Biomarker combination and its use in predicting the effect of gastric cancer treatment

Abstract

The application discloses a biomarker combination and application thereof in predicting treatment effect of gastric cancer. The biomarker combination can predict the gastric cancer effect of a gastric cancer patient receiving a Herceptin combined XELOX chemotherapy treatment, has the advantages of high sensitivity and high specificity, and provides favorable technical support for predicting the gastric cancer patient receiving the Herceptin combined XELOX chemotherapy treatment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of bioinformatics, and particularly relates to a biomarker combination and application thereof in predicting the treatment effect of Herceptin combined with XELOX chemotherapy on gastric cancer. BACKGROUND

[0002] Gastric cancer (GC) is the fourth most common malignancy worldwide and the second leading cause of cancer-related death globally. The early detection rate of gastric cancer is low, and the cure rate of advanced gastric cancer is low. The preferred treatment method for patients with advanced gastric cancer is surgery. For some patients who do not have the opportunity for surgical treatment, only drugs can improve the quality of life or even maintain survival. Although in recent years, the iterative update of first-line treatment drugs, the in-depth research and development of second-line treatment drugs and targeted drugs Herceptin and Ramucirumab (CYRAMZA) have made great progress, but the problem of drug resistance has not been solved, and the overall prognosis of gastric cancer treatment is still at a poor level. In clinical practice, there is a large individual difference in the treatment effect of tumors, but there is too little basis for drug individualization, and there is an urgent need for markers to guide individualized precision medicine, thereby alleviating the problem of drug resistance.

[0003] Therefore, in order to achieve individualized treatment of gastric cancer, it is necessary to identify subgroups according to molecular genetics and pathological characteristics, discover and apply corresponding target genes. In addition, in the study of gastric cancer, it has been reported that the prognosis of gastric cancer can be classified according to the subtype of gastric cancer. At present, a number of research patents have emerged, which are based on the gene expression level of genome and transcriptome to realize the classification of gastric cancer, such as a system for predicting the postoperative prognosis or anticancer drug suitability of patients with advanced gastric cancer (patent number CN110168106A), and a cluster classification and prognosis prediction system based on the biological characteristics of gastric cancer (patent number CN110177886A). However, in clinical practice, the first-line treatment of gastric cancer patients, Herceptin targeted therapy or combined with other chemotherapy regimens (such as combined with capecitabine / 5-FU / fluorouracil, or oxaliplatin / platinum), still cannot achieve effective treatment plan selection.

[0004] Proteomics plays a major role in revealing the complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis, and resistance to treatment. Proteomic tumor diagnosis has the advantages of high sensitivity, strong specificity, and clear background mechanism, and has been increasingly used in tumor detection in recent years. Moreover, the research of these tumor markers is often based on a certain amount of experimental data, and the types of cancer and sample size involved are relatively limited. Therefore, by collecting proteomic data and using big data analysis methods to establish a model for predicting effective treatment, it is helpful to achieve individualized chemotherapy and has important clinical significance for recommending appropriate treatment plans for patients. SUMMARY

[0005] In view of the defect that the prior art lacks technical solutions for predicting the effect of Herceptin combined with XELOX chemotherapy in treating gastric cancer, the present application provides a biomarker combination and its application in predicting the effect of Herceptin combined with XELOX chemotherapy in treating gastric cancer. The biomarker combination at least includes ACOX1, ACTL6A, AIP, AMBP, B4GALNT3, BRD8 and BSDC1, at this time the prediction accuracy, diagnostic sensitivity and specificity in the verification set are all 100%, and the biomarker combination can be extended to at most include 336 protein molecule biomarkers as described in the present application, and the prediction accuracy, diagnostic sensitivity and specificity are all high.

[0006] To solve the above technical problems, one technical solution provided by the present application is a biomarker combination, which includes the following biomarkers: ACOX1, ACTL6A, AIP, AMBP, B4GALNT3, BRD8 and BSDC1.

[0007] Preferably, the biomarker combination further includes ATP5D.

