Application of ZMYM1 as biomarker in diagnosis and / or treatment of liver cancer

Abstract

The invention discloses application of ZMYM1 as a biomarker in liver cancer diagnosis and / or treatment. The expression of ZMYM1 in liver cancer patients is up-regulated. Database analysis, cell experiments and animal model experiments prove that ZMYM1 is a cancer promoting gene of liver cancer, ZMYM1 interacts with METTL3 to promote migration of tumor cells and improve the inflammation level in a tumor microenvironment, and finally the effect of promoting the progress of liver cancer is achieved. Based on TCGA, GEO, ICGC and NGDC databases, the application of the ZMYM1 as a biomarker in liver cancer diagnosis, liver cancer prognosis risk assessment, liver cancer immunotherapy effect prediction and targeted drug therapy effect prediction is provided according to a bioinformatics analysis result, so that a prevention scheme or a treatment scheme is clinically provided for a subject, and the clinical application of the ZMYM1 in liver cancer diagnosis, liver cancer prognosis risk assessment, liver cancer immunotherapy effect prediction and targeted drug therapy effect prediction is promoted. The precise molecular treatment of the liver cancer is realized.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and specifically to the application of ZMYM1 as a biomarker in the diagnosis and / or treatment of liver cancer. Background Art

[0002] The ZMYM1 gene (Zinc Finger MYM-Type Containing 1) encodes a protein containing a MYM-type zinc finger domain and is involved in the regulation of gene expression. This protein may play an important role in biological processes such as transcriptional regulation, cell differentiation and proliferation by interacting with other transcription factors or chromatin remodeling complexes. ZMYM1 is a new member known to be involved in carcinogenesis. For example, in 60 cases of primary B-cell precursor acute lymphoblastic leukemia (BCP-ALL), ZMYM1 was considered to be one of the potential target genes affected by the minimal alteration region. Another deep learning-based framework applied to survival-related predictions in colon cancer showed that ZMYM1 is a potential biomarker for predicting progression and survival.

[0003] Notably, ZMYM1 has been highlighted as a target of methyltransferase 3 (METTL3), an mRNA highly expressed in hepatocellular carcinoma (LIHC). 6 A methyltransferase. The dysregulation of METTL3 has been shown to promote the occurrence and development of various cancers. The effect of the interaction between ZMYM1 and METTL3 in liver cancer has not been clearly reported. Summary of the invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0005] To this end, the present invention proposes the use of a reagent for detecting ZMYM1 in the preparation of a product for diagnosing liver cancer. ZMYM1 is a liver cancer biomarker, and its expression level can be used for the diagnosis of liver cancer and the prediction of prognosis.

[0006] The present invention also proposes the use of ZMYM1 as a biomarker.

[0007] The present invention also provides the use of substances for inhibiting the expression of ZMYM1.

[0008] The present invention also provides a kit.

[0009] According to a first aspect of the present invention, a method for preparing a product for diagnosing liver cancer is provided.

[0010] In some embodiments of the present invention, the ZMYM1 expression is upregulated in liver cancer patients.

[0011] In some embodiments of the present invention, the product detects the expression level of ZMYM1 by at least one of RT-PCR, real-time fluorescence quantitative PCR, in situ hybridization, northern blotting, chip and high-throughput sequencing platform.

[0012] In some embodiments of the present invention, the product for detecting the expression level of ZMYM1 by in situ hybridization includes a probe that hybridizes with the nucleic acid sequence of the ZMYM1 gene.

[0013] In some embodiments of the present invention, the product for detecting the expression level of ZMYM1 by means of a chip comprises a probe that hybridizes with the nucleic acid sequence of the ZMYM1 gene.

[0014] In some embodiments of the present invention, the reagent for detecting ZMYM1 includes a primer pair for amplifying ZMYM1.

[0015] In some embodiments of the present invention, the primer pair includes a primer pair as shown in SEQ ID NO:6 and SEQ ID NO:7.

[0016] In some embodiments of the present invention, the product for diagnosing liver cancer can diagnose liver cancer by detecting the expression level of ZMYM1 in a sample to be tested, and the sample to be tested includes but is not limited to tumor tissue and blood samples.

[0017] According to the second aspect of the present invention, the application of ZMYM1 as a biomarker in the prognostic risk assessment of liver cancer patients is proposed.

[0018] In some embodiments of the present invention, the ZMYM1 expression is upregulated in liver cancer patients with poor prognosis and downregulated in liver cancer patients with good prognosis.

[0019] According to the third aspect of the present invention, the application of ZMYM1 as a biomarker in predicting the effect of immunotherapy on liver cancer patients is proposed.

[0020] In some embodiments of the present invention, the expression level of ZMYM1 is negatively correlated with an immune-related score, and the immune-related score includes a Stromal score, an Immune score, and an ESTIMATE score calculated by an ESTIMATE algorithm.

[0021] In some embodiments of the present invention, the expression level of ZMYM1 is positively correlated with immunotherapy escape-related scores, and the immunotherapy escape-related scores include TIDE score, Exclusion score, IFNG score, Dysfunction score, MSI score and MDSC score.

[0022] In some embodiments of the present invention, the expression level of ZMYM1 is negatively correlated with the immunotherapy-related score, and the immunotherapy-related score includes the MHC-IPS score, the EC-IPS score, the SC-IPS score, the CP-IPS score and the IPS-IPS score.

[0023] According to a fourth aspect of the present invention, the application of ZMYM1 as a biomarker in predicting the therapeutic effect of tumor targeted drugs in patients with liver cancer is proposed.

