Abstract Background Glioma is one of the most common primary malignancies in the brain or spine. The transcription factor (TF) CCAAT/enhancer binding protein beta (CEBPB) is important for maintaining the tumor initiating capacity and invasion ability. To investigate the regulation mechanism of CEBPB in glioma, microarray data [35]GSE47352 was analyzed. Methods [36]GSE47352 was downloaded from Gene Expression Omnibus, including three samples of SNB19 human glioma cells transduced with non-target control small hairpin RNA (shRNA) lentiviral vectors for 72 h (normal glioma cells) and three samples of SNB19 human glioma cells transduced with CEBPB shRNA lentiviral vectors for 72 h (CEBPB-silenced glioma cells). The differentially expressed genes (DEGs) were screened using limma package and then annotated. Afterwards, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) software was applied to perform enrichment analysis for the DEGs. Furthermore, the protein-protein interaction (PPI) network and transcriptional regulatory network were constructed using Cytoscape software. Results Total 529 DEGs were identified in the normal glioma cells compared with the CEBPB-silenced glioma cells, including 336 up-regulated and 193 down-regulated genes. The significantly enriched pathways included chemokine signaling pathway (which involved CCL2), focal adhesion (which involved THBS1 and THBS2), TGF-beta signaling pathway (which involved THBS1, THBS2, SMAD5, and SMAD6) and chronic myeloid leukemia (which involved TGFBR2 and CCND1). In the PPI network, CCND1 (degree = 29) and CCL2 (degree = 12) were hub nodes. Additionally, CEBPB and TCF12 might function in glioma through targeting others (CEBPB → TCF12, CEBPB → TGFBR2, and TCF12 → TGFBR2). Conclusions CEBPB might act in glioma by regulating CCL2, CCND1, THBS1, THBS2, SMAD5, SMAD6, TGFBR2, and TCF12. Keywords: Glioma, CCAAT/enhancer binding protein beta, Differentially expressed genes, Protein-protein interaction network, Transcriptional regulatory network Background Glioma, which is known as one of the most common primary malignancies in the brain or spine, accounts for nearly 30 % of all brain and central nervous system tumors and 80 % of all malignant brain tumors [[37]1, [38]2]. Previous researches have shown that the most important hallmarks of malignant glioma are its invasion and angiogenesis [[39]3]. So far, researchers have indicated that glioma can be induced by neurofibromatoses and tuberous sclerosis complex [[40]4], electromagnetic radiation [[41]5], DNA repair genes (such as excision repair cross-complementing 1, ERCC1, and X-ray repair cross-complementing group 1, XRCC1) [[42]6]. However, the exact molecular mechanisms of glioma were still unclear. In the central nervous system, the neoplastic transformation can convert the neural cells into cells of mesenchymal phenotype which possess the ability of invasion and promoting angiogenesis [[43]7, [44]8]. What is more, it has been identified that mesenchymal stem cells (MSC)-like properties may play a role in the tumorigenesis, invasion, and recurrence of primary glioblastoma tumors [[45]8]. The transcription factor (TF) CCAAT/enhancer binding protein beta (CEBPB) is associated with the mesenchymal state of primary glioblastoma, and its expression in glioma is important for maintaining the tumor initiating capacity and invasion ability [[46]9, [47]10]. Moreover, the