[0008] More preferably, the biomarker combination further comprises one or more of the following biomarkers: ABCD1, ABCD3, ACADS, ACBD5, ACPP, ACSF2, ADAP1, ADH4, ADPGK, AGL, AGOl, AKAP9, ALYREF, ANAPC13, ANK2, AP1G1, APAF1, APBB1IP, APIP, ARHGAP4, ARHGEF17, ARL1, ARL6IP4, ARRB2, ATP11B, ATP5A1, ATP5D, ATP6V0A2, B3GNT6, B4GALT4, BAIAP2, BCKDK, BMS1, BOP1, C10orf112, C10orf76, C11orf31, C14orf169, C17orf85, C1orf198, C2orf47, C4orf33, C6orf211, CAB39, CADM1, CANT1, CAPN5, CAPN8, CASP6, CCDC47, CDC42EP1, CDK5RAP3, CEACAM1, CFL2, CHI3L1, CHORDC1, CLDN7, CLIC2, CLTC, CNOT7, COL15A1, COL4A1, COMMD3, COPS4, COPS5, COPS6, COX15, CREG1, CRYL1, CSTB, CSTF2T, CYP2S1, CYTB, DAZAP1, DCTN5, DDAH2, DDHD2, DDR1, DDX27, DDX60, DDX60L, DECR2, DES, DHCR24, DHFR, DHX38, DIDOl, DNAJA1, DNAJC11, DNASE2, DOCK11, DOPEY2, DPYSL4, DSG2, ECM1, EDF1, EIF2AK2, EIF3F, EIF5A, ELF1, ENTPD5, EPHX1, EPS15, EPS8L3, ERAP1, ERBB2IP, ERLIN2, ESYT2, ETAA1, FAM103A1, FAM105B, FAM162A, FAM96A, FIS1, FKBP10, FLG2, FMNL1, FN3KRP, FUT2, GALE, GBA2, GEMIN7, GID8, GMFG, GOLIM4, GOLT1B, GOSR1, GRAP2, GTDC2, GTF2E1, GTF2F1, GUCY1A3, GUK1, HEPH, HERC2, HGD, HLA-A, HNMT, HNRNPA1, HNRNPH3, HNRNPLL, HNRNPR, HSPA4L, HSPA6, HSPB6, HSPB7, HSPE1, HTRA3, ICT1,IFIT5, IL1RN, IMPAD1, INPPL1, INTS9, ISCA2, ITGB5, ITSN2, KDELR1, KDM2A, KHNYN, KIAA0195, KIAA0319L, KIAA1522, KIAA1704, KRT73, KRT80 KXD1, LAMC2, LAX1, LGALS4, LLGL2, LMF2, LPCAT3, LRRC1, LSM2, LSS, MAN2B1, ME1, MED14, MED4, MEMO1, MFSD5, MGST1, MGST3, MLXIPL, MMP7, MNF1, MON2, MPC2, MPV17, MRPL3, MRPL48, MRPS14, MRPS18A, MRVI1, MSLN, MTG1, MTIF2, MUC5AC, NBAS, ND2, NDUFB7, NHEJ1, NIF3L1, NIPSNAP3A, NRAS, NSFL1C, NT5C, OCA2, OCIAD2, OSBPL1A, PAFAH1B3, PARP12, PAWR, PBK, PBX1, PDCD11, PDCD6, PEA15, PFKP, PGLYRP2, PHC2, PIK3CA, PIP4K2B, PKP2, PLIN4, PODN, POLR1A, PPFIBP1, PPIG, PPP1CC, PPP3CC, PRPH, PSMA6, PTDSS1, PTGES3, PTGR1, PTPRCAP, QKI, QSER1, QSOX1, RAB10, RAB3GAP1, RANGAP1, RBM3, RBM39, RBMXL1, RCSD1, RHEB, RHOC, RHOT1, RNF181, RNF25, RPL18A, RPL23, RPP38, RPS4X, RPS6KA3, RRAGC, RUNX1, RUVBL1, S100A16, SCAF11, SCIN, SCRN3, SEL1L3, SEPSECS, SF3A3, SH3BGRL2, SLC25A35, SLC30A5, SLC35D1, SLC44A1, SLC44A2, SMPDL3B, SMTN, SNAP23, SNRPG, SOAT1, SPON1, SRSF2, SRSF9, ST14, ST6GALNAC1, STEAP4, STK17B, STK39, STXBP3, SUGP2, SYK, SYNCRIP, SYNPO, SZRD1, TACSTD2, TAF6, TBCC, TELO2, TEP1, TIMM13, TM9SF2, TMEM45B, TMEM63A, TNFAIP8L2, TNIK, TNRC6B,TOMM6, TP53BP1, TPP2, TRAM1, TRAPPC2L, TRIM14, TSPAN8, TTC39A, TTN, TUFM, UBE2Q1, UBE2S, UBXN6, UQCRQ, USP34, USP9Y, VPS51, VTI1A, WBSCR22, WDR26, WDR81, WIBG, YBX3, and ZNF706.

[0009] To solve the above technical problems, one of the technical solutions provided by the present application is a reagent for detecting the expression level of a biomarker combination as described herein, the reagent comprising biomolecules that specifically hybridize with the biomarkers respectively or simultaneously, such as primers, probes and / or antibodies; or, the reagent comprises reagents for genome, transcriptome and / or proteome sequencing for detecting the expression level of a biomarker combination as described herein.

[0010] To solve the above technical problems, one of the technical solutions provided by the present application is a kit comprising a biomarker combination as described herein and / or a reagent as described herein.

[0011] To solve the above technical problems, one of the technical solutions provided by the present application is the use of a biomarker combination as described herein, a reagent as described herein or a kit as described herein in the preparation of a preparation for predicting the effect of Herceptin combined with XELOX chemotherapy in the treatment of gastric cancer. Preferably, the preparation is a diagnostic product.

[0012] To solve the above technical problems, one of the technical solutions provided by the present application is a system for predicting the effect of Herceptin combined with XELOX chemotherapy in the treatment of gastric cancer, the system comprising:

[0013] a data processing module for receiving or inputting expression data of a biomarker combination as described herein of a gastric cancer patient;

[0014] a judgment and output module for, after the receiving or inputting is completed, processing the expression data by Firmiana software, presetting a machine learning algorithm based on a generalized linear regression model, constructing a prediction model, respectively predicting the sensitive prediction probability and the insensitive prediction probability of the gastric cancer patient, obtaining the sensitive prediction probability and the insensitive prediction probability of the gastric cancer patient, judging whether the expression data meets a preset judgment condition, to predict the effect of Herceptin combined with XELOX chemotherapy in the treatment of gastric cancer, and outputting the prediction result;

[0015] Wherein, in the judging and outputting module, when the expression data meets the judging condition, the judging condition is that the sensitive prediction probability is greater than or equal to the non-sensitive prediction probability, and the output prediction result is "Herceptin combined with XELOX chemotherapy has a therapeutic effect on gastric cancer"; when the expression data does not meet the judging condition, that is, the sensitive prediction probability is less than the non-sensitive prediction probability, the output prediction result is "Herceptin combined with XELOX chemotherapy does not have a therapeutic effect on gastric cancer".