[0024] In some embodiments of the present invention, the expression level of ZMYM1 is positively correlated with the IC50 value of tumor targeted drugs, and the tumor targeted drugs include bexarotene, bortezomib, cyclopamine, dasatinib, imatinib, parthenolide, sorafenib and gefitinib.

[0025] According to a fifth aspect of the present invention, a substance for inhibiting the expression of ZMYM1 is used in (1) or (2):

[0026] (1) Preparation of drugs for treating and / or preventing liver cancer;

[0027] (2) Prepare drugs for inhibiting the proliferation, migration or invasion of liver cancer cells.

[0028] In some embodiments of the present invention, the substance expressing ZMYM1 includes: any one of antisense oligonucleotide (ASO) of ZMYM1, small interfering RNA (siRNA), short hairpin RNA (shRNA), micro RNA (miRNA), clustered regularly interspaced short palindromic repeats (CRSIPR) system, transcription activator-like effector nucleases (TALEN) system, and zinc finger nuclease (ZFN) system.

[0029] In some embodiments of the present invention, the sequence of the siRNA is shown in SEQ ID NO:7.

[0030] In some embodiments of the present invention, the substance that inhibits the expression of ZMYM1 also includes a vector loaded with the above-mentioned ASO, siRNA, shRNA, miRNA, CRS IPR system, TALEN system, and ZFN system.

[0031] In some embodiments of the present invention, the vector comprises any one of a plasmid, a lentivirus, an adenovirus and an adeno-associated virus.

[0032] According to a sixth aspect of the present invention, a kit is provided, comprising a reagent for detecting ZMYM1, a nucleic acid extraction reagent and a reverse transcription reagent.

[0033] In some embodiments of the present invention, the nucleic acid extraction reagent includes TRIzol.

[0034] In some embodiments of the present invention, the reverse transcription reagent includes reverse transcription polymerase, dNTPs and RT primers.

[0035] In some embodiments of the invention, the reverse transcriptase polymerase comprises Evo M-MLV RTase.

[0036] In some embodiments of the present invention, the RT primer comprises at least one of a random primer and oligo dT.

[0037] In some embodiments of the present invention, the reverse transcription reagent further comprises an RNase inhibitor, a gDNA removal reagent and RNase-free water.

[0038] In some embodiments of the present invention, the kit further comprises an internal reference primer pair.

[0039] In some embodiments of the present invention, the internal reference primer pair is a primer pair for detecting β-Actin.

[0040] The present invention has at least the following beneficial effects:

[0041] The present invention provides the application of ZMYM1 as a biomarker in the diagnosis and / or treatment of liver cancer. Database analysis, cell experiments and animal model experiments confirm that ZMYM1 is a promoting oncogene of liver cancer. ZMYM1 promotes the migration of tumor cells and increases the level of inflammation in the tumor microenvironment by interacting with METTL3, ultimately promoting the progression of liver cancer. Based on the TCGA, GEO, ICGC and NGDC databases, the present invention provides the application of ZMYM1 as a biomarker in liver cancer diagnosis, liver cancer prognosis risk assessment, liver cancer immunotherapy effect prediction and targeted drug treatment effect prediction through the results of bioinformatics analysis, so as to provide subjects with a prevention plan or treatment plan in the clinic and realize the precise molecular treatment of liver cancer.

[0042] Additional aspects and advantages of the present invention will be set forth in part in, and in part will be apparent from, the description which follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The present invention will be further described below with reference to the accompanying drawings and embodiments, wherein:

[0044] Figure 1 Graphs showing evaluation results before and after integration of different data sets in Example 1 of the present invention; A is before batch removal, and B is after batch removal;

[0045] Figure 2 This is a graph showing the expression levels of ZMYM1 in different tumor cell lines of the CCLE dataset in Example 1 of the present invention;

[0046] Figure 3 The result diagrams of ZMYM1 expression levels in liver cancer tissues and normal liver tissues of different data sets in Example 1 of the present invention are shown; wherein A is the integrated RNA-seq data, B is the GSE22058 data set, C is the GSE25097 data set, D is the GSE63898 data set, and E is the GSE89377 data set;

[0047] Figure 4 The Kaplan-Meier survival curve of ZMYM1 obtained by analyzing the integrated RNA-seq data in Example 1 of the present invention;

[0048] Figure 5 The result diagram of the relationship between ZMYM1 expression and clinical pathological characteristics of patients in Example 1 of the present invention; wherein A represents different age groups, B represents different genders, C represents different N stages, D represents different M stages, E represents different AJCC Stages, F represents different T stages, and G represents different Grades;

[0049] Figure 6 The result diagram of the prognostic relationship between ZMYM1 expression and clinical pathological characteristics of patients in Example 1 of the present invention; wherein A represents different ages, B represents different AJCC Stages, C represents different N stages, and D represents different Grades;

[0050] Figure 7 The result diagram of the prognostic relationship between ZMYM1 expression and clinical pathological characteristics of patients in Example 1 of the present invention; wherein A represents different genders, B represents different T stages, and C represents different M stages;

[0051] Figure 8The Cox regression analysis result diagram and nomogram of different risk factors in Example 2 of the present invention; wherein A is a univariate Cox regression analysis, B is a multivariate Cox regression analysis, and C is a nomogram model based on ZMYM1 expression level;