transforming growth factor beta 1/SMAD family member 3 (TGFB1/SMAD3) plays a key role in the extracellular matrix (ECM) production which can lead to glioblastoma aggression [[48]11, [49]12]. It has been revealed that CEBPB can regulate the synthesis of ECM [[50]13]. However, the regulation mechanism of CEBPB on TGFB1/SMAD3 in glioma was seldom studied. In our study, in order to gain a better understanding of the regulation mechanisms of CEBPB and investigate whether CEBPB could regulate the production of ECM via the TGFB1/SMAD3 signaling pathway in glioma, the microarray data deposited by Carro et al. were further analyzed with bioinformatics methods. Firstly, the differentially expressed genes (DEGs) between SNB19 human glioma cells transduced with non-target control small hairpin RNA (shRNA) lentiviral vectors for 72 h and SNB19 human glioma cells transduced with CEBPB shRNA lentiviral vectors for 72 h were identified and annotated. Subsequently, their potential functions were predicted by enrichment analysis. Finally, protein-protein interaction (PPI) network and transcriptional regulatory network were constructed to screen key genes. Methods Microarray dataset The microarray dataset of [51]GSE19114 [[52]14] was downloaded from Gene Expression Omnibus (GEO, [53]http://www.ncbi.nlm.nih.gov/geo/) database, which was based on the platform of [54]GPL6947 IlluminaHumanHT-12 V3.0 expression beadchip. A total of 74 samples were included in the dataset, among which 3 samples of SNB19 human glioma cells transduced with non-target control shRNA lentiviral vectors for 72 h (normal glioma cells) and 3 samples of SNB19 human glioma cells transduced with CEBPB shRNA lentiviral vectors for 72 h (CEBPB-silenced glioma cells) were used to study the effect of CEBPB on glioma. Data preprocessing and DEGs screening The preprocessed microarray data were obtained from GEO2R of National Center of Biotechnology Information (NCBI, [55]http://www.ncbi.nlm.nih.gov/geo/geo2r/), including 48803 probes. The linear models for microarray data (limma) package [[56]15] were used to identify the DEGs between the normal glioma cells and the CEBPB-silenced glioma cells. Benjamini-Hochberg (BH) method [[57]16] was applied to adjust the raw p value into false discovery rate (FDR). The FDR <0.05 and |log[2] fold change (FC) >1 were used as cut-off criteria. Functional and pathway enrichment analysis Gene Ontology (GO, [58]http://www.geneontology.org/) annotations are of great importance for mining biological and functional significance from large dataset [[59]17]. The Kyoto Encyclopedia of Genes and Genomes (KEGG, [60]http://www.genome.ad.jp/kegg) database represents higher order of functions in terms of the network of the interacting molecules [[61]18]. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool [[62]19] was employed to perform GO functional and KEGG pathway enrichment analyses for the DEGs. The p value <0.05 was used as the cut-off criterion. DEGs annotation TSGene database ([63]http://bioinfo.mc.vanderbilt.edu/TSGene/), which contains detailed annotations for each tumor suppressor gene (TSG), such as cancer mutations, gene expressions, methylation sites, transcriptional regulations, and PPIs, was applied to identify the TSGs from the DEGs [[64]20]. Additionally, tumor-associated gene (TAG) database ([65]http://www.binfo.ncku.edu.tw/TAG/), which provides information about commonly