Methods for screening and verifying targeting of drugs for treating cancer and proteins thereof
By screening and validating targeted therapies for gastric cancer, and utilizing ADAM15 and EFNA3 proteins, the problem of insufficient effective treatment for gastric cancer has been addressed. This provides new drug targets and early diagnostic methods, and enhances the efficacy of immunotherapy.
Patent Information
- Application Number
- CN202511455635.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-13
- Publication Date
- 2026-02-27
AI Technical Summary
Current technologies offer limited effective drug treatments for gastric cancer, lacking effective therapeutic targets and strategies.
By analyzing plasma proteome based on two-sample Mendelian randomization, proteins causally associated with cancer risk were screened using Bonferroni correction. Robustness and multi-omics integration validation were performed, and ADAM15 and EFNA3 were identified as potential therapeutic targets. ADAM15 inhibits the growth of gastric cancer cells, while EFNA3 promotes the growth of gastric cancer cells.
ADAM15 can promote the interaction between endothelial cells and CD8+ T cells, enhance their migration, adhesion and killing effects, improve the response to immunotherapy, and promote the progression of gastric cancer by EFNA3 overexpression, providing new drug targets and early diagnostic methods.
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Figure CN121570592A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of drug targeting analysis, in particular to a method for screening and verifying cancer drug targeting and a protein thereof. BACKGROUND
[0002] In the related art, gastric carcinoma (GC) is a highly invasive and fatal malignant tumor, which is the fourth leading cause of cancer death worldwide and the fifth most common cancer in overall incidence. Due to the lack of early clinical symptoms and signs, most patients are diagnosed in the middle and late stages of the disease. Although some understanding of the etiology and pathogenesis of gastric cancer has been achieved, effective drug treatment is still limited. Therefore, it is of great significance to improve the prognosis of patients by further studying the pathogenesis of gastric cancer, finding new therapeutic targets, and developing more effective treatment strategies. SUMMARY
[0003] The main purpose of the present application is to provide a method for screening and verifying cancer drug targeting and a protein thereof, which aims to solve the technical problem of limited effective drug treatment for gastric cancer in the prior art.
[0004] To achieve the above-mentioned purpose, the present application provides an application of a protein in the preparation of an anti-gastric cancer drug, wherein the protein is ADAM15, and the ADAM15 serves as a therapeutic target to inhibit the growth of gastric cancer cells.
[0005] In addition, to achieve the above-mentioned purpose, the present application also provides an application of a protein in the preparation of a gastric cancer treatment drug, wherein the protein is EFNA3, and the EFNA3 serves as a potential therapeutic target to promote the growth of gastric cancer cells.
[0006] In addition, to achieve the above-mentioned purpose, the present application also provides a method for screening and verifying cancer drug targeting, which comprises the following steps: Based on the analysis of two-sample Mendelian randomization, the causal relationship between the plasma proteome, the protein in the plasma proteome, and the risk of cancer is obtained; Based on the causal relationship, the proteins causally related to the risk of cancer are screened out by using Bonferroni correction; The candidate proteins are screened from the proteins by robustness verification analysis on the proteins; The candidate proteins are subjected to a variety of multi-omics integrated verification respectively, and verification results are obtained to guide the preparation of cancer drugs.
[0007] In an embodiment, the multi-omics integrated verification includes systematic causal verification and mediation analysis, multi-omics functional verification and drug sensitivity analysis, and in vitro experiment and clinical translation verification.
[0008] In one embodiment, the multi-omics functional validation includes differential expression analysis, enrichment analysis, single-cell transcriptomics analysis, and protein interaction network analysis.
[0009] In one embodiment, the systematic causal verification and mediation analysis includes: A two-step mediation analysis method was used to obtain the biological characteristics of the cancer microenvironment caused by candidate proteins.
[0010] In one embodiment, the in vitro experiment includes: By knocking down the candidate protein, the effects of the candidate protein on T cell invasion, adhesion, activation, and cytotoxicity were examined to obtain the knockdown candidate protein; RNA sequencing was performed on the knockdown candidate protein to identify downstream genes and signaling pathways, in order to obtain the molecular mechanism of the knockdown candidate protein. The clinical translational validation includes: Immunohistochemical staining was performed on the candidate proteins, and Kaplan-Mayer survival analysis was conducted in conjunction with clinical outcomes.
