FOLFOX scheme drug effect prediction method and system for digestive tract tumor EBV positive patient

Abstract

The invention relates to the technical field of digestive tract tumor drug treatment and drug effect prediction, and discloses an FOLFOX scheme drug effect prediction method and system for a digestive tract tumor EBV positive patient, and the method comprises the steps: obtaining samples of a plurality of PDTX models, and carrying out the RNA sequencing of each sample, so as to obtain corresponding RNA sequencing data; wherein the PDTX model is a PDTX model of mice of the same strain; carrying out EBV detection on the samples of the plurality of PDTX models, dividing the samples of the plurality of PDTX models into EBV infection negative samples and EBV infection positive samples, forming an EBV infection positive sample group by using the EBV infection positive samples, and taking the EBV infection positive sample group as a sample basis for establishment of an FOLFOX scheme drug effect prediction model and FOLFOX scheme drug effect prediction; performing differential gene analysis and gene screening based on the EBV infection positive sample group, and then determining a gene sample set for establishing an FOLFOX scheme efficacy prediction model; establishing an FOLFOX scheme efficacy prediction model based on the gene sample set; and the prediction quality of the FOLFOX scheme drug effect prediction model is verified based on a clinical sample.

Description

Technical Field

[0001] The present invention relates to the technical field of drug treatment and drug efficacy prediction for digestive tract tumors, and in particular to a drug efficacy prediction method and system for a FOLFOX regimen for EBV-positive patients with digestive tract tumors. Background Art

[0003] The FOLFOX regimen is a classic tumor treatment regimen that includes 5-fluorouracil (5-FU), leucovorin, and oxaliplatin. Currently, the FOLFOX regimen is still widely used, especially in postoperative adjuvant therapy and initial treatment of advanced cancer. Studies have shown that this regimen can effectively prolong the patient's survival and is relatively well tolerated. However, some patients may experience side effects such as neurotoxicity, requiring individualized precision medication. New research is also exploring the possibility of combining with immunotherapy and targeted therapy to further improve efficacy.

[0004] The FOLFOX regimen is mainly used to treat colorectal cancer, especially in postoperative adjuvant therapy and metastatic disease. In addition, it is also used in the treatment of tumors such as esophageal cancer, gastric cancer, certain types of pancreatic cancer, and bile duct / cystic cancer. The combination of drugs in this regimen helps to inhibit the growth and spread of cancer cells. The FOLFOX regimen has shown good efficacy in the treatment of colorectal cancer and can significantly prolong disease-free survival and overall survival. In the treatment of esophageal cancer and gastric cancer, although the efficacy is relatively weak, it has also shown certain effects, especially when combined with other treatments. Overall, its efficacy varies depending on individual patient differences, cancer type and stage, so a treatment plan needs to be formulated according to specific circumstances.

[0005] Common side effects of the FOLFOX regimen include:

[0006] (1) Gastrointestinal reactions: nausea, vomiting, diarrhea or constipation.

[0007] (2) Effects on the blood system: Decrease in white blood cells, platelets, and red blood cells may lead to an increased risk of infection and anemia.

[0008] (3) Neurotoxicity: hand-foot syndrome (such as numbness and tingling in the hands and feet), which is usually related to platinum oxide.

[0009] (4) Fatigue: Patients may feel noticeably weak.

[0010] (5) Oral problems: oral ulcers or dry mouth.

[0011] The severity of side effects can vary from person to person and will be monitored and managed by your doctor based on your individual condition.

[0012] The efficacy and side effects of the FOLFOX regimen are indeed affected by differences in the patient's physical constitution, including the following aspects:

[0013] (1) Age: Elderly patients may be more susceptible to side effects, especially neurotoxicity.

[0014] (2) Underlying health conditions: Patients with other diseases or comorbidities may have poor tolerance.

[0015] (3) Genetic factors: Certain gene mutations may affect drug metabolism, resulting in different therapeutic effects and side effects.

[0016] (4) Nutritional status: Patients with malnutrition may experience more serious side effects during treatment.

[0017] Therefore, doctors usually adjust the treatment plan according to the patient's specific situation to ensure the best effect and minimize side effects.

[0018] The FOLFOX treatment regimen is widely used in the treatment of colorectal cancer, esophageal cancer, gastric cancer, liver cancer, pancreatic cancer, bile duct / cystic cancer and other tumors. However, the efficacy of the FOLFOX regimen is affected by differences in the patient's physical constitution. Due to differences in tumor patients themselves, the efficacy of the FOLFOX regimen will also vary from person to person. In order to reduce the side effects of the FOLFOX treatment regimen for patients with poor efficacy, the impact on the treatment of other regimens and the loss of money. Therefore, the prior art has developed a technical solution that uses clinical indicators, gene mutations, gene expression and methylation indicators on a single cancer type, and uses algorithms such as neural networks and random forests to predict the efficacy of the FOLFOX regimen for patients.

[0019] For example:

[0020] (1) The technical solution of the document “Construction of an artificial neural network efficacy prediction model for the first-line treatment of metastatic colorectal cancer with FOLFOX regimen” did not take into account EBV infection. EBV infection can affect gene expression and thus the accuracy of the model. Neural networks require a high number of training samples. In addition, this solution is only applicable to one type of cancer and cannot predict other cancers.

[0021] (2) Ahn SJ, Kim JH, Park SJ, Han JK. Prediction of the therapeutic response after FOLFOX and FOLFIRI treatment for patients with liver metastasis from colorectal cancer using computerized CT texture analysis. Eur J Radiol. 2016 Oct; 85(10): 1867-1874. doi: 10.1016 / j.ejrad.2016.08.014 IF: 3.2Q1. Epub 2016 Aug 23. PMID: 27666629. The technical solution uses clinical feature CT texture of colorectal cancer samples and logistic regression modeling to predict the efficacy of FOLFOX. However, this technical solution is only suitable for colorectal cancer and cannot predict other cancers.

