Gastric cancer prediction markers, kits and applications, pharmaceutical compositions, treatment effect prediction models and construction methods and devices based on Tag2 / YBX1 / ISGs signal axis

Abstract

The present invention belongs to the technical field of molecular-assisted diagnosis, and discloses a gastric cancer prediction marker based on the Tag2 / YBX1 / ISGs signal axis, a kit and application, a pharmaceutical composition, a treatment effect prediction model, and a construction method and device. The gastric cancer-related prediction markers provided by the present invention include the Tagln2 gene, its mRNA and expression protein, and antibodies thereof; the YBX1 gene, its mRNA and expression protein, and antibodies thereof; and, the ISGs gene set, its mRNA and expression protein, and antibodies thereof. The prediction markers provided by the present invention are highly correlated with the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy, and can be applied to the production of drugs for gastric cancer treatment and / or the prediction of gastric cancer treatment effects, providing important technical support for the screening and / or verification of drugs for gastric cancer treatment and the prediction of clinical gastric cancer chemotherapy and treatment effects.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular-assisted diagnosis, and in particular relates to a gastric cancer prediction marker based on the Tag2 / YBX1 / ISGs signal axis, a kit and its application, a pharmaceutical composition, a treatment effect prediction model and a construction method and device. Background Art

[0002] Gastric cancer is the fifth most common cancer and the fourth leading cause of cancer death worldwide. It is highly heterogeneous. Molecular classification and resistance mechanism research for the purpose of efficacy are new challenges and opportunities facing clinical individualized precision treatment.

[0003] Efficient activation of interferon (IFN) signaling is considered to be the cornerstone of successful tumor treatment, including chemotherapy, radiotherapy and immunotherapy. In recent years, the use of IFN alone, stimulating IFN production or combining with the highly anticipated immune checkpoint blockade (ICB) and targeted pattern recognition receptors (PRRs) strategies have been shown to significantly enhance anti-tumor immune responses, but also have the "paradox" of promoting treatment resistance. Studies have revealed that IFN significantly reduces the sensitivity of tumors to genotoxic treatment regimens by inducing overexpression of a resistance-signature ISGs subset (ISGs.RS) without increasing the levels of pro-apoptotic and anti-proliferative ISGs, resulting in an "IFN-related DNA damage resistance signature (IRDS)". Differentially expressed ISGs.RS exist in a variety of solid tumors such as glioma, breast cancer, head and neck cancer, prostate cancer, and lung cancer. The differentially expressed ISGs.RS constitute a subset that can predict tolerance to chemotherapy and ICB treatment.

[0004] However, there are no reports on the differentially expressed ISGs.RS subsets in gastric cancer. Summary of the invention

[0005] The present invention screened out differential gene expression data in gastric cancer tissue that are specifically related to the therapeutic effects of radiotherapy and chemotherapy from a large amount of gene expression level data of gastric cancer tissue cells, and obtained a series of gastric cancer tissue-specific efficacy prediction markers, which provide important technical support for the screening and / or verification of gastric cancer treatment drugs and the prediction of clinical gastric cancer chemotherapy and treatment effects.

[0006] The inventors found that the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy are highly correlated with the mRNA expression level and / or protein expression level on the Tagln2 / YBX1 / ISGs signal axis, more specifically including the Tagln2 gene; YBX1; and the ISGs gene set. Among them, the genes related to the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy in the ISGs gene set specifically include but are not limited to: one or more of the ISG15 gene, IFIT1 gene, IFIT2 gene, IFIT3 gene, IFI6 gene, IFI16 gene, IFI44 gene, OAS2 gene, OAS3 gene, RSAD2 gene, WARS gene and OASL gene. Further, the genes in the ISGs gene set that are highly correlated with the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy include IFIT1 gene, IFIT2 gene, IFIT3 gene, ISG15 gene, IFI16 gene, OAS3 gene and WARS gene.

[0007] Based on the above gene expression data highly related to the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy, the present invention searches and establishes a prediction model that is most suitable for predicting and classifying the treatment effect of gastric cancer through machine learning modeling, and has a better ability to distinguish the treatment sensitivity and treatment resistance of the treatment effect of gastric cancer radiotherapy and chemotherapy through comprehensive judgment of multiple data such as Tagln2 gene, YBX1 and / or ISGs gene set. Therefore, the present invention covers the above genes and their expression products as predictive markers and models constructed based on them.

[0008] The first purpose of the present invention is to solve the gap in the prior art in the prediction of chemotherapy and ICB treatment tolerance based on the differentially expressed ISGs.RS subsets in gastric cancer, and provide a gastric cancer-related predictive marker based on the Tagln2 / YBX1 / ISGs signaling axis. Through the detection of this predictive marker, the development of gastric cancer can be well judged and predicted, and then a highly accurate prediction model for the therapeutic effect of gastric cancer drugs and / or treatment methods can be established to assist in the prediction of clinical gastric cancer treatment efficacy and the formulation of individualized precision treatment plans.

[0009] The second object of the present invention is to provide a gastric cancer-related detection kit.

[0010] The third object of the present invention is to provide the application of the above-mentioned gastric cancer-related prediction markers.

[0011] The fourth object of the present invention is to provide a pharmaceutical composition for treating gastric cancer.

[0012] The fifth object of the present invention is to provide a method for constructing a gastric cancer treatment effect prediction model.

[0013] The sixth object of the present invention is to provide a method for predicting the therapeutic effect of gastric cancer.

[0014] The seventh objective of the present invention is to provide a device for predicting the therapeutic effect of gastric cancer.

[0015] Specifically, the gastric cancer-related predictive markers provided by the present invention include: Tagln2 gene and its mRNA and expressed protein and antibodies thereof; YBX1 gene and its mRNA and expressed protein and antibodies thereof; and / or ISGs gene set and its mRNA and expressed protein and antibodies thereof.

[0016] In some specific embodiments, the ISGs gene set includes IFIT1 gene, IFIT2 gene, IFIT3 gene, ISG15 gene, IFI16 gene, OAS3 gene and WARS gene.

[0017] In the application of the gastric cancer-related prediction marker provided by the present invention, the prediction marker is applied to the production of drugs for gastric cancer treatment and / or the prediction of gastric cancer treatment effects.

[0018] The gastric cancer treatment pharmaceutical composition provided by the present invention comprises a Tagln2 expression inhibitor and an anti-tumor drug.

[0019] In some specific embodiments, the Tagln2 expression inhibitor is selected from MK2206 and / or Fisetin.

[0020] In some specific embodiments, the anti-tumor drug is selected from one or more of cisplatin, oxaliplatin, 5-fluorouracil and capecitabine.

[0021] The method for constructing a gastric cancer treatment effect prediction model provided by the present invention comprises: S1, obtaining the expression level of the IRDS gene set of gastric cancer samples and relevant data of their treatment effects, and incorporating the data of chemotherapy or radiotherapy therein as a training set Q; S2, performing random forest model training based on the training set Q to construct a prediction model; wherein the IRDS gene set comprises Tagln2 gene, YBX1 gene, IFIT1 gene, IFIT2 gene, IFIT3 gene, ISG15 gene, IFI16 gene, OAS3 gene and WARS gene.

[0022] In some specific embodiments, in step S1, based on the treatment effect, the data of the training set Q includes treatment-sensitive and treatment-resistant types.

[0023] In some specific embodiments, in step S2, the random forest model training includes: introducing random attribute selection, dividing the training set Q based on the treatment effect type to calculate the information gain, and training to obtain the calculation formula of the probability score value of the IRDS gene set mRNA expression and the score cutoff value for judging the treatment effect type;

[0024] Among them, the calculation formula of the information gain is:

[0025]

[0026] Among them, p0 is the proportion of treatment-resistant types in the training set Q, and p1 is the proportion of treatment-sensitive types in the training set Q; q is the score cutoff value and has V possible values. Using q for division will generate V branch nodes.

[0027] In some specific embodiments, the probability score value is calculated as follows:

[0028] Probability Score value = ∑a i A i (i=1,2,3.....,9)

[0029] A i is the expression level of a gene in the IRDS gene set, a i is the multiplication coefficient of the gene; among them, the multiplication coefficient of Tagln2 gene is 0.0927, the multiplication coefficient of YBX1 gene is 0.2883, the multiplication coefficient of IFIT1 gene is 0.1429, the multiplication coefficient of IFIT2 gene is 0.1192, the multiplication coefficient of IFIT3 gene is 0.0176, the multiplication coefficient of ISG15 gene is 0.1542, the multiplication coefficient of IFI16 gene is 0.0410, the multiplication coefficient of OAS3 gene is 0.0730, and the multiplication coefficient of WARS gene is 0.0712;

[0030] The score cutoff value is the median value of the probability score value in the training set Q.