[0016] Preferably, in the preset, the training set parameter is set to 80%, and the validation set parameter is set to 20%.

[0017] Preferably, the system further comprises a data collection module for collecting expression data of the biomarker combination in a gastric cancer tissue sample of a patient and transmitting the expression data to the data processing module.

[0018] To solve the above technical problems, one technical solution provided by the present application is a computer-aided method for predicting the therapeutic effect of Herceptin combined with XELOX chemotherapy on gastric cancer, which comprises the following steps:

[0019] (1) receiving or inputting expression data of a biomarker combination of a gastric cancer patient as described herein, processing the expression data by Firmiana software, presetting a machine learning algorithm based on a generalized linear regression model, constructing a prediction model, and respectively predicting the sensitive prediction probability and the non-sensitive prediction probability of the gastric cancer patient;

[0020] (2) judging whether the expression data meets a preset judging condition, the judging condition being that the sensitive prediction probability is greater than or equal to the non-sensitive prediction probability, to predict the therapeutic effect of Herceptin combined with XELOX chemotherapy on gastric cancer, and outputting a prediction result;

[0021] Wherein, in step (2), when the expression data meets the judging condition, the output prediction result is "Herceptin combined with XELOX chemotherapy has a therapeutic effect on gastric cancer"; when the expression data does not meet the judging condition, the output prediction result is "Herceptin combined with XELOX chemotherapy does not have a therapeutic effect on gastric cancer".

[0022] To solve the above technical problems, one technical solution provided by the present application is a computer-readable storage medium storing a computer program, the computer program being executed by a processor to realize the functions of the system as described herein or to realize the steps of the method as described herein.

[0023] To solve the above technical problems, one technical solution provided by the present application is: an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor being configured to execute the computer program to realize the functions of the system as described herein, or to realize the steps of the method as described herein.

[0024] On the basis of common general knowledge in the art, the above-mentioned preferred conditions can be combined arbitrarily, thereby obtaining preferred embodiments of the present application.

[0025] The reagents and raw materials used in the present application are commercially available.

[0026] The positive progress effect of the present application is that:

[0027] The biomarker combination provided by the present application has significant changes in the expression levels in the clinical samples of different treatment responses of gastric cancer patients, and therefore the biomarker combination provided by the present application can predict the effect of the gastric cancer treatment of the gastric cancer patients receiving the Herceptin combined with XELOX chemotherapy, has the advantages of high sensitivity and high specificity, and provides favorable technical support for predicting the gastric cancer treatment of the gastric cancer patients receiving the Herceptin combined with XELOX chemotherapy.

[0028] Based on the biomarker combination of the present application, the corresponding system, computer readable storage medium, electronic device and developed prediction method have wide scientific research value and provide personalized prediction for gastric cancer patients, and recommend whether the patient is suitable for receiving the Herceptin combined with XELOX chemotherapy treatment scheme. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 The sensitivity and specificity results of the marker combination described in the present application in the training set.

[0030] Figure 2 The sensitivity and specificity results of the marker combination described in the present application in the validation set.

[0031] Figure 3 Structure diagram of the system for predicting the treatment effect of the gastric cancer treated by the platinum class combined with the fluorouracil class and the taxol class of drugs;

[0032] Figure 4 Structure diagram of the electronic device. DETAILED DESCRIPTION

[0033] The present application will be further described by way of examples, but the present application is not limited in the scope of the examples. The experimental methods not specified in the following examples are selected according to the conventional methods and conditions, or according to the product instructions.

[0034] The required in the examples of the patients who received Herceptin targeted therapy or combined with other chemotherapy regimens, including 2 cases of Herceptin alone (2.9%), 3 cases of Herceptin combined with paclitaxel (4.3%), 6 cases of Herceptin combined with fluorouracil (8.7%), 3 cases of Herceptin combined with camptothecin (4.3%), 1 case of Herceptin combined with platinum (1.4%), 1 case of Herceptin combined with pertuzumab (1.4%), 44 cases of Herceptin combined with XELOX chemotherapy (64%), and 9 cases of Herceptin combined with fluorouracil plus paclitaxel (13.0%) gastric cancer patients before treatment, a total of 44 cases, including 35 cases of treatment sensitive group and 9 cases of non-sensitive group. The design and implementation of this study were approved and supervised by the medical ethics committee through ethical voting. The written informed consent of all patients has been obtained.

[0035] Example 1 Pretreatment of clinical samples of gastric cancer before treatment

[0036] The clinical sample is formalin-fixed paraffin-embedded tissue. Sample pretreatment: 3-10 μm thick sections were taken from the paraffin block for macroscopic dissection, xylene dewaxing, ethanol washing, air drying, and white sheets were obtained. Hematoxylin-eosin staining was performed on 3 μm thick sections as a tumor microscopic evaluation of tumor cell content in the section and tumor area selection. 10 μm thick sections were collected into centrifuge tubes corresponding to the tumor sample and stored at -80 for future use.