[0052] Fig. 9 Result diagram of the ZMYM1 nomogram model in Example 2 of the present invention; wherein A is the calibration curve of the nomogram model, B is the prognostic Kaplan-Meier curve of the nomogram model, C is the ROC curve and AUC value of the nomogram model, and D is the DCA decision curve of different clinical characteristics and nomogram models;

[0053] Fig.10 Result diagram of the functional analysis of ZMYM1 in Example 3 of the present invention; wherein A is a volcano plot of differential gene expression between different ZMYM1 expression subgroups, and B is a KEGG analysis of ZMYM1 differential genes;

[0054] Fig.11 The figure is a functional analysis result diagram of ZMYM1 in Example 3 of the present invention; wherein A is the GO analysis of the differentially expressed genes of ZMYM1, and B is the GSVA analysis result;

[0055] Fig.12 This is a result diagram of the correlation between ZMYM1 expression and immune infiltration in the tumor microenvironment of liver cancer evaluated using the ESTIMATE algorithm in Example 4 of the present invention;

[0056] Fig.13 It is a relationship diagram between ZMYM1 and immunotherapy response and immune escape in Example 4 of the present invention; wherein A to H are TIDE score, IFNG score, MSI.Score, Dysfunction score, Exclusion score, MDSC score, MHC-IPS and EC-IPS, respectively;

[0057] Fig.14 is a relationship diagram of ZMYM1 and immunotherapy response and immune escape in Example 4 of the present invention; wherein A to B are SC-OPS, CP-IPS and IPS, respectively;

[0058] Fig.15 The result diagram of the correlation between the expression level of ZMYM1 and drug sensitivity in Example 4 of the present invention; wherein A to H are Bexarotene, Bortezomib, Cyclopamine, Dasatinib, Imatinib, Parthenolide, Sorafenib and Gefitinib, respectively;

[0059] Fig.16The result diagram of evaluating the relationship between ZMYM1 and METTL3 in Example 5 of the present invention; wherein A is the expression analysis of ZMYM1 based on primary tumor samples (n=371) and normal liver tissues (n=50), B is the Kaplan-Meier survival analysis showing the association between high / low expression of ZMYM1 and the survival of liver cancer patients; C and D are the expression of ZMYM1 in liver cancer cells after silencing or overexpressing METTL3, and E is the correlation analysis between ZMYM1 expression and METTL3 expression in liver cancer cells;

[0060] Fig.17 The results of evaluating the relationship between ZMYM1 and METTL3 in Example 5 of the present invention; wherein A is a confocal immunofluorescence analysis showing that METTL3 and ZMYM1 are co-localized in liver cancer cells; B is a possible mRNA on ZMYM1 6 A predicted modification sites;

[0061] Fig.18 The results of evaluating the effects of ZMYM1 and METTL3 on migration-related molecules in tumor cells in Example 5 of the present invention are shown in Figures 5A and 5B; A and B are the expressions of metastasis-related cadherin and vimentin in liver cancer cells after silencing METTL3 or ZMYM1, and C and D are the expressions of metastasis-related cadherin and vimentin in liver cancer cells after overexpressing METTL3 or ZMYM1;

[0062] Fig.19 The results of evaluating the effects of ZMYM1 and METTL3 on the RAS / c-FOS / ERK signaling pathway in Example 5 of the present invention are shown in Figure 5; wherein A and B are the relative expression levels of RAS / c-FOS / ERK signaling pathway related proteins in liver cancer cells (Huh-7 and Hep3B) and normal liver cells THLE-2, and C and D are the relative expression levels of RAS / c-FOS / ERK signaling pathway related proteins in Huh-7 after silencing of METTL3 or ZMYM1;

[0063] Fig. 20 This is to evaluate the effect of silencing or overexpressing ZMYM1 on tumor size and m in Example 6 of the present invention. 6 A is the result of the effect of ZMYM1 level; A is the tumor growth curve of each group of mice from day 0 to day 28 after ZMYM1 overexpression or silencing; B is the m 6 A shows the relative level, and C shows the relative expression level of METTL3 in tumors of different groups of mice after ZMYM1 overexpression or silencing;

[0064] Fig.21The results of evaluating the effect of ZMYM1 on tumor migration-related molecules in Example 6 of the present invention are shown in Figure 6; wherein A and B are the relative protein expression levels of E-cadherin and Vimentin associated with metastasis in mice after silencing of METTL3 and ZMYM1, and C and D are the relative protein expression levels of E-cadherin and Vimentin associated with metastasis in mice after overexpression of METTL3 and ZMYM1;

[0065] Fig. 22 The results of evaluating the effects of ZMYM1 and METTL3 on the RAS / c-FOS / ERK signaling pathway in Example 6 of the present invention are shown in Figure 6; wherein A and B are the relative protein expression levels of the RAS / c-FOS / ERK signaling pathway in mice after silencing of METTL3 and ZMYM1, and C and D are the relative protein expression levels of the RAS / c-FOS / ERK signaling pathway in mice after overexpression of METTL3 and ZMYM1;

[0066] Fig.23 This is a result diagram of evaluating the effects of ZMYM1 and METTL3 on inflammatory markers in Example 6 of the present invention; wherein A is the result of IL-1β, IL-6 and TNF-α, and B is the result of IL-4, IL-10 and PD-1;

[0067] Fig.24 Graph showing the validation results based on clinical samples in Example 7 of the present invention; wherein A is the ZMYM1 gene expression level in liver cancer tissue and adjacent cancer tissue of 80 liver cancer patients, and B is the survival analysis of 80 liver cancer patients based on ZMYM1 expression level. DETAILED DESCRIPTION

[0068] The following will be combined with the embodiments to clearly and completely describe the concept of the present invention and the technical effects produced, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0069] Unless otherwise specified, the reagents, methods and equipment used in the present invention are conventional reagents, methods and equipment in the art.

