Application of ITIH2 in treatment of gastric cancer

Abstract

The invention provides application of ITIH2 in treatment of gastric cancer. According to the invention, a Mendel randomization (MR) method in a drug genome range is utilized, and it is determined that ITIH2 is a potential drug target of gastric cancer. In-vitro studies show that ITIH2, as a tumor suppressor gene, significantly inhibits growth and migration of gastric cancer cells. After standard screening, a drug approved by FDA, namely amiodarone, is preferentially considered, the amiodarone serves as a reutilization drug for treating the gastric cancer, and the effectiveness of the amiodarone is verified by inhibiting growth and migration of gastric cancer cells and angiogenesis. In a word, the research provides a new insight for gene-based gastric cancer drug treatment, and potential possibility is provided for success of clinical experiments.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and in particular, the present invention relates to the application of ITIH2 in treating gastric cancer. Background Art

[0002] Gastric cancer (GC) is one of the most common cancers and ranks third among the causes of cancer-related deaths worldwide. Despite the availability of a variety of effective treatments, including surgical resection, chemotherapy, radiotherapy, and immunotherapy, the overall survival rate of GC remains low. Therefore, there is an urgent need to identify therapeutic targets and develop novel anticancer drugs to ultimately improve the treatment outcomes of GC patients. Summary of the invention

[0003] The present invention proposes the application of ITIH2 in treating gastric cancer.

[0004] In the present invention, we obtained expression quantitative trait locus (eQTL) data in the druggable genome and performed Mendelian randomization analysis using the results of genome-wide association studies (GWAS) to identify potential druggable genes associated with gastric cancer (GC). Through further co-localization analysis, we identified a gene: ITIH2, which showed strong potential as a drug target for GC. We then investigated potential drugs associated with ITIH2 through a series of criteria and screened out an FDA-approved drug: amiodarone. In addition, we explored the role of ITIH2 in GC cancer cell lines and the pharmacological effects of its target drugs. In vitro experiments showed that ITIH2 significantly inhibited the growth and migration of GC cells. Finally, the therapeutic potential of amiodarone to alleviate GC progression by possibly relying on the p53 signaling pathway was verified. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] Figure 1 Flowchart for identification of druggable genes;

[0006] Figure 2 Results of co-localization of genes identified by Mendelian randomization and acquired genetic variations; (A) Forest plot of seven important genes associated with blood GC; (B) Regional plot of co-localization evidence of ITIH2 in GC;

[0007] Figure 3 Flow chart for identifying drug candidate gene ITIH2 for GC;

[0008] Figure 4The expression of ITIH2 in different genotypes of rs1244506 and GC tumor tissues compared with adjacent normal tissues; (A) eQTL analysis of the correlation between whole blood and stomach rs1244506 genotypes and ITIH2 expression based on the GTEx database; (B) Meta-analysis of ITIH2 gene expression based on two public GC datasets downloaded from the GEO database; (C) The left side shows the representative IHC staining results of ITIH2 in GC tumor tissues and their related adjacent tissues; the right figure shows the IHC expression levels of ITIH2 in five pairs of matched GC and adjacent / normal tissues and their statistical analysis (*P<0.05);

[0009] Figure 5 The results show that ITIH2 significantly inhibited cell growth viability and migration ability; (A) qRT-PCR analysis of ITIH2 mRNA expression in AGS and HGC27 cell lines 48 hours after plasmid transfection (Vec: empty vector transfection; ITIH2: ITIH2 overexpression); (B) After overexpression of ITIH2 in AGS and HGC27 cell lines, the absorbance value was detected by CCK-8 reagent and cell proliferation analysis was performed; mean + standard deviation (n = 3); (C) Transwell analysis was used to evaluate the effect of ITIH2 on the migration of AGS and HGC27 cells, scale bar = 250μm, the results are expressed as mean ± standard deviation, compared with the control group, **P < 0.01, ***P < 0.001 and ****P < 0.0001 indicate significance;

[0010] Figure 6 Available drug information predicted by DSigDB and the effect of amiodarone on ITIH2, (A)

[0011] Display of candidate drugs predicted by DSigDB and their related action information; (B) Expression level of ITIH2 in AGS and HGC27 cells treated with different concentrations of amiodarone;