[0037] Example 2 Protein and peptide extraction from clinical samples

[0038] An equal amount of FFPE (tumor sample prepared in Example 1) tissue was collected in an EP tube, and lysis buffer (0.1M Tris-HCL pH 8.0, 0.1M DTT, 1mM PMSF) was added, then ground with a mortar for 3 minutes; sodium dodecyl sulfate (SDS) was added to a final concentration of 4%, 99°C, 1800rpm shaking for 2-2.5 hours; centrifuge at 12,000g for 5 minutes to collect the supernatant in an EP tube, add 4 times the volume of acetone, and place it at -20°C for 4 hours or overnight; centrifuge at 12,000g at 4°C for 1 minute to discard the supernatant and retain the precipitate, wash the precipitate with cold acetone three times, and dry the protein precipitate on a super-clean tornado; resuspend the protein precipitate with 8M urea and 50mM NH4HCO3, then add it to the FASP tube, and repeatedly centrifuge with 50mM NH4HCO3 to remove urea; add 50μL of 50mM NH4HCO3 containing 5.5μg of trypsin to the FASP tube, and incubate at 37°C for 18-20 hours for enzymolysis; centrifuge at 12,800g for 15 minutes to collect the peptides, and wash twice with 200μL of MS water to improve peptide yield; vacuum dry at 60°C to obtain the required peptides for mass spectrometry detection.

[0039] Mass spectrometry detection of clinical samples

[0040] The detection was performed with Q-Exactive HF-X hybrid quadrupole orbitrap mass spectrometer (Thermo Fisher Scientific, Rockford, IL, USA) and high performance liquid chromatography system (EASY nLC 1200, Thermo Fisher) and the mass spectrometry data corresponding to the peptide sample was obtained. The specific operation was as follows:

[0041] The drained peptide sample was redissolved in solvent A (0.1% formic acid in water) and loaded onto a trap column (100 pm x 2 cm; particle size, 3 pm; pore size, ), and then separated on an analytical column (150 pm x 12 cm, particle size, 1.9 pm; pore size, ) with a gradient of 5-35% mobile phase B (80% acetonitrile and 0.1% formic acid) elution at a flow rate of 600 nL / min for a total of 75 minutes. MS analysis was performed on QE-HFX with one full scan (300-1400 m / z, resolution = 12000), and the maximum number of ions allowed to enter the ion trap (automatic gain control target, AGC target) was 3E+06 ions, followed by high-energy collision-induced dissociation (isolation window of 1.6 m / z, collision energy of 27%, AGC target of 5E+04 ions, maximum injection time of 30 ms, dynamic exclusion setting of 18 seconds). The liquid chromatography tandem mass spectrometry system used Xcalibur software (Thermo Scientific) for control of data acquisition.

[0042] Example 4 Prediction of the effect of Herceptin combined with XELOX chemotherapy in the treatment of gastric cancer

[0043] All data were processed using Firmiana (V1.0). Firmiana is a workflow based on the Galaxy system, which consists of multiple functional modules such as user login interface, raw data, identification and quantification, data analysis and knowledge mining. The preset selected in this embodiment is a machine learning algorithm based on generalized linear regression model. The original file is retrieved according to the Refseq protein database of the National Center for Biotechnology Information (NCBI) (updated on 04-07-2013, 32,015 entries). Trypsin is selected as the proteolytic enzyme, with a maximum of two missed cleavage sites, fixed modification as carbamidomethyl (C), dynamic modification as protein acetyl (protein N-term), and oxidation (M). The first search quality tolerance is 20 ppm, and the main search peptide tolerance is 0.5 da. The false discovery rate (FDR) of peptide spectrum match (PSM) and protein is less than 1%. The identified peptide segment quantification result is recorded as the average value of the chromatographic fragment ion peak area in all reference spectral libraries. The intensity-based absolute quantification (iBAQ) method is used for protein quantification. We calculate the peak area value as a part of the corresponding protein. The total score (FOT) is used to represent the standardized abundance of a specific protein in a sample. FOT is defined as the iBAQ of the protein divided by the total iBAQ of all identified proteins in the sample. Proteins with at least one unique peptide and 1% FDR are selected. The above 44 clinical samples are divided into a training set including 35 cases and an internal validation set of 9 cases, and the same training set and validation set are used in each of the following groups, as follows:

[0044] First group

[0045] The relative expression levels of 7 protein molecular biomarkers (ACOX1, ACTL6A, AIP, AMBP, B4GALNT3, BRD8 and BSDC1) in the clinical samples of gastric cancer patients are calculated to obtain their prediction accuracy, sensitivity and specificity, including 35 training sets, with a prediction accuracy of 100%, a sensitivity of 100% and a specificity of 100%; the remaining 9 cases are test sets, with a prediction accuracy of 100% ( Figure 1 ), a sensitivity of 100% and a specificity of 100% ( Figure 2 ). For a patient with gastric cancer to be treated, according to the expression level of the protein molecular biomarker, the output result of the different reactions of the patient using Herceptin combined with XELOX chemotherapy is obtained, so as to recommend or not recommend the treatment plan to the patient (see Table 1).

[0046] Table 1 Prediction and output results of the first group of markers

[0047]

[0048] Group 2

[0049] On the basis of the first group of biomarkers, the protein molecular biomarker ATP5D is added, and the relative expression level of the combination of the eight protein molecular biomarkers (ACOX1, ACTL6A, AIP, AMBP, ATP5D, B4GALNT3, BRD8, BSDC1) is calculated to obtain the prediction accuracy, sensitivity and specificity. In the training set, the prediction accuracy is 100%, the diagnostic sensitivity is 100.00%, and the specificity is 100.00%; in the internal validation set, the prediction accuracy is 100%, the diagnostic sensitivity is 100.00%, and the specificity is 100%. For a patient with gastric cancer to be treated, according to the expression level of the protein molecular biomarker, the output result of different reactions of the patient to the treatment of Herceptin combined with XELOX chemotherapy is obtained, so as to recommend or not recommend the treatment scheme to the patient (see Table 2).

