Screening method of disease-related genes and construction method of animal model

Abstract

The invention provides a screening method of disease-related genes. The screening method comprises the following steps: 1) constructing a data set containing gene expression profiles of diseased and non-diseased samples; 2) according to the data set in the step 1), establishing a linear model for the expression data of each gene in the gene expression profile and whether the gene is diseased or not; and 3) according to the slope of the linear model of each gene obtained in the step 2), screening out differentially expressed genes which are the disease-related genes. Known regulatory network information and whole genome expression data are already combined through a genotype-phenotype relationship network model, the causal relationship from genes to gene products to phenotypes can be quantified through a network mathematical model, and the causal influence of singlet or polymorphic genes is simulated.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a method for screening disease-related genes and a method for constructing an animal model. Background Art

[0002] Preeclampsia is a common type of hypertensive disorder during pregnancy. Internationally, it is defined as new-onset hypertension after 20 weeks of gestation accompanied by proteinuria or maternal organ or uteroplacental dysfunction. Preeclampsia puts both mother and fetus at serious risk. For the mother, this leads to a 2-4 times increased risk of long-term hypertension, a 2-fold increased risk of cardiovascular death and major cardiovascular events, and a 1.5-fold increased risk of stroke. For the fetus, this leads to antenatal risks of intrauterine growth restriction (IUGR), oligohydramnios, preterm birth (most often due to human factors), placental abruption, fetal distress, and fetal death.

[0003] Preeclampsia progresses in two stages: Stage I: early placental dysfunction and Stage II: late multiple organ dysfunction. The main risk factors for preeclampsia include: history of preeclampsia, chronic hypertension, gestational diabetes, multiple pregnancy, antiphospholipid syndrome and obesity. For the severe clinical phenotype of preeclampsia (stage II), it includes pregnancy-induced hypertension, proteinuria, renal complications, liver damage, neurological complications, hematological complications and uteroplacental complications.

[0004] Other factors that increase the risk of preeclampsia include systemic lupus erythematosus, history of stillbirth, fasting, previous placental abruption, assisted reproductive technology, chronic kidney disease, and advanced maternal age. For details, please refer to Table 1

[0005] Table 1: Standard description of pre-eclampsia.

[0006]

[0007] Some additional risk factors include polycystic ovary syndrome, sleep-disordered breathing, various infections such as periodontal disease, urinary tract infection, and Helicobacter pylori. The search for genetic markers of preeclampsia has been conducted. A genome-wide association study of 4380 patients with the disease and 310238 controls found that a locus near the FLT1 (FMS-like tyrosine kinase 1) gene was involved in the development of preeclampsia. Some literature has reported those risk factors and pathogenesis of preeclampsia, such as the literature. The pathogenesis of preeclampsia has been described. Increased angiogenic markers such as soluble FMS-like tyrosine kinase-1 (sFlt-1) and soluble Englin (sEng). sFlt-1 reduces the levels of vascular endothelial growth factor (VEGF) and placental growth factor, both of which are important regulators of endothelial cell function. sEng is a core receptor on the cell surface that can reduce the level of transforming growth factor (TGF)-β, which is responsible for the migration and proliferation of endothelial cells. All these factors contribute to endothelial dysfunction, vasoconstrictive states, oxidative stress, and microembolism involving multiple organ systems. However, no focus has been given to the genetic and molecular functions underlying severe preeclampsia in the current state of the art. Next-generation sequencing, i.e., massively parallel sequencing technologies that enable affordable analysis of large genomic regions, is a promising tool to investigate genetic influences on preeclampsia. Utilizing genome-wide gene expression profiles associated with clinical phenotypes will show the unbiased nature of systematic analysis and identify key disease genes for severe preeclampsia, which will pave the way for further drug development.

[0008] Current approaches to managing preeclampsia include pre-pregnancy counseling, perinatal blood pressure control and monitoring, perinatal aspirin therapy for high-risk women, betamethasone for patients <34 weeks, parenteral magnesium sulfate, and postpartum blood pressure follow-up. Timely delivery of the fetus is currently the only established form of treatment. The International Society for the Study of Hypertension in Pregnancy recommends exercise to reduce / prevent the occurrence of preeclampsia, but it is not consistently supported by multiple studies. To treat severe preeclampsia, antihypertensive drugs provide good results in reducing blood pressure in preeclampsia. Anticonvulsant drugs, such as magnesium sulfate, pravastatin, and metformin, have been proposed for preventing seizures in preeclampsia. Pravastatin has shown promising results in mouse models. A clinical trial in humans is underway. Corticosteroids promote lung development in infants before delivery. Preliminary data on the effects of metformin on obese pregnant women look promising. Severe preeclampsia is a complex, multifactorial disease and one of the leading causes of death during pregnancy, and there is currently no effective treatment strategy. This is mainly due to the lack of reliable preclinical models that replicate severe preeclampsia disease in humans.

[0009] In summary, there is an urgent need for a method to identify the key disease genes of preeclampsia and to further construct a model that replicates severe human preeclampsia. Summary of the invention

[0010] Therefore, the technical problem to be solved by the present invention is to provide a method for determining the key disease genes of pre-eclampsia. The present invention adopts the following technical solution:

[0011] A method for screening disease-related genes comprises the following steps:

[0012] Step 1) constructing a dataset containing gene expression profiles of diseased and non-disease samples;

[0013] Step 2) establishing a linear model between each gene expression data in the gene expression profile and whether the disease occurs according to the data set in step 1);

[0014] Step 3) According to the slope of the linear model of each gene obtained in step 2), differentially expressed genes are screened out, and the differentially expressed genes are the disease-related genes.

[0015] Optionally, in step 1), multiple data sets are constructed; in step 2), linear models are established for each gene expression data in each data set and whether it is a pre-eclampsia sample phenotype; in step 3), genes that are differentially expressed in each data set are screened out according to the slope of the linear model of each gene.

[0016] Optionally, the step 2) specifically comprises the following steps: using each gene expression data in the gene expression profile as a predictor variable and whether the disease is present as a response variable; and setting the value of the diseased sample phenotype to 1 and the value of the non-disease phenotype to 2, to establish a linear model.

[0017] Optionally, the step 3) specifically includes the following steps: expanding the slope value of the line model of each gene to the range of [-1,1] to obtain a score value for each gene, comparing the score value with a threshold value θ representing a significant difference, and genes with a score value greater than the threshold value are differentially expressed genes.

[0018] Optionally, the disease is pre-eclampsia.

[0019] Optionally, the data set of step 1) also includes data on clinical phenotypes of pre-eclampsia samples and non-pre-eclampsia samples;

[0020] The clinical phenotype data include at least one of diagnosis result, average uterine pi, average umbilical cord pi, maximum systolic blood pressure, maximum diastolic blood pressure, proteinuria pattern, HELLP diagnosis, and gestational age;

[0021] The step 2) also includes establishing a linear model between each gene expression data in the gene expression profile and each clinical phenotype data;

[0022] After the differentially expressed genes are screened out in step 3), a scoring matrix is ​​established; the rows and columns in the scoring matrix represent the differentially expressed genes and the types of clinical phenotypes, respectively, and the slope of the linear model obtained in step 2) is filled in the corresponding position in the scoring matrix; the scoring matrix is ​​subjected to a dichotomous clustering analysis to classify the clinical phenotypes.

[0023] Optionally, pathway analysis of the screened disease-related genes is also included.