[0011] In one embodiment, the robustness verification analysis includes bidirectional Mendelian randomization analysis, Steiger filtering, Bayesian colocalization analysis, and Mendelian randomization analysis based on summary data.
[0012] In one embodiment, the two samples in the two-sample Mendelian randomization are an exposure genome-wide association analysis and a first-outcome genome-wide association analysis; After screening for proteins causally associated with the cancer risk using Bonferroni correction, and before performing robustness validation analysis on the proteins, the method for screening and validating therapeutic cancer biomarkers further includes: The recurrent proteins were obtained after performing two-sample Mendelian randomization and Bonferroni correction based on two second-outcome genome-wide association analyses; wherein the first-outcome genome-wide association analysis and the second-outcome genome-wide association analysis were different.
[0013] In one embodiment, where the cancer is gastric cancer, the verification results are ADAM15 and EFNA3.
[0014] One or more technical solutions proposed in this application have at least the following technical effects: The ADAM15 provided in this application can promote the interaction between endothelial cells and CD8+ T cells, enhancing their migration, adhesion, activation, and killing effects. High ADAM15 levels are associated with stronger CD8+ T cell infiltration and better immunotherapy responses. Mechanistically, ADAM15 enhances anti-tumor immunity through the LITAF-TNF pathway. Therefore, the ADAM15 protein provided in this application not only serves as a potential drug target, providing potential drug candidates for future gastric cancer drug development, but also, due to the low expression of ADAM15 in gastric cancer patient tissues, can help improve the early diagnosis of gastric cancer. This application also provides another protein—EFNA3. Overexpression of EFNA3 can promote gastric cancer progression, and this protein is associated with neuro-immune interactions; therefore, it can be used as an extended target in potential clinical applications. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart illustrating a method for screening and validating targeted therapies for cancer treatment, as described in an embodiment of this application.
[0017] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.
[0019] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.
[0020] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text includes three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution that simultaneously satisfies A and B. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.
[0021] In this application, when numerical intervals (i.e., numerical ranges) are involved, unless otherwise specified, the distribution of selectable numerical values within the numerical interval is considered continuous, and includes the two endpoints of the numerical interval (i.e., the minimum and maximum values), as well as every numerical value between these two endpoints. Unless otherwise specified, when a numerical interval refers only to integers within that numerical interval, it includes the two endpoint integers of the numerical range, as well as every integer between the two endpoints, which is equivalent to directly listing every integer. When multiple numerical ranges are provided to describe features or characteristics, these numerical ranges can be merged. In other words, unless otherwise specified, the numerical ranges disclosed in this application should be understood to include any and all subranges included therein. The "numerical value" in the numerical interval can be any quantitative value, such as a number, percentage, ratio, etc. The term "numerical interval" can be broadly included to include percentage intervals, ratio intervals, proportion intervals, etc.
[0022] This application provides an example of the application of a protein, ADAM15, in the preparation of an anti-gastric cancer drug. ADAM15 serves as a therapeutic target to inhibit the growth of gastric cancer cells.
[0023] In this embodiment, ADAM15 (de-integrin metalloproteinase 15) is used as an anti-gastric cancer immunomodulatory factor, which can be applied in diagnosis, prediction and immunotherapy sensitization. Specifically, inhibiting ADAM15 with antibodies can slow the growth of gastric cancer cells.
[0024] Specifically, ADAM15 can promote the interaction between endothelial cells and CD8+ T cells, enhancing their migration, adhesion, activation, and killing effects. High ADAM15 levels are associated with stronger CD8+ T cell infiltration and better immunotherapy responses. Mechanistically, ADAM15 enhances anti-tumor immunity through the LITAF-TNF pathway. Therefore, the ADAM15 protein provided in this application not only serves as a potential drug target, providing potential drug candidates for future gastric cancer drug development, but also, due to the low expression of ADAM15 in gastric cancer patient tissues, this protein may help improve the early diagnosis of gastric cancer.
[0025] In addition, this application also provides the application of a protein in the preparation of a drug for treating gastric cancer, wherein the protein is EFNA3, and EFNA3 serves as a potential therapeutic target to promote the growth of gastric cancer cells.
[0026] This embodiment provides another protein, EFNA3 (hepatocyte glycoside A3), which serves as an expanded target for potential clinical applications. EFNA3 is associated with neuro-immune interactions and therefore can be used as an extended target. Specifically, EFNA3 overexpression can promote gastric cancer progression.
