S by centrifuging at 10000 rpm for 20 min in 4uC. The protein concentration was analyzed by Bradford protein assay (Bio-Rad, USA). The entire protein was separated with 10 SDS-PAGE and then transferred to a PVDF membrane (0.45 mm) for two h. Soon after 2 h of blocking by five milk in TBST, incubated the membrane with mouse anti-HIF-1a (Santa Cruz, CA, USA) at 1:200 dilution and mouse anti-b-actin (proteintech, USA) at 1:2000 dilution in 4uC for 12 h and followed by two h incubating with goat anti-mouse IgG (proteintech, USA) at 1:2000 dilution. Right after washing by TBST, detected the membrane signals working with enhanced chemiluminescence ECL (Beyotime, China). The Image J application was applied for quantitative evaluation of HIF-1a signal intensities with normalized with b-actin levels. Information were analyzed with GraphPad Prism Version 5.0, variations between groups were statistically evalu-Analysis of differentially expressed genes in cancer versus typical tissuesGeneChip Operating Software was applied to analyze the chips and extract the raw pictures signal data. The GEO DataSets of NCBI accession number of our study is: GSE56807. Raw signal information had been then imported and analyzed with Limma algorithm to determine the differentially expressed genes. The linear models and empirical Bayes strategies have been to analyze the information. This prevented a gene with a quite small fold change from being judged as differentially expressed just because of an accidentally tiny residual SD. The resulting P values had been adjusted using the BH FDR algorithm. Genes were regarded as to be considerably differentially expressed if each the FDR values was ,0.05(controlling the expected FDR to no far more than five ) and gene expression PKD3 Formulation showed a minimum of 2-fold changes between cancer andTable 1. GENETIC_ASSOCIATION_DB_DISEASE_CLASS evaluation of 82 genes in TF-gene regulatory network.Term CancerP-Value two.53E-Fold enrichment two.Benjamini four.55E-Genes TLR2, RRM2B, MDK, MMP1, TIMP1, TAP1, SERPINA1, FAS, FCGR3A, FN1, HLA-A, IGF1, CFTR, HLA-C, HLA-B, HGF, SOD1, BRCA1, CDKN1B, TFRC, PLA2G2A, IRF1, PCNA, MDM2, COL1A1, CTSB, PGK1, PARP1, GSTP1 TLR2, HLA-A, CFTR, HLA-C, OAS2, HLA-B, STAT1, MMP1, PSMB9, IFNAR2, TFRC, TAP1, IRF1, JAK1, FAS,SERPINA1, FCGR3A, GSTP1 TLR2, MMP1, TIMP1, TAP1, SERPINA3, SERPINA1, FAS, FN1,HSPA4, MYB, FCGR3A, HLA-A, IGF1, HLA-C, CFTR, HGF, HLA-B, STAT3, PSMB9, CDKN1B, PLA2G2A, COL1A2, MDM2, COL1A1, GSTP1 TLR2, OAS2, MMP1, TIMP1, NOD-like Receptor (NLR) drug CXCL10, TAP1, SERPINA3, SERPINA1, FAS, FCGR3A, HLA-A, IGF1, CFTR, HLA-C, HLA-B, STAT3, PSMB9, IFNAR2, CYBB, CD86, CTSB, IRF1, TNFRSF10B, COL1A1, PARP1, GSTPInfection Cardiovascular4.82E-06 four.77E-3.59 two.4.34E-05 two.15E-Immune2.13E-1.7.66E-doi:10.1371/journal.pone.0099835.tPLOS 1 | plosone.orgHIF-1a and Gastric CancerFigure 3. TF-gene network of those 82 differentially expressed genes in gastric cancer tissues. Red circles within a are up-regulated genes, whereas green circles are down-regulated genes plus the yellow triangles are these 5 key TFs. B, The brief framework of this network. The circles would be the clustered genes and the variety of genes is shown inside. The path of the arrow is in the Supply to the Target. doi:10.1371/journal.pone.0099835.gated by sample one-tailed Student’s t-test with p worth ,0.05 thought of as considerable.Construction of transcription issue gene network according to gene expression profile and transcriptional regulatory element databaseTranscription aspect (TF) gene network was constructed determined by gene expression profile and transcriptional r.