[0031] The method for predicting the therapeutic effect of gastric cancer provided by the present invention is constructed by the method for constructing the prediction model of the therapeutic effect of gastric cancer, and the method specifically comprises: obtaining the IRDS gene set expression level data of the sample to be predicted, and calculating the probability score value:

[0032] Probability Score value = ∑a i A i (i=1,2,3.....,9)

[0033] The median value of the probability score value in the training set Q is used as the cutoff score value to judge the treatment effect, specifically including: probability score value ≤ Score cutoff value, the treatment effect is predicted to be sensitive; probability score value > Score cutoff value, the treatment effect is predicted to be resistant.

[0034] The device for predicting the therapeutic effect of gastric cancer provided by the present invention runs the above-mentioned method for constructing a prediction model for the therapeutic effect of gastric cancer and / or the method for predicting the therapeutic effect of gastric cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a Tagln2 immunohistochemical staining image of normal tissue adjacent to the cancer in one of the gastric cancer tissue chips provided in Example 1 of the present invention (scale bar is 20 μm);

[0036] Figure 2 This is a Tagln2 immunohistochemical staining image of gastric cancer tissue in one of the gastric cancer tissue chips provided in Example 1 of the present invention (scale bar is 20 μm);

[0037] Figure 3 The staining intensity score statistical diagram of 75 gastric cancer tissue chips provided in Example 1 of the present invention (IS, N is adjacent normal tissue cells, T is gastric cancer tissue cells);

[0038] Figure 4 This is a statistical graph of the immunohistochemical staining scores of Tagln2 expression in the cell membrane, cytoplasm and nucleus in 75 gastric cancer tissue chips provided in Example 1 of the present invention (PP, N is adjacent normal tissue cells, T is gastric cancer tissue cells);

[0039] Figure 5 The statistical graph of the Tagln2 immunohistochemical staining scores of the gastric cancer tissue chip at different distant metastasis stages provided in Example 1 of the present invention (PP×IS);

[0040] Figure 6 The statistical graph of the Tagln2 immunohistochemical staining scores of the gastric cancer tissue chip at different T stages provided in Example 1 of the present invention (PP×IS);

[0041] Figure 7 The statistical graph of the Tagln2 immunohistochemical staining scores of the gastric cancer tissue chip at different overall stages provided in Example 1 of the present invention (PP×IS);

[0042] Figure 8 Figure 1 of the experimental results of the effect of Tagln2 gene expression on the tolerance of BGC-823 gastric cancer cells to chemotherapeutic drugs provided in Example 2 of the present invention (cisplatin, TAGLN2 expression knockdown);

[0043] Fig. 9 Figure 2 of the experimental results of the effect of Tagln2 gene expression on the tolerance of HGC-27 gastric cancer cells to chemotherapeutic drugs provided in Example 2 of the present invention (cisplatin, TAGLN2 overexpression);

[0044] Fig.10 Figure 3 of the experimental results of the effect of Tagln2 gene expression on the tolerance of BGC-823 gastric cancer cells to chemotherapeutic drugs provided in Example 2 of the present invention (oxaliplatin, TAGLN2 expression knockdown);

[0045] Fig.11 Figure 4 of the experimental results of the effect of Tagln2 gene expression on the tolerance of HGC-27 gastric cancer cells to chemotherapeutic drugs provided in Example 2 of the present invention (oxaliplatin, TAGLN2 overexpression);

[0046] Fig.12 Figure 5 (5-FU, TAGLN2 expression knockdown) of the experimental results of the effect of Tagln2 gene expression on the tolerance of BGC-823 gastric cancer cells to chemotherapy drugs in Example 2 of the present invention;

[0047] Fig.13 Figure 6 (5-FU, TAGLN2 overexpression) of the experimental results of the effect of Tagln2 gene expression on the tolerance of HGC-27 gastric cancer cells to chemotherapeutic drugs provided in Example 2 of the present invention;

[0048] Fig.14 Figure 7 of the experimental results of the effect of Tagln2 gene expression on the tolerance of BGC-823 gastric cancer cells to chemotherapeutic drugs provided in Example 2 of the present invention (capecitabine, TAGLN2 expression knockdown);

[0049] Fig.15 Figure 8 of the experimental results of the effect of Tagln2 gene expression on the tolerance of HGC-27 gastric cancer cells to chemotherapeutic drugs provided in Example 2 of the present invention (capecitabine, TAGLN2 overexpression);

[0050] Fig.16 Figure 1 (IFIT1) of RNA-Seq detection results of reducing the chemotherapeutic drug resistance of BGC-823 gastric cancer cells by knocking down the expression of Tagln2 gene provided in Example 2 of the present invention;

[0051] Fig.17 Figure 2 (IFIT2) of RNA-Seq detection results of reducing the resistance of BGC-823 gastric cancer cells to chemotherapy drugs by knocking down the expression of Tagln2 gene provided in Example 2 of the present invention;

[0052] Fig.18Figure 3 (IFIT3) of RNA-Seq detection results of reducing the resistance of BGC-823 gastric cancer cells to chemotherapy drugs by knocking down the expression of Tagln2 gene provided in Example 2 of the present invention;

[0053] Fig.19 Figure 4 (ISG15) of the RNA-Seq detection results of reducing the chemotherapeutic drug tolerance of BGC-823 gastric cancer cells by knocking down Tagln2 gene expression in Example 2 of the present invention;

[0054] Fig. 20 Figure 5 (HLA-B) is the RNA-Seq detection result of reducing the chemotherapeutic drug resistance of BGC-823 gastric cancer cells by knocking down the expression of Tagln2 gene provided in Example 2 of the present invention;

[0055] Fig.21 This is the first graph of the experimental results of the ability of knocking down Tagln2 gene expression to promote the growth inhibition of BGC-823 gastric cancer cells by radiotherapy provided in Example 2 of the present invention (4Gy);

[0056] Fig. 22 Figure 2 (6Gy) shows the experimental results of the ability of knocking down Tagln2 gene expression to promote the growth inhibition of BGC-823 gastric cancer cells by radiotherapy provided in Example 2 of the present invention;

[0057] Fig.23 This is a graph showing the experimental results of the ability of knocking down Tagln2 gene expression to promote radiotherapy to inhibit the formation of BGC-823 gastric cancer colonies provided in Example 2 of the present invention (4Gy and 6Gy);

[0058] Fig.24 This is one of the RNA-Seq detection result graphs showing that knockdown of Tagln2 gene expression inhibits the enhanced expression of IGSs related to BGC-823 gastric cancer cell tolerance induced by radiotherapy provided in Example 2 of the present invention;

[0059] Fig.25 Figure 2 is the RNA-Seq detection result of knocking down Tagln2 gene expression to inhibit the enhanced expression of IGSs related to BGC-823 gastric cancer cell tolerance induced by radiotherapy provided in Example 2 of the present invention;

[0060] Fig.26 This is a graph showing the results of a western blot experiment in which the inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis provided in Example 2 of the present invention reduces the expression of Tagln2 and YBX1 in gastric cancer cells;

[0061] Fig. 27This is a diagram showing the RNA-Seq detection results of the inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis provided in Example 2 of the present invention inhibiting the expression of ISGs in gastric cancer cells;

[0062] Fig.28 This is one of the experimental result diagrams of the cytotoxicity of the inhibitor MK2206 targeting the Tagln2 / YBX1 / ISGs signaling axis on Tagln2 knockdown gastric cancer cells provided in Example 2 of the present invention;

[0063] Fig.29 Figure 2 is the experimental result of the cytotoxicity of the inhibitor MK2206 targeting the Tagln2 / YBX1 / ISGs signal axis on gastric cancer cells overexpressing Tagln2 provided in Example 2 of the present invention;

[0064] Fig.30 The figure is a graph showing the experimental results of the cytotoxicity of MK2206, cisplatin and the combination of the two on multiple gastric cancer cells (BGC-823, MGC-803 and SGC-7901) provided in Example 2 of the present invention;

