The Different Molecular Networks And Mechanism Function Prediction Insulin-Like Growth Factor 2 MRNA Binding Protein 3 (IGF2BP3) In No-Tumor Hepatitis/Cirrhotic Tissues (HBV Or HCV Infection) And HCC

Based on web reasoning methods and IGF2BP3 network integrated approach of gene ontology analysis, by the GEO data set gene regulation, build no tumor hepatitis/ cirrhotic liver tissue-related and liver-related networks. By biological compute methods, the close ties between the Center functional module of Center molecular and the disease points such as liver cancer invasion and proliferation of more far-reaching guidance are better researched, which is significant to identify potential new therapeutic target genes as well as disease diagnosis and treatment. By experiments, integrated bio-computing method was successfully applied to microarray data analysis, and Insulin-Like Growth Factor 2 MRNA Binding Protein 3 (IGF2BP3) Chemotaxis-Mediated Signal with Protein Transport Coupling Metabolism for Positive Regulation of Survival Gene Product Activity Network in No-Tumor Hepatitis/Cirrhotic Tissues (HBV or HCV infection) by Biocomputation, Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) Inhibited Network of Signal Coupling DNA Replication to transcription for Cell Differentiation and Apoptosis in No-Tumor Hepatitis/Cirrhotic Tissues (HBV or HCV infection) by Biocomputation, Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) Signal Coupling Metabolism to DNA Replication for Regulation of Cell Growth Network in Human Hepatocellular Carcinoma (HCC) by Biocomputation, Insulin-like growth factor 2 mRNA binding protein 3 (IGF2BP3) Inhibited Network of Signal Coupling Metabolism to Cell Cycle for Cell Differentiation in Human Hepatocellular Carcinoma (HCC) by Biocomputation are analyzed, get the subtle relationship of center molecular and disease, thus make a meaningful exploration to disease prevention and treatment. And develop more effective drugs. Our goal is to build, interpret and verify that the network the upper building in liver cancer. In the previous article. In this study, we have established different IGF2BP3 relevant network. Conforming to the corresponding network, the reasonable assumptions are brought up. For the four assumptions of IGF2BP3 activated up-inhibited and down-inhibited network in no-tumor hepatitis/cirrhotic tissues, IGF2BP3 activated up-inhibited and down-inhibited network in HCC. GO analysis can be verified, through a cluster of 3.0 we first verify that the identified gene can separated no-tumor hepatitis/cirrhotic tissues from HCC. The results show that upstream SPINK1, RAB3B, CDH13, ST6GALNAC, MMP9, SULT1C2, CHRNA4 activated IGF2BP3, and downstream IGF2BP3-activated GDPD5, LLGL2, ST6GALNAC, PRKCG, ELAVL3, IRF5, S100P, SSTR5 in no-tumor hepatitis/cirrhotic tissues, upstream MCM2, TNFRSF9, CYP21A2, CEBPA, HIST1H2AD, PTHLH, MMP11 inhibited IGF2BP3, and downstream IGF2BP3-inhibited SFRP4, MYBL2, MDK, MKRN3, MAPK3, CAD, PTHLH, TBL3, MMP11 in no-tumor hepatitis/cirrhotic tissues, upstream MKRN3, TPSD1, ESM1, LEF1, REG1A, PHLDA2, PTHR2, MMP11, TSR1, LAPTM4B activated IGF2BP3, and downstream IGF2BP3-activated E2F1, SPINK1, ORC1L, RFC4, CORO2A, NUP62, RABGGTA in HCC. upstream LCN2, SPINK1, GRM1, ACTN2, S100P, STMN1, TSHB inhibited IGF2BP3, and downstream IGF2BP3-inhibited TROAP, MDK, RAB3B, PIGC, SLC4A3, MCM2, MAP4K4, AMELY, SEMA3B, KIAA0513, CSTF2, CPD, TNFRSF9, YWHAE, SBF1, EIF1AX, GRM1, ARHGDIG, LLGL2, ACTN2, DDX10, RNF185, RBM34, SORT1, CEBPA, CST6, MYH6, CAMK1, SFTPA2, NFKBIB, HMGB2, FOLR1, LGALS3, ITGA2, EPHA4, GJA5, CKS1B, ORC6L, FGF9, AFP, CCL20, PRKG2, CHL1, PTHLH, CRYGA, MYOM1, ZNF43, GAS7, CYP51A1, EYA1, ALDH3A1, FLJ33790, DKK1, PTHR2, MMP11, PROK1, ADAMDEC1, NINJ2 in HCC.