shared functional domains in well-characterized oncogenes and TSGs, was used for screening the TAGs from the DEGs [[66]21]. Besides, as a collection of data about the transcriptional regulatory network, the Encyclopedia of DNA Elements (ENCODE) project was introduced for screening the TFs from the DEGs [[67]22]. PPI network construction The PPI pairs were searched using the Search Tool for the Retrieval of Interacting Genes (STRING, [68]http://string-db.org/) online tool [[69]23]. The required confidence (combined score) >0.4 was used as the cut-off criterion. Then, the Cytoscape software [[70]24] was used to visualize the PPI network. Furthermore, connectivity degree analysis was performed to search the hub nodes of PPI networks. The degree of a node was corresponded to the number of interactions involved it [[71]25]. In addition, hub nodes were nodes with higher degrees. Transcriptional regulatory network construction ENCODE project is a collection of data about the transcriptional regulatory network, which helps illuminate TF-binding sites, histone marks, chromatin accessibility, DNA methylation, RNA expression, RNA binding, and other cell-state indicators [[72]22]. Based on the transcriptional regulation interactions derived from ENCODE project, the regulatory network containing CEBPB and TGFB1/SMAD3 was constructed by Cytoscape software [[73]24]. Results Identification of DEGs According to the analysis of the microarray dataset, a total of 529 DEGs (including 336 up-regulated genes and 193 down-regulated genes) were identified in the normal glioma cells compared with the CEBPB-silenced glioma cells. Among them, the top ten significantly up-regulated genes (such as thrombospondin 1 (THBS1) and chemokine (C-C motif) ligand 2 (CCL2)) and down-regulated genes (such as cyclin D1 (CCND1)) are displayed in Table [74]1. Table 1. The top ten up- and down-regulated genes DEGs Gene symbol FDR Log[2] FC Up-regulated AXL 9.39E−07 1.846031 SERPINE1 8.58E−07 1.741651 ITGB1 6.28E−08 1.739866 PRPF31 6.28E−08 1.644503 TXNDC5 3.26E−08 1.629988 WDFY1 3.26E−08 1.622947 AXL 1.57E−07 1.554728 SLC1A3 5.96E−08 1.484443 SET 3.90E−07 1.477058 ITGB1 2.66E−07 1.466634 Down−regulated AKR1B10 3.26E−08 −2.19537 SLC2A3 6.28E−08 −2.01825 HMOX1 6.28E−08 −1.58464 CCND1 9.30E−08 −1.49158 HIST1H2BK 1.16E−07 −1.38961 STX3 3.36E−07 −1.2468 TDG 8.98E−08 −1.23629 SRXN1 8.97E−07 −1.22479 DICER1 5.00E−07 −1.20817 STK40 9.14E−07 −1.19625 [75]Open in a new tab DEGs differentially expressed genes, FDR false discovery rate, FC fold change Functional and pathway enrichment analysis For the up-regulated genes, the enriched functions included transcription from RNA polymerase II promoter (p = 1.01E−03), cytoskeleton organization (p = 2.76E−04), and endocytosis (p = 2.57E−05) (Table [76]2A). Meanwhile, the down-regulated genes were mainly enriched in the function of enzyme-linked receptor protein signaling pathway (p = 2.89E−03), skin development (p = 4.97E−03), and response to hyperoxia (p = 2.97E−05) (Table [77]2B). Table 2. The top ten functions enriched for the differentially expressed genes GO ID Description Gene number p value Gene symbols (A)  GO:0006366 Transcription from RNA polymerase II promoter 47 1.01E−03 SOX21, TCF25, TOP2A, GTF2F2, CIAO1, SERPINE1, DKK1, CYR61, SOX18, PAF1…  GO:0007010 Cytoskeleton organization 32 2.76E−04 PTK2, DPYSL2, CNN3, BICD2, CLIC4, CTGF, EDN1, NRAS, ITGB1, RHOG…  