[0050] Table 2 Prediction and output result of the second group of markers

[0051]

[0052] Group 3

[0053] On the basis of the second set, 336 protein biomarkers were expanded (ABCD1, ABCD3, ACADS, ACBD5, ACOX1, ACPP, ACSF2, ACTL6A, ADAP1, ADH4, ADPGK, AGL, AGOl, AIP, AKAP9, ALYREF, AMBP, ANAPC13, ANK2, APIGl, APAF1, APBB1IP, APIP, ARHGAP4, ARHGEF17, ARL1, ARL6IP4, ARRB2, ATP11B, ATP5A1, ATP5D, ATP6V0A2, B3GNT6, B4GALNT3, B4GALT4, BAIAP2, BCKDK, BMS1, BOP1, BRD8, BSDC1, C10orf112, C10orf76, C11orf31, C14orf169, C17orf85, C1orf198, C2orf47, C4orf33, C6orf211, CAB39, CADM1, CANT1, CAPN5, CAPN8, CASP6, CCDC47, CDC42EP1, CDK5RAP3, CEACAM1, CFL2, CHI3L1, CHORDC1, CLDN7, CLIC2, CLTC, CNOT7, COL15A1, COL4A1, COMMD3, COPS4, COPS5, COPS6, COX15, CREG1, CRYL1, CSTB, CSTF2T, CYP2S1, CYTB, DAZAP1, DCTN5, DDAH2, DDHD2, DDR1, DDX27, DDX60, DDX60L, DECR2, DES, DHCR24, DHFR, DHX38, DIDOl, DNAJA1, DNAJC11, DNASE2, DOCK11, DOPEY2, DPYSL4, DSG2, ECM1, EDF1, EIF2AK2, EIF3F, EIF5A, ELF1, ENTPD5, EPHX1, EPS15, EPS8L3, ERAP1, ERBB2IP, ERLIN2, ESYT2, ETAA1, FAM103A1, FAM105B, FAM162A, FAM96A, FIS1, FKBP10, FLG2, FMNL1, FN3KRP, FUT2, GALE, GBA2, GEMIN7, GID8, GMFG, GOLIM4, GOLT1B, GOSR1, GRAP2, GTDC2, GTF2E1, GTF2F1, GUCY1A3, GUK1, HEPH, HERC2, HGD, HLA-A, HNMT, HNRNPA1, HNRNPH3, HNRNPLL, HNRNPR, HSPA4L,HSPA6, HSPB6, HSPB7, HSPE1, HTRA3, ICT1, IFIT5, IL1RN, IMPAD1, INPPL1, INTS9, ISCA2, ITGB5, ITSN2, KDELR1, KDM2A, KHNYN, KIAA0195, KIAA0319L, KIAA1522, KIAA1704, KRT73, KRT80 KXD1, LAMC2, LAX1, LGALS4, LLGL2, LMF2, LPCAT3, LRRC1, LSM2, LSS, MAN2B1, ME1, MED14, MED4, MEMO1, MFSD5, MGST1, MGST3, MLXIPL, MMP7, MNF1, MON2, MPC2, MPV17, MRPL3, MRPL48, MRPS14, MRPS18A, MRVI1, MSLN, MTG1, MTIF2, MUC5AC, NBAS, ND2, NDUFB7, NHEJ1, NIF3L1, NIPSNAP3A, NRAS, NSFL1C, NT5C, OCA2, OCIAD2, OSBPL1A, PAFAH1B3, PARP12, PAWR, PBK, PBX1, PDCD11, PDCD6, PEA15, PFKP, PGLYRP2, PHC2, PIK3CA, PIP4K2B, PKP2, PLIN4, PODN, POLR1A, PPFIBP1, PPIG, PPP1CC, PPP3CC, PRPH, PSMA6, PTDSS1, PTGES3, PTGR1, PTPRCAP, QKI, QSER1, QSOX1, RAB10, RAB3GAP1, RANGAP1, RBM3, RBM39, RBMXL1, RCSD1, RHEB, RHOC, RHOT1, RNF181, RNF25, RPL18A, RPL23, RPP38, RPS4X, RPS6KA3, RRAGC, RUNX1, RUVBL1, S100A16, SCAF11, SCIN, SCRN3, SEL1L3, SEPSECS, SF3A3, SH3BGRL2, SLC25A35, SLC30A5, SLC35D1, SLC44A1, SLC44A2, SMPDL3B, SMTN, SNAP23, SNRPG, SOAT1, SPON1, SRSF2, SRSF9, ST14, ST6GALNAC1, STEAP4, STK17B, STK39, STXBP3, SUGP2, SYK, SYNCRIP, SYNPO, SZRD1, TACSTD2, TAF6, TBCC, TELO2, TEP1, TIMM13, TM9SF2,TMEM45B, TMEM63A, TNFAIP8L2, TNIK, TNRC6B, TOMM6, TP53BP1, TPP2, TRAM1, TRAPPC2L, TRIM14, TSPAN8, TTC39A, TTN, TUFM, UBE2Q1, UBE2S, UBXN6, UQCRQ, USP34, USP9Y, VPS51, VTI1A, WBSCR22, WDR26, WDR81, WIBG, YBX3, and ZNF706), the prediction accuracy of the relative expression levels of the 336 protein biomolecular markers was calculated, including 35 cases of training set, the prediction accuracy was 100%, the diagnostic sensitivity was 100.00%, the specificity was 100.00%, and the remaining 9 cases were internal validation set, the prediction accuracy was 100%, the diagnostic sensitivity was 100%, and the specificity was 100%. For a patient with gastric cancer to be treated, according to the expression level of the protein biomolecular marker, the output result of different reactions of the patient to the Herceptin combined with XELOX chemotherapy is obtained, so as to recommend or not recommend the treatment scheme to the patient (see Table 3),