[0070] Example 1 ZMYM1 expression in different data sets and its relationship with patient clinical characteristics and prognosis

[0071] This example performs bioinformatics analysis based on TCGA-LIHC data, ICGC-LIRI-JP data, OEP000321 in the NGDC database, GSE148355, GSE22058, GSE25097, GSE63898, GSE89377 in the GEO database, and CCLE database. Among them, TCGA-LIHC data, ICGC-LIRI-JP data, OEP000321 in the NGDC database, and GSE148355 in the GEO database are all RNA-seq data, so this example integrates these four data sets and removes batches using the sva package of R software. The data evaluation results before batch removal are as follows: Figure 1 As shown in A, the data evaluation results after batch removal are as follows Figure 1 As shown in B, it can be seen that the data sets were scattered before batch removal, and the data were merged after batch removal. Subsequent RNA-seq analysis is based on the RNA-seq data after batch removal.

[0072] 1. Expression of ZMYM1 in different datasets

[0073] In order to comprehensively describe the expression of ZMYM1 in normal tissues and cancer samples, this example uses the data from the CCLE dataset (https: / / sites.broadinstitute.org / ccle) and finds that ZMYM1 is highly expressed in 32 tumor cell lines ( Figure 2 ), among which ZMYM1 is also highly expressed in liver cancer (LIHC).

[0074] The expression of ZMYM1 gene in liver cancer and control tissues in RNA-seq data was further analyzed, and the results were as follows Figure 3 As shown in A; Figure 3 As shown in A, the expression of ZMYM1 in liver cancer is significantly upregulated compared with normal tissues; in addition, the expression of ZMYM1 in the datasets GSE22058, GSE25097, GSE63898, and GSE89377 is the same as that in the RNA-seq data, as shown in Figure 3 As shown in B~3E.

[0075] like Figure 4 The Kaplan-Meier curve shown indicates that patients with high expression of ZMYM1 exhibited a shorter overall survival (OS) compared with patients with low expression of ZMYM1. The above data indicate that ZMYM1 is upregulated in liver cancer and is associated with a poor prognosis.

[0076] 2. Relationship between ZMYM1 expression level and clinicopathological characteristics of patients with liver cancer

[0077] The association between ZMYM1 expression levels and clinicopathological characteristics of G. cane patients was analyzed in RNA-seq data, and the results were as follows Figure 5 shown by Figure 5 It can be seen that ZMYM1 showed significant differential expression in different T stages and grades of liver cancer patients, with marginal significance in AJCC Stage, but no significant differences in gender, age, N stage and M stage.

[0078] 3. Relationship between ZMYM1 expression level and prognosis of HCC patients with different clinicopathological characteristics

[0079] This example analyzes the relationship between the expression of ZMYM1 and prognosis in tumor patients of different ages, genders, T stages, N stages, M stages, AJCC stages and grades based on the aforementioned integrated RNA-seq data; by using the R software survminer package to select the optimal cutoff value for grouping, the analysis results are as follows: Figure 6 and Figure 7 Kaplan-Meier curves are shown.

[0080] Depend on Figure 6 and Figure 7 It can be seen that ZMYM1 expression is associated with poor prognosis in young and old groups, male patients, stage I-II, stage III-IV, stage T1-T2, stage T3-T4, stage N0, stage M0, stage G1-G2, and stage G3-G4 liver cancer patients; in female patients, stage M1-Mx, and stage N1-Nx liver cancer patients, ZMYM1 expression is not associated with prognosis.

[0081] Example 2 Cox risk regression analysis of ZMYM1 expression level and patient prognosis and nomogram model construction

[0082] Based on the aforementioned integrated RNA-seq data, this example uses univariate and multivariate Cox regression analysis to determine the independent predictors of OS in patients with liver cancer. The variables studied include AJCC Stage, age, gender, and ZMYM1 expression level; the nomogram model is constructed using the R software rms package, and the model is evaluated using the calibration curve. Finally, the relationship between the information nomogram model and other factors is analyzed using the DCA decision curve. The results are shown in the figure below. Figure 8 and Fig. 9 shown.

[0083] Depend on Figure 8 A and Figure 8 B It can be seen that univariate Cox regression analysis and multivariate Cox regression analysis showed that ZMYM1, gender and AJCC Stage were independent prognostic risk factors for OS in patients with liver cancer, among which ZMYM1 had a higher hazard ratio (HR).

[0084] Depend on Figure 8 C and Fig. 9 A shows that by constructing the nomogram model, it was found that ZMYM1 is a highly effective predictor of poor prognosis in patients at 1, 3, and 5 years, and the calibration curve of the nomogram also verifies this. Fig. 9 B and Fig. 9 C shows that the Kaplan-Meier curve shows that the nomogram model can well divide patients into two groups of high and low risk. Further ROC curve analysis shows that the AUC value is above 0.64. Fig. 9 D shows that the DCA decision curve analysis nomogram found that compared with age, gender and AJCCStage stage, the effect rate of the ZMYM1 nomogram model is significantly higher.