[0024] The present invention also proposes a gene set obtained based on the above screening method, wherein the gene set comprises the following genes: ANG, C7orf49, DONSON, SLC38A10, DSCR4, HERPUD1, PDLIM2, HSPA2, LACTB, ADAM17, ANO8, GNPTG, DDIT4L, TMCC3, PMEL, PGRMC1, VASN, PXN, PGK1, FGD6, BCAP31, KRT15, ​​SPOCK2, VWA1, MT1F, BIN2, DDR1, RPS19BP1, PJA1, DAB2IP, KIAA1217, RANBP2, CD9, ISM2, CCDC 127, TMEM141, DDX58, PFKFB3, NEBL, HPCA, EPHB3, MED12L, SEMA3G, REEP4, HSPG2, CCDC115, GALNT18, LRRC32, GRHL3, KRT7, KLHDC7A, METTL6, FABP4, SH 3BP2, TNNI2, LAIR2, ARSK, HRASLS2, LAMC2, FAM219A, GPER, GREB1L, CLDN5, MIDN, DNAJB2, ITGB6, MAPK8IP3, FPGS, SIPA1L1, VEGFC, MAP3K8, OXGR1, SPIC E1, PTPN12, ADRA2C, RIOK1, DLX5, SHANK2, ATP6V0E1, JUNB, NANS, LIMK1, GFOD2, ZFP36L1, CKMT2, TPM1, SFR1, STC2, PTGES, ATG16L2, FBLIM1, UBR1, SLC 2A5, CDK19, PWWP2B, SSR1, PPARG, CEBPA, DEGS1, PFKL, TCF7L2, TNR, ERP44, GSE1, DCP2, ABCA12, GPR32, IRF9, TNIP2, ZBTB2, FOXJ3, CA4, JUP, CAV3, FGF2 2. PDIA5, KCTD11, FAM8A1, BCAR3, AP3S1, IFI44L, STK11IP, DVL1, LBH, SNX9, FN1, NLRP2, MTMR4, TMEM179B, VIT, ANKRD23, ANXA4, ELMO3, PFKP, SSFA2, S LC11A2, MAP3K9, B9D2, POLR2H, INSIG1, FOSL2, DOCK5, UBASH3B, SLC35F2, SYN3, MAPK11, IL1R2, DGKZ, HMOX1, ELF3, SNX33, LY6D, AHNAK, GATA3, MAN1C1,<h2 style=";text-align:left;direction:ltr">ABCA7, CXXC1, KCTD9, AVL9, ISLR2, DNAH11, PREP, JPH1, ASTE1, IGSf3, CLDN9, NARF, SH3RF1, UPK1B, BMPR2, PDK1, BCAT2, SWAP70, SERPINE1, SLC2A1, EB I3,PMEPA1,TRIP10,SLC28A1,AMOTL2,MORF4L2,RHOB,SLC38A3,TIMP3,NLR P7,CITED2,C5orf30,PFKFB4,SLC2A3,BASP1,FAM184A,HPCAL1,RIMKLA,FBX O22,PLAC8,EPHA1,KLF4,ANKRD13B,CYP11A1,STX1A,MFAP5,GLA,ELF4,SUV 39H1,SLC1A6,EFHD1,HN1L,FAM156A,P2RY2,RAB3D,IRF2BPL,CSNK2A2,RHOF ,OAS3,TGM1,HK1,STK24,RASSF7,CCRN4L,KIF17,C14orf37,CCDC113,MYCN ,SLCO4A1,AQP1,RARRES3,LYN,MED13L,BTNL9,MBD2,ASCL2,ALPK3,KIT,PRR 15,FAM43A,AGRN,CRKL,PAM,RHOU,MIIP,PPP1R16B,FOXJ2,PTPRU,FKBP2,M MP14,PLEC,CRH,IL1RAP,TMEM59,PADI1,VTCN1,GLIS3,PLBD1,PNCK,BAD,R NF24,SUN2,SPTLC3,ING1,CPOX,ARNT2,GREM2,UGP2,DNAJC1,ACSF2,HEY1, USP18,EFNA1,IGSF8,FRMD4A,MAML2,SLC16A1,KIAA1919,HPS3,PPP1R3B,TN FAIP8,RAB25,MIF,FKBP8,TREM1,FAM120AOS,PPL,ITGA5,HMGN3,EPS8L1,R ASSF8,F11R,C4orf3,KIF2A,MXI1,MTUS1,GALE,GAPDH,RUSC2,HCAR2,TPI1, LTBP1,GPX3,KLF3,TES,JPH2,TMEM66,SLC2A14,CDO1,NEK6,PARD3,RASGRF 2,HMGB3,SEC23A,TMEM184A,GSN,RAB8B,ILVBL,SLC16A3,MOB3A,SERPINB9,<h2 style=";text-align:left;direction:ltr">EDEM2,SFRP1,PPP1R12C,TRIM24,ANKRD37,ARID3A,LEPROTL1,KDM3A,CSNK 1E,FHL2,CCSAP,NEURL,S100A6,CXCR6,FUT11,DSP,EAF1,GALK1,FAM120A, ERRFI1, VWCE, ALDOA, LRRC8A, AK4, ZMYND8, EZR, TACC1, NTRK2, ADAM9, ADORA2A, HTRA1, GUCA2A, GPR17, ARDC3, EGLN3, PRKAB2, SERPINA3, PRSS22, EGLN 1,C5orf46,MID1,GPT2,PTBP3,LRRC1,NRIP1,PAPPA2,CCBP2,ARHGEF4,MGA T3,AIF1L,SLCO2A1,KRT17,NOG,PLOD2,DUSP1,PKM,ASPHD2,PLIN2,CYP2J2 ,QPCT,HILPDA,RDH13,NEK11,CORO2A,GRHL1,SCARB1,GALNT2,DNM2,PIK3A P1,SYDE1,PRRG1,PIK3R1,LIMCH1,UBE2Q2,GDPD3,HK2,MLL5,TLE6,GPR146, BTD, PHYHIP, MYOF, PRKD3, ZCCHC2, CALM1, SIGLEC6, BCL6, LDHA, RASEF, HEXB, ENG, INHA, INHBA, MME, PVRL4, BACH1, PLA2G16, TSC22D1, TRIM14, TBC1D2 2A, CCBL1, WDR60, LIMD1, NPNT, TMEM45A, SEMA4C, IPMK, ERGIC1, MTSS1L, BHLHE40, SLC6A8, RFK, CST6, DIO2, C12orf75, PROCR, PIK3CB, ERO1L, LEP, SFXN 3,SH3BP5,MYO7B,MICAL3,KRT19,HTRA4,TPBG,SPAG4,EFNB1,SASH1,OCRL, SMARCA1,P4HA1,NDRG1,SH3PXD2A,COL17A1,FSTL3,FLNB,FLT1,ELMO1,TRPV 6,EPHB6,HSD17B1,ARL4C,TMEM139,PKD2,TRIM40,SEMA3D,PDE7B,SLC20A1 ,HIST1H1T,ANXA6,BTG2,FBN2,ASAH1,SLC25A27,RAB15,ZC2HC1A,TMEM136,<h2 style=";text-align:left;direction:ltr">KLRD1,TMEM168,CAP2,HSD11B2,SELE,STON2,SCN9A,ABCD3,PISD,PPAP2B, F13A1,CYTH3,OLFML3,FAM101B,SOD1,CSHL1,LRRC4,SNX27,CYBASC3,SOX5 ABAT, ADAMTSL1, C18orf8, LEPREL1, LRIG3, B3GNT2, ABCG2, GRIK2, DISP1, CNTN5, GABARAPL1, CATSPERB, ARPC5, PLCXD2, SLC16A10, DAB2, TKT, CD28, MO RC4, EGFL6, CADM3, KCTD3, SLCO2B1, SYNPO2L, SLC23A2, PXK, SLC9A9, PDE5A, IPO13, GCLM, OPRK1, ATP8B4, DNMT3A, LAMA2, IL12RB2, GSTA3, C12orf39, RN F114,MTSS1,TMEM133,CDYL2,MOB3C,CLDN16,C10orf10,ECM1,ACSS1,NPC1 ,IGF1,SLAMF1,ATP1A1,HSD3B2,NUFIP2,LRRN3,ADAP2,MAF,LSS,EML2,POST N,RAPH1,SPATA6,LAMC1,APLN,KL,ANK3,TRAK2,WWC1,CPEB4,MAPK8,GNG2, GOT1,FAM20B,ZNF555,AKIP1,SPON1,TLR7,FAM84B,PRKCE,KCNIP3,RCAN1, HDAC9,RALYL,THSD4,ESRRG,HNRNPA0,IL36RN,CLIC5,VNN3,ST5,ACO1,LGI 2,LCP1,CYBB,TXNRD1,KCNIP1,SH3BGRL2,TRAF3,ZNF554,MRS2,KLHL3,ADCY 7,RAD54L2,HPGDS,PLEKHO2,UNC5C,HLF,RAP1GAP2,UBALD2,CD200R1,ATRN L1,ENPP1,LGALS14,NFIA,ZNF682,PSEN2,DOCK8,C5,SIK3,MKNK1,FES,BMX, SPRED1,ZBTB39,FOXRED2,ZMYND11,GPR34,TNFRSF11A,UBB,OR51B5,FOLR2 ,SLC19A3,CLCN5,TGFBR3,ABHD3,PMAIP1,TOM1L1,OR51B4,MRPS30,NFE2L2,<h2 style=";text-align:left;direction:ltr">SLC2A12,SCG2,CYSLTR1,MYO1A,VCP,OTUB2,NCEH1,SLIT2,KIAA0226L,PIT PNA,LYPD6,PLAC1,HMGN2,HIST2H2BE,SUPT7L,DCAF11,PCBD1,GRINA,CABL ES1, C5orf42, ALDH4A1, RAB5C, FRZB, SFXN4, CHKA, APMAP, SLC26A2, CERS2, ATXN2, MAP7D2, NEDD4L, ITPR1, ENPP2, DSN1, IQGAP2, FRY, LYVE1, MB21D2, LA MB2,CARD16,GSTK1,GSK3B,MATN2,MFF,NALCN,PIKFYVE,KIRREL,TBC1D9,P DPN,TMEM55A,ICA1,APBB2,ARHGAP17,PIK3CG,BMP5,SOX6,PSMC5,AFF3,FGF 10,GUCY1A3,RBM47,PRKG1,GLUL,ASB2,STRADA,CCND2,FILIP1,ST3GAL4,L ANCL2,SLC37A1,PDZRN3,TSPAN33,VAV1,DEPDC5,PER3,WNT2,CLDN1,RUNX1T 1,AMPD3,OR51B2,CYP4V2,IDO1,PC,XPO6,ZNF583,ZNF641,TYRO3,CYTH4,N SMAF,NCKAP1L,PTPN1,FRMD6,SRPX,ELK1,ACSS3,PIAS2,KLF12,TM9SF3,STX BP6,BHLHE41,FLRT3,PTPRD,TMEM106C,ZNF610,PHTF2,ZEB2,HSPB11,P2RY 1,RAB3IL1,CRYAB,ABCA11P,LAMC3,TMPRSS3,FMO1,SLC4A4,BMP4,RBBP4,DU SP4,CXorf57,HYOU1,KLHL42,ANGPTL2,PARP6,AGPAT5,WDSUB1,ABCA9,ATP 5B,C11orf63,DUS2L,PIGX,ZNF542,DACH1,LRRTM2,LRRC39,SEC22C,LINC00 472, TSPAN7, GABRA3, CHN1, ASPA, FXYD3, PRTFDC1, GUCY1A2, ANKS1B, SLC25A6, AIF1, PDHA1, BIN1, PCOLCE, DLG2, VTN, FXN, CTLA4, SLC17A5, APBA1, NINTC2N, PTP4A1, TXK, MAD2L2, FURIN, ERCC6, SGCB, OSBPL11, RDH10, LONRF2, TMEM176A, EID1, VAV3, SLC35F6, K CNJ5, CX3CR1, TMEM86A, NPL, MAT2A, HDAC3, GRHPR, SPEF2, ST6GAL1, BNC2, PRKCD, CLTC, LRRC42, DYRK2, KLC2, PROM1, SLC18B1, NCOA2, SLC16A4, PRKCZ, SEMA6A, MSC, ATP9B, XPNPEP2, APBB3, PALLD, KITLG, CHST10, LRRC41 , WDR11, LGALS13, GAP43, ACO2, CAPRIN2, AZIN1, MYLK, GUCY1B3, POMGNT1, HSD17B2, GABRB1, PIP5K1B, MSRB2. ,