[0054] Table 3 Prediction and output results of the 3rd group of markers

[0055]

[0056] From the above results, it can be seen that the 7 protein biomolecular markers (see the 1st group) in the clinical sample of the gastric cancer patient are combined, which can be used to predict whether the gastric cancer patient receiving Herceptin combined with XELOX chemotherapy can produce effective treatment effect of tumor regression.

[0057] On this basis, increasing the protein biomolecular markers (such as the 2nd group and the 3rd group) can well predict whether the gastric cancer patient receiving Herceptin combined with XELOX chemotherapy can produce effective treatment effect of tumor regression.

[0058] Example 5 System for predicting treatment effect of gastric cancer platinum combined with fluorouracil drugs

[0059] The system 61 for predicting treatment effect of gastric cancer platinum combined with fluorouracil drugs 52 and the judgment and output module 53 further comprises a data collection module 51. Figure 3 ).

[0060] The data collection module 51 is used to collect the expression data of the biomarker combination in the gastric cancer tissue sample of the patient, and transmit it to the data processing module.

[0061] The data processing module 52 is configured to analyze the received or input expression data of the biomarker combination according to the data analysis method as described in Embodiment 4 to obtain a calculation result. The expression data of the biomarker combination can be collected by the data collection module 51, or can be obtained from other sources.

[0062] The judging and outputting module 53 is configured to judge whether the calculation result meets a preset judging condition, i.e., the sensitive prediction probability is greater than the non-sensitive prediction probability, to predict the effect of the platinum drug combined with the fluorouracil drug in treating gastric cancer, and output a prediction result. In the judging and outputting module, when the expression data meets the judging condition that the sensitive prediction probability is greater than or equal to the non-sensitive prediction probability, the prediction result output is "the platinum drug combined with the fluorouracil drug has a therapeutic effect on gastric cancer"; when the expression data does not meet the judging condition that the sensitive prediction probability is less than the non-sensitive prediction probability, the prediction result output is "the platinum drug combined with the fluorouracil drug does not have a therapeutic effect on gastric cancer".

[0063] Embodiment 6 Electronic device

[0064] The embodiment provides an electronic device which can be in the form of a computing device (for example, can be a server device) including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for predicting the effect of the platinum drug combined with the fluorouracil drug in treating gastric cancer in Embodiment 4 when executing the computer program.

[0065] Figure 4 A hardware structure schematic diagram of the embodiment is shown, and the electronic device 9 specifically includes:

[0066] at least one processor 91, at least one memory 92, and a bus 93 for connecting different system components including the processor 91 and the memory 92, wherein:

[0067] The bus 93 includes a data bus, an address bus, and a control bus.

[0068] The memory 92 includes a volatile memory, such as a random access memory (RAM) 921 and / or a cache memory 922, and can further include a read-only memory (ROM) 923.

[0069] The memory 92 further includes programs / utilities 925 having a set of (at least one) program modules 924, such as an operating system, one or more application programs, other program modules, and program data, each of which or a combination of which can include the implementation of a network environment.

[0070] The processor 91 performs the various functionality application and data processing by running the computer programs stored in the memory 92, such as the data analysis method of embodiment 4 of the present application.

[0071] The electronic device 9 can further communicate with one or more external devices 94 such as a keyboard, a pointing device, etc. through an input / output (I / O) interface 95. Further, the electronic device 9 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with the other modules of the electronic device 9 through the bus 93. It should be appreciated that although not shown, other hardware and / or software modules could be used in conjunction with the electronic device 9, including but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID (Redundant Array of Independent Disks) systems, tape drives, and data archival storage systems, etc.

[0072] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the foregoing detailed description, such division is merely exemplary and not mandatory. Indeed, according to an implementation of the present application, the features and functionalities of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functionalities of one unit / module described above can be further divided into embodied by multiple units / modules.

[0073] Embodiment 7 computer readable storage medium

[0074] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to realize the steps of the method for predicting the treatment effect of platinum combined with fluorouracil drugs on gastric cancer in embodiment 4 of the present application.

[0075] In which, the more specific readable storage medium can include but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device or any suitable combination of the above.

[0076] In possible implementation manners, the present application can also be implemented in the form of a program product, which includes program codes for causing terminal equipment to perform the steps of the method for predicting the treatment effect of platinum combined with fluorouracil drugs on gastric cancer in embodiment 4 of the present application when the program product is run on the terminal equipment.

[0077] Wherein, the program code for executing the present application can be written in any combination of one or more programming languages, and can be executed completely on the user device, partially on the user device, as an independent software package, partially on the user device and partially on a remote device, or completely on a remote device.

[0078] Predictive effect of the combination of less kinds of protein molecular biomarkers

[0079] On the basis of the 7 protein molecular biomarkers in Group 1, the combination of less kinds of protein molecular biomarkers was formed by reducing the kinds of protein molecular biomarkers, and the prediction accuracy of each group was calculated using the same database, analysis method, training set and validation set as in Examples 1-4.