GO:0006897 Endocytosis 23 2.57E−05 PTK2, PIK3R2, THBS1, SERPINE1, DKK1, CYFIP2, AXL, RABEPK, LRP1B, ABCA1…  GO:0071375 Cellular response to peptide hormone stimulus 15 5.75E−04 PTK2, PIK3R2, GNG10, PPM1A, GNG5, PIK3R1, ATP6V1G1, NRAS, SOCS2, GNG12…  GO:0000398 mRNA splicing, via spliceosome 10 1.02E−02 PABPC1, GTF2F2, LSM7, LSM3, POLR2C, UPF3B, MBNL2, C1QBP, PRPF31, PAPOLA  GO:0048469 Cell maturation 9 8.96E−04 SOX18, AXL, GJA1, DLD, FOXO3,TYMS,CLN5,EPAS1,PTBP3  GO:0043200 Response to amino acid stimulus 7 6.71E−04 CTGF, EDN1, CEBPB, TYMS, CCL2, LAMTOR3, LAMTOR1  GO:0006112 Energy reserve metabolic process 7 4.38E−02 GNG10, GNG5, GFPT2, RAP1B, PPP1CC, GNG12, PYGB  GO:0018279 Protein N-linked glycosylation via asparagine 6 1.02E−02 UGGT1, MLEC, GFPT2, B4GALT5, PGM3, STT3B  GO:0006261 DNA-dependent DNA replication 6 1.49E−02 POLB, MCM3, RFC5, TOP2A, BAZ1A, RPAIN (B)  GO:0007167 Enzyme-linked receptor protein signaling pathway 19 2.89E−03 KANK1, RTN4, ATP6V1D, PTPRK, EEF2K, ERRFI1, CGN, TGFBR2, ATP6V0A1, MVP…  GO:0043588 Skin development 9 4.97E−03 PTHLH, ALDH3A2, ERRFI1, YAP1, STK4, EMP1, COL5A2, NCOA3, DICER1  GO:0030330 DNA damage response, signal transduction by p53 class mediator 7 1.41E−04 NDRG1, SPRED1, PSME3, CDKN1A, E2F7, CASP2, HIPK2  GO:0001890 Placenta development 7 4.74E−04 TXNRD1, ADM, CCNF, SPP1, STK4, NDP, E2F7  GO:0031100 Organ regeneration 5 6.05E−05 ADM, TGFBR2, CCND1, LCP1, CDKN1A  GO:0071456 Cellular response to hypoxia 5 2.26E−03 HMOX1, NPEPPS, NDRG1, BNIP3, HIPK2  GO:0048002 Antigen processing and presentation of peptide antigen 5 4.35E−02 CTSD, NPEPPS, PSME3, AP1S1, AP1S2  GO:0055093 Response to hyperoxia 4 2.97E−05 TXNRD1, BNIP3, CAV1, CDKN1A  GO:0000188 Inactivation of MAPK activity 4 1.36E−04 DUSP5, SPRED1, CAV1, DUSP22  GO:0060443 Mammary gland morphogenesis 4 2.15E−03 PTHLH, TGFBR2, CAV1, NCOA3 [78]Open in a new tab GO Gene Ontology, ID identification (A) The top ten functions enriched for the up-regulated genes. (B) The top ten functions enriched for the down-regulated genes Among the up-regulated genes, CCL2 was significantly enriched in the pathway of chemokine signaling pathway (p = 1.63E−03). THBS1 and thrombospondin 2 (THBS2) were significantly involved in the pathway of focal adhesion (p = 7.54E−03). And the up-regulated genes, such as THBS1, THBS2, SMAD family member 5 (SMAD5) and SMAD family member 6 (SMAD6), were significantly enriched in transforming growth factor beta (TGF-beta) signaling pathway (p = 4.83E−02) (Table [79]3A). Meanwhile, the down-regulated transforming growth factor beta receptor II (TGFBR2) and CCND1 were significantly enriched in both the pathways of chronic myeloid leukemia (p = 9.85E−03) and pancreatic cancer (p = 4.69E−02) (Table [80]3B). Table 3. The pathways enriched for the differentially expressed genes KEGG ID Name Gene number p value Gene symbols (A)  4062 Chemokine signaling pathway 12 1.63E−03 PTK2, PIK3R2, GNG10, GNG5, RAP1B, PIK3R1, NRAS, IL8, GNG12, CSK, FOXO3, CCL2  4510 Focal adhesion 11 7.54E−03 PTK2, PIK3R2, THBS1, THBS2, RAP1B, PPP1CC, PIK3R1, ITGB1, ACTG1, FLNB, CAV2  4810 Regulation of actin cytoskeleton 11 1.18E−02 PTK2, PIK3R2, CYFIP2, PPP1CC, PIK3R1, NRAS, ITGB1, GNG12, ACTG1, CSK, ARHGEF6  4910 Insulin signaling pathway 9 5.22E−03 PIK3R2, PPP1CC, PIK3R1, NRAS, SOCS2, PTPN1, PYGB, CALM2, PTPRF  3013 RNA transport 9 9.27E−03 PABPC1, EIF3A, NUP54, EIF3G, UPF3B, NUP155, KPNB1, NUP37, EIF2S3  