[0085] Example 3 Functional analysis of ZMYM1

[0086] Based on the aforementioned integrated RNA-seq data, this example groups the genes according to their expression levels, and then performs differentially expressed gene analysis. Here, the R software limma package is used, and the thresholds |log1.5 FC|>1 and adj.P.Val<0.05 are selected for differential gene screening. Then, GO and KEGG enrichment analysis is performed on these differential genes. The reference set of the R software GSVA package is h.all.v2024.1.Hs.symbols.gmt. The results are shown in the figure. Fig.10 and Fig.11 shown.

[0087] Depend on Fig.10 It can be seen that Fig.10 A total of 2292 differentially expressed genes were identified in the volcano plot of A, of which 2080 genes were upregulated in the ZMYM1 high expression group and 212 genes were downregulated in the ZMYM1 high expression group; Fig.10 KEGG analysis of B showed that the ZMYM1 high expression group was mainly enriched in signal pathways closely related to tumorigenesis functions such as cell cycle, DNA repair and mismatch repair.

[0088] Depend on Fig.11 It can be seen that GO functional enrichment analysis showed that the ZMYM1 high expression group was mainly enriched in biological processes such as DNA replication, nuclear division, DNA template and DNA replication; and GSVA analysis found that the ZMYM1 high expression group was mainly related to cancer occurrence pathways such as MITOTIC_SPINDLE, while the low expression group was related to the OXIDATIVE_PHOSPHORYLATION pathway; the above processes are closely related to tumor development, indicating that ZMYM1 is likely to be involved in the occurrence and development of tumors.

[0089] Example 4 Relationship between ZMYM1 and immune cell infiltration, immunotherapy response and targeted drug therapy response

[0090] 1. Relationship between ZMYM1 and immune cell infiltration in the setting of liver cancer

[0091] Based on the aforementioned integrated RNA-seq data, this example used the R software immunedeconv package to calculate three immune infiltration scores, namely, Stromal Score, Immune Score, and ESTIMATE score. The R software "ggpubr" and "reshape2" packages were used to perform correlation analysis between the immune infiltration score and the expression level of ZMYM1. The results are shown in Figure 2. Fig.12 shown.

[0092] Depend on Fig.12 It can be seen that the expression level of ZMYM1 shows a significant negative correlation with the immune score (Immune Score), stromal score (Stromal Score) and ESTIMATE score. It is speculated that liver cancer patients with high expression of ZMYM1 are not suitable for immunotherapy (CAR-T, PD-1 and PD-L1, etc.), which may lead to poor prognosis after treatment.

[0093] 2. Relationship between ZMYM1 and immunotherapy response and immune escape

[0094] This example analyzes the immune escape and immune dysfunction of the high and low expression groups of ZMYM1 based on the TIDE database (http: / / tide.dfci.harvard.edu / ), and obtains the TIDE score, Exclusion score, IFNG score, Dysfunction score, MSI.Score and MDSC score. This example also uses the deconvo_ips function of the R software IOBR package to score four different immune phenotypes (antigen presenting MHC-IPS, effector cell EC-IPS, inhibitory cell SC-IPS, checkpoint CP-IPS). IPS-IPS is a comprehensive integrated score, and the higher the IPS-IPS score, the stronger the immunogenicity of the sample. The results are as follows: Fig.13 and Fig.14 shown.

[0095] Depend on Fig.13 A~ Fig.13F shows that the results showed that the immune escape-related scores (TIDE score, Exclusion score, IFNG score, Dysfunction score, MSI.Score and MDSC score) in the ZMYM1 high expression group were significantly higher than those in the ZMYM1 low expression group, indicating that liver cancer patients with high expression of ZMYM1 are more likely to have immune escape, which is not conducive to immunotherapy. Fig.13 G. Fig.13 H and Fig.14 It can be seen that the immune phenotype scores (MHC-IPS, EC-IPS, SC-IPS, CP-IPS and IPS-IPS) of the ZMYM1 high expression group were significantly lower than those of the ZMYM1 low expression group, indicating that liver cancer patients with high expression of ZMYM1 are not suitable for immunotherapy.

[0096] 3. Relationship between ZMYM1 and sensitivity of tumor targeted drugs

[0097] Based on the aforementioned integrated RNA-seq data, this example uses the “pRRophetic” package of R to predict the drug sensitivity of the high and low expression groups of ZMYM1 (p<0.05). The results are as follows: Fig.15 shown.

[0098] Depend on Fig.15 It can be seen that the IC50 of Bexarotene, Bortezomib, Cyclopamine, Dasatinib, Imatinib, Parthenolide, Sorafenib and Gefitinib in the ZMYM1 high expression group were significantly higher than those in the low expression group (p<0.05), indicating that liver cancer patients with high ZMYM1 expression showed higher resistance to a variety of tumor targeted drugs, and the ability of drugs to induce apoptosis was weakened and the specificity was reduced, which is not conducive to the treatment of liver cancer with targeted drugs.

[0099] Example 5 The relationship between ZMYM1 and METTL3 and their effects on tumor migration

[0100] 1. Relationship between ZMYM1 and METTL3

[0101] 1) Expression and prognosis of ZMYM1 in TCGA database:

[0102] Use UALCAN (https: / / ualcan.path.uab.edu / index.html) to predict m 6 A METTL3 methyltransferase, including the expression profile in the METTL3 data, was used to determine the impact of high or low expression of METTL3 and ZMYM1 on the survival of LIHC patients; the results obtained are shown in Fig.16 A and Fig.16 As shown in B.

[0103] Depend on Fig.16 A and Fig.16 B It can be seen that based on TCGA data, the expression of ZMYM1 in tumor tissue is significantly higher than that in normal liver tissue, and high expression of ZMYM1 is also associated with a lower survival rate.