[0027] In addition, to achieve the above objectives, refer to Figure 1 This application also proposes a method for screening and validating targeted therapies for cancer, the method comprising steps S10-S40: Step S10 involves analyzing plasma proteome and the causal relationship between proteins in plasma proteome and cancer risk based on two-sample Mendelian randomization.
[0028] It should be noted that the two samples in two-sample Mendelian randomization refer to exposure genome-wide association analysis (exposed GWAS) and first-outcome genome-wide association analysis (first-outcome GWAS). Specifically, all proteins in the plasma proteome were integrated from public databases, and multiple single nucleotide polymorphisms (SNPs) were integrated from the public databases as instrumental variables. The selected 13,365 SNPs were strongly correlated with the integrated 4,972 proteins (P<5e-8, genome-wide significance). The integrated 4,972 proteins were used as exposure variables to obtain the first sample (exposed GWAS). The public database contained GWAS data from 7,921 cases and 159,201 controls, and cancer risk was used as the outcome variable to obtain the second sample (first-outcome GWAS).
[0029] For example, the cancer risk is the risk of gastric cancer. The following uses gastric cancer as an example to illustrate the method provided in the embodiments of this application for screening and validating drug targets for treating cancer.
[0030] Public databases can be Genome-Wide Association Study (GWAS) databases. For example, GWAS databases can be the UK BioBank, the European Bioinformatics Institute (EBI), or the Genome-Wide Association Study Catalog (GWAS Catalog).
[0031] Analysis based on two-sample Mendelian randomization refers to the use of working variables SNPs to obtain the causal relationship between plasma proteome, proteins in plasma proteome and cancer risk. Specifically, each working variable SNP is calculated to estimate the causal effect of the exposure variable (4972 proteins) on the outcome variable (cancer), thereby obtaining multiple causal relationships.
[0032] Furthermore, the GWAS in this embodiment includes a larger case and control group and analyzes a large number of SNPs. Since SNPs are determined at birth, the natural randomized controlled design in this embodiment can minimize many biases and confounding factors, providing a more reliable framework for discovering novel therapeutic targets.
[0033] Step S20: Based on causal relationships, use Bonferroni correction to screen for proteins that are causally related to cancer risk; In this embodiment, Bonferroni correction is used to perform rigorous statistical tests on each causal relationship obtained, thereby achieving preliminary screening and identifying proteins that are causally related to cancer risk. The screened proteins meet the condition that the working variable SNPs only affect the outcome variable through the exposure variable and that the working variable SNPs are not related to other confounding variables. Only then is the causal inference between the exposure variable and the outcome variable considered valid. For example, when the cancer is gastric cancer, Bonferroni correction was used to screen for 22 proteins causally associated with gastric cancer risk. This indicates that 22 proteins have a causal relationship with gastric cancer risk. Among them, genetic prediction showed that elevated levels of LY6D and EFNA3 were positively correlated with increased gastric cancer risk. The remaining 20 proteins (CLEC14A, AMY2A, INSR, PTPRM, SELP, IL3RA, ADGRF5, ISLR2, THSD1, PLXND1, PECAM1, GKN2, IGF1R, STC1, ADAM15, SLURP1, TLR4, CD36, KDR, and TLL1) were negatively correlated with gastric cancer risk. Furthermore, no heterogeneity or pleiotropic effects were found (P_heterogeneity > 0.05, P_pleiotropic effect > 0.05).
[0034] The Bonferroni correction p-value is used as the significance criterion. When the p-value is <0.05 / 4972, the probability of false positives during the entire correction process can be controlled to below 5%. Here, 0.05 refers to the single significance threshold, and 4972 refers to the number of corrections.
[0035] Step S30: Candidate proteins are obtained by performing robustness validation analysis on the proteins. In this embodiment, to further verify the causality of the proteins, various robustness verification analyses were conducted from different perspectives to ensure the reliability and rigor of the causal relationship between the screened proteins and cancer. The 22 proteins screened in steps S10 to S20 were verified, and their significance levels all reached P<0.05.
[0036] This embodiment performs four robustness verification analyses. In one embodiment, the robustness verification analyses include bidirectional Mendelian randomization analysis, Steiger filtering, Bayesian colocalization analysis, and Mendelian randomization analysis based on summary data.