[0065] Fig.31 This is one of the in vivo treatment experiment results (tumor growth curve) of the inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis to enhance the chemotherapy sensitivity of gastric cancer tissue provided in Example 2 of the present invention;

[0066] Fig.32 Figure 2 (tumor weight) of the in vivo treatment experiment results of the inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis to enhance the chemotherapy sensitivity of gastric cancer tissue provided in Example 2 of the present invention;

[0067] Fig.33 Figure 3 (ex vivo tumor tissue) is the result of an in vivo treatment experiment of an inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis to enhance the chemotherapy sensitivity of gastric cancer tissue provided in Example 2 of the present invention

[0068] Fig.34 This is one of the results of tumor tissue immunohistochemistry in an in vivo treatment experiment in which an inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis enhances the chemosensitivity of gastric cancer tissue provided in Example 2 of the present invention (inhibiting the expression of Tagln2 in tumor tissue);

[0069] Fig.35 Figure 2 of the results of tumor tissue immunohistochemistry in an in vivo treatment experiment of an inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis to enhance the chemotherapy sensitivity of gastric cancer tissue cells provided in Example 2 of the present invention (inhibition of YBX1 expression in tumor tissue);

[0070] Fig.36 Figure 3 of the results of tumor tissue immunohistochemistry in an in vivo treatment experiment of an inhibitor targeting the Tagln2 / YBX1 / ISGs signaling axis to enhance the chemotherapy sensitivity of gastric cancer tissue cells provided in Example 2 of the present invention (inhibition of ISG15 expression in tumor tissue);

[0071] Fig.37 Figure 4 is the result of tumor tissue immunohistochemistry in an in vivo treatment experiment of an inhibitor targeting the Tagln2 / YBX1 / ISGs signal axis to enhance the chemotherapy sensitivity of gastric cancer tissue cells provided in Example 2 of the present invention (inhibition of OAS3 expression in tumor tissue);

[0072] Fig.38 Figure 5 is the result of tumor tissue immunohistochemistry in an in vivo treatment experiment of an inhibitor targeting the Tagln2 / YBX1 / ISGs signal axis to enhance the chemotherapy sensitivity of gastric cancer tissue cells provided in Example 2 of the present invention (inhibition of IFIT1 expression in tumor tissue);

[0073] Fig.39 Figure 6 of the results of tumor tissue immunohistochemistry in an in vivo treatment experiment in which the inhibitor targeting the Tagln2 / YBX1 / ISGs signal axis provided in Example 2 of the present invention enhances the chemotherapy sensitivity of gastric cancer tissue cells (inhibition of IFIT2 expression in tumor tissue);

[0074] Fig.40 Figure 7 of the immunohistochemistry results of tumor tissues in an in vivo treatment experiment of the inhibitor targeting the Tagln2 / YBX1 / ISGs signal axis to enhance the chemotherapy sensitivity of gastric cancer tissue cells provided in Example 2 of the present invention (inhibition of IFIT3 expression in tumor tissues);

[0075] Fig.41 This is a statistical graph of the expression levels of the IRDS gene set for different first treatment effects obtained using the probability score value in Example 3 of the present invention;

[0076] Fig.42 is a ROC curve diagram of the prediction of the treatment effect based on the prediction model in Example 3 of the present invention;

[0077] Fig.43 The survival function curve of the training set obtained based on the prediction model in Experimental Example 3 of the present invention;

[0078] Fig.44 In Experimental Example 3 of the present invention, the prediction model is used to divide the samples into high-level group and low-level group using probability score to analyze the correlation with immune cells. DETAILED DESCRIPTION

[0079] The gastric cancer-related predictive markers provided by the present invention specifically include: Tagln2 gene, its mRNA, expressed protein, and antibodies thereof; YBX1 gene, its mRNA, expressed protein, and antibodies thereof; and, ISGs gene set, its mRNA, expressed protein, and antibodies thereof. Wherein, the Tagln2 gene, its mRNA, expressed protein, and antibodies thereof specifically refer to: based on the expression level of the Tagln2 gene, by detecting the mRNA and / or expressed protein expressed by it, or by using antibodies that can specifically bind to the mRNA and / or protein of Tagln2 as detection substances, the tolerance and / or sensitivity of specific gastric cancer tissues to drugs and / or treatments are judged. The YBX1 gene, its mRNA, expressed protein, and antibodies thereof specifically refer to: based on the expression level of the YBX1 gene, by detecting the mRNA and / or expressed protein expressed by it, or by using antibodies that can specifically bind to the mRNA and / or protein of YBX1 as detection substances, the tolerance and / or sensitivity of specific gastric cancer tissues to drugs and / or treatments are judged. The ISGs gene set, its mRNA, expressed protein, and its antibodies specifically refer to: based on the expression level of the ISGs gene set, by detecting the mRNA and / or expressed protein expressed therefrom, or by using antibodies that can specifically bind to the mRNA and / or protein of ISGs as detection substances, judging the tolerance and / or sensitivity of specific gastric cancer tissues to drugs and / or treatment methods.

[0080] In the present invention, the specific genes in the ISGs gene set associated with gastric cancer include but are not limited to: one or more of the ISG15 gene, IFIT1 gene, IFIT2 gene, IFIT3 gene, IFI6 gene, IFI16 gene, IFI44 gene, OAS2 gene, OAS3 gene, RSAD2 gene, WARS gene and OASL gene. In some preferred embodiments, the genes in the ISGs gene set that are highly correlated with the tolerance and / or sensitivity of gastric cancer to chemoradiotherapy include: IFIT1 gene, IFIT2 gene, IFIT3 gene, ISG15 gene, IFI16 gene, OAS3 gene and WARS gene.

[0081] The detection kit provided by the present invention specifically includes the above-mentioned gastric cancer-related prediction markers, and / or substances for detecting the prediction markers. Among them, the substances for detecting the prediction markers are specifically necessary reagents for detecting the expression level of related genes, which can be, but are not limited to: one or more of: nucleic acid purification reagents, protein purification reagents, PCR-related reaction systems, primer systems, and expression protein-specific antibodies.

[0082] The present invention also provides the application of the above-mentioned gastric cancer-related predictive markers and / or detection kits, which can be specifically applied to but not limited to: the production of drugs for gastric cancer treatment and / or the prediction of gastric cancer treatment effects. Based on the high correlation between the above predictive markers and the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy, the expression level changes of the predictive markers in gastric cancer tissue cells are directly detected or based on the detection kit after corresponding treatment, the state changes are inferred to achieve the prediction of the treatment effect; and / or, based on the expression level changes of the predictive markers, a strategy of suppressing the expression level changes of the predictive markers manifested by the expected poor treatment effect is adopted, providing new ideas for the research and development of drugs for gastric cancer treatment.

[0083] The present invention provides a gastric cancer treatment composition based on the high correlation between the Tagln2 gene, the YBX1 gene and the ISGs gene set and the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy. The gastric cancer treatment composition specifically includes a Tagln2 expression inhibitor and an anti-tumor drug. Among them, the inhibitory effect of the Tagln2 expression inhibitor on the expression of Tagln2 in gastric cancer tissue cells can improve the sensitivity of gastric cancer tissue cells to anti-tumor drugs, and the synergistic cooperation of the two can achieve better therapeutic effects.

[0084] In the present invention, the Tagln2 expression inhibitor specifically refers to a class of compounds that can act on the Tagln2 expression metabolic pathway, which can prevent the start of Tagln2 expression by inhibiting the expression of Tagln2 upstream genes, or can act on the transcription and / or translation stage of Tagln2 expression to stop the expression of Tagln2. In some specific embodiments, specific examples of the Tagln2 expression inhibitor include but are not limited to: MK2206 and / or Fisetin.

[0085] In the present invention, the anti-tumor drug is a type of chemotherapy drug conventionally used in the treatment of gastric cancer, which inhibits the proliferation of tumor cells, induces cell apoptosis or inhibits tumor angiogenesis through different mechanisms, thereby playing a role in treating gastric cancer. In some specific embodiments, specific examples of the anti-tumor drug include but are not limited to: one or more of cisplatin, oxaliplatin, 5-fluorouracil and capecitabine.