[0012] Figure 7 The results of amiodarone significantly inhibiting cell growth, migration and angiogenesis; (A) After treatment with a specific concentration of amiodarone for 48 hours, the cell viability of AGS and HGC27 cells was detected by CCK-8, and the IC 50; (B) The line graph shows the absorbance measurements of AGS and HGC27 cells treated with 0 μM, 5 μM and 10 μM amiodarone for different times, assessed using the CCK-8 assay; (C) Treatment with 10 μM amiodarone effectively inhibited cell migration, and the right panel shows the statistical analysis of the number of migrated cells, scale bar = 250 μM; (D) Angiogenesis assay evaluated the angiogenic capacity of HUVECs treated with 5 μM and 10 μM amiodarone, scale bar 500 μM; the lower panel shows a bar graph showing the fold changes in connectivity, segmentation, segment length and mesh; (E) Western blot detection of ITIH2, p21 and p53 protein levels after AGS and HGC27 cells were cultured with 5 and 10 μM amiodarone, and relative quantification was performed using GAPDH as the standard; the results are expressed as mean ± SD, and **P < 0.01, ***P < 0.001 and ****P < 0.0001 indicate significance compared with the control group;

[0013] Figure 8 This is the result of eQTL analysis of the correlation between rs1244506 genotype and ITIH2 expression in other tissues based on the GTEx database;

[0014] Fig. 9 The molecular docking analysis results of amiodarone and phenobarbital with ITIH2 protein are shown, (A) ITIH2 docking with amiodarone, (B) ITIH2 docking with phenobarbital. DETAILED DESCRIPTION

[0015] In order to make the technical personnel in the field understand the present invention more clearly and intuitively, the present invention will be further described below in conjunction with the accompanying drawings. The experimental methods without specific conditions in the following examples are selected according to conventional methods and conditions, or according to the product instructions.

[0016] Materials and Methods

[0017] Gastric cancer GWAS data

[0018] FinnGen (www.FinnGen.fi / en) is a genomic and personalized medicine research project that collects and analyzes genomic and health data from Finnish biobank donors. We downloaded the DF10 version of the GC data (including 1423 GC cases and 314193 controls) as the GC dataset (C3_STOMACH), which contains all individuals of European ancestry. Our analysis was based on publicly available GWAS summary statistics, eliminating the need for institutional review board approval.

[0019] eQTL dataset

[0020] The eQTL Gen (www.eQTLGen.org / ) consortium dataset provides cis-eQTL data from 31,684 healthy blood samples of European ancestry. We downloaded a full set of statistically significant cis-eQTLs (false discovery rate (FDR) < 0.05), and the allele frequencies were updated in 2019. To avoid tissue specificity, we verified the co-localization of the final target genes based on the GTEx database through the following series of analyses

[0021] eQTL (https: / / www.gtexportal.org / home / ).

[0022] Experimental verification of ITIH2 expression in GC

[0023] In the immunohistochemical comparison of gastric cancer tissue and adjacent tissue, all five pairs of samples from our hospital showed that the expression of ITIH2 in gastric cancer tissue was significantly reduced. Furthermore, cell culture experiments showed that amiodarone treatment of gastric cancer (GC) cells could upregulate the expression of ITIH2, which was consistent with the expected analysis results.

[0024] Potentially druggable genetic data

[0025] Our druggable gene data were compiled from a review article (The druggable genome and support for target identification and validation in drug development, PMID: 28356508) and the Drug Gene Interaction Database (DGIdb, https: / / www.dgidb.org / ). We downloaded various categories of druggable gene data updated in February 2022 from DGIdb. In order to expand the list of genes with drug potential, we integrated the above two sources and summarized 5642 protein-coding genes with drug potential. The process is as follows Figure 1 shown.