[0037] Specifically, bidirectional Mendelian randomization (BMR) analysis is used to demonstrate that the causal relationship is unidirectional, while Steiger filtering is used to further ensure that the directionality of the causal relationship is accurate. By combining BMR and Steiger filtering, conclusions that may be caused by reverse causal relationships can be highlighted. Bayesian colocalization analysis strongly indicates that the protein and cancer have the same variants. Summary data-based Mendelian randomization (SMR) analysis is used to test the association between gene expression levels and complex traits of interest, and can be used to identify target genes in GWAS signals for subsequent functional studies.
[0038] For example, all four robustness validation analyses supported the causal relationship between the 22 proteins and the risk of gastric cancer. Among them, the Bayesian colocalization analysis showed that CD36, IGF1R, KDR and TLL1 were supported by strong genetic colocalization evidence (PP4>0.8).
[0039] Step S40: Perform multi-omics integration validation on candidate proteins to obtain validation results for guiding cancer drug development.
[0040] In this embodiment, multi-omics integrated validation is used to comprehensively validate candidate proteins to identify more complete, accurate, and translationally promising proteins for guiding cancer drug development. In one embodiment, multi-omics integrated validation includes systematic causal validation and mediation analysis, multi-omics functional validation and drug sensitivity analysis, and in vitro experimental and clinical translational validation.
[0041] In one embodiment, systematic causal verification and mediation analysis include: A two-step mediation analysis method was used to obtain the biological characteristics of the cancer microenvironment caused by candidate proteins.
[0042] The biological characteristics of the cancer microenvironment include cancer risk factors, immune cell phenotypes, and metabolites; for example, the biological characteristics of the gastric cancer microenvironment include gastric cancer risk factors, immune cell phenotypes, gut microbiota species, and metabolites.
[0043] In this embodiment, various biological characteristics are used as potential mediating variables to screen the causal relationships between all potential mediating variables and candidate proteins and gastric cancer, thereby achieving systematic causal verification of candidate proteins. Specifically, a two-step mediation analysis method was used to verify whether the biological characteristics of the cancer microenvironment play a mediating role between candidate proteins and cancer, and the mediating effect of candidate proteins in the relationship between cancer risk factors and cancer was quantified, thereby determining the mechanism of action of candidate proteins on cancer risk.
[0044] It should be noted that the two-step mediation analysis method refers to a method combining two steps of Mendelian randomization with mediation analysis to obtain the causal relationship between candidate proteins and various biological characteristics, as well as the causal relationship between various biological characteristics and cancer. Based on these two causal relationships, and the causal relationship between candidate proteins and cancer obtained in steps S10 and S20, the corresponding immune cell phenotypes that lead to cancer caused by the candidate proteins are then obtained. For example, when the cancer is gastric cancer, the various biological characteristics include 8 gastric cancer risk factors, 731 immune cell phenotypes, 233 gut microbiota species, and 1400 metabolites.
[0045] Specifically, after analyzing the causal relationships between candidate proteins and various biological characteristics, Bonferroni correction was applied, and biological characteristics with significant causal associations were included in subsequent analyses. These significant causal relationships were designated as direct effects (beta_1), which constitutes the first step of Mendelian randomization. Next, after Bonferroni correction and exclusion of heterogeneity and pleiotropy, the causal relationships between various biological characteristics and cancer were analyzed. Biological characteristics with significant causal associations were included in subsequent analyses, and these significant causal relationships were designated as direct effects (beta_2), which constitutes the second step of Mendelian randomization. For example, using a two-step mediation analysis, two gastric cancer risk factors, 16 immunophenotypes, three gut microbiota, and 15 plasma metabolites were jointly identified as causally related to gastric cancer in both the discovery and validation GWAS cohorts.
[0046] Furthermore, the effect estimates beta_1 and beta_2 from two-step Mendelian randomization were used in conjunction with the total effect beta_0 from the main Mendelian randomization to conduct mediation analysis, so as to obtain the mediation effect and the mediation ratio. The biological characteristics corresponding to the mediation ratio falling within a specific range were used as the mechanism of action of candidate proteins on gastric cancer risk.
[0047] The formulas for calculating the mediation effect and the mediation ratio are: Mediation effect = beta_1 × beta_2, Mediation ratio = beta_1 × beta_2 / beta_0; where beta_0 is the total effect beta_0, which is the causal relationship between the candidate proteins obtained in steps S10 and S20 and gastric cancer.