[0022] However, the FOLFOX chemotherapy regimen is a multi-target, multi-signal pathway chemotherapy drug. Changes in the functions of multiple genes in the signal pathway will directly affect the changes in the efficacy of the FOLFOX regimen. Existing technologies use DNA to find changes in sites, but changes in sites do not necessarily change the function of genes. Whether the various pathways in tumor cells are affected still requires attention to the actual expression of genes, so the prediction accuracy is low. Summary of the invention

[0023] In order to solve the problems existing in the prior art, the present invention provides the following technical solutions: a method and system for predicting the efficacy of the FOLFOX regimen for patients with EBV (Epstein-Barr Virus)-positive digestive tract tumors, using the PDTX model of the same strain of mice to obtain pure FOLFOX regimen efficacy data for modeling, and finally using the data of clinical personnel to verify the model. The data modeling of digestive tract tumor samples such as gastric cancer, intestinal cancer, liver cancer, pancreatic cancer, esophageal cancer and bile duct / cystic cancer is used, so that the model is not limited to a single cancer type, which expands the applicability of the method and system.

[0024] In one aspect, the present invention provides a method for predicting the efficacy of the FOLFOX regimen for EBV-positive patients with digestive tract tumors, comprising:

[0025] S1, obtaining samples of multiple PDTX (patient-derived tumor xenografts) models and performing RNA sequencing on the samples of each PDTX model to obtain RNA sequencing data corresponding to the samples of the multiple PDTX models; wherein the PDTX models are PDTX models of the same strain of mice;

[0026] S2, performing EBV detection on samples of multiple PDTX models, thereby dividing the samples of the multiple PDTX models into EBV infection-negative samples and EBV infection-positive samples, and after removing the EBV infection-negative samples, the EBV infection-positive samples constitute an EBV infection-positive sample group as a sample basis for establishing a FOLFOX regimen efficacy prediction model and a FOLFOX regimen efficacy prediction;

[0027] S3, determining a gene sample set for establishing a drug efficacy prediction model for the FOLFOX regimen after differential gene analysis and gene screening based on the EBV infection positive sample group;

[0028] S4, establishing a drug efficacy prediction model for the FOLFOX regimen based on the gene sample set;

[0029] S5. Verify the prediction quality of the FOLFOX regimen efficacy prediction model based on clinical samples.

[0030] Preferably, samples of multiple PDTX models are obtained and WES sequencing is performed on the samples of each PDTX model to obtain DNA sequencing data corresponding to the samples of the multiple PDTX models; wherein: S1 includes:

[0031] S11, performing WES sequencing on each sample of the PDTX model; the WES sequencing uses an Illumina NovaSeqV6000 sequencer, uses whole exome sequencing: Agilent V6 is used as a sequencing reagent, and uses a capture sequencing sequencing method; the sequencing data output is 25G, and the sequencing data requirements are: base quality Q30>90%, GC content is 50±5%, the average depth of the sequencing capture region is 200X-250X, and the coverage requirements are that the capture region coverage is above 95%, and the 10X coverage is above 80%;

[0032] S12, RNA sequencing was performed on each sample of the PDTX model; the RNA sequencing used an Illumina NovaSeqV6000 sequencer, VAHTS mRNA Capture Beads+mRNAVAHTS Universal V8 RNA-seqLibrary Prep Kit for Illumina as sequencing reagents, and a capture sequencing method was used; the sequencing data output was 10G, and the sequencing data requirements were: base quality Q30>90%, and GC content was 50±5%.

[0033] Preferably, S2 includes:

[0034] S21, obtaining the drug efficacy data and cancer type information data of the samples of the multiple PDTX models;

[0035] S22, based on the cluster analysis of the drug efficacy data and the cancer information data, the samples of the multiple PDTX models are regionally classified; the regional classification includes enriched gene regions and non-enriched gene regions;

[0036] S23, performing gene enrichment analysis on all genes in the enriched gene region to obtain the EBV pathway;

[0037] S24, performing compatibility verification on all genes in the enriched gene region and the EBV pathway, wherein the EBV genes that pass the compatibility verification are EBV genes that match the EBV pathway;

[0038] S25, drawing a heat map for the samples of the multiple PDTX models based on the EBV gene, and distinguishing the EBV-infected positive samples from the EBV-infected negative samples by using the heat map to obtain a first distinction result;

[0039] S26, performing statistical analysis on the DNA sequencing data and RNA sequencing data of the samples of the multiple PDTX models to obtain the number of EBV virus sequences; determining whether each sample in the samples of the multiple PDTX models is infected with the EBV virus one by one based on the number of EBV virus sequences, thereby obtaining a second distinction result for distinguishing EBV-infected positive samples from EBV-infected negative samples;

[0040] S27, determining whether the first distinction result and the second distinction result are consistent; if they are consistent, determining the final EBV infection positive sample and EBV infection negative sample; if they are inconsistent, repeating S21-S26 and performing different regional classifications until a consistent distinction result is obtained.

[0041] Preferably, S3 includes:

[0042] S31, dividing the samples in the EBV infection positive sample group into FOLFOX regimen positive efficacy samples and FOLFOX regimen negative efficacy samples;

[0043] S32, based on determining that the gene expression levels of the FOLFOX regimen-positive sample and the FOLFOX regimen-negative sample do not conform to a normal distribution, performing a DESeq2 differential analysis on the FOLFOX regimen-positive sample and the FOLFOX regimen-negative sample to obtain a plurality of differentially expressed genes;

[0044] S33, performing pathway enrichment analysis on the multiple differentially expressed genes, and obtaining the most significant pathway with the highest correlation with the efficacy of the FOLFOX regimen based on the analysis results;

[0045] S34, performing differential gene analysis on the multiple differential genes, drawing a heat map for the expression amounts of the top M most significant differential genes in the differential gene analysis results, performing PCA sample clustering, and then screening N drug efficacy classification genes, wherein the drug efficacy classification genes have a clear classification effect on the drug efficacy of the FOLFOX regimen of multiple EBV infection-positive samples;

[0046] S35, removing the collinear genes among the top M most significantly differentially expressed genes, verifying and confirming that the N pharmacodynamic classification genes are partially or completely on the most significant pathway, and determining the gene sample set for establishing the pharmacodynamic prediction model of the FOLFOX regimen based on the most significantly differentially expressed genes after removing the collinear genes, the N pharmacodynamic classification genes and combining gene functions.

[0047] Preferably, S4 includes:

[0048] S41, using random forest to establish a drug efficacy prediction model for the FOLFOX regimen;

[0049] S42, testing whether the performance of the FOLFOX regimen efficacy prediction model has reached the best based on multiple test indicators; determining that the FOLFOX regimen efficacy prediction model for EBV-positive patients with gastrointestinal tumors that has reached the best performance corresponds to the FOLFOX regimen efficacy prediction model.