[0025] The present invention also proposes a gene set obtained based on the above screening method, wherein the gene set comprises the following genes: AHNAK, ANG, ANXA4, BCAR3, BCL6, BMPR2, BTD, CALM1, CRH, CSNK2A2, CXXC1, CYP11A1, DCP2, DDR1, DUSP1, DVL1, EBI3, EFHD1, ELF3, ENG, EPS8L1, ERO1L, FAM120A, FGD6, FKBP2, FLT1, GALE, GALNT2, GATA3, GFOD2, GREM2, HEY1, HTRA1, IL1RAP, ILVBL, INHA, INHBA, ITGA5, JUNB, KIAA19 19, KIF2A, KLF4, KRT19, LEP, LRRC1, MAN1C1, MBD2, MTMR4, NDRG1, NEBL, NEK11, OCRL, PAM, PAPPA2, PDLIM2, PHYHIP, PIK3AP1, PJA1, PPL, PROCR, PRRG1, PVRL4, RA B25, RASSF7, SCARB1, SH3BP5, SH3PXD2A, SIPA1L1, SLC11A2, SLC2A1, SLC35F2, SLC6A8, SLCO2A1, SLCO4A1, SPAG4, SSFA2, SYDE1, TBC1D22A, TNIP2, TPBG, ZMYND8.

[0026] The present invention also provides a pre-eclampsia diagnostic marker, which comprises at least one of the genes NEU1, NEU2, NEU3, and NEU4.

[0027] Optionally, the pre-eclampsia diagnostic marker comprises the gene NEU1.

[0028] The present invention also proposes the use of the above gene set and pre-eclampsia diagnostic markers in preparing a product for diagnosing pre-eclampsia.

[0029] This application also proposes a method for constructing a disease animal model, comprising the following steps:

[0030] Constructing data sets of gene expression profiles of diseased and non-disease samples of humans and animals respectively; co-clustering the data sets of gene expression profiles of diseased and non-disease samples of humans and animals by coupled non-negative matrix decomposition to obtain coupled gene clusters in the human and animal data sets;

[0031] Determining transferable genes between humans and animals based on the coupled gene cluster, the screening results of the above-mentioned disease-related gene screening method, and the gene expression levels of animals;

[0032] The disease animal model is constructed based on the transferable gene.

[0033] Optionally, the animal is a mouse.

[0034] The present application also proposes the use of the disease animal model obtained by the above-mentioned disease animal model construction method in verifying the effective treatment identification of pre-eclampsia.

[0035] The technical solution of the present invention has the following advantages:

[0036] The present invention has begun to combine known regulatory network information with whole genome expression data through a network model of genotype-phenotype relationship. The network mathematical model can quantify the causal relationship from gene to gene product to phenotype and simulate the causal influence of monomorphic or polymorphic genes.

[0037] The present invention proposes that the NEU gene can be used as a diagnostic marker for preeclampsia, and proposes the feasibility of a new preeclampsia model based on dysfunction of the key vascular protein NEU1 (an important protein involved in the development of new blood vessels). In the future, this model can be used to test patients and develop new therapies for these important mechanisms of preeclampsia. This application determines that NEU1 is transferable between human and mouse models based on a method of coupled non-negative matrix decomposition. Based on this gene, a reliable preclinical model can be constructed to replicate severe human preeclampsia. By combining network and quantitative gene expression variation with phenotypic modeling, it is found that key genes on the network are more likely to contribute to disease or adaptive progression. These genes for severe preeclampsia are recommended as potential druggable targets. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Interventions to reduce the risk of pre-eclampsia.

[0039] Figure 2 Example of GLM analysis for FLT1 gene and PE classification phenotype;

[0040] Figure 3 This is the cluster analysis result diagram of 844 genes;

[0041] Figure 4 This is the result diagram of IPA and KEGG analysis;

[0042] Figure 5 translational models for gene expression fitting from mouse to human;

[0043] Figure 6 for the co-clustering relationship between human and mouse gene expression;

[0044] Figure 7 It is the structure of vascular endothelial glycocalyx (VEGLX);

[0045] Figure 8 This is a complete picture of the vascular endothelial glycocalyx;

[0046] Fig. 9 This is the clustering result diagram of the rating matrix S2. DETAILED DESCRIPTION

[0047] The following examples are provided for a better understanding of the present invention, but are not intended to limit the best mode of implementation, nor to limit the content and protection scope of the present invention. Any product identical or similar to the present invention obtained by anyone under the inspiration of the present invention or by combining the features of the present invention with other prior arts shall fall within the protection scope of the present invention.

[0048] If no specific experimental steps or conditions are specified in the examples, the conventional experimental steps or conditions described in the literature in the field can be used. If no manufacturer is specified for the reagents or instruments used, they are all conventional reagent products that can be obtained commercially.

[0049] Example 1

[0050] This embodiment provides a method for screening disease-related genes, comprising the following steps:

[0051] Step 1) constructing a dataset containing gene expression profiles of diseased and non-disease samples;

[0052] Analysis of gene expression changes has great potential in dissecting gene regulatory networks that control important biological processes. Network models of genotype-phenotype relationships have begun to combine known regulatory network information with genome-wide expression data, with the goal of predicting the phenotype of an organism. Here, a phenotype is a "high-dimensional" entity: a combination of clinical phenotypes and transcriptional readouts associated with a specific combination of gene expression. Network mathematical models can quantify the causal relationship from gene to gene product to phenotype, and they can simulate the causal effects of single or polymorphic genes, which suggests that key gene expression variations may be more likely to affect complex clinical phenotypic variation. In order to identify genes involved in severe preeclampsia, 263 preeclampsia patients were collected and linked to a preeclampsia mouse model. By integrating the patient's gene expression variation, including fine-grained as well as genome-wide RNA profiling and connecting with clinical phenotypes, the network and quantitative gene expression variation and phenotypic modeling were integrated, and it was found that key genes on the network are more likely to contribute to disease or adaptive progression. These genes for severe preeclampsia are recommended as potential druggable targets

[0053] Four datasets (3 human and 1 mouse) from the Gene Expression Omnibus Database were used. The contents of these datasets are as follows:

[0054] GSE75010 contains gene expression profiles of 157 human placental tissue samples. Of these 157 samples, 80 are preeclampsia (PE) samples and 77 are non-PE samples. In addition to gene expression data, this dataset also contains 29 clinical phenotypes such as diagnosis, mean uterine PI, mean cord PI, maximum systolic blood pressure, maximum diastolic blood pressure, proteinuria pattern, HELLP diagnosis, and gestational age.

[0055] GSE25906 contains expression profiles of 60 human placenta samples, 23 of which are preeclampsia samples and 37 are control samples. This dataset contains 4 phenotypic features such as sex, classification, gestational age, and induction of labor.

[0056] GSE14722 contains 46 human basal plate biopsy samples, 22 preterm births and 24 severe preeclampsia samples. This dataset does not contain any clinical phenotypes.