[0086] The present invention provides a method for constructing a gastric cancer treatment effect prediction model based on the high correlation between the Tagln2 gene, the YBX1 gene and the ISGs gene set and the tolerance and / or sensitivity of gastric cancer tissue cells to radiotherapy and chemotherapy. The method for constructing a gastric cancer treatment effect prediction model specifically includes: S1, obtaining the expression level of the IRDS gene set of gastric cancer samples and the relevant data of the treatment effect, and including the data of chemotherapy or radiotherapy as a training set Q; S2, performing random forest model training based on the training set Q to construct a prediction model; wherein, the IRDS gene set includes Tagln2 gene, YBX1 gene, IFIT1 gene, IFIT2 gene, IFIT3 gene, ISG15 gene, IFI16 gene, OAS3 gene and WARS gene.

[0087] In the present invention, in step S1, based on the treatment effect, the data of the training set Q includes treatment-sensitive type and treatment-resistant type.

[0088] In the present invention, in step S2, the random forest model training includes: introducing the selection of random attributes, dividing the training set Q based on the treatment effect type to calculate the information gain, and training to obtain the calculation formula of the probability score value of the mRNA expression of the IRDS gene set and the score cutoff value for judging the treatment effect type;

[0089] Among them, the calculation formula of the information gain is:

[0090]

[0091] Among them, p0 is the proportion of treatment-resistant types in the training set Q, and p1 is the proportion of treatment-sensitive types in the training set Q; q is the score cutoff value and has V possible values. Using q for division will generate V branch nodes.

[0092] In the present invention, the calculation formula of the probability score value is specifically:

[0093] Probability Score value = ∑a i A i (i=1,2,3.....,9)

[0094] A i is the expression level of a gene in the IRDS gene set, a iis the multiplication coefficient of the gene; among them, the multiplication coefficient of Tagln2 gene is 0.0927, the multiplication coefficient of YBX1 gene is 0.2883, the multiplication coefficient of IFIT1 gene is 0.1429, the multiplication coefficient of IFIT2 gene is 0.1192, the multiplication coefficient of IFIT3 gene is 0.0176, the multiplication coefficient of ISG15 gene is 0.1542, the multiplication coefficient of IFI16 gene is 0.0410, the multiplication coefficient of OAS3 gene is 0.0730, and the multiplication coefficient of WARS gene is 0.0712;

[0095] The score cutoff value is the median value of the probability score value in the training set Q.

[0096] The method for predicting the therapeutic effect of gastric cancer provided by the present invention is constructed by the method for constructing the prediction model of the therapeutic effect of gastric cancer, and the method specifically comprises: obtaining the IRDS gene set expression level data of the sample to be predicted, and calculating the probability score value:

[0097] Probability Score value = ∑a i A i (i=1,2,3.....,9)

[0098] The median value of the probability score value in the training set Q is used as the cutoff score value to judge the treatment effect, specifically including: probability score value ≤ Score cutoff value, the treatment effect is predicted to be sensitive; probability score value > Score cutoff value, the treatment effect is predicted to be resistant.

[0099] The device for predicting the therapeutic effect of gastric cancer provided by the present invention runs the method for constructing a gastric cancer therapeutic effect prediction model and / or the method for predicting the therapeutic effect of gastric cancer to predict the therapeutic effect of chemotherapy and / or radiotherapy for gastric cancer.

[0100] Embodiments of the present invention are described in detail below, and the examples of the embodiments are intended to be used to explain the present invention, but should not be construed as limiting the present invention. In the embodiments, if specific techniques or conditions are not specified, the techniques or conditions described in the literature in this area or the product specifications are used. If the manufacturer is not specified for reagents or instruments used, they are all conventional products that can be obtained commercially.

[0101] Example 1

[0102] This example is used to illustrate the differential expression of gastric cancer tissue cells on the Tagln2 / YBX1 / ISGs signal axis, specifically including:

[0103] 1. High expression of Tagln2 gene in gastric cancer tissue

[0104] (1) Take the EnVision Immunochromogenic Kit TM FLEX+, Mouse, HighpH (Dako) were used to stain 75 gastric cancer tissue chips (including 75 cancer tissues and paired adjacent normal tissues) provided by Shanghai Xinchao Biotechnology Co., Ltd. according to the instructions. The stained gastric cancer tissue chips were then baked in a constant temperature oven at 63°C for 1 hour.

[0105] (2) The gastric cancer tissue chip obtained by the treatment in (1) was dewaxed using a fully automatic staining machine. The dewaxing process specifically included: sequentially passing through xylene (2 cylinders, 15 min each), 100% anhydrous ethanol (2 cylinders, 7 min each), 90% ethanol (1 cylinder, 5 min), 80% ethanol (1 cylinder, 5 min) and 70% ethanol (1 cylinder, 5 min).

[0106] (3) Perform antigen repair on the gastric cancer tissue chip obtained by the treatment in (2) using an antigen repair instrument according to the instructions, and place the chip in distilled water to cool at room temperature for 20 minutes.

[0107] (4) Dilute the Tagln2 primary antibody (Proteintech Cat. No. 15508-1-Ap) with deionized water at a ratio of 1:750, take 400 μL of the diluted primary antibody solution and incubate the gastric cancer tissue chip obtained by (3) at 4°C overnight; then rinse with PBS solution three times (3 min each time), remove the PBS solution and add the secondary antibody (Dako, Cat. No. K8002) dropwise, and incubate at room temperature for 10 min; then rinse with PBS solution three times (3 min each time).

[0108] (5) Take 400 μL of freshly prepared DAB staining solution and incubate the gastric cancer tissue chip obtained by (4) at room temperature for 2 min; then use hematoxylin staining for 1 min, immerse in 0.25% hydrochloric acid alcohol (prepared by mixing 400 mL of 70% alcohol and 1 mL of 12 mol / L concentrated hydrochloric acid) for 10 s, and rinse with tap water for 15 min.

[0109] (6) The gastric cancer tissue chip obtained by the process of (5) was sliced ​​and dehydrated using a fully automatic staining machine. The slice dehydration process specifically includes: sequentially passing through 75% ethanol (2 cylinders, 2 minutes per cylinder), 95% ethanol (2 cylinders, 2 minutes per cylinder), anhydrous ethanol (2 cylinders, 2 minutes per cylinder) and transparent liquid (2 cylinders, 5 minutes per cylinder).

[0110] (7) The gastric cancer tissue chip obtained by the treatment of (6) was dried at room temperature, and then the ultrasensitive chemiluminescence detection kit EnVision + System HRP was taken and stained according to the instructions, and then the slide was sealed with a neutral resin. The chip image was acquired using an Aperio scanner (Aperio XT, Leica), and the staining intensity (IS, divided into 0 [negative], 1 [weak], 2 [moderate] and 3 [strong]), positive (PP) cell percentage (0 [PP ≤ 5%], 1 [6% ≤ PP ≤ 25%], 2 [26% ≤ PP ≤ 50%], 3 [51% ≤ PP ≤ 75%] and 4 [PP ≥ 75%]) was scored, and the immune response score = PP × IS, and the scoring results were analyzed. The results are as follows: Figures 1 to 7 shown.

[0111] Depend on Figure 1 and 2 The electron micrographs of the cells shown and Figure 3 The IS score statistics shown in the figure show that compared with adjacent normal tissues (IS=0.656±0.071; P<0.0001), Tagln2 staining in gastric cancer tissues was significantly increased (IS=2.109±0.098, and the expression of Tagln2 in gastric cancer tissues was significantly higher than that in paired normal tissues, and high expression of Tagln2 (IS≥2) was more common in gastric cancer tissues.

[0112] Depend on Figure 1 and 2 The electron micrographs of the cells shown and Figure 4 From the PP score statistics shown, it can be seen that the Tagln2 gene can be expressed on the cell membrane, cytoplasm and cell nucleus. About 81% of the normal tissues adjacent to the cancer showed positive Tagln2 staining on the cell membrane, while gastric cancer tissue cells showed negligible Tagln2 gene expression on the cell membrane, and about 48% of the tumor tissue cells had positive Tagln2 staining in the nuclei (22.5% of the tumor tissues had a Score ≥ 1).

[0113] Depend on Figures 5 to 7 From the statistical graph of immune response scores at different stages of gastric cancer, it can be seen that Tagln2 expression in the cytoplasm of gastric cancer cells is significantly correlated with distant metastasis stage, T stage and total cancer stage (stages I to IV). The Tagln2 gene is abnormally expressed in the early stage of gastric cancer (stage I), and the expression level of the Tagln2 gene gradually increases with the development of gastric cancer.