[0050] Preferably, the multiple test indicators include:

[0051] (1) Sensitivity or true positive rate (TPR) = TP / (TP+FN);

[0052] (2) precision or positive predictive value (PPV) = TP / (TP + FP);

[0053] (3) True negative rate (TNR) = TN / (FP+TN);

[0054] (4) Negative predictive value (NPV) = TN / (FN + TN);

[0055] (5) Accuracy = (TP + TN) / (TP + TN + FP + FN).

[0056] Preferably, S5 includes:

[0057] The clinical samples were input into the FOLFOX regimen efficacy prediction model for prediction, and the prediction quality was determined based on the prediction accuracy, true negative rate, true positive rate, negative predictive value, positive predictive value and AUC values.

[0058] The second aspect of the present invention is to provide a FOLFOX regimen efficacy prediction system for EBV-positive patients with digestive tract tumors, which is used to implement the method of the first aspect, comprising:

[0059] A sample and sequencing data acquisition module, used to acquire samples of multiple PDTX models and perform RNA sequencing on the samples of each PDTX model to acquire RNA sequencing data corresponding to the samples of multiple PDTX models; wherein the PDTX models are PDTX models of the same strain of mice;

[0060] An EBV monitoring module, used to perform EBV detection on samples of multiple PDTX models, thereby dividing the samples of the multiple PDTX models into EBV infection-negative samples and EBV infection-positive samples, and after removing the EBV infection-negative samples, the EBV infection-positive samples constitute an EBV infection-positive sample group as a sample basis for establishing a FOLFOX regimen efficacy prediction model and a FOLFOX regimen efficacy prediction;

[0061] A differential gene analysis and gene screening module, used to determine a gene sample set for establishing a FOLFOX regimen efficacy prediction model after differential gene analysis and gene screening based on the EBV infection positive sample group;

[0062] A drug efficacy prediction model establishment module, used to establish the drug efficacy prediction model of the FOLFOX regimen based on the gene sample set;

[0063] The prediction quality verification module is used to verify the prediction quality of the FOLFOX regimen efficacy prediction model based on clinical samples.

[0064] A third aspect of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is configured to read the instructions and execute the method described in the first aspect.

[0065] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor and execute the method described in the first aspect.

[0066] The method, system and electronic device provided by the present invention have the following beneficial effects:

[0067] (1) The efficacy of the FOLFOX regimen using PDTX was verified using clinical samples to reduce the impact of individual differences and living environment on the efficacy.

[0068] (2) Detect EBV before use, model and predict EBV samples, so as to avoid the interference of EBV on drug efficacy.

[0069] (3) The model is applicable to more types of cancer and is not limited to one type of cancer. The model covers gastric cancer, intestinal cancer, liver cancer, pancreatic cancer, esophageal cancer, and bile duct / cystic cancer.

[0070] (4) It can screen and identify patients for whom treatment is ineffective, thereby reducing the side effects and money wasted on ineffective patients caused by drugs.

[0071] (5) Only 10 gene parameters are required, the number of genes tested is small, and the testing workload is reduced.

[0072] (6) The model with the best performance in the random forest algorithm modeling is selected as the final prediction model, which improves the accuracy and credibility of the prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 This is a flow chart of the method for predicting the efficacy of the FOLFOX regimen for EBV-positive patients with digestive tract tumors according to an embodiment of the present invention;

[0074] Figure 2 This is a flow chart of step S1 of the method for predicting the efficacy of the FOLFOX regimen for EBV-positive patients with digestive tract tumors according to an embodiment of the present invention;

[0075] Figure 3 This is a flow chart of step S2 of the method for predicting the efficacy of the FOLFOX regimen for EBV-positive patients with digestive tract tumors according to an embodiment of the present invention;

[0076] Figure 4 This is a schematic diagram of the results of cluster analysis of samples of the PDTX model provided by the present invention according to the efficacy of the FOLFOX regimen and the type of cancer;

[0077] Figure 5 A heat map drawn for the EBV gene pair samples provided by the present invention;

[0078] Figure 6 Flow chart of step S3 of the method for predicting the efficacy of the FOLFOX regimen for EBV-positive patients with digestive tract tumors according to an embodiment of the present invention;

[0079] Figure 7 The analysis results obtained after pathway enrichment analysis of multiple differentially expressed genes provided by the present invention;

[0080] Fig. 8A and Figure 8B They are respectively heat maps and PCA maps of all genes used for obtaining drug efficacy classification genes provided by the present invention;

[0081] Fig.9A and Fig. 9B They are respectively the gene heat map and PCA map of the determined drug efficacy classification genes provided by the present invention;

[0082] Fig.10 This is a flow chart of step S4 of the method for predicting the efficacy of the FOLFOX regimen for EBV-positive patients with digestive tract tumors according to an embodiment of the present invention;

[0083] Fig.11 This is a diagram of the architecture of the FOLFOX regimen efficacy prediction system for EBV-positive patients with digestive tract tumors provided by the present invention;

[0084] Fig.12 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0085] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0086] The method provided by the present invention can be implemented in the following terminal environment, and the terminal may include one or more of the following components: a processor, a memory, and a display screen. The memory stores at least one instruction, and the instruction is loaded and executed by the processor to implement the method described in the following embodiment.

[0087] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts of the entire terminal, and executes various functions of the terminal and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory.

[0088] The memory may include random access memory (RAM) or read-only memory (ROM). The memory may be used to store instructions, programs, codes, code sets or instructions.

[0089] The display screen is used to display the user interface of each application.

[0090] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal, and the terminal may include more or fewer components, or combine certain components, or arrange the components differently. For example, the terminal also includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be described in detail here.

[0091] In addition to biomarkers, specific molecular features, such as gene mutations, can also be incorporated into the prognostic model for patients with early-stage liver cancer after surgery. Gene mutations, especially driver gene mutations, are an important cause of cancer development. Gene mutations are also important targets for cancer treatment and serve as important markers for prognosis.

[0092] Embodiment 1

[0093] like Figure 1 As shown, this embodiment provides a method for predicting the efficacy of the FOLFOX regimen for EBV-positive patients with digestive tract tumors, comprising:

[0094] S1, obtain samples of multiple PDTX models and perform RNA sequencing on the samples of each PDTX model to obtain RNA sequencing data corresponding to the samples of multiple PDTX models; wherein the PDTX model is a PDTX model of the same strain of mice; of course, step S1 can also be implemented as follows: obtain samples of multiple PDTX models and perform WES sequencing and RNA sequencing on the samples of each PDTX model to obtain DNA sequencing data and RNA sequencing data corresponding to the samples of multiple PDTX models; wherein the PDTX model is a PDTX model of the same strain of mice; that is, step S1 is implemented by considering both DNA sequencing data and RNA sequencing data at the same time, wherein the DNA sequencing data is used as verification data for the RNA sequencing data, thereby ensuring the accuracy of the basic data for analysis.