[0026] Mendelian randomization (MR)

[0027] We used the eQTL data from the druggable genome as exposure factors for MR analysis. By matching the transcription start site (TSS) position of each gene symbol, we retained only SNPs within the TSS ± ​​250 kb range of each protein-coding gene with druggable potential, which had an FDR < 0.05. Using European samples from the 1000Genomes Project database, each eQTL-associated SNP was identified with a linkage disequilibrium threshold of r. 2<0.1 for clustering. We then used the R package “FastTraitR” (version 1.0.0) to identify phenotypes associated with instrumental variables (IVs) and excluded SNPs and traits directly associated with GC, including alcohol intake, smoking, and body mass index. The remaining SNPs were used as instrumental variables for MR analysis. For genes with only one SNP retained, we applied the Wald ratio method to calculate MR estimates. For genes with multiple SNPs retained, we used the inverse variance weighted (IVW) method. We performed Cochran's Q test to assess the heterogeneity between the causal effects of individual SNPs, and used the intercept of MR-Egger regression to assess the pleiotropy at the SNP level. We applied the Bonferroni correction to adjust the P value, with FDR<0.05 as the significance threshold. Finally, the Steiger test was used to verify the directionality of the association. The present invention uses the “TwoSampleMR” toolkit (version0.5.6) in R software (version 4.1.1) for MR analysis.

[0028] Colocalization analysis

[0029] To determine whether two phenotypes are driven by the same causal variant in a specific region, we used the default prior probability: P1 = 10 -4 , P2=10 -4 , P12=10 -5 , to enhance the association between the two phenotypes. P1, P2, and P12 are the prior probabilities indicating significant correlations between SNPs in the test region and GC results, gene expression, or both. Through analysis, the posterior probabilities of different hypotheses (PPH0-PPH4) will be obtained: PPH0, indicating no association with any trait; PPH1, indicating an association with gene expression but no association with GC risk; PPH2, indicating an association with GC risk but no association with gene expression; PPH3, indicating an association with GC risk and gene expression, with significant causal differences;

[0030] PPH4, which showed an association with both GC risk and gene expression, was significantly associated with SNP loci in the same genomic region driven by common pathogenic variants. We favored PPH4 because the higher posterior probability indicated a greater likelihood that the model was valid given the data. Therefore, we will focus on genes with PPH4 ≥ “0.80” for subsequent analyses. We used the R packages “colon” ​​(version 5.2.3) and “locuscomparer” (version 1.0.0) for the analysis.

[0031] Differential expression of druggable target genes in GC

[0032] The Gene Expression Omnibus (GEO, https: / / www.ncbi.nlm.nih.gov / geo / ) is a public functional genomics database. We downloaded two GC-related datasets (GSE29272 and GSE37023) from the GEO database and performed differential expression analysis of druggable target genes between tumor and adjacent / normal tissues in each dataset.

[0033] Immunohistochemistry (IHC) staining

[0034] IHC staining was performed on 4 μm paraffin-embedded tissue sections. The sections were dewaxed, hydrated, and antigen retrieved, and then treated with hydrogen peroxide to inhibit endogenous peroxidase activity. ITIH2 (1:200; Affinity Biosciences, #DF13454, China) primary antibody was incubated overnight at 4°C, and then the slides were washed with PBS and incubated with horseradish peroxidase-conjugated goat anti-mouse / rabbit secondary antibodies at room temperature for 15 min, followed by staining with DAB substrate. Subsequently, the sections were counterstained with hematoxylin.

[0035] Drug candidate prediction

[0036] Considering that the Drug Signature Database (DSigDB, https: / / dsigdb.tanlab.org / ) provides extensive drug target information and related gene expression data, we utilized this database to identify potential reusable drugs that could act on the final druggable targets validated by MR and colocalization analysis.

[0037] Cell culture and treatment

[0038] AGS (RRID: CVCL_0139) and HGC27 (RRID: CVCL_1279) cell lines were obtained from ATCC. AGS cells were cultured with RPMI-1640 medium (Gibco, USA), while HGC27 cells were cultured with DMEM medium (Gibco, USA). Both cell types were grown in an antibiotic environment enriched with 10% fetal bovine serum (NEWZERUM, #FBS S500, New Zealand) and containing 100U / ml penicillin and 100μg / ml streptomycin. Incubation conditions were set at 37°C and maintained at 5% CO2 atmosphere to ensure optimal growth conditions. All human cell lines have been verified by STR (or SNP) analysis within the past three years, and all experiments were performed with mycoplasma-free cells.