[0114] 2. Upregulation of YBX1 and ISGs Gene Expression

[0115] 1. Construction of Tagln2 recombinant plasmid:

[0116] (1) The nucleotide sequence synthesized by the synthesis company, such as the human Tagln2 CDS sequence shown in SEQ ID NO: 1, was subjected to PCR amplification to obtain a PCR amplification product including a stop codon and the human Tagln2 CDS sequence; and the PCR amplification product was recovered by gel electrophoresis and a DNA gel recovery kit (Magen, catalog number D2110-02) according to the instructions.

[0117] The reaction system for PCR amplification included: 2.5 μL of 2 mM dNTP mixture, 2.5 μL of 10× KOD buffer, 1.5 μL of 25 mM MgSO4, 0.5 μL of human Tagln2 CDS sequence, 0.5 μL of Tagln2HindIIIF, 0.3 μL of primer Tagln2KpnIR, 0.3 μL of KOD Plus Neo, and 17.1 μL of ddH2O;

[0118] The reaction conditions of PCR amplification included: pre-denaturation at 94°C for 5 min; denaturation at 98°C for 30 s, annealing at 58°C for 30 s, extension at 68°C for 40 s, 32 cycles; and extension at 68°C for 5 min.

[0119] (2) Take 15 μL of the PCR amplification product obtained in (1) and 15 μL of pcDNA3.1+ vector and add them to two sterile EP reaction tubes. Add 15 μL of HindIII enzyme, 15 μL of KpnI, 5 μL of 10×B buffer and 27 μL of ddH2O to each tube, and react at 37°C for 3 h to obtain double enzyme digestion products; and use gel electrophoresis and DNA gel recovery kit according to the instructions to recover the double enzyme digestion products.

[0120] (3) Take 3 μL of the double-enzyme digestion product of the PCR amplification product, 2 μL of the double-enzyme digestion product of the pcDNA3.1+ vector, 1 μL of 10× Ligase Buffer, 1 μL of T4 DNA ligase and 3 μL of ddH2O, react at 16°C for 2 h to obtain a ligation product.

[0121] (4) In an ice bath, take 5 μL of the ligation product and add it to 50 μL of DH5α competent cells (cell density of 70%), gently rotate to mix, and place on ice for 30 min; after heat shock in a 42°C water bath for 90 s, quickly transfer the tube to an ice bath and continue to place on ice for 2 min; add 200 μL of LB medium (Tryptone 10 g; Yeast Extract 5 g; NaCl 10 g) and mix well, and culture at 37°C and 200 rpm for 1 h; in an ultra-clean workbench, evenly spread the bacterial liquid on an LB plate containing pcDNA3.1 + ampicillin (concentration of 100 μg / mL), leave it at room temperature until the liquid is absorbed, invert the plate, and culture it at 37°C overnight.

[0122] (5) Pick 4 single colonies with good growth status and mix them with 3 mL of LB culture medium, and culture them in a shaker at 37°C overnight; use a plasmid mini-preparation kit (Magen, catalog number P112-02) and extract the plasmid from the culture medium according to the instructions, send samples, sequence and analyze, and obtain the Tagln2 recombinant plasmid inserted with the human Tagln2 CDS sequence (including the nucleotide fragment shown in SEQ ID NO:4).

[0123] 2. Construction and testing of BGC-823 cells with overexpression and knockdown of Tagln2 expression:

[0124] (1) BGC-823 cells were added to RPMI-1640 medium (BioInd, Catalog No. 01-100-1ACS) containing 10% fetal bovine serum (Sigma, Catalog No. f8313) and 1% penicillin / streptomycin (MeilunBio, Catalog No. MA0110) and cultured in a CO2 incubator with 5% CO2, saturated humidity and 37°C.

[0125] (2) Construction of BGC-823 cells overexpressing Tagln2: 1 μg of Tagln2 recombinant plasmid was mixed with Opti-MEM medium (invitrogen, catalog number 31985070) to obtain a final volume of 100 μL of solution A, 1 μL of transfection reagent Lipofectamine 2000 (invitrogen, catalog number 11668019) was mixed with Opti-MEM medium to obtain a final volume of 100 μL of solution B, solution A and solution B were mixed for 5 min, then allowed to stand for 20 min, added to a culture dish with a cell confluence of 70-80%, and incubated for 6 h in a CO2 incubator at 5% CO2, saturated humidity and 37°C to obtain BGC-823 cells overexpressing Tagln2, and then replaced with RPMI-1640 medium containing 10% fetal bovine serum and 1% penicillin / streptomycin for continued culture. Samples were collected after 36 h and 48 h of culture for test analysis.

[0126] (3) Construction of Tagln2 knockdown BGC-823 cells: 1.25 μL of 20 μM siTAG2 stock solution and siYBX1 stock solution were respectively mixed with Opti-MEM culture medium to obtain a final volume of 100 μL of solution A; 1 μL of transfection reagent Lipofectamine 2000 was mixed with Opti-MEM culture medium to obtain a final volume of 100 μL of solution B; solution A and solution B were mixed for 5 min and then allowed to stand for 20 min, then added to a culture dish with a cell confluence of 30-50%, and incubated in a CO2 incubator at 5% CO2, saturated humidity and 37°C for 4 h to obtain Tagln2 knockdown BGC-823 cells, which were then replaced with RPMI-1640 culture medium containing 10% fetal bovine serum and 1% penicillin / streptomycin for continued culture. After a period of culture, samples were collected for testing and analysis.

[0127] The siTAG2 mother solution includes the siTAG2 fragments with nucleotide sequences as shown in SEQ ID NOs: 5 and 6; and the siYBX1 includes the siYBX1 fragments with nucleotide sequences as shown in SEQ ID NOs: 7 and 8.

[0128] Table 2. Nucleotide sequences involved in this example

[0129]

[0130]

[0131] (4) ITRAQ-2DLC-MS / MS detection: After replacing the cell growth medium and culturing for 48 h, a portion of the culture medium was taken for protein extraction, SDS electrophoresis, BCA quantification, reductive alkylation, enzymatic hydrolysis, iTRAQ / TMT labeling, SCX separation, liquid phase tandem mass spectrometry, and database retrieval.

[0132] Among them, the cold acetone method was used for protein extraction; sequencing-grade modified trypsin (Promega, Madison, WI) was used for enzymatic hydrolysis at 37°C; iTRAQ / TMT labels (AB Sciex, Dublin, CA, USA) were used and iTRAQ / TMT labeling was performed according to the instructions; the reagents and conditions used in each detection step referred to the reagents and conditions commonly used in the relevant test methods in the prior art, and the reagents and conditions used in the ITRAQ-2DLC-MS / MS detection of BGC-823 cells overexpressing Tagln2 and BGC-823 cells overexpressing Tagln2 were kept consistent.

[0133] A total of 45,068 significant chromatographic signals were identified in ITRAQ-2DLC-MS / MS detection, and 2,778 proteins were retrieved based on the chromatographic signals and related peptide fragments. The results are shown in Table 3.

[0134] Table 3. Differential protein expression caused by overexpression of Tagln2 in BGC-823 cells

[0135]

[0136]

[0137] In Table 3, R value = differential protein expression caused by overexpression of Tagln2 in BGC-823 cells / differential protein expression caused by overexpression of Tagln2 in BGC-823 control cells.

[0138] From the results shown in Table 3, it can be seen that compared with the control BGC-823 cells overexpressing Tagln2, 62 differentially expressed proteins were identified, of which 46 were upregulated and 16 were downregulated; among them, the upregulation fold of genes such as ISG15, IFIT1, IFIT2, IFIT3, OAS3 and IFI16 (all ISGs) was greater than 1.5.

[0139] (5) RNA-Seq detection: After replacing the cell growth medium and culturing for 36 h, part of the culture medium was taken for total RNA extraction, total RNA quality detection, mRNA purification, cDNA synthesis, PCR enrichment of library fragments, library quality inspection and Illumina platform double-end sequencing.

[0140] Oligo(dT) magnetic beads are used to purify mRNA; cDNA synthesis includes using an ion shearing method to break RNA into fragments of about 300 bp in length, and using 6-base random primers and reverse transcriptase to synthesize the first cDNA chain, and then PCR amplifying the first cDNA chain. The reagents and conditions used in each detection step refer to the reagents and conditions commonly used in the relevant test methods in the prior art, and the reagents and conditions used in the ITRAQ-2DLC-MS / MS detection of BGC-823 cells overexpressing Tagln2 and BGC-823 cells overexpressing Tagln2 are consistent.