[0095] Using the FOLFOX regimen efficacy obtained from the PDTX model as the basis for analysis can reduce the defects of unstable efficacy caused by individual differences and differences in living environment, which affects the accuracy and reliability of prediction. WES (whole exome sequencing) uses sequence capture technology to capture and enrich the DNA in the exon region of the whole genome for high-throughput sequencing.

[0096] like Figure 2 As shown, as a preferred embodiment, the S1 includes:

[0097] S11, performing WES sequencing on each sample of the PDTX model; the WES sequencing uses IIlumina NovaSeqV6000 sequencer, uses whole exome sequencing: Agilent V6 is used as sequencing reagent, and uses capture sequencing sequencing; the sequencing data output is 25G, and the sequencing data requirements are: base quality Q30>90%, GC content is 50±5%, the average depth of the sequencing capture area is 200X-250X, and the coverage requirements are that the capture area coverage is above 95%, and the 10X coverage is above 80%;

[0098] S12, RNA sequencing was performed on each sample of the PDTX model; the RNA sequencing used an IIlumina NovaSeqV6000 sequencer, using VAHTS mRNA Capture Beads+mRNAVAHTS Universal V8RNA-seqLibrary Prep Kit for Illumina as sequencing reagents, and a capture sequencing sequencing method; the sequencing data output was 10G, and the sequencing data requirements were: base quality Q30>90%, and GC content was 50±5%.

[0099] In this embodiment, S1 involves RNA data and DNA data of 100 samples of the PDTX model, and a total of 20 cases of clinical RNA data are obtained.

[0100] S2, performing EBV detection on samples of multiple PDTX models, thereby dividing the samples of the multiple PDTX models into EBV infection-negative samples and EBV infection-positive samples, and after removing the EBV infection-negative samples, the EBV infection-positive samples constitute an EBV infection-positive sample group as a sample basis for establishing a FOLFOX regimen efficacy prediction model and a FOLFOX regimen efficacy prediction;

[0101] Implementation principle of S2: Samples not infected by EBV are not suitable for modeling together with positive samples. This is because whether or not the sample is infected with EBV will affect the expression of genes in the sample. Therefore, if the efficacy of the FOLFOX regimen is studied based on the transcript level, it is necessary to consider whether the patient is infected with EBV. The subsequent modeling of the present invention based on the EBV-positive samples obtained after EBV detection belongs to this situation, so the EBV-negative samples are eliminated.

[0102] like Figure 3 As shown, as a preferred embodiment, the S2 includes:

[0103] S21, obtaining the drug efficacy data and cancer type information data of the samples of the multiple PDTX models;

[0104] S22, based on the cluster analysis of the drug efficacy data and the cancer information data, the samples of the multiple PDTX models are regionally classified; the regional classification includes enriched gene regions and non-enriched gene regions;

[0105] S23, performing gene enrichment analysis on all genes in the enriched gene region to obtain the EBV pathway;

[0106] S24, performing compatibility verification on all genes in the enriched gene region and the EBV pathway, wherein the EBV genes that pass the compatibility verification are EBV genes that match the EBV pathway;

[0107] S25, drawing a heat map for the samples of the multiple PDTX models based on the EBV gene, and distinguishing the EBV-infected positive samples from the EBV-infected negative samples by using the heat map to obtain a first distinction result;

[0108] S26, performing statistical analysis on the DNA sequencing data and RNA sequencing data of the samples of the multiple PDTX models to obtain the sequence number of EBV virus; determining whether each sample in the samples of the multiple PDTX models is infected with EBV virus one by one based on the sequence number of EBV virus, thereby obtaining a second distinction result for distinguishing EBV-infected positive samples from EBV-infected negative samples;

[0109] S27, determining whether the first distinction result and the second distinction result are consistent; if they are consistent, determining the final EBV infection positive sample and EBV infection negative sample; if they are inconsistent, repeating S21-S26 and performing different regional classifications until a consistent distinction result is obtained.

[0110] In this embodiment, specifically, 80 samples of the PDTX model were clustered according to the efficacy of the FOLFOX regimen and the type of cancer. The 80 samples can be divided into two categories, namely, the enriched gene region (upper left corner) and the non-enriched gene region (other regions except the upper left corner). Figure 4 shown.

[0111] right Figure 4 Gene enrichment analysis was continued for the genes in the enriched gene region in the upper left corner, and Epstein-Barrvirus infection was obtained as the most significant pathway, namely the EBV pathway. A total of 27 genes in the enriched gene region matched the EBV pathway. The 27 genes are as follows: CDKN1A, CD40, TNFAIP3, PIK3CD, ICAM1, HLA-DMA, HLA-DMB, CCND2, BLNK, PLCG2, E2F2, JAK3, LYN, ENTPD1, ​​SYK, HLA-B, TAP2, TAP1, TYK2, HLA-A, RUNX3, NFKB2, HLA-E, TR AF3, IRF7, FAS, NFKBIE. A heat map of 80 samples was made based on the expression of 27 EBV genes, as shown in the figure below. Figure 5 As shown, EBV infection-positive samples and EBV infection-negative samples are clearly distinguished.

[0112] By analyzing the DNA and RNA sequencing data of 80 samples, the number of EBV virus sequences was counted as shown in Table 1 to determine whether the samples were infected with EBV virus. The results were consistent with the cluster analysis samples, and finally the EBV infection-positive samples were determined.

[0113] Table 1

[0114] Sample name DNA_EBV_reads RNA_EBV_reads sample1 85 3260 sample2 7455 2460 sample3 13535 4032 sample4 21826 738 sample5 4505 2935 sample6 17757 1493 sample7 18212 5918 sample8 10709 1107 sample9 0 2418 sample10 4566 437 sample11 6846 1104 sample12 3442 732 sample13 15407 798 sampIe14 2856 2599 sample15 8952 1363 sampIe16 6301 354 sample17 6758 4023 sample18 29437 77

[0115] In this embodiment, 18 samples were detected to be infected by EBV virus, which were used for modeling later. After EBV infection, the expression of genes will be affected. To study the efficacy of the FOLFOX regimen from the transcript level, it is necessary to consider whether the patient is infected by EBV. This embodiment is based on EBV detection positive samples for subsequent steps.