[0039] Quantitative real-time polymerase chain reaction (qRT-PCR)

[0040] Total RNA was extracted from cells using RNAiso PLUS reagent (Takara, #9109, Japan). TM The extracted RNA was reverse transcribed into cDNA using the RT Master Mix Kit (Takara, #RR036A, Japan). qPCR amplification was performed using the TB Green Premix Ex Taq II qPCR Quantification Kit (Takara, #RR820A, Japan) according to the instructions. All gene expressions were normalized to β-actin. All primer sequences used in this study are shown in Table 1.

[0041] Table 1

[0042]

[0043]

[0044] Cell transfection

[0045] Plasmids were synthesized by NewHelix (Shanghai, China). TM Plasmid transfection was performed using the 3000 reagent (Thermo Fisher Scientific, #L3000015, USA). 48 hours after transfection, the overexpression effect of ITIH2 was analyzed by qRT-PCR.

[0046] Cell proliferation assay

[0047] The cell viability was analyzed using the CCK-8 kit (Cell Counting Kit-8, Dojindo, #CK04, Japan). 3 The cells were inoculated and cultured in 100ul culture medium in a 96-well microplate (Corning, USA) at a density of 10 cells / well. After the specified incubation time, 10μl CCK-8 reagent was added to each well and incubated at 37°C for 2 hours. The absorbance was measured at a wavelength of 450nm using a UV / VIS spectrophotometer based on a high-quality monochromator (Thermo Scientific Multiskan Go). Cell viability and IC were calculated using GraphPad Prism software v.8 (GraphPad, La Jolla, USA). 50 value and expressed as absorbance.

[0048] Transwell migration assay

[0049] In vitro migration assays were performed using 24-well transwell plates (Corning, #3422, USA). Cells were plated at 4 × 104 The cells were seeded at a density of 100 cells in serum-free medium in the chamber of a 24-well plate. The lower chamber included a medium containing 20% ​​FBS. After incubation for 24 hours at 37°C and 5% CO2, the cells in the chamber of the 24-well plate were fixed with 4% paraformaldehyde fixative and stained with crystal violet for 10 minutes at room temperature.

[0050] Immunoblot analysis

[0051] Total protein of treated cells was extracted by SDS lysis buffer supplemented with protease and phosphatase inhibitor cocktail (Beyotime, #P1050, China) and quantified using BCA protein assay kit (Applygen, #P1511, China). Protein bands were separated by SDS-PAGE (EpiZyme, #PG113, China) and then transferred to PVDF membrane (MerckMillipore, #ISEQ 00010, Germany). After blocking with protein-free rapid sealing solution (Servicebio, #G2052, China) for 10 min, the PVDF membrane was incubated with primary antibodies such as ITIH2 (1:1000; Affinity Biosciences, #DF13454, China), p53 (1:1000, CST, #2527T, USA), p21 (1:1000, CST, #2947, USA), GAPDH (1:50000; Proteintech, #60004, China) at 4°C overnight. After washing three times, the membrane was incubated with horseradish peroxidase-conjugated anti-rabbit / anti-mouse secondary antibodies at room temperature for 1 h. Finally, the membrane was incubated with Omni ECL TM Femto Light Chemiluminescence Kit (EpiZyme,

[0052] #SQ201, China) and the membrane was analyzed and imaged with an imager (Amersham Imager600 UV, USA). The intensity of the Western bands was determined by ImageJ software (National Institutes of Health, USA).

[0053] Angiogenesis assay

[0054] Human umbilical vein endothelial cells (HUVECs) were pretreated in ECM medium supplemented with different concentrations of amiodarone for 48 h. After preincubation, the cells were seeded on 24-well plates, each plate was coated with 300 ul of Matrigel (BD Biosciences, CA), and the cell density was 1.5 × 10 5Subsequently, the plates were incubated for 4 h and photographed under an optical microscope (Leica DMI4000, Germany). The resulting tubular network structures were captured and quantitatively analyzed using ImageJ software (National Institutes of Health, USA).

[0055] Statistical analysis

[0056] The results are expressed as mean ± standard deviation. The data were evaluated using GraphPad statistical software (GraphPad software Prism 8.0, USA). The statistical significance of the comparison between the two groups was analyzed using a two-sided unpaired Student t test. P values ​​less than 0.05 were considered statistically significant.