4145 Phagosome 8 2.82E−02 TAP1, THBS1, THBS2, ATP6V1G1, ITGB1, ACTG1, LAMP2, DYNC1LI2  5100 Bacterial invasion of epithelial cells 7 1.24E−03 PTK2, PIK3R2, PIK3R1, ITGB1, RHOG, ACTG1, CAV2  5142 Chagas disease (American trypanosomiasis) 7 1.13E−02 PIK3R2, SERPINE1, GNA11, PIK3R1, IL8, IFNGR1, CCL2  4722 Neurotrophin signaling pathway 7 3.05E−02 PIK3R2, RAP1B, PIK3R1, NRAS, CALM2, CSK, FOXO3  4360 Axon guidance 7 3.28E−02 PTK2, DPYSL2, SEMA4F, NRAS, ITGB1, SLIT2, EFNA1 5131 Shigellosis 6 3.01E−03 ITGB1, IL8, RHOG, ACTG1, FBXW11, CSK  5211 Renal cell carcinoma 5 2.45E−02 PIK3R2, RAP1B, PIK3R1, NRAS, EPAS1  5412 Arrhythmogenic right ventricular cardiomyopathy (ARVC) 5 3.03E−02 ITGB1, DAG1, GJA1, ACTG1, CDH2  5410 Hypertrophic cardiomyopathy 5 4.62E−02 TPM3, ITGB1, DAG1, TPM1, ACTG1  4350 TGF-beta signaling pathway 5 4.83E−02 THBS1, THBS2, SMAD6, ID3, SMAD5  20 Citrate cycle (TCA cycle) 4 5.11E−03 CS, DLD, DLAT, SDHA 5144 Malaria 4 3.19E−02 THBS1, THBS2, IL8, CCL2  5213 Endometrial cancer 4 3.39E−02 PIK3R2, PIK3R1, NRAS, FOXO3  5223 Non-small cell lung cancer 4 3.82E−02 PIK3R2, PIK3R1, NRAS, FOXO3  3410 Base excision repair 3 4.23E−02 POLB, PARP1, PARP3 (B)  4144 Endocytosis 9 4.62E−04 ASAP2, VPS36, TGFBR2, ASAP1, CAV1, SH3KBP1, EHD1, RAB22A, DNM3  4142 Lysosome 7 4.55E−04 CTSD, TPP1, ATP6V0A1, ABCB9, AP1S1, AP1S2, NEU1  2010 ABC transporters 4 1.58E−03 ABCC2, ABCC3, ABCB9, ABCC5  10 Glycolysis/gluconeogenesis 4 6.56E−03 ENO2, ALDH3A2, PGAM1, PGK1  5220 Chronic myeloid leukemia 4 9.85E−03 TGFBR2, CCND1, CDKN1A, BCL2L1  561 Glycerolipid metabolism 3 1.98E−02 ALDH3A2, AGPAT9, LCLAT1  5212 Pancreatic cancer 3 4.69E−02 TGFBR2, CCND1, BCL2L1  4966 Collecting duct acid secretion 2 3.85E−02 ATP6V1D, ATP6V0A1   650 Butanoate metabolism 2 4.67E−02 AKR1B10, HMGCS1 [81]Open in a new tab (A) The pathways enriched for the up-regulated genes. (B) The pathways enriched for the down-regulated genes. Kyoto Encyclopedia of Genes and Genomes, KEGG; identification, ID The annotation of DEGs A total of 54 DEGs were screened as TAGs, including 33 up-regulated and 21 down-regulated genes. Among the 33 up-regulated genes, there were 22 TSGs (such as THBS1), 6 oncogenes, and 5 other genes (such as CCL2). Meanwhile, there were 13 TSGs, 4 oncogenes (such as CCND1), and 4 other genes in the 21 down-regulated genes. Additionally, 9 DEGs were screened as the TFs, including 8 up-regulated and 1 down-regulated genes (Table [82]4). Table 4. The identified transcription factors (TFs) and tumor associated genes (TAGs) among the differentially expressed genes (DEGs). Tumor suppressed genes, TSGs DEGs TF numbers TFs TAG numbers TAGs TSGs Oncogenes Others Up-regulated 1 KLF12 33 BAP1, THBS1, DKK1, PAF1, ST13, LRP1B, PDGFRL, ITGB1, TPM1, GJA1, CDH11, SLIT2, GLIPR1, FAT1, SOD2, FOXO3, EFNA1, GAS1, PTPRF, RAD51C, CAV2, SDHA SET, CCNA2, AXL, NRAS, ROS1, SCK GTF2F2, CTGF, FHL2, C1QBP, CCL2 Down-regulated 8 ASCL1, ETV4, HSF1, LMO3, PML, RUNX3, TCF7, USF2 21 HIPK2, YAP1, ERRFI1, PTPRK, KANK1, BNIP3L, DUSP22, SASH1, CDKN1A, NDRG4, ZFHX3, NDRG1, TGFBR2, BCL2L2, NCOA3, CCND1, CDC25B PTHLH, EMP1, CAV1, GLS [83]Open in a new tab PPI network analysis The constructed PPI network was consisted of 810 interactions (such as CCND1-THBS1 and THBS1-CCL2) (Fig. [84]1). Besides, the top 10 % nodes with higher degrees in the PPI network were identified, including CCND1 (degree = 29) and CCL2 (degree = 