[0104] 2) Expression of ZMYM1 in liver cancer cell lines after silencing or overexpressing METTL3:

[0105] The liver cancer cell line Hep3B (cells purchased from the Chinese Academy of Sciences Cell Bank) was silenced or overexpressed by liposome transfection, and the METTL3 overexpression vector (abbreviated as oe-METTL3) was constructed using pcDNA3.1 plasmid (purchased from Invitrogen, V79020), and the empty vector was used as a negative control. At the same time, METTL3-specific siRNA (si-METTL3#1 and si-METTL3#2) and negative control siRNA with scramble sequence were synthesized by Shanghai Sangon Biotechnology Co., Ltd., and the specific sequence is shown in Table 1. According to the instructions provided by the supplier, the above-mentioned overexpression vector, empty vector and siRNA were transfected into the tumor cell lines Hep3B and Huh-7 using Lipofectamine 2000 transfection reagent (purchased from Invitrogen, 11668027), respectively. After 6 h of culture, total RNA was extracted using a TriZol kit, and cDNA was prepared using a HiScript IIQ RT SuperMix for qPCR kit (purchased from Vazyme, R223). Then, qRT-PCR was performed in an ABI7500 real-time PCR system (Thermo Fisher) using SYBR Green PCR Master Mix (purchased from Applied Biosystems, 4309155) to detect the expression level of ZMYM1. GAPDH was used as an internal reference for relative quantification. The sequences of the specific primers used are shown in Table 2. The results are shown in Table 2. Fig.16 C~ Fig.16 As shown in E.

[0106] Table 1. Sequence information of siRNA

[0107]

[0108] Table 2 Primer sequence information for qRT-PCR

[0109]

[0110] Depend on Fig.16 C~ Fig.16E It can be seen that silencing METTL3 can inhibit the expression of ZMYM1 in liver cancer cells, while overexpression of METTL3 can increase the expression of ZMYM1, and the expression levels of METTL3 and ZMYM1 are positively correlated.

[0111] 3) Co-localization of METTL3 and ZMYM1 in liver cancer cells:

[0112] Fluorescence confocal technique was used to analyze the localization of METTL3 and ZMYM1 in cells. Hep3B and Huh-7 liver cancer cells (transfected with si-NC and si-METTL3, respectively) on slides were fixed with 4% fixative at room temperature for 30 min, permeabilized with 0.4% Triton X-100 (purchased from Sangon Biotech, A110694), and blocked with 2% bovine serum albumin (purchased from Sangon Biotech, C102301) for 30 min. Subsequently, METTL3 primary antibody (H00056339-B01P, 1:1000 dilution, Novus Biologicals) and ZMYM1 primary antibody (NBP-76523, 1:5000 dilution, Novus Biologicals) were added and incubated overnight at 4°C. The next day, the excess primary antibody was washed off, and goat anti-mouse IgG secondary antibody (Alexa Fluor 488, ab150113, 1:1000 dilution, Abcam) and goat anti-rabbit IgG secondary antibody (Alexa Fluor 647, ab150079, 1:1000 dilution, Abcam) were incubated at room temperature for 1 hour; after rinsing with PBS three times, the cell nuclei were stained with DAPI staining solution (E607303, Sangon Biotech) for 10 minutes; a Zeiss LSM 780 confocal microscope (Carl Zeiss GmbH) was used for observation and photography. The results are shown in Fig.17 As shown in A.

[0113] Depend on Fig.17 A shows that METTL3 and ZMYM1 are co-localized in liver cancer cells.

[0114] 4) m in ZMYM1 6 A modification site prediction:

[0115] SRAMP was used to predict the m in ZMYM1 6 A modification site (http: / / www.cuilab.cn / sramp), the results are as follows Fig.17 As shown in B.

[0116] 2. Effects of ZMYM1 and METTL3 on migration-related molecules in tumor cells

[0117] The liver cancer cell line Hep3B (cells purchased from the Chinese Academy of Sciences Cell Bank) was silenced or overexpressed by lipofectamine transfection, and the METTL3 overexpression vector (abbreviated as oe-METTL3) and ZMYM1 overexpression vector (abbreviated as oe-ZMYM1) were constructed using pcDNA3.1 plasmid (purchased from Invitrogen, V79020), and the empty vector was used as a negative control. At the same time, METTL3-specific siRNA (si-METTL3#1 and si-METTL3#2), ZMYM1-specific siRNA (si-ZMYM1) and negative control siRNA with scramble sequence were synthesized by Shanghai Sangon Biotechnology Co., Ltd. According to the instructions provided by the supplier, the above-mentioned overexpression vector, empty vector and siRNA were transfected into the tumor cell lines Hep3B and Huh-7 using Lipofectamine 2000 transfection reagent (purchased from Invitrogen, 11668027), respectively.After 6 h of culture, protein samples were collected using RIPA lysis buffer; protein quantification was performed using a BCA protein detection kit (P0011, Beyotime); 20 mg of protein samples were then separated using 10% SDS-PAGE separation gel electrophoresis, and the proteins were transferred to a polyvinylidene fluoride (PVDF) microporous membrane (FFP36, Beyotime); the PVDF membrane was placed in a Tris buffer (ST661, Beyotime) containing 0.1% Tween-20 (ST825, Beyotime) and 5% skim milk and blocked at room temperature for 2 h; then, the primary antibodies METTL3 (ab195352, 1:1000 dilution, Abcam), N-cadherin (22018-1-AP, 1:1000 dilution, Proteintech Group), and E-cadherin (1:1000 dilution, 20874-1-AP, Proteintech Group) were used. Group), Vimentin primary antibody (1:1500 dilution, 10366-1-AP, Proteintech Group), RAS primary antibody (ab52939, 1:5000 dilution, Abcam), c-FOS primary antibody (1:1000 dilution, Abcam), p-ERK1 / 2 primary antibody (28733-1-AP, 1:5000 dilution, Proteintech Group), ERK1 / 2 primary antibody (11257-1-AP, 1:1000 dilution, Proteintech The membrane was incubated with primary antibody (Abcam, 1:5000 dilution) and internal reference GAPDH primary antibody (ab8245, 1:5000 dilution, Abcam) at 4°C overnight; then, the excess primary antibody was washed off, and the PVDF membrane was incubated with goat anti-rabbit IgG secondary antibody (ab205718, 1:5000 dilution, Abcam) and goat anti-mouse IgG secondary antibody (ab205719, 1:5000 dilution, Abcam) conjugated with horseradish peroxidase at room temperature for 2 hours, and then developed using an ECL chemiluminescence kit (P0018FS, Beyotime), and exposed and photographed in a Tanon 4600 automatic chemiluminescence analysis system (Tanon); the relative gray value was calculated using ImageJ software (version 1.45s) for statistics, and the results are shown in FIG. Fig.18 and Fig.19 shown.