[0057] result

[0058] Druggable genome

[0059] After integrating the data of the two sets of druggable genes and excluding pseudogenes, we identified a total of 5642 druggable genes. Using the “FastTraitR” R package to remove confounders, we found 83 SNPs associated with these confounders. Based on the selection criteria of IV injection, we utilized 31489 SNPs associated with 2996 blood druggable genes for further MR analysis.

[0060] Cancer drug candidate genes

[0061] We used MR analysis to identify potential druggable targets in blood, resulting in a total of seven promising targets: ITIH2, CNR1, LY75, RECQL, CDH2, CHN2, and THBS3 ( Figure 2A). This analysis depends on the odds ratio (OR). An OR greater than 1 indicates an increased risk of the outcome associated with the exposure, whereas an OR less than 1 indicates a decreased risk. Notably, our MR analysis results showed that the expression of ITIH2, CNR1, LY75, and RECQL was associated with a decreased risk of GC, with ORs of 0.63 (95% CI 0.51-0.76), 0.56 (95% CI 0.47-0.69), 0.69 (95% CI 0.50-0.81), and 0.53 (95% CI 0.42-0.68), respectively. In contrast, the expression of CDH2, CHN2, and THBS3 was associated with an increased risk of GC, with ORs of 1.29 (95% CI 1.15-1.45), 1.33 (95% CI 1.18-1.50), and 1.45 (95% CI 1.31-1.61), respectively. In addition, our MR analysis showed that there were genes with more than 2 SNPs in the blood. We performed sensitivity analysis on these genes, and the results showed that all genes with multiple SNPs passed the strict test of Cochran's Q test and the intercept test of the MR-Egger method, except for RECQL, which was significant at P < 0.05 (Table 2).

[0062] Table 2 Druggable genes associated with GC when SNPs within ±250 kb of TSS meet FDR < 0.05 criteria

[0063]

[0064] In addition, we performed colocalization analysis to investigate the significant genes identified in the MR analysis in combination with the GC GWAS signal. Through this analysis, we identified a set of genes that colocalized with the GWAS variants with a high posterior probability of heterogeneity (PPH4>0.80). Table 3 summarizes the results of the colocalization analysis for all six potentially druggable genes. Especially for the ITIH2 gene with PPH4 greater than 0.80, we found a total of 18 cis-eQTLs associated with its expression: rs1244506, rs61833218, rs10508335, rs2277227, rs7067698, rs139152455, rs11255289, rs141963988, rs112920556, rs35136024, rs11255250, rs74120509, rs118050580, rs117147712, rs149572377, rs7907251, rs138763149, and rs111457346. Among these 18 SNPs, we found that both the expression of the gene and the risk of GC were significantly associated with the major SNP rs1244506 demonstrated by colocalization analysis ( Figure 2B). Therefore, the ITIH2 gene showed a protective effect in GC through the expression of the cis-eQTL rs1244506. Flowchart for identifying candidate drug genes for GC is shown in Figure 3 shown.

[0065] Table 3

[0066]

[0067]

[0068] ITIH2 is downregulated in cancer tissues

[0069] To explore the relationship between rs1244506, ITIH2 expression and GC risk, we investigated whether the presence of the rs1244505 mutant allele in blood and gastric samples coincided with the downregulation of ITIH2. Figure 4 A and Figure 8 As shown, this association has been documented in various tissues in the GTEx database. In addition, we performed a meta-analysis based on two publicly available GEO datasets to examine the expression level of ITIH2 in GC. The results showed that ITIH2 was significantly decreased in GC samples compared with adjacent / normal tissues ( Figure 4 B). To further confirm the above results, we performed IHC staining using paired samples from five groups of GC tissues and adjacent non-cancerous tissues. The results also showed that the expression of ITIH2 in tumor tissues was significantly decreased compared with that in adjacent non-cancerous tissues ( Figure 4 C) These results suggest that the ITIH2 gene may play a suppressive role in the occurrence and development of tumors.