12) (Table [85]5). Fig. 1. Fig. 1 [86]Open in a new tab The protein-protein interaction (PPI) network for the differentially expressed genes (DEGs). The red circles represent the up-regulated genes. The green circles indicate the down-regulated genes Table 5. The top 10 % DEGs with higher degrees in the protein-protein interaction (PPI) network Gene Degree Gene Degree Gene Degree Gene Degree CCND1 29 SOD2 19 CENPN 16 KIF11 15 PIK3R1 25 TYMS 19 CAV1 16 PTK2 15 PGK1 22 CDKN1A 18 PIK3R2 16 EDN1 14 NUP37 22 PARP1 18 CTGF 15 CS 13 CALM2 21 TOP2A 18 RFC5 15 CCL2 13 MCM3 21 ITGB1 18 NUP155 15 RSL24D1 12 GMNN 20 TCP1 18 NRAS 15 CDCA7 12 CCNA2 20 SERPINE1 17 NIP7 15 BCL2L1 12 [87]Open in a new tab Transcriptional regulatory network analysis For further study, the regulation of TGFB1/SMAD3 by CEBPB, the transcriptional regulation interactions related to TGFB1/SMAD3, and the members of TGFB family were screened out from the ENCODE database and the transcriptional regulatory network was visualized by Cytoscape software (Fig. [88]2). The transcriptional regulation network showed that the CEBPB could regulate SMAD3, transcription factor 12 (TCF12), transforming growth factor beta 2 (TGFB2), TGFBR2, and TGFBR3 directly. Additionally, TCF12 targeted TGFB1, TGFBR1, TGFBR2, TGFBR3, and SMAD3. Fig. 2. Fig. 2 [89]Open in a new tab The transcriptional regulatory network involving CEBPB and TGFB1/SMAD3. The red and green nodes represent the up-regulated and down-regulated genes, respectively. The blue nodes stand for non-differentially expressed genes (DEGs). The arrows represent regulatory relationships Discussion In this study, a total of 529 DEGs were obtained, including 336 up-regulated genes and 193 down-regulated genes. Enrichment analysis indicated that the up-regulated CCL2 was significantly enriched in the chemokine signaling pathway. Reports have found that chemokine expressed by stromal cells or endogenously produced in glioma cells may play key roles in tumor cell migration, invasion, proliferation, angiogenesis and immune cell infiltration in the tumor mass [[90]26]. The chemokine CCL2 can promote glioma tumor aggressiveness by promoting attraction of T regulatory cells (which suppress the lymphocyte anti-tumor effector function) and microglial cells (which can reduce the anti-tumor functions and secrete pro-invasive metalloproteinases) [[91]27, [92]28]. Meanwhile, metalloproteinases can promote the glioma invasion through the detachment of ECM [[93]29]. Besides, results of DEGs annotation showed that CCL2 was screened out as a TAG. Therefore, we speculated that the increased expression of CCL2 could promote glioma aggressiveness through the pathway of chemokine signaling. In addition, some up-regulated genes (such as THBS1, THBS2, SMAD5, and SMAD6) were significantly enriched in the TGF-beta signaling pathway in our study. Recently, it has been reported that the TGFB is a key factor in controlling migration, invasion and angiogenesis in glioblastoma and induces profound immunosuppression [[94]30]. Besides, the THBS1 (belonging to thrombospondin family), which is referred as a TGFB activating protein, induces the glioma invasion [[95]31]. THBS1 is a powerful anti-angiogenesis protein in glioblastoma [[96]32]. These suggested that THBS1 might play a key role in regulating the angiogenesis in glioma. As another member of thrombospondin family, THBS2 may be a potential