[0141] In the RNA-Seq test, 72 genes with a change fold greater than 2 were analyzed, and the results are shown in Table 4.

[0142] Table 4. Differentially expressed genes caused by overexpression of Tagln2 in BGC-823 cells

[0143]

[0144]

[0145] The results shown in Table 4 show that the log2 FC =1.87, log2 of IFIT1 FC =2.47, log2 of IFIT2 FC =3.32, log2 of IFIT3 FC =3.30, log2 of OAS2 FC =1.84, log2 of RSAD2 FC 2.40, log2 of IFI6 FC is 1.24, log2 of OASL FC =3.46 and log2 of IFI44 FC was 2.35, and gene expression was significantly upregulated.

[0146] Comprehensive analysis of the data in Tables 3 and 4 shows that the continuous expression of Tagln2 in gastric cancer tissue cells can create an "illusion" similar to chronic pathogen infection, and then stimulate the continuous, low-level expression of IFN and ISGs in gastric cancer cells by activating the AKT-YBX1 signaling pathway, that is, YBXI in gastric cancer cells mediates the regulatory effect of Tagln2 on ISGs expression.

[0147] Example 2

[0148] This example is used to illustrate the sensitization method and drug for promoting gastric cancer treatment based on the Tagln2 / YBX1 / ISGs signal axis, specifically including:

[0149] 1. Effect of Tagln2 gene expression on the tolerance of gastric cancer tissue cells to chemotherapy and radiotherapy

[0150] 1. Tagln2 gene expression enhances the tolerance of gastric cancer tissue cells to chemotherapy drugs:

[0151] (1) BGC-823 cells and HGC-27 cells were treated according to the method provided in Example 1 to construct BGC-823 cells with Tagln2 expression knockdown (shTAGLN2) and HGC-27 cells with Tagln2 overexpression (oe-TAGLN2). The constructed cells were cultured at 2×10 3 The cells were plated and grown in a 96-well plate at a density of 1 × 10 / well. After the cells adhered to the wall, the cells were digested and dispersed by blowing. The cells were counted and the cell concentration was adjusted to 1 × 10 5 / mL, divided into 96-well plates at 100μL / well, and treated with chemotherapy drugs:

[0152] (a) The constructed cells were treated with cisplatin at concentrations of 0.2, 0.4, 0.8, 1.6 and 3.2 μg / mL (BGC-823 cells for 24 h, HGC-27 cells for 48 h), and untreated BGC-823 cells or HGC-27 cells were treated with the same series of cisplatin concentrations and the same culture conditions as controls;

[0153] (b) The constructed cells were treated with oxaliplatin at concentrations of 0.16, 0.32, 0.64, 1.28, 2.56, 5.12, 10.24 and 20.48 μM (BGC-823 cells for 24 h, HGC-27 cells for 48 h), and untreated BGC-823 cells or HGC-27 cells were treated with the same series of oxaliplatin concentrations and the same culture conditions as controls;

[0154] (c) The constructed cells were treated with 5-fluorouracil (5-Fu) at concentrations of 5, 10, 20, 40, 80, 160, 320, 640 and 1280 μM (BGC-823 cells for 24 h, HGC-27 cells for 48 h), and untreated BGC-823 cells or HGC-27 cells were treated with the same series of 5-Fu concentrations and the same culture conditions as controls;

[0155] (d) The constructed cells were treated with capecitabine at concentrations of 0.01875, 0.0375, 0.075, 0.15, 0.3, 0.6, 1.2, 2.4, and 4.8 mM (BGC-823 cells for 24 h, HGC-27 cells for 48 h), and untreated BGC-823 cells or HGC-27 cells were treated with the same series of capecitabine concentrations and the same culture conditions as controls.

[0156] (2) After each cell culture was cultured for a certain period of time, the culture medium was removed; 10 μL of CCK8 detection solution (MeilunBio, catalog number MA0218) was mixed with 100 μL of culture medium and incubated for 4 h. The OD value of the culture medium was read using an ELISA reader. 490 ; GraphPad Prism8 was used to analyze the measured data, and the drug concentration data were converted to log10 (common) in Transfrom concentratin (X) and IC was calculated by nonlinear regression. 50 , the results are as follows Figures 8 to 15 shown.

[0157] Depend on Figures 8 to 15 The results shown in the figure show that compared with normal BGC-823 cells, BGC-823 cells with Tagln2 expression knockdown treated with cisplatin, oxaliplatin, capecitabine and 5-FU had an IC 50 Compared with normal HGC-27 cells, the IC values ​​of HGC-27 cells overexpressing Tagln2 were reduced when treated with cisplatin, oxaliplatin, capecitabine and 5-FU. 50 The value increased, showing a completely opposite trend of change, indicating that the overexpression of Tagln2 gene in gastric cancer tissue cells will enhance the cells' tolerance to four common chemotherapy drugs: cisplatin, oxaliplatin, capecitabine and 5-FU.

[0158] (3) According to the "RNA-Seq Detection" provided in Example 1, the mRNA expression levels of IFIT1, IFIT2, IFIT3, ISG15 and HLA-B in BGC-823 cells knocked down by Tagln2 expression and BGC-823 cells treated with 1 μg / mL cisplatin in experimental group 1 and control experimental group 1 were tested. The results are as follows: Figures 16-20 shown.

[0159] Depend on Figures 16-20It can be seen that in normal gastric cancer tissue cells, 1 μg / mL cisplatin treatment will lead to an increase in the mRNA expression of IFIT1, IFIT2, IFIT3, ISG15 and HLA-B, while the knockdown of Tagln2 expression can inhibit the expression of the above-related ISGs gene sets, thereby improving the sensitivity of gastric cancer cells to chemotherapeutic drugs and achieving better therapeutic effects.

[0160] 2. Tagln2 gene expression knockdown enhances the sensitivity of gastric cancer tissue cells to radiotherapy:

[0161] (1) Tagln2 knockdown BGC-823 cells were cultured at 2×10 3 The cells were plated and grown in a 96-well plate at a density of 1 × 10 / well. After the cells adhered to the wall, the cells were digested and dispersed by blowing. The cells were counted and the cell concentration was adjusted to 1 × 10 5 / mL, and dispense into 96-well plates at 100μL / well for irradiation treatment:

[0162] Tagln2 knockdown BGC-823 cells were irradiated with 0, 4 and 6 Gy X-ray radiation (RadSource-2000 X-ray irradiator) for 4 days as the experimental group, and the constructed cells were not irradiated with X-rays and treated with the same culture conditions as the blank control group 1;

[0163] Untreated HGC-27 cells were treated under the same irradiation conditions and the same culture conditions as a control group, and the constructed cells were treated without X-ray radiation and under the same culture conditions as a blank control group 2.

[0164] (2) Collect the irradiated cells by trypsin digestion, remove the culture medium, take 10 μL of the test solution and 100 μL of the culture medium, mix them and incubate for 4 h, and read the OD value of the culture medium using an ELISA reader. 490 The relative cell survival rate was calculated according to the following formula. Fig.21 and 22 shown.

[0165] Relative cell survival rate (%) = (A1-A0) / (A2-A0')

[0166] Among them, A1 is the OD of the experimental group 490 Value, A0 is the OD of blank control group 1 490 A2 is the OD of the control group 490 A0' is the OD of blank control group 2 490 value.

[0167] (3) Collect the irradiated cells by trypsin digestion, remove the culture medium, count, and adjust the cell concentration to 1×103 / mL, divided into 96-well plates at 200μL / well, and each well was supplemented with cell growth medium to a total volume of 300μL / well. Observed every day. When single cells were observed to grow into clustered clones, the medium was aspirated, rinsed twice with PBS, and crystal violet staining solution was added at 200μL / well. After staining for 20 minutes, it was rinsed with tap water with a slow flow, dried, and the plate was inverted. The number of clone groups larger than 10 cells was counted under a microscope with a low-power microscope, and the clone formation rate was calculated according to the following formula. The results are shown in Fig.23 shown.

[0168] Cloning rate (%) = number of clones / number of inoculated cells × 100%

[0169] Depend on Figures 21 to 23 The results shown show that compared with untreated BGC-823 cells, after BGC-823 cells with Tagln2 expression knocked down were irradiated with X-rays, the cells showed lower growth ability, that is, knocking down Tagln2 expression can reduce the tolerance of gastric cancer tissue cells to radiotherapy.