[0070] Overexpression of ITIH2 inhibits the proliferation and migration of GC cells in vitro

[0071] To investigate the effect of ITIH2 on GC cells, we performed overexpression experiments in AGS and HGC27 cell lines. The overexpression efficiency was verified using qRT-PCR, which increased the expression of ITIH2 by more than 15-fold ( Figure 5 A). CCK-8 assay showed that ITIH2 overexpression could significantly inhibit GC cell proliferation ( Figure 5 B). Meanwhile, transwell analysis showed that upregulated ITIH2 could inhibit the cell migration ability of AGS and HGC27 cells ( Figure 5 C). Therefore, ITIH2 can inhibit the proliferation and migration of GC cells in vitro.

[0072] Drug candidate prediction

[0073] We used DSigDB to predict potentially effective intervention drugs and compiled a list of such drugs based on the search results ( Figure 6 A). Based on the above results, we identified amiodarone as a potential interventional drug that has been approved by the U.S. Food and Drug Administration and the World Health Organization and may increase the expression of ITIH2. Since our in vitro results indicated that ITIH2 functions as a tumor suppressor gene, we then performed Western blot analysis on GC cells treated with amiodarone. The results showed that amiodarone significantly increased the protein level expression of ITIH2 in a concentration-dependent manner ( Figure 7 B). In addition, we analyzed the molecular docking of amiodarone, phenobarbital and ITIH2 protein: first, we used UniProt (https: / / www.uniprot.org / ) to download the 3D structure of the protein, then obtained the SDF file of the drug from the PubChem compound database (https: / / pubchem.ncbi.nlm.nih.gov / ), and then used PROTEIN PLUS (https: / / proteins.plus / ) to predict the small molecule active center of ITIH2 protein, and used AutoDock 4.2.6 software for molecular docking (http: / / autodock.scripps.edu / ). Finally, we used PyMol3.1.0 to visualize the docking results (https: / / www.pymol.org / ); through computational simulation, we obtained the drug-protein docking results between amiodarone, phenobarbital and ITIH2 protein. The results showed that the binding between ITIH2 protein and amiodarone showed extremely high affinity, and the binding energy was as low as -8.77 kcal / mol, which significantly indicated that the complex formed between the two had strong stability ( Fig. 9 ). Together, these findings suggest that ITIH2 is a promising target for amiodarone.

[0074] Amiodarone inhibits GC cell growth, migration and angiogenesis in vitro

[0075] To further elucidate the effect of amiodarone on cell growth, we first determined its IC 50 , 6.76μM and 9.19μM respectively ( Figure 7 A). Subsequently, we cultured these two GC cell lines with final concentrations of 5μM and 10μM amiodarone, and the results showed that after amiodarone treatment, cell survival was significantly inhibited in a dose-dependent manner ( Figure 7 B). In addition, we evaluated the migration ability of cells at the indicated concentrations and observed that amiodarone reduced the number of migrated cells by approximately 50% ( Figure 7C). In addition, amiodarone treatment resulted in a decrease in connections, segments, segment length, and mesh formation ( Figure 7 D). In addition, it has been reported that desethylamiodarone, a metabolite of amiodarone, can inhibit the growth of melanoma cells in vitro and in vivo and arrest the cell cycle at the G0 / G1 phase in a p53-dependent manner. To test whether amiodarone inhibits the growth, migration, and angiogenesis of GC cells through the p53 signaling pathway, we investigated the protein expression of p21 and p53 after amiodarone treatment. The results showed that amiodarone induced a significant increase in the protein expression levels of p21 and p53 in a dose-dependent manner ( Figure 7 E). Collectively, these findings suggest that amiodarone effectively inhibits the proliferation and migration of GC cells, as well as the angiogenesis of HUVECs, and that amiodarone may exert its inhibitory effects on GC cells via the p53 signaling pathway.

[0076] Therefore, the present invention identifies new therapeutic targets for GC by integrating large-scale GC-GWAS data and druggable gene information. We investigated more than 5,000 potential gene targets by a systematic MR approach and obtained seven genes: ITIH2, CNR1, LY75, RECQL, CDH2, CHN2, and THBS3 as target sites. In addition, we performed colocalization analysis and singled out ITIH2 as a promising candidate for drug repurposing in GC treatment, and its expression showed a protective effect in GC. Subsequently, the biological function of ITIH2 was further tested in GC cells, and the results showed that the expression of ITIH2 was reduced in GC tumor tissues compared with adjacent non-tumor tissues, and its overexpression inhibited the growth and migration of GC cells. Further studies found that amiodarone has the potential to increase the expression of ITIH2. After further verification, amiodarone may become a therapeutic agent for the treatment of GC.