[0057] GSE116751 contains expression profiles of 10 pregnant mouse placenta samples. Of these 10 samples, 4 are saline-perfused (control) samples and 6 are AVP-perfused (preeclampsia) samples.

[0058] Step 2) establishing a linear model between each gene expression data in the gene expression profile and whether the disease occurs according to the data set in step 1);

[0059] A general linear model (GLM) was used to associate each gene with the clinical phenotype. i ) i As a predictor variable, the phenotypic characteristics pf j As the response variable, a general linear model was established as shown in the following formula (1):

[0060]

[0061] If there are N genes in the dataset, N such models are generated for each phenotypic feature. All N slope values ​​are scaled to the range [-1,1]. Let sb i,j .express Therefore, sb i,j Can be used as gene i Phenotype pf j score.

[0062] exist Figure 6 In the figure, you can see a scatter plot of FLT1 gene expression and sample diagnosis (PE / non-PE) for the GSE75010 dataset. The diagnosis phenotype can have two values: non-PE = 1 and PE = 2. The GLM constructed on this gene and diagnosis phenotype has the following form:

[0063] y=-2.558+0.359x (2)

[0064] Step 3) According to the slope of the linear model of each gene obtained in step 2), differentially expressed genes are screened out, and the differentially expressed genes are the disease-related genes

[0065] Still taking formula (2) as an example, where x is gene expression, y is diagnosis, and 0.359 is the slope term of the gene. Expanding this slope value to the range of [-1,1], the score obtained is 0.6590. In other words, the phenotypic diagnostic score of the FLT1 gene is 0.6590.

[0066] When the phenotype is "diagnosis" and the available values ​​are pre-eclampsia (PE) and non-pre-eclampsia (non-PE), a threshold θ is used to filter genes. i,j |>θ then gene g i The present invention sets the threshold value θ according to the statistical p value of the model. When θ is equal to 0.13, the corresponding statistical p value is 0.05; when θ is equal to 0.35, the corresponding p value is 0.0001.

[0067] In order to further understand the relationship between differentially expressed genes and clinical phenotypes, we first constructed a scoring matrix S1 F×D Where F is the number of clinical phenotypes and D is the number of differentially expressed genes. Thus, the matrix S1 F×D Each entry in is the score of the gene in that column for the phenotype of that row. Based on S1 F×D . Perform dichotomy on the following content. In the dichotomy results, you can see which phenotypes are grouped together and their associations with genes.

[0068] Calculate the differentially expressed genes of all three human datasets (GSE75010, GSE25906, and GSE14722). Among them, a gene set containing 844 genes was selected from the GSE75010 dataset:

[0069] <h2 style=";text-align:left;direction:ltr">ANG,C7orf49,DONSON,SLC38A10,DSCR4,HERPUD1,PDLIM2,HSPA2,LACTB,A DAM17,ANO8,GNPTG,DDIT4L,TMCC3,PMEL,PGRMC1,VASN,PXN,PGK1,FGD6,BC AP31,KRT15,SPOCK2,VWA1,MT1F,BIN2,DDR1,RPS19BP1,PJA1,DAB2IP,KIA A1217,RANBP2,CD9,ISM2,CCDC127,TMEM141,DDX58,PFKFB3,NEBL,HPCA,EP HB3,MED12L,SEMA3G,REEP4,HSPG2,CCDC115,GALNT18,LRRC32,GRHL3,KRT 7,KLHDC7A,METTL6,FABP4,SH3BP2,TNNI2,LAIR2,ARSK,HRASLS2,LAMC2,FA M219A,GPER,GREB1L,CLDN5,MIDN,DNAJB2,ITGB6,MAPK8IP3,FPGS,SIPA1L 1,VEGFC,MAP3K8,OXGR1,SPICE1,PTPN12,ADRA2C,RIOK1,DLX5,SHANK2,ATP 6V0E1,JUNB,NANS,LIMK1,GFOD2,ZFP36L1,CKMT2,TPM1,SFR1,STC2,PTGES ,ATG16L2,FBLIM1,UBR1,SLC2A5,CDK19,PWWP2B,SSR1,PPARG,CEBPA,DEGS1 ,PFKL,TCF7L2,TNR,ERP44,GSE1,DCP2,ABCA12,GPR32,IRF9,TNIP2,ZBTB2 ,FOXJ3,CA4,JUP,CAV3,FGF22,PDIA5,KCTD11,FAM8A1,BCAR3,AP3S1,IFI44 L,STK11IP,DVL1,LBH,SNX9,FN1,NLRP2,MTMR4,TMEM179B,VIT,ANKRD23,A NXA4,ELMO3,PFKP,SSFA2,SLC11A2,MAP3K9,B9D2,POLR2H,INSIG1,FOSL2,D OCK5,UBASH3B,SLC35F2,SYN3,MAPK11,IL1R2,DGKZ,HMOX1,ELF3,SNX33,LY 6D,AHNAK,GATA3,MAN1C1,ABCA7,CXXC1,KCTD9,AVL9,ISLR2,DNAH11,PREP,<h2 style=";text-align:left;direction:ltr">JPH1,ASTE1,IGSF3,CLDN9,NARF,SH3RF1,UPK1B,BMPR2,PDK1,BCAT2,SWAP 70,SERPINE1,SLC2A1,EBI3,PMEPA1,TRIP10,SLC28A1,AMOTL2,MORF4L2,RH OB,SLC38A3,TIMP3,NLRP7,CITED2,C5orf30,PFKFB4,SLC2A3,BASP1,FAM1 84A,HPCAL1,RIMKLA,FBXO22,PLAC8,EPHA1,KLF4,ANKRD13B,CYP11A1,STX1 A,MFAP5,GLA,ELF4,SUV39H1,SLC1A6,EFHD1,HN1L,FAM156A,P2RY2,RAB3D ,IRF2BPL,CSNK2A2,RHOF,OAS3,TGM1,HK1,STK24,RASSF7,CCRN4L,KIF17,C 14orf37,CCDC113,MYCN,SLCO4A1,AQP1,RARRES3,LYN,MED13L,BTNL9,MBD 2,ASCL2,ALPK3,KIT,PRR15,FAM43A,AGRN,CRKL,PAM,RHOU,MIIP,PPP1R16B ,FOXJ2,PTPRU,FKBP2,MMP14,PLEC,CRH,IL1RAP,TMEM59,PADI1,VTCN1,GL IS3,PLBD1,PNCK,BAD,RNF24,SUN2,SPTLC3,ING1,CPOX,ARNT2,GREM2,UGP2 ,DNAJC1,ACSF2,HEY1,USP18,EFNA1,IGSF8,FRMD4A,MAML2,SLC16A1,KIAA 1919,HPS3,PPP1R3B,TNFAIP8,RAB25,MIF,FKBP8,TREM1,FAM120AOS,PPL,I TGA5, HMGN3, EPS8L1, RASSF8, F11R, C4orf3, KIF2A, MXI1, MTUS1, GALE, GAPDH, RUSC2, HCAR2, TPI1, LTBP1, GPX3, KLF3, TES, JPH2, TMEM66, SLC2A14, CDO 1,NEK6,PARD3,RASGRF2,HMGB3,SEC23A,TMEM184A,GSN,RAB8B,ILVBL,SLC 16A3,MOB3A,SERPINB9,EDEM2,SFRP1,PPP1R12C,TRIM24,ANKRD37,ARID3A,<h2 style=";text-align:left;direction:ltr">LEPROTL1,KDM3A,CSNK1E,FHL2,CCSAP,NEURL,S100A6,CXCR6,FUT11,DSP,EAF1,GALK1,FAM120A,ERRFI 1,VWCE,ALDOA,LRRC8A,AK4,ZMYND8,EZR,TACC1,NTRK2,ADAM9,ADORA2A,HTRA1,GUCA2A,GPR17,ARRDC3,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0070] <h2 style=";text-align:left;direction:ltr"> EGLN3,PRKAB2,SERPINA3,PRSS22,EGLN1,C5orf46,MID1,GPT2,PTBP3,LRRC1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0071] <h2 style=";text-align:left;direction:ltr"> NRIP1,PAPPA2,CCBP2,ARHGEF4,MGAT3,AIF1L,SLCO2A1,KRT17,NOG,PLOD2,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0072] <h2 style=";text-align:left;direction:ltr"> DUSP1,PKM,ASPHD2,PLIN2,CYP2J2,QPCT,HILPDA,RDH13,NEK11,CORO2A,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0073] <h2 style=";text-align:left;direction:ltr"> GRHL1,SCARB1,GALNT2,DNM2,PIK3AP1,SYDE1,PRRG1,PIK3R1,LIMCH1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0074] <h2 style=";text-align:left;direction:ltr"> UBE2Q2,GDPD3,HK2,MLL5,TLE6,GPR146,BTD,PHYHIP,MYOF,PRKD3,ZCCHC2,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0075] <h2 style=";text-align:left;direction:ltr"> CALM1,SIGLEC6,BCL6,LDHA,RASEF,HEXB,ENG,INHA,INHBA,MME,PVRL4,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0076] <h2 style=";text-align:left;direction:ltr"> BACH1,PLA2G16,TSC22D1,TRIM14,TBC1D22A,CCBL1,WDR60,LIMD1,NPNT,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0077] <h2 style=";text-align:left;direction:ltr"> TMEM45A,SEMA4C,IPMK,ERGIC1,MTSS1L,BHLHE40,SLC6A8,RFK,CST6,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0078] <h2 style=";text-align:left;direction:ltr"> DIO2,C12orf75,PROCR,PIK3CB,ERO1L,LEP,SFXN3,SH3BP5,MYO7B,MICAL3,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0079] <h2 style=";text-align:left;direction:ltr">KRT19,HTRA4,TPBG,SPAG4,EFNB1,SASH1,OCRL,SMARCA1,P4HA1,NDRG1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0080] <h2 style=";text-align:left;direction:ltr"> SH3PXD2A,COL17A1,FSTL3,FLNB,FLT1,ELMO1,TRPV6,EPHB6,HSD17B1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0081] <h2 style=";text-align:left;direction:ltr"> ARL4C,TMEM139,PKD2,TRIM40,SEMA3D,PDE7B,SLC20A1,HIST1H1T,ANXA6,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0082] <h2 style=";text-align:left;direction:ltr"> BTG2,FBN2,ASAH1,SLC25A27,RAB15,ZC2HC1A,TMEM136,KLRD1,TMEM168,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0083] <h2 style=";text-align:left;direction:ltr"> CAP2,HSD11B2,SELE,STON2,SCN9A,ABCD3,PISD,PPAP2B,F13A1,CYTH3,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0084] <h2 style=";text-align:left;direction:ltr"> OLFML3, FAM101B, SOD1, CSHL1, LRRC4, SNX27, CYBASC3, SOX5, ABAT, ADAMTSL1<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0085] <h2 style=";text-align:left;direction:ltr"> C18orf8,LEPREL1,LRIG3,B3GNT2,ABCG2,GRIK2,DISP1,CNTN5,GABARAPL1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0086] <h2 style=";text-align:left;direction:ltr"> CATSPERB,ARPC5,PLCXD2,SLC16A10,DAB2,TKT,CD28,MORC4,EGFL6,CADM3,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0087] <h2 style=";text-align:left;direction:ltr"> KCTD3, SLCO2B1, SYNPO2L, SLC23A2, PXK, SLC9A9, PDE5A, IPO13, GCLM<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0088] <h2 style=";text-align:left;direction:ltr"> OPRK1,ATP8B4,DNMT3A,LAMA2,IL12RB2,GSTA3,C12orf39,RNF114,MTSS1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0089] <h2 style=";text-align:left;direction:ltr"> TMEM133,CDYL2,MOB3C,CLDN16,C10orf10,ECM1,ACSS1,NPC1,IGF1,SLAMF1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0090] <h2 style=";text-align:left;direction:ltr">ATP1A1,HSD3B2,NUFIP2,LRRN3,ADAP2,MAF,LSS,EML2,POSTN,RAPH1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0091] <h2 style=";text-align:left;direction:ltr"> SPATA6,LAMC1,APLN,KL,ANK3,TRAK2,WWC1,CPEB4,MAPK8,GNG2,GOT1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0092] <h2 style=";text-align:left;direction:ltr"> FAM20B,ZNF555,AKIP1,SPON1,TLR7,FAM84B,PRKCE,KCNIP3,RCAN1,HDAC9,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0093] <h2 style=";text-align:left;direction:ltr"> RALYL,THSD4,ESRRG,HNRNPA0,IL36RN,CLIC5,VNN3,ST5,ACO1,LGI2,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0094] <h2 style=";text-align:left;direction:ltr"> LCP1,CYBB,TXNRD1,KCNIP1,SH3BGRL2,TRAF3,ZNF554,MRS2,KLHL3,ADCY7,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0095] <h2 style=";text-align:left;direction:ltr"> RAD54L2,HPGDS,PLEKHO2,UNC5C,HLF,RAP1GAP2,UBALD2,CD200R1,ATRNL1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0096] <h2 style=";text-align:left;direction:ltr"> ENPP1,LGALS14,NFIA,ZNF682,PSEN2,DOCK8,C5,SIK3,MKNK1,FES,BMX,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0097] <h2 style=";text-align:left;direction:ltr"> SPRED1,ZBTB39,FOXRED2,ZMYND11,GPR34,TNFRSF11A,UBB,OR51B5,FOLR2,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0098] <h2 style=";text-align:left;direction:ltr"> SLC19A3,CLCN5,TGFBR3,ABHD3,PMAIP1,TOM1L1,OR51B4,MRPS30,NFE2L2,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0099] <h2 style=";text-align:left;direction:ltr"> SLC2A12,SCG2,CYSLTR1,MYO1A,VCP,OTUB2,NCEH1,SLIT2,KIAA0226L,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0100] <h2 style=";text-align:left;direction:ltr"> PITPNA,LYPD6,PLAC1,HMGN2,HIST2H2BE,SUPT7L,DCAF11,PCBD1,GRINA,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0101] <h2 style=";text-align:left;direction:ltr">CABLES1,C5orf42,ALDH4A1,RAB5C,FRZB,SFXN4,CHKA,APMAP,SLC26A2,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0102] <h2 style=";text-align:left;direction:ltr"> CERS2,ATXN2,MAP7D2,NEDD4L,ITPR1,ENPP2,DSN1,IQGAP2,FRY,LYVE1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0103] <h2 style=";text-align:left;direction:ltr"> MB21D2,LAMB2,CARD16,GSTK1,GSK3B,MATN2,MFF,NALCN,PIKFYVE,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0104] <h2 style=";text-align:left;direction:ltr"> KIRREL,TBC1D9,PDPN,TMEM55A,ICA1,APBB2,ARHGAP17,PIK3CG,BMP5,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0105] <h2 style=";text-align:left;direction:ltr"> SOX6,PSMC5,AFF3,FGF10,GUCY1A3,RBM47,PRKG1,GLUL,ASB2,STRADA,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0106] <h2 style=";text-align:left;direction:ltr"> CCND2,FILIP1,ST3GAL4,LANCL2,SLC37A1,PDZRN3,TSPAN33,VAV1,DEPDC5,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0107] <h2 style=";text-align:left;direction:ltr"> PER3,WNT2,CLDN1,RUNX1T1,AMPD3,OR51B2,CYP4V2,IDO1,PC,XPO6,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0108] <h2 style=";text-align:left;direction:ltr"> ZNF583,ZNF641,TYRO3,CYTH4,NSMAF,NCKAP1L,PTPN1,FRMD6,SRPX,ELK1,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0109] <h2 style=";text-align:left;direction:ltr"> ACSS3,PIAS2,KLF12,TM9SF3,STXBP6,BHLHE41,FLRT3,PTPRD,TMEM106C,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0110] <h2 style=";text-align:left;direction:ltr"> ZNF610,PHTF2,ZEB2,HSPB11,P2RY1,RAB3IL1,CRYAB,ABCA11P,LAMC3,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0111] <h2 style=";text-align:left;direction:ltr"> TMPRSS3,FMO1,SLC4A4,BMP4,RBBP4,DUSP4,CXorf57,HYOU1,KLHL42,ANGPTL2,<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0112] PARP6, AGPAT5, WDSUB1, ABCA9, ATP5B, C11orf63, DUS2L, PIGX, ZNF542, DACH1,

[0113] LRRTM2, LRRC39, SEC22C, LINC00472, TSPAN7, GABRA3, CHN1, ASPA, FXYD3,

[0114] PRTFDC1, GUCY1A2, ANKS1B, SLC25A6, AIF1, PDHA1, BIN1, PCOLCE, DLG2,

[0115] VTN, FXN, CTLA4, SLC17A5, APBA1, NIN, TC2N, PTP4A1, TXK, MAD2L2, FURIN, ERCC6, SGCB, OSBPL11, RDH10, LONRF2, TMEM 176A, EID1, VAV3, SLC35F6, KCNJ5, CX3CR1, TMEM86A, NPL, MAT2A, HDAC3, GRHPR, SPEF2, ST6GAL1, BNC2, PRKCD, CLTC, LR RC42, DYRK2, KLC2, PROM1, SLC18B1, NCOA2, SLC16A4, PRKCZ, SEMA6A, MSC, ATP9B, XPNPEP2, APBB3, PALLD, KITLG, CHST 10, LRRC41, WDR11, LGALS13, GAP43, ACO2, CAPRIN2, AZIN1, MYLK, GUCY1B3, POMGNT1, HSD17B2, GABRB1, PIP5K1B, MSRB2

[0116] Overlapping genes upregulated in severe pre-eclampsia conditions were selected in all 3 datasets. A gene set containing 81 genes was obtained:

[0117] AHNAK, ANG, ANXA4, BCAR3, BCL6, BMPR2, BTD, CALM1, CRH, CSNK2A2, CXXC1, CYP11A1, DCP2, DDR1, DUSP1, DVL1, EBI3, EFHD1, ELF3, ENG, EPS8L1, ERO1L, FAM120A, FGD6, FKBP2, FLT1, GALE, GALNT2, GATA3, GFOD2, GREM2, HEY1, HTRA1, IL1RAP, ILVBL, INHA, INHBA, ITGA5, JUNB, KIAA1919, KIF2A, KLF4, KRT 19, LEP, LRRC1, MAN1C1, MBD2, MTMR4, NDRG1, NEBL, NEK11, OCRL, PAM, PAPPA2, PDLIM2, PHYHIP, PIK3AP1, PJA1, PPL, PROCR, PRRG1, PVRL4, RAB25, RASS F7, SCARB1, SH3BP5, SH3PXD2A, SIPA1L1, SLC11A2, SLC2A1, SLC35F2, SLC6A8, SLCO2A1, SLCO4A1, SPAG4, SSFA2, SYDE1, TBC1D22A, TNIP2, TPBG, ZMYND8

[0118] The above genes are taken as potential targets for the treatment of preeclampsia. Then pathway analysis is performed on the above genes. Pathway analysis is a very convenient and effective method to understand the different roles and functions of a genome in different metabolic pathways. For this purpose, Ingenuity Pathway Analysis (IPA) and KEGG programs are used. IPA is a genomics analysis tool used to interpret complex biological and molecular pathway data. Its main functions include information mining based on literature databases, disease and biological function association analysis, gene pathway modeling, and prediction of new biomarkers and drug targets. KEGG is a tool for understanding cellular molecular functions and system relationships, including integrated analysis from macromolecular information (such as genomes, transcriptomes, and proteomes) to biochemical pathways and disease information. The differentially expressed genes of GSE75010 are the most differentiated and comprehensive gene set, so it is used for pathway analysis. Enter the differentially expressed genes obtained from the data set GSE75010 into IPA and KEGG, and view the results. The results are as follows Figure 3 shown.

[0119] It can be seen that according to the results of pathway analysis, neuraminidase genes (NEU1, NEU2, NEU3, NEU4) are closely related to the clinical phenotype of preeclampsia

[0120] Furthermore, in order to determine the association between neuraminidase genes (NEU1, NEU2, NEU3, NEU4) and clinical phenotypes of preeclampsia, a new scoring matrix S2 was established with 81 target genes as columns. The selected phenotypes and four genes NEU1, NEU2, NEU3, and NEU4 from the datasets GSE75010 and GSE25906 constituted the rows. The matrix entries of target genes and phenotypes are the scores obtained using GLM (in the range of [-1,1]), while the matrix entries of target genes and NEU genes are their correlation coefficients, also in the range of [-1,1]. For example, the target gene g from GSE75010 i The matrix entries for the phenotype maternal age and phenotype are the g obtained by the GLM i Scores and phenotypes Maternal age on dataset GSE75010.

[0121] g from GSE75010 i The matrix entries for NEU1 and NEU1 are the gene expression of NEU1 in preeclampsia (PE) samples and the gene expression of NEU1 in PE samples in the dataset GSE75010. i The correlation between expression. The di-clustering of this score matrix S2 will provide clustering of NEU genes with different phenotypes. The clustering results are shown in Fig. 9 shown.

[0122] Example 2

[0123] In order to build a mouse model, it is necessary to identify potential target genes that can be transferred from the mouse model, that is, genes that show similar patterns and behaviors in the mouse and human samples of preeclampsia. The goal is to identify genes that overlap and show similar patterns in human and mouse models. These genes can then be considered transferable between human and mouse models. The goal of this work is to clarify whether a gene is really a target for the treatment of preeclampsia through experimental validation in mouse models. To identify transferable gene patterns between human and mouse models, the coupled non-negative matrix factorization (CoupledNMF) method was applied to analyze the gene expression data of mouse and human samples to find similar gene clusters from the dataset.

[0124] Non-negative matrix factorization (NMF) is a method used in signal processing to solve the blind source deconvolution problem and is widely used in cluster analysis, face recognition and text mining. NMF can be defined by the following model:

[0125] X=WH+Er.....W,H≥0 (3)

[0126] Where X is an n×p data matrix, W and H are n×r and r×p matrices respectively; Er is an error term used to account for inaccurate reconstruction. NMF attempts to explain the main sources of variance in the dataset X with a small number of vectors (r). NMF does not require orthogonality among the components.

[0127] Coupled NMF formulates the clustering problem of the same type of data from two different sources as an optimization problem to solve. Let M and E be two data matrices, M is the score matrix of mice, and E is the score matrix of humans. Couple the M and E matrices and simultaneously maximally decompose the matrices M and E. This problem is transformed into the following optimization problem to obtain the log-likelihood function L defined as:

[0128]

[0129] In the above model, L has three adjustment parameters: λ1, λ2 and μ. λ1 and λ2 regulate the matrix factorization of human E and mouse M respectively, and μ regulates the interaction between human E and mouse M. is the Frobenius norm, defined as the square root of the sum of the absolute squares of its elements, W1 and H1 are the weight decomposition of the mouse matrix M on the vector genes and samples. W2 and H2 are the weight decomposition of the human matrix M on the vector genes and samples. tr(*) is the trace of the square matrix, defined as the sum of the elements on the main diagonal of the square matrix. Basically, the operation of coupling two factorizations is performed to map the consistency features from E and M. The fourth term is the scalar weight factors W1 and W2. Using the particle swarm optimization (PSO) algorithm (see Yongsheng Ding, Lijun Cheng, Witold Pedrycz, Kuangrong Hao. Global Nonlinear Kernel Prediction for Large Data Set With a Particle Swarm-Optimized Interval Support Vector Regression. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26 (10)), the log-likelihood function L solves the optimal parameters W1 and W2. Using the empirical Bayesian biclustering Bi-EB method (see Yazdanparast A, Li L, Zhang C, Cheng L. Bi-EB: Empirical Bayesian biclustering for pattern recognition in multi-omics data integration across species. Gene (Basel). 2022 Oct 30; 13(11): 1982. doi: 10.3390 / gene 13111982. PMID: 36360219; PMCID: PMC9690013.), weight factors W1 and W2 biclusters and coupled clusters were drawn. Figure 3 The concept of coupled clustering on human and mouse datasets is illustrated. The mouse GSE116751 and human GSE75010 datasets are co-clustered (via CoupledNMF) to obtain coupled gene clusters for both datasets. Figure 4 The results of the co-clustering procedure for mouse data (GSE116751) and human data (GSE75010) are shown. We can see that 3 co-clusters are highlighted in the figure. Table 2 lists the genes in co-cluster 1. From the table, we can see that all the functions are related to angiogenesis. Among the genes belonging to the co-cluster, ANG, GREM2 and NEU1 were found to be present. Comparing the differential expression of NEU genes in mice and humans, it was found that only NEU1 can be reliably recommended as a potential target for the mouse model. The other genes were discarded due to their low expression in the mouse dataset.

[0130] Table 2 Functional annotation of cluster 1

[0131]

[0132]

[0133] Example 3 Experimental Verification

[0134] The NEU gene was used to establish a preeclampsia diagnostic model, and the diagnostic accuracy of the model was verified. The results are as follows: The NEU target of mice was interfered with by drugs L-NAME and zanamivir. In the mouse model, L-NAME was administered by subcutaneous injection, and zanamivir was administered by intravenous injection. Five groups of mice with different treatments were obtained. The following data confirmed that the NEU target is related to preeclampsia, and it is shown that a mouse model for preeclampsia research can be constructed by interfering with the NEU gene target.

[0135] Table 3 Systolic blood pressure data

[0136]

[0137]

[0138] Table 4 Diastolic blood pressure data

[0139]

[0140] Table 5 Average pulsating pressure

[0141]

[0142]

[0143] Table 6 24-hour urine protein

[0144]

[0145]

[0146] Table 7 Number of pups

[0147]

[0148] The outer membrane of the cell is covered by a highly hydrated network of carbohydrate fibers. This covering is called a glycocalyx. The general structural components of the glycocalyx are glycoproteins and proteoglycans. The precise biochemical composition and structure of the glycocalyx vary with the cell type. Glycoproteins are glycoconjugates in which one or more sugars are covalently attached to the polypeptide backbone of a protein. Proteoglycans are core proteins covalently bound to glycosaminoglycan (GAG) chains, also known as silicic acid polysaccharide proteins.The vascular endothelium is a monolayer of cells that wraps around the inner walls of arteries, capillaries, and veins. It is not only a barrier between blood and tissues, but also actively controls the degree of relaxation and contraction of the blood vessels, and the extravasation of solutes, fluids, macromolecules, hormones, platelets, and blood cells. The structure of the vascular endothelial glycocalyx (VEGLX) is shown in Figure 3 .

[0149] VEGLX not only protects the vascular endothelium from turbulent wear caused by blood flow, but also plays an important role in various endothelial functions. The healthy vascular endothelium covered by VEGLX contains cytokines, chemokines, receptors, growth factors, gap-binding proteins, extracellular superoxide dismutase (ecSOD) and endothelial nitric oxide. Figure 4 As shown, the synthase (eNOS).