[0080] Group 4

[0081] In the absence of the BSDC1 protein, the marker combination is ACOX1, ACTL6A, AIP, AMBP, B4GALNT3 and BRD8. On the training set, the accuracy is 97.14%, the sensitivity and specificity are 94.12% and 100% respectively; on the test set, the accuracy is 100%, the sensitivity and specificity are 100% and 100% respectively.

[0082] Group 5

[0083] In the absence of the BSDC1 and BRD8 proteins, the marker combination is ACOX1, ACTL6A, AIP, AMBP and B4GALNT3. On the training set, the accuracy is 77.14%, the sensitivity and specificity are 64.71% and 88.89% respectively; on the test set, the accuracy is 88.89%, the sensitivity and specificity are 100% and 83.33% respectively.

[0084] Group 6

[0085] In the absence of the BSDC1, BRD8 and B4GALNT3 proteins, the marker combination is ACOX1, ACTL6A, AIP and AMBP. On the training set, the accuracy is 68.57%, the sensitivity and specificity are 70.59% and 66.67% respectively; on the test set, the accuracy is 88.89%, the sensitivity and specificity are 100% and 83.33% respectively.

[0086] Group 7

[0087] In the case of missing BSDC1, BRD8, B4GALNT3, AMBP, AIP proteins, the marker combination is ACOX1 and ACTL6A. On the training set, the accuracy is 71.43%, the sensitivity and specificity are 52.94% and 88.89% respectively; on the test set, the accuracy is 66.67%, the sensitivity and specificity are 33.33% and 83.33% respectively.

[0088] Group 8

[0089] In the case of missing BSDC1, BRD8, B4GALNT3, AMBP, AIP proteins, the marker combination is ACOX1 and ACTL6A. On the training set, the accuracy is 71.43%, the sensitivity and specificity are 52.94% and 88.89% respectively; on the test set, the accuracy is 66.67%, the sensitivity and specificity are 33.33% and 83.33% respectively.

[0090] This result shows that with the successive deletion of the number of protein in the minimum marker combination, the accuracy of the marker combination prediction decreases, and the sensitivity and specificity also decrease; therefore, the proteins in the marker combination ACOX1, ACTL6A, AIP, AMBP, B4GALNT3, BRD8 and BSDC1 are the essential protein markers to ensure the prediction accuracy, sensitivity and specificity.

[0091] Finally, the above specific implementation methods are only used to illustrate the technical solutions of the present application, but not to limit them.

Claims

1. Use of a reagent for detecting a biomarker combination in the manufacture of a preparation for predicting the therapeutic effect of Herceptin combined with XELOX chemotherapy on gastric cancer; the biomarker combination consists of the following biomarkers: ACOX1, ACTL6A, AIP, AMBP, B4GALNT3, BRD8, BSDC1, and ATP5D; The reagent is a biomolecule that specifically hybridizes with the biomarker combination respectively or simultaneously; or, the reagent is a reagent for genome, transcriptome and / or proteome sequencing for detecting the expression level of the biomarker combination.

2. Use of reagents for detecting a biomarker combination in the manufacture of a preparation for predicting the therapeutic effect of Herceptin combined with XELOX chemotherapy on gastric cancer; the biomarker combination consists of the following biomarkers: ACOX1, ACTL6A, AIP, AMBP, B4GALNT3, BRD8, BSDC1, and ATP5D; The reagent is a biomolecule that specifically hybridizes with the biomarker combination respectively or simultaneously; or, the reagent is a reagent for genome, transcriptome and / or proteome sequencing for detecting the expression level of the biomarker combination.

