficient between METTL14 and YTHDC1 is 0.66, and the correlation coefficient between METTL14 and LRPRC is 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 widely applied to cancer 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 method6A transcription regulation subtype cluster1\2\3, showing that the TCGA-CCRCC is divided into the 3 types of m6A best effect of modulation subclass (fig. 1C); principal component analysis showed three classes of m6The a regulatory subclass gene expression patterns were significantly different (fig. 1D).
Two, m6Clinical phenotypic differential exploration of A transcriptional regulatory subtypes
The sex, age, tumor stage and grade of the patient were taken together, and m was searched6Clinical differences between the a regulatory subtypes showed significantly more fatality outcome in cluster3 (fig. 2A). Further analysis showed that cluster3 regulated subtype was significantly associated with poor survival and also with higher T stage. And using the Kaplan-Meier method to treat three types of m6The overall survival rate of the A regulatory subtype is compared, and the survival rate of cluster3 is far worse than that of cluster1\ 2; compared with clustere1 was similar to the overall survival of patients in cluster2, with the overall survival of patients in cluster3 being significantly worse than those in cluster1 and cluster2 (p 0.002) (fig. 2B). 21 m were evaluated using a one-way regression method6The analysis of the prognostic value of 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).
III, m6Exploration 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 m6The relation between A modification pattern and tumor immune microenvironment, all immune checkpoint related gene expressions are extracted, and compared with cluster1, cluster2 and cluster3, CTLA4, PDCD1, TNFSF14, LAG3 and the like show remarkably high expression (log)2FoldChange>1，p<0.05), suggesting that cluster3 may be associated with an immunosuppressive microenvironment, suggesting kidney cancer m6The 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 8) in cluster3 renal cancers compared to cluster1 and cluster2<0.001) and Tfh cell infiltration (p)<0.001) whereas infiltration of macrophages type M2 was significantly reduced in cluster3 (fig. 3C).
Four, m6Construction of A Regulation subtype classifier
We classified cluster1/2 into one class, and Kaplan-Meier analysis showed significantly poorer prognosis for cluster3 (FIG. 4). Construction of m Using binary logistic regression6A, adjusting a subtype classifier to obtain a formula: prediction score 1.889 HNRNPA2B1-0.451 akbh 5. 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.
Fifth, the exploration of the correlation between the prediction score and the curative effect of immunotherapy
Cluster3 differed significantly from cluster1\2 in immune checkpoint expression and used publicly published immunotherapy cohort (IMvigor210) data for prediction of immunotherapy efficacy. 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 dH2O without RNase using SYBR Green qPCR (Takara Biotechnology Co.) using the primer sequences shown in Table 1. GAPDH RNA expression was measured for normalization. According toGreen qPCR premix (Applied Biosystems) manufacturer's protocol, specific operating cycle conditions for mRNA and GAPDH were determined and passed 2-ΔΔCtThe 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 counterdenier, and the relative amounts of HNRNPA2B1 and ALKBH5 in the specimens were evaluated using RT-qPCR technique. And evaluating the prediction scores of each sample by using a formula pair, dividing the samples into a high-low score group according to the prediction scores, and staining CD3, CD4, CD8, CK, FOXP3 and PD-L1 in the samples of the high-low score group 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 were stained clearly, and the expression of PD-L1 molecule 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 predictive 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 the specificity and sensitivity of the diagnosis, and the diagnostic ability was determined by performing area under the curve (AUC) analysis.
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 obtained a predictive score. Using a selected 2 m6The 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 m6A modifies the associated unique gene expression subtype. And found that m is of one class6The 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 by using the classifier and the prediction score, wherein the prediction score is a combined genome of HNRNPA2B1 and ALKBH 5.
The combined genome of the invention has obvious verification results in IMvigor210 cohort and real-world cohort of FUSCC, and can effectively predict the response of patients to immunotherapy. Thus, in this study, systematic analysis of the primary tissues of ccRCC can screen and identify promising biomarker expression profiles to predict the efficacy of immunotherapy.
In summary, the present study identifies m likely to be involved in the formation of ccRCC inhibitory immune microenvironment6And (B) modified subtype A. The expression levels of HNRNPA2B1 and ALKBH5 have higher value of predicting the curative effect of immunotherapy, and are possibly helpful for more accurately applying immune checkpoint inhibitors to renal cancer patients for treatment.
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
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<211> 19
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 3
ccctgctctg aaacccaag 19
<210> 4
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 4
gttctcttcc ttgtccatct cc 22
<210> 5
<211> 22
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 5
gtcttctcca ccatggagaa gg 22
<210> 6
<211> 23
<212> DNA
<213> Artificial Sequence (Artificial Sequence)
<400> 6
catgccagtg agcttcccgt tca 23