[0077] The inter-α-trypsin inhibitor (ITI) family includes serine protease inhibitors that are assembled from two precursor proteins: a light chain and one or two heavy chains. The light chain is represented by a single type encoded by the AMBP protein, while the heavy chain (ITIH family) exists in five homologous forms encoded by different proteins, including ITIH1, ITIH2, ITIH3, ITIH4, and ITIH5. The main function of the ITI family is to protect matrix activity through protease inhibition, subsequently regulating various cell biological functions such as inflammation and cell motility. ITIH genes are significantly downregulated in a variety of human solid tumors, including breast, colon, and gastric.

[0078] ITIH2 is a member of the ITIH superfamily, located on chromosome 10p15, secreted by the liver, and circulates in the blood and body fluids. In GC patients, ITIH2 is greatly reduced in bone metastatic tissues compared with tissues without bone metastasis, and high expression of ITIH2 is associated with prolonged overall survival.

[0079] Amiodarone is a widely used and effective antiarrhythmic drug for the control of life-threatening ventricular tachyarrhythmias, but some patients with long-term treatment have side effects, mainly including pulmonary complications and thyroid dysfunction. Amiodarone has also been explored as a potential treatment for cancers such as prostate cancer and unresectable hepatocellular carcinoma. Interestingly, one study showed that amiodarone sensitized glioma cells to TRAIL-mediated apoptosis. In addition, amiodarone can reduce the migration ability of cancer triple-negative cell lines and reduce the number of lung metastases in vivo. Our results are consistent with the above studies, and amiodarone inhibited GC cell growth, migration, and angiogenesis in vitro, indicating that amiodarone may be a potential drug for GC patients. Further experimental trials are needed to determine the optimal usage and dosage of amiodarone to inhibit GC.

[0080] The present invention uses druggable genomic MR to study potential drug targets and identify promising GC therapeutic drugs, and colocalization analysis helps reduce false negatives and false positives, thereby improving the credibility of the results. The present invention obtains ITIH2 as a druggable target gene and validates its role as a tumor suppressor in GC by demonstrating its inhibitory effects on cell growth and migration in vitro, a function that has not been emphasized in the literature as a priority drug target for GC treatment. In addition, our study shows for the first time that amiodarone can upregulate the expression level of ITIH2 and inhibit the growth, migration and angiogenesis of GC cells in vitro, indicating its potential as a GC therapeutic agent.

[0081] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to these embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the embodiments herein, and improvements and modifications made to the present invention by those skilled in the art based on the disclosure of the present invention should be within the scope of protection of the present invention.

Claims

1. Use of ITIH2 detection reagents in the preparation of products for diagnosis or prognosis of patients with gastric cancer, breast cancer or colon cancer.

2. The use according to claim 1, characterized in that The ITIH2 detection reagent is used to detect the ITIH2 gene expression level, or to detect the ITIH2 mRNA expression level, or to detect the ITIH2 protein expression level.

3. A kit for diagnosis or prognosis of gastric cancer patients, characterized in that: Includes detection reagents for ITIH2.

4. The kit according to claim 3, characterized in that It includes reagents for detecting the expression level of ITIH2 gene, or detecting the expression level of ITIH2 mRNA, or detecting the expression level of ITIH2 protein.

5. Application of ITIH2 promoter in the preparation of drugs for preventing and treating gastric cancer.

6. The use according to claim 5, characterized in that The ITIH2 promoter is a substance that promotes the expression of the ITIH2 gene, promotes the expression of ITIH2 mRNA, or promotes the activity of the ITIH2 protein.

7. Use of amiodarone as an ITIH2 promoter in the preparation of drugs for the prevention and treatment of gastric cancer.

8. A drug for preventing and treating gastric cancer, characterized in that: Includes ITIH2 accelerator.

9. The drug according to claim 8, characterized in that The ITIH2 promoter is a substance that promotes the expression of the ITIH2 gene, or promotes the expression of ITIH2 mRNA, or promotes the activity of the ITIH2 protein.

10. A drug for preventing and treating gastric cancer, characterized in that: Including amiodarone.

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