3. Use of a reagent for detecting a biomarker combination in the manufacture of a preparation for predicting the therapeutic effect of Herceptin combined with XELOX chemotherapy on gastric cancer; the biomarker combination consists of the following biomarkers: ACOX1, ACTL6A, AIP, AMBP, B4GALNT3, BRD8, BSDC1, ABCD1, ABCD3, ACADS, ACBD5, ACPP, ACSF2, ADAP1, ADH4, ADPGK, AGL, AGO1, AKAP9, ALYREF, ANAPC13, ANK2, AP1G1, APAF1, APBB1IP, APIP, ARHGAP4, ARHGEF17, ARL1, ARL6IP4, ARRB2, ATP11B, ATP5A1, ATP5D, ATP6V0A2, B3GNT6, B4GALT4, BAIAP2, BCKDK, BMS1, BOP1, C10orf112, C10orf76, C11orf31, C14orf169, C17orf85, C1orf198, C2orf47, C4orf33, C6orf211, CAB39, CADM1, CANT1, CAPN5, CAPN8, CASP6, CCDC47, CDC42EP1, CDK5RAP3, CEACAM1, CFL2, CHI3L1, CHORDC1, CLDN7, CLIC2, CLTC, CNOT7, COL15A1, COL4A1, COMMD3, COPS4, COPS5, COPS6, COX15, CREG1, CRYL1, CSTB, CSTF2T, CYP2S1, CYTB, DAZAP1, DCTN5, DDAH2, DDHD2, DDR1, DDX27, DDX60, DDX60L, DECR2, DES, DHCR24, DHFR, DHX38, DIDO1, DNAJA1, DNAJC11, DNASE2, DOCK11, DOPEY2, DPYSL4, DSG2, ECM1, EDF1, EIF2AK2, EIF3F, EIF5A, ELF1, ENTPD5, EPHX1, EPS15, EPS8L3, ERAP1, ERBB2IP, ERLIN2, ESYT2, ETAA1, FAM103A1, FAM105B, FAM162A, FAM96A, FIS1, FKBP10, FLG2, FMNL1, FN3KRP, FUT2, GALE, GBA2, GEMIN7, GID8, GMFG, GOLIM4, GOLT1B, GOSR1, GRAP2, GTDC2, GTF2E1, GTF2F1, GUCY1A3, GUK1, HEPH, HERC2, HGD, HLA-A, HNMT, HNRNPA1, HNRNPH3, HNRNPLL, HNRNPR, HSPA4L, HSPA6, HSPB6, HSPB7, HSPE1,HTRA3, ICT1, IFIT5, IL1RN, IMPAD1, INPPL1, INTS9, ISCA2, ITGB5, ITSN2, KDELR1, KDM2A, KHNYN, KIAA0195, KIAA0319L, KIAA1522, KIAA1704, KRT73, KRT80 KXD1, LAMC2, LAX1, LGALS4, LLGL2, LMF2, LPCAT3, LRRC1, LSM2, LSS, MAN2B1, ME1, MED14, MED4, MEMO1, MFSD5, MGST1, MGST3, MLXIPL, MMP7, MNF1, MON2, MPC2, MPV17, MRPL3, MRPL48, MRPS14, MRPS18A, MRVI1, MSLN, MTG1, MTIF2, MUC5AC, NBAS, ND2, NDUFB7, NHEJ1, NIF3L1, NIPSNAP3A, NRAS, NSFL1C, NT5C, OCA2, OCIAD2, OSBPL1A, PAFAH1B3, PARP12, PAWR, PBK, PBX1, PDCD11, PDCD6, PEA15, PFKP, PGLYRP2, PHC2, PIK3CA, PIP4K2B, PKP2, PLIN4, PODN, POLR1A, PPFIBP1, PPIG, PPP1CC, PPP3CC, PRPH, PSMA6, PTDSS1, PTGES3, PTGR1, PTPRCAP, QKI, QSER1, QSOX1, RAB10, RAB3GAP1, RANGAP1, RBM3, RBM39, RBMXL1, RCSD1, RHEB, RHOC, RHOT1, RNF181, RNF25, RPL18A, RPL23, RPP38, RPS4X, RPS6KA3, RRAGC, RUNX1, RUVBL1, S100A16, SCAF11, SCIN, SCRN3, SEL1L3, SEPSECS, SF3A3, SH3BGRL2, SLC25A35, SLC30A5, SLC35D1, SLC44A1, SLC44A2, SMPDL3B, SMTN, SNAP23, SNRPG, SOAT1, SPON1, SRSF2, SRSF9, ST14, ST6GALNAC1, STEAP4, STK17B, STK39, STXBP3, SUGP2, SYK, SYNCRIP, SYNPO, SZRD1, TACSTD2, TAF6, TBCC, TELO2, TEP1, TIMM13, TM9SF2, TMEM45B, TMEM63A, TNFAIP8L2,TNIK, TNRC6B, TOMM6, TP53BP1, TPP2, TRAM1, TRAPPC2L, TRIM14, TSPAN8, TTC39A, TTN, TUFM, UBE2Q1, UBE2S, UBXN6, UQCRQ, USP34, USP9Y, VPS51, VTI1A, WBSCR22, WDR26, WDR81, WIBG, YBX3, and ZNF706; The reagent is a biomolecule that specifically hybridizes with the biomarker combination respectively or simultaneously; or, the reagent is a reagent for genome, transcriptome and / or proteome sequencing for detecting the expression level of the biomarker combination.

4. Use according to any one of claims 1 to 3, wherein The biomolecule is a primer, a probe and / or an antibody.

5. The application as described in any one of claims 1-3, characterized in that, The preparation is a diagnostic product.

6. A system for predicting the therapeutic effect of a combination of Herceptin and XELOX chemotherapy on gastric cancer, characterized by, The system comprises: a data processing module for receiving or inputting the expression data of the biomarker combination in the application of any one of claims 1-3 of a gastric cancer patient; a judging and outputting module for, after the receiving or inputting is completed, processing the expression data by Firmiana software, presetting a machine learning algorithm based on a generalized linear regression model to construct a prediction model, respectively predicting the sensitive prediction probability and the insensitive prediction probability of the gastric cancer patient, judging whether the expression data meets a preset judging condition, to predict the treatment effect of Herceptin combined with XELOX chemotherapy on gastric cancer, and outputting a prediction result; In the judging and outputting module, when the expression data meets the judging condition, the judging condition is that the sensitive prediction probability is greater than or equal to the insensitive prediction probability, and the output prediction result is "Herceptin combined with XELOX chemotherapy has a treatment effect on gastric cancer"; when the expression data does not meet the judging condition, i.e., the sensitive prediction probability is less than the insensitive prediction probability, the output prediction result is "Herceptin combined with XELOX chemotherapy does not have a treatment effect on gastric cancer".

7. The system of claim 6, wherein, In the preset, the training set parameter is set to 80%, and the validation set parameter is set to 20%.

8. The system of claim 6, wherein, The system further comprises a data collection module for collecting the expression data of the biomarker combination in a gastric cancer tissue sample of a patient and transmitting it to the data processing module.

9. A computer-readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, can implement the functions of the system of any one of claims 6-8.

10. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor is configured to execute the computer program to implement the functions of the system of any one of claims 6-8.

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