[0116] S3, determining a gene sample set for establishing a drug efficacy prediction model for the FOLFOX regimen after differential gene analysis and gene screening based on the EBV infection positive sample group;

[0117] like Figure 6 As shown, as a preferred implementation, S3 includes:

[0118] S31, dividing the samples in the EBV infection negative sample group into FOLFOX regimen efficacy positive samples and FOLFOX regimen efficacy negative samples;

[0119] In this embodiment, after EBV detection, there were 18 EBV-positive samples. 33 EBV-positive PDTX samples were obtained through searching and identifying in the own biological sample library, so a total of 51 EBV-positive samples were included in the analysis. Among them, 20 samples were positive for FOLFOX efficacy and 31 samples were negative for efficacy. Gastric cancer, intestinal cancer, liver cancer, pancreatic cancer, esophageal cancer and bile duct / cystic cancer were covered.

[0120] S32, based on determining that the sample expression levels of the FOLFOX regimen-positive samples and the FOLFOX regimen-negative samples do not conform to a normal distribution, performing DESeq2 differential analysis on the FOLFOX regimen-positive samples and the FOLFOX regimen-negative samples to obtain multiple differentially expressed genes.

[0121] In this example, DESeq2 is an R language package designed for normalization, visualization and differential expression analysis of high-dimensional quantitative data. It estimates the prior values ​​of log2foldchange and dispersion through empirical Bayes techniques, and calculates the posterior values ​​of these statistics. The two groups of samples were differentially analyzed by DESeq2, and a total of 294 differential genes (p < 0.05) were screened, as shown in Table 2.

[0122] Table 2

[0123]

[0124]

[0125]

[0126] S33, performing pathway enrichment analysis on the multiple differentially expressed genes, and obtaining the most significant pathway with the highest correlation with the efficacy of the FOLFOX regimen based on the analysis results;

[0127] In this example, pathway enrichment analysis was performed on 294 genes using KOBAS (http: / / bioinfo.org / kobas). The analysis results are shown in Figure 7 As shown in the figure, 294 genes were significantly enriched in pathways such as Metabolic pathways, Cytokine-cytokinereceptor interaction, Aminoacyl-tRNA biosynthesis and Lysosome (p < 0.05). Metabolic pathways, Cytokine-cytokine receptor interaction, Aminoacyl-tRNA biosynthesis and Lysosome were the most significant pathways with the highest correlation with the efficacy of the FOLFOX regimen.

[0128] Metabolic pathways: Metabolic pathways are a series of interrelated chemical reactions that work together in cells to transform and utilize nutrients. These pathways include decomposition pathways (such as glycolysis, tricarboxylic acid cycle) and synthesis pathways (such as fatty acid synthesis, protein synthesis). Metabolic pathways are responsible for energy production, synthesis and decomposition of biomacromolecules, and maintaining the stability of the intracellular environment.

[0129] Cytokine-cytokine receptor interaction: This is an important component of intercellular signaling and involves the binding of cytokines (a class of small molecule proteins) to their specific receptors on the surface of target cells. This interaction plays a key regulatory role in a variety of biological processes such as immune response, inflammation, cell proliferation, differentiation and apoptosis. Aminoacyl-tRNA biosynthesis: The interaction between cytokines and their receptors regulates processes such as immune response and inflammation. Aminoacyl-tRNA is a key molecule in the translation stage of protein biosynthesis. They bring amino acids to the ribosome and synthesize proteins according to the codon sequence on the mRNA.

[0130] Lysosome: Lysosomes are organelles that contain a variety of digestive enzymes that can break down a variety of biomacromolecules. They are responsible for intracellular waste disposal, digestion of endocytosed substances, and autophagy during apoptosis. Lysosomes are also involved in maintaining intracellular homeostasis and regulating cell death.

[0131] S34, performing differential gene analysis on the multiple differential genes, drawing a heat map for the expression levels of the top M most significant differential genes in the differential gene analysis results, and performing PCA sample clustering to screen N efficacy classification genes, wherein the efficacy classification genes have a clear classification effect on the FOLFOX regimen efficacy of multiple FOLFOX regimen-positive samples.

[0132] like Fig. 8A and Figure 8B As shown in the figure, M is 100 and N is 10, that is, there are 10 genes that have a good classification effect on the FOLFOX efficacy of the samples. The heat map of the expression of the top 100 most significant differential genes in the 294 gene screening analysis results and PCA sample clustering. From the figure below, it can be found that some genes have a good classification effect on the FOLFOX efficacy of the samples.

[0133] S35, removing the collinear genes among the top M most significantly differentially expressed genes, verifying and confirming that the N pharmacodynamic classification genes are partially or completely on the most significant pathway, and determining the gene sample set for establishing the pharmacodynamic prediction model of the FOLFOX regimen based on the most significantly differentially expressed genes after removing the collinear genes, the N pharmacodynamic classification genes and combining gene functions.

[0134] In this embodiment, the colinear genes in the top 100 most significantly differentially expressed genes were removed, and 10 genes related to the gene function and FOLFOX efficacy were combined for mathematical modeling. Ten genes were finally selected for modeling in the EBV-positive sample group, including 7 up-regulated genes in FOLFOX efficacy-positive samples and 3 down-regulated genes in FOLFOX efficacy-positive samples. Up-regulated genes: MT-TW, CHTF8, LINC00649, NIFK-AS1, RPS26, TNFSF11, CHAC1, down-regulated genes CD68, IGF2BP3, GAB1. Among them, MT-TW is in the Aminoacyl-tRNA biosynthesist pathway, TNFSF11 is in the Cytokine-cytokine receptorinteraction pathway, and CD68 is in the Lysosome pathway.

[0135] Functions of the 10 genes:

[0136] Upregulated genes: MT-TW, CHTF8, LINC00649, NIFK-AS1, RPS26, TNFSF11, CHAC1; downregulated genes: CD68, IGF2BP3, GAB1.

[0137] MT-TW: Mitochondrial tRNA gene, involved in mitochondrial protein synthesis.