[0170] (4) According to the "RNA-Seq detection" provided in Example 1, the mRNA expression levels of Tagln2 and ISGs gene sets in BGC-823 cells and BGC-823 cells with Tagln2 expression knocked down were tested. The results are as follows: Fig.24 and 25 shown.

[0171] Depend on Fig.24 and 25 The test results shown show that compared with untreated BGC-823 cells, after BGC-823 cells with knocked-down Tagln2 expression were irradiated with X-rays, the expression levels of Tagln2 and a group of Tagln2-induced ISGs gene sets (IFIT1, IFIT2, IFIT3, IFI16, ISG15, HLA-B, OAS3, WARS and TAP1) in the cells were significantly reduced. Knocking down Tagln2 expression can effectively inhibit the overexpression of the ISGs gene set, thereby improving the sensitivity of gastric cancer tissue to radiation therapy.

[0172] 2. Inhibitors targeting the Tagln2 / YBX1 / ISGs signaling axis enhance the sensitivity of gastric cancer tissue cells to chemotherapy and radiotherapy

[0173] 1. In vitro experiment: BGC-823 cells were cultured at a rate of 2×10 3 The cells were plated and grown in a 96-well plate at a density of 1 × 10 / well. After the cells adhered to the wall, the cells were digested and dispersed by blowing. The cells were counted and the cell concentration was adjusted to 1 × 10 5 / mL, and dispensed into 96-well plates at 100μL / well, and performed the following treatments and tests:

[0174] (1) BGC-823 cells were treated according to the method provided in Example 1 to construct BGC-823 cells overexpressing Tagln2, and MK2206 (final concentration of 10.4 μM) or Fisetin (final concentration of 50 μM) was added to the culture medium for 12, 24 and 48 h, respectively. The changes in the expression levels of Tagln2 and YBX1 proteins in BGC-823 cells were detected by western blot. The results are as follows: Fig.26 shown.

[0175] Depend on Fig.26 The results shown show that in BGC-823 cells overexpressing Tagln2, treatment with MK2206 (final concentration of 10.4 μM) or Fisetin (final concentration of 50 μM) had an inhibitory effect on the expression of Tagln2 and YBX1 at 12 h of culture, and the inhibitory effect was significant at 48 h of culture, and the inhibitory effect of MK2206 and Fisetin on the expression of Tagln2 and YBX1 was time-dependent.

[0176] (2) The normal BGC-823 cell line was irradiated with 6 Gy of X-ray radiation for 4 days, and the cell growth medium containing Fisetin (final concentration of 10 μM), MK2206 (final concentration of 200 nM) or DMSO (content 0.01%) was replaced. After further culture for 48 hours, the cell samples were collected and the mRNA expression levels of IFIT1, IFIT2, IFI16 and RPS6 in BGC-823 cells were detected by qRT-PCR. The results are shown in Figure 2. Fig. 27 shown.

[0177] Depend on Fig. 27 The results shown show that Fisetin and MK2206 significantly inhibited the upregulation of IFIT1, IFIT2, IFI16 and RPS6 gene expression levels in BGC-823 cells caused by 6Gy X-ray radiation.

[0178] (3) BGC-823 cells were treated according to the method provided in Example 1 to construct BGC-823 cells with Tagln2 expression knocked down. BGC-823 cells with Tagln2 expression knocked down and normal BGC-823 cells were incubated with MK2206 at final concentrations of 0.13, 0.26, 0.52, 1.04, 1.65, 3.25, 6.5 and 13 μM for 48 h, respectively. Cytotoxicity was determined by CCK8. The results were as follows: Fig.28 shown.

[0179] HGC-27 cells were treated according to the method provided in Example 1 to construct the Tagln2-overexpressing HGC-27 cells and normal HGC-27 cells, respectively, and incubated with MK2206 at final concentrations of 0.01625, 0.0325, 0.065, 0.13, 0.26, 0.52 and 1.04 μM for 48 h. The cytotoxicity was determined by CCK8. The results are as follows: Fig.29 shown.

[0180] Depend on Fig.28 and 29 The results show that MK2206 at a concentration below 6.5 μM has almost no cytotoxicity to BGC-823 cells, and MK2206 at a concentration below 1.04 μM has almost no cytotoxicity to HGC-27 cells.

[0181] (4) BGC-823 cells, MGC-803 cells, and SGC-7901 were incubated with MK2206 (final concentration of 200 nM), cisplatin (final concentration of 0.4 μg / ml), and MK2206 (final concentration of 200 nM) + cisplatin (final concentration of 0.4 μg / ml) for 48 h, and the cytotoxicity was determined by CCK8. The results are as follows: Fig.30 shown.

[0182] Depend on Fig.30 The results shown show that the combined treatment of MK2206 and cisplatin significantly enhanced the cytotoxicity of low-concentration cisplatin to gastric cancer tissue cells.

[0183] Depend on Figures 26-30 The results shown show that the small molecule inhibitors MK2206 or Fisetin can target the Tagln2-YBX1-ISGs signaling axis, thereby reducing the expression of genes such as Tagln2, YBX1 and ISGs in gastric cancer tissue cells, and have the potential for synergistic effects when combined with radiotherapy and chemotherapy.

[0184] 2. In vivo treatment experiment: (1) BALB / c nude mice (male, 4 weeks old, SPF-grade breeding conditions) were used as experimental animals. The needle was inserted subcutaneously on the right back of the nude mice. The needle went about 5 mm subcutaneously to the right back of the nude mice near the armpit. 0.2 mL of cell suspension of BGC-823 cells overexpressing Tagln2 and normal BGC-823 cells were injected respectively (2×10 cells were inoculated per nude mouse). 6 After the injection is completed, the needle is rotated and withdrawn, and the needle hole is pressed with a cotton swab until no liquid flows out, completing the subcutaneous inoculation of cells. When the average volume of the tumor in the mouse body grows to 64-100mm 3 When the following groups are used for subsequent processing:

[0185] Experimental group 1 was treated with normal BGC-823 cells + PBS, experimental group 2 was treated with normal BGC-823 cells + cisplatin, experimental group 3 was treated with normal BGC-823 cells + MK2206, experimental group 4 was treated with normal BGC-823 cells + MK2206 + cisplatin, experimental group 5 was treated with BGC-823 cells overexpressing Tagln2 + PBS, experimental group 6 was treated with BGC-823 cells overexpressing Tagln2 + cisplatin, experimental group 7 was treated with BGC-823 cells overexpressing Tagln2 + MK2206, and experimental group 8 was treated with BGC-823 cells overexpressing Tagln2 + MK2206 + cisplatin, with 8 mice in each group. The mice were administered with the following methods: (1) cisplatin treatment: nude mice were intraperitoneally injected with 0.2 mL 3 mg / kg of cisplatin once every 4 days; (2) MK-2206 treatment: nude mice were administered with 120 mg / kg of cisplatin once every 4 days; (3) MK2206+cisplatin treatment: the above two methods were combined, and the first MK2206 gavage was performed 2 days before the intraperitoneal injection of cisplatin. The dosing cycle was 28 days, and the tumor volume was measured every 3 days. The results are shown in Table 1. Fig.31 shown.

[0186] Depend on Figures 31-33 The test results shown show that BGC-823 cells overexpressing Tagln2 have a stronger tolerance to cisplatin treatment, and the combined treatment of MK2206 and cisplatin in nude mice transplanted with normal BGC-823 cells and BGC-823 cells overexpressing Tagln2 showed a significant inhibitory effect on tumor growth and tumor weight in nude mice.

[0187] (2) After the administration cycle was completed, the experiment was terminated, the tumor was peeled off to obtain gastric cancer tissue cells, and the expression of Tagln2 and related ISGs proteins in gastric cancer tissues was stained and detected according to the "tissue immunohistochemistry detection" provided in Example 1, and the corresponding score values ​​were calculated. The results are as follows: Figures 32-38 shown.

[0188] Depend on Figures 34-40The results shown show that the expression levels of Tagln2, YBX1, IFIT1, IFIT2, IFIT3, OAS3 and ISG15 in the Tagln2 overexpression group were significantly higher than those in the control group, and the expression of the above proteins was further increased after cisplatin treatment; MK2206 treatment alone had an inhibitory effect on the expression of the above proteins in gastric cancer tissue cells, and the combined treatment group of cisplatin and MK2206 (Cisplatin+MK) also significantly inhibited the chemotherapy-induced increase in the expression of the above proteins. Therefore, MK2206 enhances the tumor treatment effect by targeted inhibition of AKT-YBX1-ISGs axis activation, and can cooperate with chemotherapeutic drugs such as cisplatin to achieve better therapeutic effects.