[0048] For example, the mechanism of action obtained is that gastric cancer risk factors and immune cell phenotypes partially mediate the causal effect of candidate proteins on gastric cancer risk. Specifically, gastric squamous epithelium, MYC targets, cell cycle, tumor metabolism, and immune characteristics may be involved in the gastric cancer risk induced by candidate proteins. In one example, with a mediating percentage between 2.8% and 5.0%, candidate proteins CD36, INSR, PLXND1, IGF1R, IL3RA, ADGRF5, PTPRM, and PECAM1 partially reduced gastric cancer risk by decreasing the effect of processed meat intake; TLL1 exerted a protective effect by reducing the risk of gastritis; and SLURP1, THSD1, and TLR4 reduced gastric cancer risk by increasing the SSC-A level of HLA DR+ natural killer cells. In another example, CD4+ T cells not only have a causal relationship with gastric cancer themselves but also mediate a 3.3% causal effect of EFAN3 on gastric cancer. THSD1, ADGRF5, IL3RA, PECAM1, IGF1R, and ISLR2 partially reduce the risk of gastric cancer by increasing the proportion of newly matured B cells.
[0049] In one embodiment, multi-omics functional validation includes differential expression analysis, enrichment analysis, single-cell transcriptomics analysis, and protein-protein interaction network analysis.
[0050] In this embodiment, differential expression analysis is used to compare the expression levels of target proteins in cancer tumors and normal tissues. Specifically, it is used to determine whether the gene expression difference between cancer tumors and normal tissues affects the causal relationship between candidate proteins and cancer risk by comparing the expression levels of target proteins in cancer tumors and normal tissues. This allows for further screening of carcinogenic targets from candidate proteins.
[0051] For example, a comparative analysis of tumor tissue (T) from TCGA-STAD gastric cancer patients and normal gastric tissue (G) from the GTEX database revealed that among 22 candidate proteins, seven proteins—AMY2A, PTPRM, PLXND1, GKN2, ADAM15, CD36, and TLL1—were significantly expressed lower in tumor tissue than in normal tissue. Since ADAM15 is highly expressed in normal endothelial cells and downregulated in tumor tissue, it can inhibit the growth of gastric cancer cells; therefore, ADAM15 can be considered a protective protein causally related to gastric cancer. Conversely, EFAN3 and LY6D proteins showed significantly increased expression in tumor tissue, which can promote the growth of gastric cancer cells. Further comparison between tumors (T) and adjacent normal tissue (N) within the TCGA-STAD cohort showed that seven of the nine differentially expressed proteins maintained the same expression trend. It should also be noted that a target refers to a protein that can directly bind to a drug.
[0052] In this embodiment, enrichment analysis was performed based on GSVA scores calculated using over 6,000 features to explore the potential mechanisms of candidate proteins. These features included cell type, immune cells, and the Hallmark pathway; potential mechanisms included endothelial-immune cell interactions and cell differentiation regulation.
[0053] For example, enrichment analysis revealed that most of the 22 candidate proteins were associated with low scores on signature pathways such as MYC targets, E2F targets, cell cycle (mitotic spindle, G2M checkpoint), and cancer metabolism (oxidative phosphorylation, glycolysis). This reveals the potential molecular mechanism by which these 22 candidate proteins reduce the risk of gastric cancer. Furthermore, most of the 22 candidate proteins were positively correlated with the levels of immune cells (such as T cells and B cells) and immune signatures (interferon response); while EFNA3 gene expression showed a negative correlation, suggesting that immune deficiency may be involved in the development of gastric cancer.
[0054] In this embodiment, single-cell transcriptomics analysis is used to locate the cell types in which candidate proteins are enriched in gastric tissue, so as to obtain the role of candidate proteins in immune cell communication, thereby gaining a deeper understanding of how candidate proteins prevent or promote the occurrence of gastric cancer.
[0055] For example, a single-cell level cross-cell type analysis was performed on 22 candidate proteins. This analysis included 63,229 tumor cells and 47,879 normal cells from 24 tumor and paired normal tissues. These cells were clustered into 31 subpopulations and further subdivided into 11 cell types. Single-cell transcriptomic analysis revealed that 8 of the 22 candidate proteins (including ADAM15 and EFNA3) showed significant enrichment in endothelial cells (logFC>3, p<1e-5). Differential analysis between tumor and normal tissues further confirmed the reduction of ADAM15, particularly in the endothelial cells of tumor tissues.