[0138] CHTF8: Chromosome cohesion factor is involved in sister chromatid cohesion and the fidelity of chromosome transmission. CTF18 replication factor C (CTF18-RFC) is a component of one of the nuclear antigen loading complexes, which consists of CTF18, CTF8, DSCC1, RFC2, RFC3, RFC4, and RFC5. The CTF18-RFC complex binds to single-stranded and primed DNA and has weak ATPase activity, which is stimulated in the presence of primed DNA, replication protein A (RPA), and proliferating cell nuclear antigen (PCNA). The CTF18-RFC complex catalyzes the ATP-dependent loading of PCNA onto primed and gapped DNA. It also interacts with and stimulates POLH, suggesting a protein network that coordinates DNA repair, recombination, and chromosome cohesion reactions with replication fork progression.

[0139] LINC00649: a long intergenic noncoding RNA that may play a role in gene regulation.

[0140] NIFK-AS1: a long noncoding RNA that may play a role in the regulation of gene expression.

[0141] RPS26: encodes ribosomal protein, involved in protein synthesis.

[0142] TNFSF11: This gene encodes a member of the tumor necrosis factor (TNF) cytokine family, which is a ligand for osteoprotegerin, a key factor in osteoclast differentiation and activation. This protein has been shown to be a dendritic cell survival factor and is involved in the regulation of T cell-dependent immune responses. This protein activates the anti-apoptotic kinase AKT / PKB through a signaling complex involving SRC kinase and tumor necrosis factor receptor-associated factor (TRAF) 6, suggesting that this protein may play a role in the regulation of apoptosis.

[0143] CHAC1: catalyzes the cleavage of glutathione into 5-oxo-L-proline and Cys-Gly dipeptide. It specifically acts on glutathione but not other γ-glutamyl peptides. Glutathione depletion is an important factor in the occurrence and execution of apoptosis. It acts as a pro-apoptotic component of the unfolded protein response pathway by mediating the pro-apoptotic effects of the ATF4-ATF3-DDIT3 / CHOP cascade.

[0144] CD68: May play a role in the phagocytic activity of tissue macrophages, including intracellular lysosomal metabolism and extracellular cell-cell and cell-pathogen interactions. Binds to tissue- and organ-specific lectins or selectins, allowing macrophage subsets to home to specific sites.

[0145] IGF2BP3: RNA binding factor that recruits target transcripts into cytoplasmic protein-RNA complexes (mRNPs). Binds to the 3′-UTR of CD44 mRNA and stabilizes it, thereby promoting cell adhesion and invasive pseudopodia formation in cancer cells.

[0146] GAB1: An adaptor protein that plays a role in intracellular signal transduction cascades triggered by activated receptor-type kinases. Plays a role in FGFR1 signal transduction. May be involved in epidermal growth factor receptor (EGFR) and insulin receptor (INSR) signaling. Participates in the MET / HGF signaling pathway.

[0147] The 10 genes had a certain FOLFOX efficacy classification effect in 51 samples. From the gene heat map and PCA diagram, it can be seen that the 10 genes were inaccurate in a few samples in the FOLFOX efficacy classification, because the expression of individual cancer genes in multi-cancer samples was inconsistent in these genes, which was later corrected by the model algorithm.

[0148] Ten genes had a certain role in the FOLFOX regimen efficacy classification in 51 samples. Fig.9A Gene heatmap and Fig. 9B The PCA graph shows that there are a few samples inaccurate in the efficacy classification of the FOLFOX regimen for the 10 genes, because the expression of individual cancer genes in multi-cancer samples is inconsistent among these genes, which are then corrected using the model algorithm.

[0149] S4, establishing a drug efficacy prediction model for the FOLFOX regimen based on the gene sample set;

[0150] like Fig.10 As shown, as a preferred embodiment, the S4 includes:

[0151] S41, using Random Forest to establish a drug efficacy prediction model for the FOLFOX regimen;

[0152] S42, testing whether the performance of the FOLFOX regimen efficacy prediction model has reached the best based on multiple test indicators; determining that the FOLFOX regimen efficacy prediction model for EBV-positive patients with gastrointestinal tumors that has reached the best performance corresponds to the FOLFOX regimen efficacy prediction model.

[0153] As a preferred implementation, the multiple test indicators include:

[0154] (1) Sensitivity or true positive rate (TPR) = TP / (TP+FN);

[0155] (2) precision or positive predictive value (PPV) = TP / (TP + FP);

[0156] (3) True negative rate (TNR) = TN / (FP+TN);

[0157] (4) Negative predictive value (NPV) = TN / (FN + TN);

[0158] (5) Accuracy = (TP + TN) / (TP + TN + FP + FN).

[0159] In this embodiment, the performance of the Random Forest model was comprehensively compared. Table 3 shows the performance evaluation results of the FOLFOX regimen efficacy prediction model established by the Random Forest model.

[0160] Table 3

[0161] algorithm Accuracy TNR TPR NPV PPV AUC Random Forest 0.86 0.90 0.75 0.90 0.75 0.83

[0162] Random forest is a classifier that contains many decision trees. It can be used to handle classification and regression problems, as well as dimensionality reduction problems. It is also very tolerant to outliers and noise, and has better prediction and classification performance than decision trees.

[0163] The establishment process of Random Forest:

[0164] Each decision tree is built according to the following algorithm:

[0165] 1. Use N to represent the number of training cases (samples), and M to represent the number of features.

[0166] 2. Input the number of features m, which is used to determine the decision result of a node on the decision tree; m should be much smaller than M.

[0167] 3. Take samples N times from N training cases (samples) with replacement to form a training set (bootstrap sampling), and use the unsampled cases (samples) to make predictions and evaluate their errors.

[0168] 4. For each node, randomly select m features. The decision of each node in the decision tree is determined based on these features. Based on these m features, calculate the best split method.

[0169] 5. Each tree will grow completely without pruning, which may be adopted after building a normal tree classifier).

[0170] S5. Verify the prediction quality of the FOLFOX regimen efficacy prediction model based on clinical samples.

[0171] In this example, 20 clinical samples were used to verify the prediction quality of the FOLFOX regimen efficacy prediction model, as shown in Table 4.