[0118] Depend on Fig.18It can be seen that silencing METTL3 or ZMYM1 can reduce the protein expression levels of tumor migration-related proteins N-cadherin and Vimentin in Huh-7 and increase the protein expression level of E-cadherin; while overexpression of METTL3 and ZMYM1 can increase the protein expression levels of N-cadherin and Vimentin in Hep3B and reduce the protein expression level of E-cadherin; the above shows that METTL3 and ZMYM1 have a promoting effect on the invasion and metastasis of liver cancer cells.

[0119] Depend on Fig.19 It can be seen that the expression of the RAS / ERK / c-FOS signaling pathway downstream of METTL3 was detected. Compared with the normal liver cell line THLE-2, the RAS and c-FOS levels and the phosphorylation degree of ERK1 / 2 in the liver cancer cell lines Huh-7 and Hep3B were significantly reduced; by silencing METTL3 and ZMYM1, the expression of the above signaling pathways in Huh-7 cells was quantified again. The results showed that silencing of METTL3 and ZMYM1 could significantly upregulate the protein expression levels of RAS and c-FOS and the phosphorylation degree of ERK1 / 2 in Huh-7.

[0120] Example 6 Animal model verification of the effects of ZMYM1 and METTL3 on tumors

[0121] 1. Effect of silencing or overexpressing ZMYM1 on tumor size

[0122] Male BALB / c nude mice (4 weeks old) were purchased from Shanghai Experimental Animal Research Center and housed in a specific pathogen-free environment at room temperature of 24°C and relative humidity of 70%. All mice were fed freely under a strict 12-h light / dark cycle. Lentiviruses overexpressing or knocking down METTL3 and ZMYM1 were prepared using plasmids pKD-CMV-MCS-EF1-PURO (CD510VA-1, System Biosciences) and et-plko-puro (21915, Addgene), and empty vectors were used as negative controls. The constructed lentiviruses were used to infect Hep3B cells at an infection multiplicity of 50, and then infected cells were selected with 2 μg / mL puromycin (E607054, Sangon) for 3 days. Stably infected cells were plated with PBS at 1×10 6 The cells were resuspended at a density of 100 μL and injected subcutaneously into the right armpit of the mouse. The tumor volume was recorded with a vernier caliper on the 7th day after injection and measured once a week for 4 weeks. The changes in tumor volume were as follows: Fig. 20 As shown in A.

[0123] Depend on Fig. 20As shown in A, overexpression of ZMYM1 can significantly accelerate tumor growth, while silencing ZMYM1 can significantly delay tumor growth.

[0124] 2. Effects of silencing or overexpressing ZMYM1 on m 6 Impact of A Levels

[0125] On the 28th day after the tumor cells were injected into the above tumor-bearing nude mice, all mice were killed by inhalation of 3% isoflurane (R510-22-10, RWD) and cervical dislocation. Subcutaneous tumor samples were collected and the m 6 The content of A and the expression level of METTL3 gene were detected, and the results were as follows Fig. 20 B and Fig. 20 C; the method for detecting gene expression level by qRT-PCR is shown in the steps described in Example 5, m 6 The detection method of A content is as follows:

[0126] Total RNA from tumor samples was extracted using the TriZol assay kit (15596026, Invitrogen). Poly(A)mRNA Magnetic Isolation Module (E7490S, New England Biolabs) was used to purify mRNA from total RNA. 6 A, 200 ng of mRNA was digested with enzymes, 3 μL of ammonium bicarbonate (A610032, Sangon Biotech) and alkaline phosphatase (D7027, Beyotime) were added, and incubated at 37°C for 2 h; the sample was diluted to 100 μL, filtered, and m was determined by LC-MS / MS. 6 The content of A.

[0127] Depend on Fig. 20 B and Fig. 20 C shows that silencing of ZMYM1 reduces the m 6 A and METTL3 levels, overexpression of ZMYM1 increased m 6 A and METTL3 levels.

[0128] 3. Effects of silencing or overexpressing ZMYM1 on tumor migration-related molecules

[0129] Western blotting was used to detect the protein expression levels of tumor migration-related molecules in the above-mentioned mouse subcutaneous tumor samples. The detection method was as described in Example 5. The results are as follows: Fig.21 shown.