[0056] In this embodiment, protein-protein interaction (PPI) network analysis is used to obtain the molecular interaction patterns of candidate proteins. For example, a protein-protein interaction (PPI) network is constructed using the STRING database to determine whether strong interactions exist between candidate proteins.
[0057] In this embodiment, drug sensitivity analysis assesses the differences in drug response based on the high and low expression levels of different candidate proteins by combining cancer cell lines with multiple targeted compounds. For example, 256 targeted compounds (including targeted drugs and cytotoxic drugs) from 28 gastric cancer cell lines are used.
[0058] Specifically, the half-maximal inhibitory concentration (IC50) values of 265 targeted compounds and the mRNA expression levels of genes encoding target proteins in 28 gastric cancer cell lines were extracted from the Genomics of Cancer Drug Sensitivity (GDSC) database. Pearson correlation analysis was then performed on the gene expression levels of target proteins and their IC50 values, confirming that the expression of SLURP1, TLL1, KDR, CD36, ADAM15, INSR, PTPRM, and GKN among the 22 candidate proteins was significantly correlated with lower IC50 values for most compounds, suggesting higher drug sensitivity. Among these, ADAM15 expression was associated with better drug responses in 12 out of 14 targeted compounds.
[0059] In one embodiment, the in vitro experiment includes: knocking down a candidate protein, detecting the effect of the candidate protein on T cell invasion, adhesion, activation, and cytotoxicity to obtain the knockdown candidate protein; and performing RNA sequencing on the knockdown candidate protein to identify downstream genes and signaling pathways to obtain the molecular mechanism of the knockdown candidate protein. Clinical translational validation includes: performing immunohistochemical staining on the candidate proteins and conducting Kaplan-Mayer survival analysis in conjunction with clinical outcomes.
[0060] In this embodiment, gene knockout experiments were conducted in vascular endothelial cells and cancer cell lines to evaluate the functional detection of cell adhesion, migration, invasion, and immune cell interactions (T cell toxicity and activation assays). Downstream signaling pathways of the candidate protein were also obtained using high-throughput RNA sequencing technology. For example, to further identify therapeutic targets for gastric cancer, the newly discovered immune-related candidate target ADAM15 was subjected to in vitro experiments and clinical translation verification. Experimental results showed that candidate protein ADAM15 promotes the interaction between endothelial cells and CD8+ T cells, enhancing their migration, adhesion, activation, and killing effects. Candidate protein ADAM15 also enhances anti-tumor immunity through the LITAF-TNF pathway.
[0061] In addition, this embodiment also collected clinical patient samples and performed immunohistochemical (IHC) staining, and combined with clinical outcomes to perform Kaplan-Meier survival analysis, successfully demonstrating the potential value of the candidate protein in predicting the survival outcomes of cancer patients receiving immunotherapy.
[0062] For example, in the case of gastric cancer, samples were collected from 30 gastric cancer patients who received immunotherapy. Immunohistochemical staining for ADAM15, EFNA3, and LY6D was performed, and Kaplan-Meier survival analysis was conducted in conjunction with clinical outcomes. The results showed that in the patient cohort, high ADAM15 levels were associated with stronger CD8+ T cell infiltration and better immunotherapy response, and ADAM15 expression levels were associated with improved survival in gastric cancer patients receiving immunotherapy.
[0063] In another example, in order to obtain a therapeutic target for gastric cancer, an in vitro experiment was conducted on EFNA3, a newly discovered candidate target related to immunity in this embodiment. The experimental results showed that EFNA3 was identified as another potential immune regulatory factor, which is related to neuro-immune interactions and can be used as an extended application.
[0064] This embodiment provides a method for screening and validating targeted therapies for cancer. The method uses causal inference screening and Bonferroni correction to initially screen proteins, followed by various robustness validation analyses to verify the reliability of the initial screening results from different perspectives. Furthermore, multiple multi-omics integrated validation methods are employed to verify whether the screened candidate proteins can guide precision cancer treatment and drug development. Because the multi-omics integrated validation method provided in this embodiment is more comprehensive, the candidate proteins obtained through this validation have greater translational potential. Therefore, using the method provided in this embodiment for screening and validating targeted therapies for cancer can discover new immune regulatory targets and guide precision cancer treatment and drug development.