[0172] Table 4

[0173] Sample name CHTF8 MT_TW NIFK_AS1 RPS26 TNFSF11 LINC00649 CHAC1 IGF2BP3 CD68 GAB1 Real efficacy Predicting drug efficacy clin1 15.11 14.91 7.18 616.32 12.81 5.92 15.52 0.73 1.18 1.12 1 1 clin2 15.03 16.07 7.02 511.28 11.15 6.3 12.81 4.96 2.21 2.34 1 1 clin3 14.72 23.24 8.4 637.12 8.85 7.19 13.3 0.61 3.08 1.28 1 1 clin4 12.31 18.27 7.22 537.89 8.51 4.38 10.93 2.21 3.02 3.31 1 0 clin5 15.11 20.83 6.07 635.81 10.06 6.47 16.01 1.62 1.06 1.21 1 1 clin6 15.32 22.37 633 544.77 9.3 6.1 14.76 1.89 1.18 1.08 1 1 clin7 14.86 15.82 6.32 604.6 10.48 5.49 15.74 2.99 1.97 1.16 1 1 clin8 11.63 11.28 6.23 480.55 8.17 4.02 10.12 2.94 4.28 1.1 1 0 clin9 6.5 20.44 4.78 344.36 7.96 8.15 4.48 7.03 6.24 2.22 0 0 clin10 7.44 18.89 516 347.38 748 276 55 602 926 2.17 0 0 clin11 8.41 30.37 6.11 385.2 10.04 4.37 10.03 6.48 5.76 1.29 0 0 clin12 7.11 12.78 4.58 327.69 7.41 2.92 4.67 9.45 7.21 2.09 0 0 clin13 7.33 14.62 6.21 288.51 5.95 3.65 7.53 9.8 8.96 2.24 0 0 clin14 7.82 10.43 5.16 334.46 8.27 4.23 4.78 5.79 6.19 2.24 0 0 clin15 9.29 12.59 4.91 454.06 8.61 5.05 9.53 4.12 5.32 2.18 0 0 clin16 10.15 15.67 6.44 554.07 7.08 5.41 9.06 6.84 4.26 2.15 0 1 clin17 7.17 14.45 4.69 329.82 10.54 3.28 4.42 5.68 5.25 2.2 0 0 clin18 11.37 17.87 7.35 518.33 9.55 4.19 12.07 3.08 3.81 2.23 0 1 clin19 10.59 10.63 5.58 570.21 10.45 5.05 12.34 3.42 4.88 1.11 0 1 clin20 7.17 12.24 5.63 381.99 7.93 4.22 4.47 6.98 5.76 2.34 0 0

[0174] Thus, the values ​​of accuracy, true negative rate, true positive rate, negative predictive value, positive predictive value and AUO are obtained, as shown in Table 5. AUC (Area Under Curve) is a commonly used performance indicator used to evaluate the performance of classification models. In machine learning, AUC is usually used to evaluate the prediction quality of binary classification models (such as logistic regression, support vector machine, etc.), and the prediction quality is evaluated here.

[0175] Table 5

[0176]

[0177] Embodiment 2

[0178] like Fig.11 As shown, this embodiment provides a FOLFOX regimen efficacy prediction system for EBV-positive patients with digestive tract tumors, which is used to implement the method of the first aspect, including:

[0179] A sample and sequencing data acquisition module 101 is used to acquire samples of multiple PDTX models and perform RNA sequencing on the samples of each PDTX model to acquire RNA sequencing data corresponding to the samples of multiple PDTX models; wherein the PDTX models are PDTX models of the same strain of mice;

[0180] The EBV monitoring module 102 is used to perform EBV detection on samples of multiple PDTX models, thereby dividing the samples of the multiple PDTX models into EBV infection-negative samples and EBV infection-positive samples, and after removing the EBV infection-negative samples, the EBV infection-positive samples constitute an EBV infection-positive sample group as a sample basis for establishing a FOLFOX regimen efficacy prediction model and a FOLFOX regimen efficacy prediction;

[0181] A differential gene analysis and gene screening module 103 is used to determine a gene sample set for establishing a FOLFOX regimen efficacy prediction model after differential gene analysis and gene screening based on the EBV infection negative sample group;

[0182] A drug efficacy prediction model building module 104 is used to build the drug efficacy prediction model of the FOLFOX regimen based on the gene sample set;

[0183] The prediction quality verification module 105 is used to verify the prediction quality of the FOLFOX regimen efficacy prediction model based on clinical samples.

[0184] The present invention also provides a computer-readable storage medium storing a plurality of instructions, wherein the instructions are used to implement the method as in the first embodiment.

[0185] like Fig.12 As shown, the present invention further provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301. The memory 302 stores a plurality of instructions, which can be loaded and executed by the processor, so that the processor can execute the method as in the first embodiment.

[0186] Although preferred embodiments of the present invention have been described, additional changes and modifications may be made to these embodiments by those skilled in the art once the basic inventive concepts are known. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for predicting the efficacy of FOLFOX regimen for EBV-positive patients with digestive tract tumors, characterized in that: include: S1, obtaining samples of multiple PDTX models and performing RNA sequencing on the samples of each PDTX model to obtain RNA sequencing data corresponding to the samples of multiple PDTX models; wherein the PDTX models are PDTX models of the same strain of mice; S2, performing EBV detection on samples of multiple PDTX models, thereby dividing the samples of the multiple PDTX models into EBV infection-negative samples and EBV infection-positive samples, and after removing the EBV infection-negative samples, the EBV infection-positive samples constitute an EBV infection-positive sample group as a sample basis for establishing a FOLFOX regimen efficacy prediction model and a FOLFOX regimen efficacy prediction; S3, determining a gene sample set for establishing a drug efficacy prediction model for the FOLFOX regimen after differential gene analysis and gene screening based on the EBV infection positive sample group; S4, establishing a drug efficacy prediction model for the FOLFOX regimen based on the gene sample set; S5. Verify the prediction quality of the FOLFOX regimen efficacy prediction model based on clinical samples.

2. A method for predicting the efficacy of FOLFOX regimen for EBV-positive patients with digestive tract tumors according to claim 1, characterized in that: The S1 further comprises: Obtain samples of multiple PDTX models and perform WES sequencing on each sample of the PDTX model to obtain DNA sequencing data corresponding to the samples of the multiple PDTX models; wherein: S1 includes: S11, performing WES sequencing on each sample of the PDTX model; the WES sequencing uses IIlumina NovaSeqV6000 sequencer, uses whole exome sequencing: Agilent V6 is used as sequencing reagent, and uses capture sequencing sequencing; the sequencing data output is 25G, and the sequencing data requirements are: base quality Q30>90%, GC content is 50±5%, the average depth of the sequencing capture area is 200X-250X, and the coverage requirements are that the capture area coverage is above 95%, and the 10X coverage is above 80%; S12, RNA sequencing was performed on each sample of the PDTX model; the RNA sequencing used an Illumina NovaSeqV6000 sequencer, VAHTS mRNA Capture Beads+mRNAVAHTS Universal V8 RNA-seqLibrary Prep Kit for Illumina as sequencing reagents, and a capture sequencing method was used; the sequencing data output was 10G, and the sequencing data requirements were: base quality Q30>90%, and GC content was 50±5%.