[0150] Damage to VEGLX has been implicated in endothelial dysfunction, severe infection, Kawasaki disease, pregnancy-induced hypertension, nephropathy (preeclampsia), gestational diabetes, sepsis, acute lung injury, trauma, ischemic cerebral embolism, acute coronary syndrome and shock.

[0151] Obviously, the above embodiments are merely examples for the purpose of clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the invention.

Claims

1. A method for screening disease-related genes, characterized in that: The steps include: Step 1) constructing a dataset containing gene expression profiles of diseased and non-disease samples; Step 2) establishing a linear model between each gene expression data in the gene expression profile and whether the disease occurs according to the data set in step 1); Step 3) According to the slope of the linear model of each gene obtained in step 2), the genes differentially expressed between the diseased and non-disease samples are screened out, and the differentially expressed genes are the disease-related genes.

2. The method for screening disease-related genes according to claim 1, characterized in that: In the step 1), multiple data sets are constructed; in the step 2), linear models are established for each gene expression data in each data set and whether it is a diseased sample phenotype; in the step 3), genes that are differentially expressed in each data set are screened out according to the slope of the linear model of each gene.

3. The method for screening disease-related genes according to claim 1, characterized in that: The step 2) specifically includes the following steps: using each gene expression data in the gene expression spectrum as a predictor variable and whether the disease is present as a response variable; and setting the value of the diseased sample phenotype to 1 and the value of the non-disease phenotype to 2 to establish a linear model.

4. The method for screening disease-related genes according to claim 1, characterized in that: The step 3) specifically includes the following steps: expanding the slope value of the line model of each gene to the range of [-1, 1] to obtain the score value of each gene, comparing the score value with a threshold representing a significant difference, and the gene with a score value greater than the threshold is a differentially expressed gene.

5. The method for screening disease-related genes according to any one of claims 1 to 4, characterized in that: The disease is pre-eclampsia.

6. The method for screening disease-related genes according to claim 5, characterized in that: The data set of step 1) also includes data on clinical phenotypes of pre-eclampsia samples and non-pre-eclampsia samples; The clinical phenotype data include at least one of diagnosis result, average uterine pi, average umbilical cord pi, maximum systolic blood pressure, maximum diastolic blood pressure, proteinuria pattern, HELLP diagnosis, and gestational age; The step 2) also includes establishing a linear model between each gene expression data in the gene expression profile and each clinical phenotype data; After the differentially expressed genes are screened out in step 3), a scoring matrix is ​​established; the rows and columns in the scoring matrix represent the differentially expressed genes and the clinical phenotype types, respectively, and the scores of the linear model obtained in step 2) are filled into the corresponding positions in the scoring matrix; The scoring matrix was subjected to a dichotomous clustering analysis to classify the clinical phenotypes.

7. The method for screening disease-related genes according to claim 1, characterized in that: Pathway analysis was performed on the screened disease-related genes.