[0065] It should also be noted that when a mutation at a certain gene locus is found to be highly associated with a certain type of cancer, then that gene locus is a candidate gene. If the protein encoded by this candidate gene may regulate gastric cancer metabolism, then that protein is a candidate protein. For example, mutations in the ADAM15 gene are highly associated with gastric cancer, and the ADAM15 protein encoded by the ADAM15 gene may regulate gastric cancer metabolism; therefore, the ADAM15 protein is a candidate protein. Multiple multi-omics integration validations were performed on ADAM15 as a candidate protein to clarify that the ADAM15 protein encoded by the ADAM15 gene can effectively regulate gastric cancer metabolism and to obtain its regulatory mechanism. Based on this regulatory mechanism, an ADAM15 antibody drug capable of inhibiting the function of the ADAM15 protein was developed, successfully achieving anti-gastric cancer treatment.
[0066] To confirm the reliability of the screening results obtained in steps S10 to S30, in one embodiment, after screening for proteins causally related to cancer risk using Bonferroni correction, and before performing robustness verification analysis on the proteins, the method for screening and validating cancer treatment biomarkers further includes: After performing two-sample Mendelian randomization and Bonferroni correction based on two second-outcome genome-wide association studies, reproducible proteins were obtained. Among them, the first-outcome genome-wide association study and the second-outcome genome-wide association study were different.
[0067] In this embodiment, two independent second-outcome genome-wide association study (GWAS) validation cohorts were used to recheck the proteins preliminarily screened from S10 to S20. The reproducible proteins obtained from the recheck were aggregated with the results of the preliminary screening from S10 to S20, and the aggregated proteins were subjected to subsequent robustness verification analysis and various multi-omics integration verifications. Among them, "independent" means that the first-outcome genome-wide association study and the second-outcome genome-wide association study are different.
[0068] Exemplarily, after recheck, all the proteins preliminarily screened from S10 to S20 were verified in the first validation cohort, and the direction of the effect size remained consistent and reached the significance level after Bonferroni correction (P < 0.05 / 4,972). In the second validation cohort, the direction of the effect size was generally consistent with the preliminary screening, but 4 out of 22 candidate proteins did not reach significance (P > 0.05), and another 9 did not reach the significance level after Bonferroni correction (0.05 / 4,972 < P < 0.05), and the remaining 9 proteins did reach the significance threshold after Bonferroni correction (P < 0.05 / 4,972). The reason why some proteins did not reach Bonferroni-corrected significance may be that the sample size of the second validation cohort was small. Therefore, the reproducible proteins obtained from the recheck in this example were the same as the results of the preliminary screening from S10 to S20.
[0069] In one embodiment, when the cancer is gastric cancer, the verification results are ADAM15 and EFNA3.
[0070] In this embodiment, the protective proteins causally related to gastric cancer obtained by various multi-omics integration verifications include ADAM15 and EFNA3. Among them, only ADAM15 always satisfies all multi-omics integration verifications. Therefore, ADAM15 can be used as a potent protective factor against gastric cancer. As an immunoregulatory factor against gastric cancer, ADAM15 can be applied in diagnosis, prediction, and immunotherapy sensitization. Specifically, inhibiting ADAM15 with an antibody can slow down the growth of gastric cancer cells.
[0071] Specifically, ADAM15 can promote the interaction between endothelial cells and CD8+ T cells, enhancing their migration, adhesion, activation, and killing effects. High ADAM15 levels are associated with stronger CD8+ T cell infiltration and better immunotherapy responses. Mechanistically, ADAM15 enhances anti-tumor immunity through the LITAF-TNF pathway. Therefore, the ADAM15 protein provided in this application not only serves as a potential drug target, providing potential drug candidates for future gastric cancer drug development, but also, due to the low expression of ADAM15 in gastric cancer patient tissues, this protein may help improve the early diagnosis of gastric cancer.
[0072] Specifically, through two-sample Mendelian randomization, Bonferroni correction, robustness verification analysis, and systematic causal verification and mediation analysis, ADAM15 was found to be a protective protein causally associated with gastric cancer.