3. A method for predicting the efficacy of FOLFOX regimen for EBV-positive patients with digestive tract tumors according to claim 2, characterized in that: The S2 includes: S21, obtaining the drug efficacy data and cancer type information data of the samples of the multiple PDTX models; S22, based on the cluster analysis of the drug efficacy data and the cancer information data, the samples of the multiple PDTX models are regionally classified; the regional classification includes enriched gene regions and non-enriched gene regions; S23, performing gene enrichment analysis on all genes in the enriched gene region to obtain the EBV pathway; S24, performing compatibility verification on all genes in the enriched gene region and the EBV pathway, wherein the EBV genes that pass the compatibility verification are EBV genes that match the EBV pathway; S25, drawing a heat map for the samples of the multiple PDTX models based on the EBV gene, and distinguishing the EBV-infected positive samples from the EBV-infected negative samples by using the heat map to obtain a first distinction result; S26, performing statistical analysis on the DNA sequencing data and RNA sequencing data of the samples of the multiple PDTX models to obtain the sequence number of EBV virus; determining whether each sample in the samples of the multiple PDTX models is infected with EBV virus one by one based on the sequence number of EBV virus, thereby obtaining a second distinction result for distinguishing EBV-infected positive samples from EBV-infected negative samples; S27, determining whether the first distinction result and the second distinction result are consistent; if they are consistent, determining the final EBV infection positive sample and EBV infection negative sample; if they are inconsistent, repeating S21-S26 and performing different regional classifications until a consistent distinction result is obtained.

4. A method for predicting the efficacy of FOLFOX regimen for EBV-positive patients with digestive tract tumors according to claim 3, characterized in that: The S3 includes: S31, dividing the samples in the EBV infection positive sample group into FOLFOX regimen positive efficacy samples and FOLFOX regimen negative efficacy samples; S32, based on determining that the gene expression levels of the FOLFOX regimen-positive sample and the FOLFOX regimen-negative sample do not conform to a normal distribution, performing a DESeq2 differential analysis on the FOLFOX regimen-positive sample and the FOLFOX regimen-negative sample to obtain a plurality of differentially expressed genes; S33, performing pathway enrichment analysis on the multiple differentially expressed genes, and obtaining the most significant pathway with the highest correlation with the efficacy of the FOLFOX regimen based on the analysis results; S34, performing differential gene analysis on the multiple differential genes, drawing a heat map for the expression amounts of the top M most significant differential genes in the differential gene analysis results, performing PCA sample clustering, and then screening N drug efficacy classification genes, wherein the drug efficacy classification genes have a clear classification effect on the drug efficacy of the FOLFOX regimen of multiple EBV infection-positive samples; S35, removing the collinear genes among the top M most significantly differentially expressed genes, verifying and confirming that the N pharmacodynamic classification genes are partially or completely on the most significant pathway, and determining the gene sample set for establishing the pharmacodynamic prediction model of the FOLFOX regimen based on the most significantly differentially expressed genes after removing the collinear genes, the N pharmacodynamic classification genes and combining gene functions.

5. A method for predicting the efficacy of FOLFOX regimen for EBV-positive patients with digestive tract tumors according to claim 4, characterized in that: The S4 includes: S41, using random forest to establish a drug efficacy prediction model for the FOLFOX regimen; S42, testing whether the performance of the FOLFOX regimen efficacy prediction model has reached the best based on multiple test indicators; determining that the FOLFOX regimen efficacy prediction model for EBV-positive patients with gastrointestinal tumors that has reached the best performance corresponds to the FOLFOX regimen efficacy prediction model.

6. A method for predicting the efficacy of FOLFOX regimen for EBV-positive patients with digestive tract tumors according to claim 5, characterized in that: The multiple test indicators include: (1) Sensitivity or true positive rate (TPR) = TP / (TP+FN); (2) precision or positive predictive value (PPV) = TP / (TP + FP); (3) True negative rate (TNR) = TN / (FP+TN); (4) Negative predictive value (NPV) = TN / (FN + TN); (5) Accuracy = (TP + TN) / (TP + TN + FP + FN).

7. A method for predicting the efficacy of FOLFOX regimen for EBV-positive patients with digestive tract tumors according to claim 6, characterized in that: The S5 includes: The clinical samples were input into the FOLFOX regimen efficacy prediction model for prediction, and the prediction quality was determined based on the prediction accuracy, true negative rate, true positive rate, negative predictive value, positive predictive value and AUC values.

8. A FOLFOX regimen efficacy prediction system for EBV-positive patients with digestive tract tumors, used to implement the prediction method according to any one of claims 1 to 7, characterized in that: include: A sample and sequencing data acquisition module (101), used to acquire samples of multiple PDTX models and perform RNA sequencing on each sample of the PDTX model to acquire RNA sequencing data corresponding to the samples of the multiple PDTX models; wherein the PDTX models are PDTX models of the same strain of mice; An EBV monitoring module (102) is used to perform EBV detection on samples of multiple PDTX models, thereby dividing the samples of the multiple PDTX models into EBV infection-negative samples and EBV infection-positive samples, and after removing the EBV infection-negative samples, the EBV infection-positive samples constitute an EBV infection-positive sample group as the establishment of a FOLFOX regimen efficacy prediction model and the sample basis for FOLFOX regimen efficacy prediction; a differential gene analysis and gene screening module (103) is used to determine a gene sample set for establishing a FOLFOX regimen efficacy prediction model after performing differential gene analysis and gene screening based on the EBV infection-positive sample group; A drug efficacy prediction model building module (104), used to build the drug efficacy prediction model of the FOLFOX regimen based on the gene sample set; The prediction quality verification module (105) is used to verify the prediction quality of the FOLFOX regimen efficacy prediction model based on clinical samples.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a plurality of instructions, and the processor is used to read the instructions and execute the prediction method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the plurality of instructions can be read by a processor to execute the prediction method as described in any one of claims 1-7.

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