[0189] Example 3

[0190] This example is used to illustrate the prediction model of the clinical treatment effect of gastric cancer based on the Tagln2 / YBX1 / ISGs signaling axis, which specifically includes:

[0191] 1. Construction of prediction model

[0192] (1) The expression levels of the IRDS gene set of gastric cancer samples and their treatment effect data were obtained, and the data of patients receiving chemotherapy or radiotherapy were included as the training set Q. The data in the training set were divided into treatment-sensitive type (including complete remission, partial remission and stable disease) and treatment-resistant type according to the treatment effect.

[0193] Among them, the IRDS gene set includes Tagln2 gene, YBX1 gene, IFIT1 gene, IFIT2 gene, IFIT3 gene, ISG15 gene, IFI16 gene, OAS3 gene and WARS gene.

[0194] (2) A random forest model is trained based on the training set, and in the selection of random attributes introduced during the training of the random forest model, the training set Q is divided based on the treatment effect type and the following formula is used to obtain information gain:

[0195]

[0196] Among them, p0 is the proportion of treatment-resistant type in the training set Q, and p1 is the proportion of treatment-sensitive type in the training set Q; q is the Score cutoff value and has V possible values. Using q for division will generate V branch nodes, q v is the value of all treatment effect types in the training set Q, Q v For q V The sample set composed.

[0197] (i) Based on the training set, the random forest model is trained, and the calculation formula for the probability score value of the mRNA expression of the IRDS gene set is obtained as follows:

[0198] Probability Score value = ∑a i A i (i=1,2,3.....,9)

[0199] A i is the expression level of a gene in the IRDS gene set, a i is the information entropy of the treatment-sensitive type in the training set Q about the expression level of the gene.

[0200] More specifically, the information entropy of each gene in the IRDS gene set is shown in Table 5:

[0201] Table 5. Information entropy of IRDS gene set

[0202] Gene <![CDATA[a i ]]> Gene <![CDATA[a i ]]> Gene <![CDATA[a i ]]> Tagln2 0.0927 iFit2 0.1192 IFI16 0.0410 YBX1 0.2883 iFit3 0.0176 OAS3 0.0730 IFIT1 0.1429 ISG15 0.1542 WARS 0.0712

[0203] Fig.41 is a scatter plot of the probability score distribution of each sample in the training set Q. Fig.41 It can be seen that the probability score value of the expression level of the treatment-sensitive IRDS gene set is significantly lower than that of the treatment-resistant type.

[0204] (ii) The random forest model is trained based on the training set. The obtained score cutoff value for judging the type of treatment effect is the median value of the probability score value in the training set Q. The judgment for predicting the treatment effect is specifically:

[0205] When the probability score value ≤ Score cutoff value, the output is 1, which means that the treatment effect is predicted to be sensitive;

[0206] When the probability Score value > Score cutoff value, the output is 0, which means that the treatment effect is predicted to be resistance type.

[0207] 2. Evaluation of prediction models

[0208] (1) Receiver operating characteristic (ROC) curve analysis was used to evaluate the prediction accuracy and sensitivity of the treatment prediction model obtained above. The obtained ROC curve is as follows: Fig.42 shown.

[0209] Depend on Fig.42From the results shown, it can be seen that the AUC value of the treatment effect prediction model provided in this embodiment for predicting the treatment effect of patients receiving chemotherapy and / or radiotherapy for the first time is 0.87 (95% CI: 0.84-0.89), which has high prediction accuracy and can well achieve auxiliary clinical tumor treatment efficacy prediction and the formulation of individualized precision treatment plans.

[0210] (2) Based on the Score cutoff value, the training set is divided into a high-level group and a low-level group, the probability Score values ​​are sorted by survival time, the survival probability of the remaining individuals at each time point is calculated, and the survival function curve of the training set is drawn, which specifically includes:

[0211] (i) Sort the individuals in the sample from shortest to longest survival time and record the time point when an event (such as death, disease recurrence, etc.) occurs for each individual;

[0212] (ii) Starting from the shortest survival time, calculate the survival probability of the remaining individuals at each time point according to the following formula:

[0213] Survival probability = (xd) / x

[0214] Among them, x is the number of individuals that have not experienced an event before the current time point, and d is the number of individuals that have experienced an event at the current time point;

[0215] (iii) Based on the survival probability at each time point, draw the survival function curve of the entire sample;

[0216] (iv) Based on the Score cutoff value, the training set was divided into a high-level group and a low-level group, and the number of events at each time point was weighted and summed according to the following formula to calculate the Log-Rank statistic, and the corresponding P value was obtained using statistical software:

[0217]

[0218] Among them, O H is the sum of the observations in the high level group, E H is the sum of the expected numbers of the high-level group; L is the sum of the observations in the high level group, E L is the sum of the expected numbers of the high-level group; and E H and E L It is calculated by the following formula:

[0219]

[0220] Where d is the total sample size, d = 171; n is the total number of risk individuals; n H is the number of risk individuals in the high-level group. The obtained survival curve is as follows Fig.43shown.

[0221] Depend on Fig.43 As shown in the survival curve diagram, the overall survival probability of the high-level expression group is lower than that of the low-level group, that is, the high expression level of the IRDS gene set indicates a worse overall survival outcome.

[0222] (3) Based on the score cutoff value, the training set was divided into a high-level group and a low-level group, and the CIBERSORT web version tool was used to perform immune cell correlation analysis. The results are as follows Fig.44 shown.

[0223] Depend on Fig.44 The results shown show that the abundance of immune-related cells such as CD8+T cells, activated memory CD4+T cells and T follicular helper cells in the high-level group was significantly lower than that in the low-level group, that is, the high expression of the IRDS gene set is significantly correlated with the immunosuppressive phenotype.

[0224] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention without departing from the principles and intent of the present invention.

Claims

1. A method for constructing a gastric cancer treatment effect prediction model, characterized in that: The method includes: S1. Obtain the expression level of the IRDS gene set of gastric cancer samples and the relevant data of their treatment effects, and include the data of those receiving chemotherapy or radiotherapy as the training set Q; S2. Perform random forest model training based on the training set Q to construct a prediction model; Among them, the IRDS gene set includes Tagln2 gene, YBX1 gene, IFIT1 gene, IFIT2 gene, IFIT3 gene, ISG15 gene, IFI16 gene, OAS3 gene and WARS gene.

2. The method for constructing a gastric cancer treatment effect prediction model according to claim 1, characterized in that: In step S1, based on the treatment effect, the data of the training set Q includes treatment-sensitive type and treatment-resistant type.

3. The method for constructing a gastric cancer treatment effect prediction model according to claim 2, characterized in that: In step S2, the random forest model training includes: introducing random attribute selection, dividing the training set Q based on the treatment effect type to calculate the information gain, and training to obtain the calculation formula of the probability score value of the mRNA expression of the IRDS gene set and the score cutoff value for judging the treatment effect type; Among them, the calculation formula of the information gain is: Among them, p0 is the proportion of treatment-resistant type in the training set Q, and p1 is the proportion of treatment-sensitive type in the training set Q; q is the score cutoff value and has V possible values. Using q for division will generate V branch nodes; Optionally, the probability score value is calculated as: Probability Score value = ΣaiAi (i = 1, 2, 3....., 9) A i is the expression level of a gene in the IRDS gene set, a i is the multiplication coefficient of the gene; among them, the multiplication coefficient of Tagln2 gene is 0.0927, the multiplication coefficient of YBX1 gene is 0.2883, the multiplication coefficient of IFIT1 gene is 0.1429, the multiplication coefficient of IFIT2 gene is 0.1192, the multiplication coefficient of IFIT3 gene is 0.0176, the multiplication coefficient of ISG15 gene is 0.1542, the multiplication coefficient of IFI16 gene is 0.0410, the multiplication coefficient of OAS3 gene is 0.0730, and the multiplication coefficient of WARS gene is 0.0712; The score cutoff value is the median value of the probability score value in the training set Q.

4. A device for predicting the therapeutic effect of gastric cancer, characterized in that: The prediction device runs the method for constructing a gastric cancer treatment effect prediction model according to any one of claims 1 to 3.

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