[0073] Furthermore, multi-omics functional validation revealed that ADAM15 is enriched in lymphatic endothelial cells of gastric cancer tissues, highly expressed in normal endothelial cells, and downregulated in tumor tissues. Additionally, ADAM15 was found to be crucial for communication between endothelial cells and other cell types (especially CD4+ T cells, CD8+ T cells, and B cells), suggesting that ADAM15 may exert its anti-tumor effect by recruiting these immune cells to endothelial cells. Drug sensitivity analysis showed that ADAM15 globally conferred better drug sensitivity on gastric cancer cell lines.
[0074] Furthermore, through in vitro experiments and clinical translation verification, it was confirmed that ADAM15 has an immunostimulatory effect and can predict improved survival in gastric cancer patients receiving immunotherapy. Specifically, ADAM15 promotes the interaction between endothelial cells and CD8+ T cells, enhances the migration, adhesion, activation and killing effects of endothelial cells and CD8+ T cells, and high ADAM15 levels are associated with stronger CD8+ T cell infiltration and better immunotherapy response. Mechanistically, ADAM15 enhances anti-tumor immunity through the LITAF-TNF pathway.
[0075] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.
Claims
1. Use of a protein in the preparation of a medicament for the treatment of gastric cancer, characterized in that, The protein is ADAM15, which inhibits the growth of gastric cancer cells as a therapeutic target.
2. Use of a protein in the preparation of a medicament for the treatment of gastric cancer, characterized in that, The protein is EFNA3, which promotes the growth of gastric cancer cells as a potential therapeutic target.
3. A method for screening and validating therapeutic cancer drug targets, characterized by, The method for screening and verifying the targeting of cancer drugs comprises: Based on the analysis of two-sample Mendelian randomization, the causal relationship between the plasma proteome and the risk of cancer is obtained; Based on the causal relationship, the proteins causally related to the risk of cancer are screened out using Bonferroni correction; Through robustness verification analysis of the proteins, candidate proteins are screened from the proteins; The candidate proteins are subjected to a variety of multi-omics integrated verification, and the verification results are obtained to guide the preparation of cancer drugs.
4. The method for screening and validating therapeutic cancer biomarkers of claim 3, wherein, The multi-omics integrated verification includes systematic causal verification and mediation analysis, multi-omics functional verification and drug sensitivity analysis, and in vitro experiments and clinical translation verification.
5. The method for screening and validating therapeutic cancer biomarkers of claim 4, wherein, The multi-omics functional verification includes differential expression analysis, enrichment analysis, single-cell transcriptomics analysis, and protein interaction network analysis.
6. The method for screening and validating therapeutic cancer biomarkers of claim 4, wherein, The systematic causal verification and mediation analysis includes: Using a two-step mediation analysis method, the candidate protein causes the biological characteristics of the cancer microenvironment.
7. The method for screening and validating therapeutic cancer biomarkers of claim 4, wherein, The in vitro experiment includes: By knocking down the candidate protein, the effects of the candidate protein on T cell invasion, adhesion, activation, and cytotoxicity are detected to obtain the knockdown candidate protein; RNA sequencing of the knockdown candidate protein is performed to identify downstream genes and signaling pathways to obtain the molecular mechanism of the knockdown candidate protein; The clinical translation verification includes: Immunohistochemical staining of the candidate protein is performed, and Kaplan-Meier survival analysis is performed in combination with clinical outcomes.
8. The method for screening and validating therapeutic cancer biomarkers of claim 3, wherein, The robustness verification analysis includes two-way Mendelian randomization analysis, Steiger filtering, Bayesian colocalization analysis, and Mendelian randomization analysis based on pooled data.
9. The method for screening and validating biomarkers for the treatment of cancer of claim 3, wherein, The two samples in the two-sample Mendelian randomization are exposure genome-wide association analysis and first outcome genome-wide association analysis; After the proteins causally related to the risk of cancer are screened out using Bonferroni correction, before the robustness verification analysis of the proteins, the method for screening and verifying cancer biomarkers further comprises: After the two-sample Mendelian randomization and Bonferroni correction based on two second outcome genome-wide association analyses, a reproducible protein is obtained; wherein the first outcome genome-wide association analysis and the second outcome genome-wide association analysis are different.
10. The method for screening and validating therapeutic cancer biomarkers of claim 3, wherein, In the case of gastric cancer, the verification results are ADAM15 and EFNA3.