inhibitor of tumor growth and angiogenesis [[97]33]. Moreover, it has been shown that THBS2 can function as an endogenous inhibitor of angiogenesis through directly affecting endothelial cell migration, proliferation, survival, and apoptosis [[98]34]. In our study, we also found that THBS1 and THBS2 were significantly involved in the pathway of focal adhesion. Previous study reported that focal adhesion can suppress the migration and metastasis of tumor cells [[99]35]. Therefore, we speculated that THBS1 and THBS2 could regulate angiogenesis and invasion in glioma via TGF-beta signaling pathway and focal adhesion pathway. Former researches have shown that SMAD6 is an inhibitor of TGFB signaling and blocked the phosphorylation of receptor-regulated SMADs (such as SMAD5) in the cytoplasm [[100]36]. As a result, we assumed that SMAD5 and SMAD6 might affect glioma by regulating the TGFB signaling. In the PPI network, THBS1 could interact with CCL2, to some extent, indicating that THBS1 might play key roles in glioma through regulating CCL2. Consequently, THBS1, THBS2, SAMD5 and SMAD6 could be key factors involved in the CEBPB-silenced glioma. Moreover, CCND1, as a member of the cyclin family, possessed the highest degree in the PPI network. Cyclins can modulate tumor cell cycle through alterations in cyclin-dependent kinase activity [[101]37]. What’s more, researchers have discovered that overexpression of CCND1 can elevate the proliferation and invasion potential of human glioblastoma cells [[102]38]. In the PPI network, we also found that CCND1 had interaction with THBS1, suggesting that CCND1 could be involved in regulating proliferation and invasion of glioma via interacting with THBS1. TGFBR2 plays a key role in TGFB signal propagation via activating TGFBR1 and the phosphorylation of SMAD proteins [[103]39]. Moreover, silencing of TGFBR2 can abolish TGFB-induced invasion and migratory responses of glioblastoma in vitro [[104]40]. In our study, we also discovered that the up-regulated TCF12 could regulate TGFB1 and SMAD3, indicating that CEBPB might regulate TGFB1 and SMAD3 through TCF12. Previous studies have shown that TGFB1/SMAD3 can promote tumor cell migration, invasion and metastasis through inducing epithelial-mesenchymal transition [[105]41, [106]42]. What is more, TCF12 has been found to suppress the expression of E-cadherin, which can lead to the metastasis of tumor cells [[107]43]. Therefore, we assumed that CEBPB might regulate TGFBR2 and SMAD3 through TGF-β1/SMAD3 signaling pathway in glioma, and CEBPB could also affect metastasis of glioma by regulating TCF12. However, in our study, TGFB1 and SMAD3 were not significantly expressed, which might due to the relatively short time for CEBPB silencing. In our further research, the regulation of CEBPB on TGFB1/SMAD3 will be studied with CEBPB-silenced for a relatively long time. Conclusions We conducted a comprehensive bioinformatics analysis to identify genes which may be correlated with CEBPB-silenced glioma. A total of 529 DEGs were identified in the normal glioma cells compared with the CEBPB-silenced glioma cells. Besides, The identified DEGs, such as TCF12, TGFBR2, CCL2, THBS1, THBS2, SMAD5, SMAD6, and CCND1, might play important roles in the progression of glioma via the regulation of CEBPB. However, further researches are still needed to unravel their action mechanisms in glioma. Acknowledgements