8. A gene set, characterized in that The following genes are included: ANG, C7orf49, DONSON, SLC38A10, DSCR4, HERPUD1, PDLIM2, HSPA2, LACTB, ADAM17, ANO8, GNPTG, DDIT4L, TMCC3, PMEL, PGRMC1, VASN, PXN, PGK1,FGD6,BCAP31,KRT15,SPOCK2,VWA1,MT1F,BIN2,DDR1,RPS19BP1,PJA1,DAB2IP,KIAA1217,RANBP2,CD9,ISM2,CCDC127,TMEM141,DDX58,PFKFB3, NEBL,HPCA,EPHB3,MED12L,SEMA3G,REEP4,HSPG2,CCDC115,GALNT18,LRRC32,GRHL3,KRT7,KLHDC7A,METTL6,FABP4,SH3BP2,TNNI2,LAIR2,ARSK,HRAS LS2,LAMC2,FAM219A,GPER,GREB1L,CLDN5,MIDN,DNAJB2,ITGB6,MAPK8IP3,FPGS,SIPA1L1,VEGFC,MAP3K8,OXGR1,SPICE1,PTPN12,ADRA2C,RIOK1,DLX 5, SHANK2, ATP6V0E1, JUNB, NANS, LIMK1, GFOD2, ZFP36L1, CKMT2, TPM1, SFR1, STC2, PTGES, ATG16L2, FBLIM1, UBR1, SLC2A5, CDK19, PWWP2B, SSR1, PPARG ,CEBPA,DEGS1,PFKL,TCF7L2,TNR,ERP44,GSE1,DCP2,ABCA12,GPR32,IRF9,TNIP2,ZBTB2,FOXJ3,CA4,JUP,CAV3,FGF22,PDIA5,KCTD11,FAM8A1,BCAR3 ,AP3S1,IFI44L,STK11IP,DVL1,LBH,SNX9,FN1,NLRP2,MTMR4,TMEM179B,VIT,ANKRD23,ANXA4,ELMO3,PFKP,SSFA2,SLC11A2,MAP3K9,B9D2,POLR2H,IN SIG1,FOSL2,DOCK5,UBASH3B,SLC35F2,SYN3,MAPK11,IL1R2,DGKZ,HMOX1,ELF3,SNX33,LY6D,AHNAK,GATA3,MAN1C1,ABCA7,CXXC1,KCTD9,AVL9,ISLR2,<h2 style=";text-align:left;direction:ltr">DNAH11, PREP, JPH1, ASTE1, IGSf3, CLDN9, NARF, SH3RF1, UPK1B, BMPR2, PDK1, BCAT2, SWAP70, SERPINE1, SLC2A1, EBI3, PMEPA1, TRIP10, SLC28A1, AMOT L2, MORF4L2, RHOB, SLC38A3, TIMP3, NLRP7, CITED2, C5orf30, PFKFB4, SLC2A3, BASP1, FAM184A, HPCA1, RIMKLA, FBXO22, PLAC8, EPHA1, KLF4, ANKRD13B ,CYP11A1,STX1A,MFAP5,GLA,ELF4,SUV39H1,SLC1A6,EFHD1,HN1L,FAM156 A,P2RY2,RAB3D,IRF2BPL,CSNK2A2,RHOF,OAS3,TGM1,HK1,STK24,RASSF7, CCRN4L,KIF17,C14orf37,CCDC113,MYCN,SLCO4A1,AQP1,RARRES3,LYN,ME D13L,BTNL9,MBD2,ASCL2,ALPK3,KIT,PRR15,FAM43A,AGRN,CRKL,PAM,RHOU ,MIIP,PPP1R16B,FOXJ2,PTPRU,FKBP2,MMP14,PLEC,CRH,IL1RAP,TMEM59, PADI1,VTCN1,GLIS3,PLBD1,PNCK,BAD,RNF24,SUN2,SPTLC3,ING1,CPOX,A RNT2, GREM2, UGP2, DNAJC1, ACSF2, HEY1, USP18, EFNA1, IGSF8, FRMD4A, MAML2, SLC16A1, KIAA1919, HPS3, PPP1R3B, TNFAIP8, RAB25, MIF, FKBP8, TREM1 FAM120AOS,PPL,ITGA5,HMGN3,EPS8L1,RASSF8,F11R,C4orf3,KIF2A,MXI1 ,MTUS1,GALE,GAPDH,RUSC2,HCAR2,TPI1,LTBP1,GPX3,KLF3,TES,JPH2,TME M66,SLC2A14,CDO1,NEK6,PARD3,RASGRF2,HMGB3,SEC23A,TMEM184A,GSN, RAB8B,ILVBL,SLC16A3,MOB3A,SERPINB9,EDEM2,SFRP1,PPP1R12C,TRIM24,<h2 style=";text-align:left;direction:ltr">ANKRD37,ARID3A,LEPROTL1,KDM3A,CSNK1E,FHL2,CCSAP,NEURL,S100A6,C Xcr6 , fut11 , DSP , EAF1 , Galk1 , Fam120A , Errfi1 , vwce , aldoa , lrc8a , ak4 , ZMYND8, EZR, TACC1, NTRK2, ADAM9, ADORA2A, HTRA1, GUCA2A, GPR17, ARDC3, EGLN3, PRKAB2, SERPINA3, PRSS22, EGLN1, C5orf46, MID1, GPT2, PTBP3, LRR C1,NRIP1,PAPPA2,CCBP2,ARHGEF4,MGAT3,AIF1L,SLCO2A1,KRT17,NOG,PL OD2,DUSP1,PKM,ASPHD2,PLIN2,CYP2J2,QPCT,HILPDA,RDH13,NEK11,CORO2 A,GRHL1,SCARB1,GALNT2,DNM2,PIK3AP1,SYDE1,PRRG1,PIK3R1,LIMCH1,U BE2Q2,GDPD3,HK2,MLL5,TLE6,GPR146,BTD,PHYHIP,MYOF,PRKD3,ZCCHC2,C ALM1, SIGLEC6, BCL6, LDHA, RASEF, HEXB, ENG, INHA, INHBA, MME, PVRL4, BACH1, PLA2G16, TSC22D1, TRIM14, TBC1D22A, CCBL1, WDR60, LIMD1, NPNT, TMEM4 5A, SEMA4C, IPMK, ERGIC1, MTSS1L, BHLHE40, SLC6A8, RFK, CST6, DIO2, C12orf75, PROCR, PIK3CB, ERO1L, LEP, SFXN3, SH3BP5, MYO7B, MICAL3, KRT19, HTR A4,TPBG,SPAG4,EFNB1,SASH1,OCRL,SMARCA1,P4HA1,NDRG1,SH3PXD2A,CO L17A1,FSTL3,FLNB,FLT1,ELMO1,TRPV6,EPHB6,HSD17B1,ARL4C,TMEM139,P KD2,TRIM40,SEMA3D,PDE7B,SLC20A1,HIST1H1T,ANXA6,BTG2,FBN2,ASAH1, SLC25A27,RAB15,ZC2HC1A,TMEM136,KLRD1,TMEM168,CAP2,HSD11B2,SELE,<h2 style=";text-align:left;direction:ltr">STON2, SCN9A, ABCD3, PISD, PPAP2B, F13A1, CYTH3, OLFML3, FAM101B, SOD1, CSHL1, LRRC4, SNX27, CYBASC3, SOX5, ABAT, ADAMTSL1, C18orf8, LEPREL1, L RIG3, B3GNT2, ABCG2, GRIK2, DISP1, CNTN5, GABARAPL1, CATSPERB, ARPC5, PLCXD2, SLC16A10, DAB2, TKT, CD28, MORC4, EGFL6, CADM3, KCTD3, SLCO2B1, SY NPO2L, SLC23A2, PXK, SLC9A9, PDE5A, IPO13, GCLM, OPRK1, ATP8B4, DNMT3A, LAMA2, IL12RB2, GSTA3, C12orf39, RNF114, MTSS1, TMEM133, CDYL2, MOB3C CLDN16,C10orf10,ECM1,ACSS1,NPC1,IGF1,SLAMF1,ATP1A1,HSD3B2,NUFI P2,LRRN3,ADAP2,MAF,LSS,EML2,POSTN,RAPH1,SPATA6,LAMC1,APLN,KL,AN K3,TRAK2,WWC1,CPEB4,MAPK8,GNG2,GOT1,FAM20B,ZNF555,AKIP1,SPON1, TLR7,FAM84B,PRKCE,KCNIP3,RCAN1,HDAC9,RALYL,THSD4,ESRRG,HNRNPA0 ,IL36RN,CLIC5,VNN3,ST5,ACO1,LGI2,LCP1,CYBB,TXNRD1,KCNIP1,SH3BG RL2,TRAF3,ZNF554,MRS2,KLHL3,ADCY7,RAD54L2,HPGDS,PLEKHO2,UNC5C,H LF,RAP1GAP2,UBALD2,CD200R1,ATRNL1,ENPP1,LGALS14,NFIA,ZNF682,PS EN2,DOCK8,C5,SIK3,MKNK1,FES,BMX,SPRED1,ZBTB39,FOXRED2,ZMYND11,G PR34,TNFRSF11A,UBB,OR51B5,FOLR2,SLC19A3,CLCN5,TGFBR3,ABHD3,PMA IP1,TOM1L1,OR51B4,MRPS30,NFE2L2,SLC2A12,SCG2,CYSLTR1,MYO1A,VCP,<h2 style=";text-align:left;direction:ltr">OTUB2,NCEH1,SLIT2,KIAA0226L,PITPNA,LYPD6,PLAC1,HMGN2,HIST2H2BE ,SUPT7L,DCAF11,PCBD1,GRINA,CABLES1,C5orf42,ALDH4A1,RAB5C,FRZB, SFXN4, CHKA, APMAP, SLC26A2, CERS2, ATXN2, MAP7D2, NEDD4L, ITPR1, ENPP2, DSN1, IQGAP2, FRY, LYVE1, MB21D2, LAMB2, CARD16, GSTK1, GSK3B, MATN2, MF F,NALCN,PIKFYVE,KIRREL,TBC1D9,PDPN,TMEM55A,ICA1,APBB2,ARHGAP17 ,PIK3CG,BMP5,SOX6,PSMC5,AFF3,FGF10,GUCY1A3,RBM47,PRKG1,GLUL,AS B2, STRADA, CCND2, FILIP1, ST3GAL4, LANCL2, SLC37A1, PDZRN3, TSPAN33, VAV1, DEPDC5, PER3, WNT2, CLDN1, RUNX1T1, AMPD3, OR51B2, CYP4V2, IDO1, PC XPO6: ZNF583: ELK1,ACSS3,PIAS2,KLF12,TM9SF3,STXBP6,BHLHE41,FLRT3,PTPRD,TMEM1 06C,ZNF610,PHTF2,ZEB2,HSPB11,P2RY1,RAB3IL1,CRYAB,ABCA11P,LAMC3 ,TMPRSS3,FMO1,SLC4A4,BMP4,RBBP4,DUSP4,CXorf57,HYOU1,KLHL42,ANGP TL2,PARP6,AGPAT5,WDSUB1,ABCA9,ATP5B,C11orf63,DUS2L,PIGX,ZNF542 ,DACH1,LRRTM2,LRRC39,SEC22C,LINC00472,TSPAN7,GABRA3,CHN1,ASPA,F XYD3, PRTFDC1, GUCY1A2, ANKS1B, SLC25A6, AIF1, PDHA1, BIN1, PCOLCE, DLG2, VTN, FXN, CTLA4, SLC17A5, APBA1, NIN, TC2N, PTP4A1, TXK, MAD2L2, FURINERCC6, SGCB, OSBPL11, RDH10, LONRF2, TMEM176A, EID1, VAV3, SLC35F6, KCNJ5, CX3CR1, TMEM86A, NPL, MAT2A, HDAC3, GRHPR, SPEF2, ST6GAL1, BNC2, PRKCD, CLTC, LRRC42, DYRK2, KLC2, PROM1, SLC18B1, NCOA2, SLC16A4, PRKCZ, SEMA6A, MSC, ATP9B, XPNPEP2, APBB3, PALLD, KITLG, CHST10, LRRC41, WDR11, LGALS13, GAP43, ACO2, CAPRIN2, AZIN1, MYLK, GUCY1B3, POMGNT1, HSD17B2, GABRB1, PIP5K1B, MSRB2., 9. A gene set, characterized in that: The gene set includes the following genes: AHNAK, ANG, ANXA4, BCAR3, BCL6, BMPR2, BTD, CALM1, CRH, CSNK2A2, CXXC1, CYP11A1, DCP2, DDR1, DUSP1, DVL1, EBI3, EFHD1, ELF3, ENG, EPS8L1, ERO1L, FAM120A, FGD6, FKBP2, FLT1, GALE, GALNT2, GATA3, GFOD2, GREM2, HEY1, HTRA1, IL1RAP, ILVBL, INHA, INHBA, ITGA5, JUNB, KIAA1919, KIF2A, KLF 4. KRT19, LEP, LRRC1, MAN1C1, MBD2, MTMR4, NDRG1, NEBL, NEK11, OCRL, PAM, PAPPA2, PDLIM2, PHYHIP, PIK3AP1, PJA1, PPL, PROCR, PRRG1, PVRL4, RAB25, RA SSF7, SCARB1, SH3BP5, SH3PXD2A, SIPA1L1, SLC11A2, SLC2A1, SLC35F2, SLC6A8, SLCO2A1, SLCO4A1, SPAG4, SSFA2, SYDE1, TBC1D22A, TNIP2, TPBG, ZMYND8.

10. A diagnostic marker for pre-eclampsia, characterized in that: The pre-eclampsia diagnostic marker comprises at least one of the genes NEU1, NEU2, NEU3, and NEU4.

11. The pre-eclampsia diagnostic marker according to claim 10, characterized in that: The pre-eclampsia diagnostic marker comprises the gene NEU1.

12. Use of the gene set according to claim 8 or 9, or the pre-eclampsia diagnostic marker according to claim 10 or 11 in the preparation of a product for diagnosing pre-eclampsia.

13. A method for constructing a disease animal model, characterized in that: The steps include: Constructing data sets of gene expression profiles of diseased and non-disease samples of humans and animals respectively; co-clustering the data sets of gene expression profiles of diseased and non-disease samples of humans and animals by coupled non-negative matrix decomposition to obtain coupled gene clusters in the human and animal data sets; Determining transferable genes between humans and animals based on the coupled gene cluster, the screening results of the disease-related gene screening method according to any one of claims 1 to 7, and the gene expression level of the animal; The disease animal model is constructed based on the transferable gene.

14. The method for constructing an animal model according to claim 13, characterized in that: The animal is a mouse.

15. The method for constructing an animal model according to claim 13, characterized in that: The transferable gene is NEU1.

16. Use of the disease animal model obtained according to the method for constructing a disease animal model according to claim 13 or 14 in verifying the effective treatment identification of pre-eclampsia.

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