[0130] Depend on Fig.21It can be seen that silencing METTL3 or ZMYM1 can reduce the protein expression levels of tumor migration-related proteins N-cadherin and Vimentin in tumors and increase the protein expression level of E-cadherin in vivo; while overexpression of METTL3 and ZMYM1 can increase the protein expression levels of N-cadherin and Vimentin in tumors and reduce the protein expression level of E-cadherin in vivo; the above in vivo experimental results verify that METTL3 and ZMYM1 have a promoting effect on the invasion and metastasis of liver cancer cells.

[0131] 4. Effects of silencing or overexpressing ZMYM1 on the RAS / ERK / c-FOS signaling pathway

[0132] Western blotting was used to detect the protein expression level of the RAS / ERK / c-FOS signaling pathway in the above mouse subcutaneous tumor samples. The detection method was as described in Example 5. The results are as follows: Fig. 22 shown.

[0133] Depend on Fig. 22 It can be seen that silencing METTL3 or ZMYM1 will increase the RAS protein level, c-FOS protein level and ERK1 / 2 phosphorylation level, while overexpressing METTL3 or ZMYM1 will reduce the RAS protein level, c-FOS protein level and ERK1 / 2 phosphorylation level. A series of results similar to the trends of in vitro cell experiments were obtained here.

[0134] 5. Effects of silencing or overexpressing ZMYM1 on tumor inflammation levels

[0135] The gene expression levels of inflammatory markers in the above mouse subcutaneous tumor samples were detected by qRT-PCR. The detection method was as described in Example 5. The results are as follows: Fig.23 shown.

[0136] Depend on Fig.23 It can be seen that inhibiting ZMYM1 can increase the gene expression levels of proinflammatory factors IL-1β, IL-6 and TNF-α and reduce the gene expression levels of anti-inflammatory factors IL-4, IL-10 and PD-1; overexpression of ZMYM1 has the opposite effect. The above results indicate that ZMYM1 has the effect of inhibiting proinflammatory factors, thereby indirectly promoting the occurrence and development of tumors.

[0137] Example 7 Clinical Tumor Sample Verification

[0138] In order to further verify the performance of ZMYM1 as a liver cancer biomarker in clinical samples, this example collected matched liver cancer tissue samples and adjacent tissue samples (80 cases each), all of which were taken from liver cancer patients diagnosed in Shenzhen People's Hospital; all included patients met the following criteria: 1) did not receive any chemotherapy or radiotherapy; 2) were not diagnosed with other cancers, autoimmune diseases, infectious diseases, etc.; all tissue samples were collected by biopsy or surgical resection, rinsed with saline and stored in liquid nitrogen for inspection.

[0139] The gene expression level of ZMYM1 in the above clinical tissue samples was detected by qRT-PCR. The nucleic acid extraction and detection methods refer to the steps described in Example 5. The results are as follows: Fig.24 As shown in A.

[0140] Depend on Fig.24 A shows that the gene expression level of ZMYM1 in liver cancer tissue is much higher than that in adjacent cancer tissue, indicating that ZMYM1 is significantly upregulated in liver cancer tissue and is a key cancer-promoting target.

[0141] The prognostic analysis method described in Example 1 was used to divide ZMYM1 into a high expression group and a low expression group and a Kaplan-Meier curve was drawn. The results are shown in FIG. Fig.24 As shown in B.

[0142] Depend on Fig.24 B shows that in clinical samples, patients with high expression of ZMYM1 showed shorter OS than those with low expression of ZMYM1, indicating that ZMYM1 is a cancer-promoting gene and high expression of ZMYM1 indicates a poor prognosis.

[0143] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the purpose of the present invention. In addition, the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

Claims

1. Use of reagents for detecting ZMYM1 in the preparation of products for diagnosing liver cancer.

2. The use according to claim 1, characterized in that: The expression of ZMYM1 is upregulated in patients with liver cancer.

3. The use according to claim 1, characterized in that: The product detects the expression level of ZMYM1 by at least one of RT-PCR, real-time fluorescence quantitative PCR, in situ hybridization, northern blotting, chip and high-throughput sequencing platform; Preferably, the reagent for detecting ZMYM1 comprises a primer pair for amplifying ZMYM1; More preferably, the primer pair comprises the primer pair shown as SEQ ID NO:6 and SEQ ID NO:

7.

4. Application of ZMYM1 as a biomarker in prognostic risk assessment of patients with liver cancer.

5. Application of ZMYM1 as a biomarker in predicting the efficacy of immunotherapy in patients with liver cancer.

6. Application of ZMYM1 as a biomarker in predicting the therapeutic effect of tumor targeted drugs in patients with liver cancer.

7. Use of a substance that inhibits ZMYM1 expression in (1) or (2): (1) Preparation of drugs for treating and / or preventing liver cancer; (2) Prepare drugs for inhibiting the proliferation, migration or invasion of liver cancer cells.

8. The use according to claim 7, characterized in that: The substance that inhibits the expression of ZMYM1 includes: any one of antisense oligonucleotides, siRNA, shRNA, miRNA, CRS IPR system, TALEN system and ZFN system of ZMYM1.

9. The use according to claim 8, characterized in that: The siRNA sequence is shown in SEQ ID NO:

3.

10. A kit, characterized in that: The kit comprises a reagent for detecting ZMYM1, a nucleic acid extraction reagent and a reverse transcription reagent.

Images