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Objective To analyze and identify hub genes of temozolomide resistance in glioma by an integrated bioinformatics approach, and to provide new directions for the study of drug resistance mechanisms and clinical treatment of glioma. Methods R-language was used to screen for differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to analyze their functions and pathways. Protein-protein interaction (PPI) analysis was used to screen for hub genes. The Gliovis database was used to verify the mRNA expression of hub genes in primary and recurrent glioma tissues and to analyze their survival. Results A total of 1 230 DEGs were screened, including 648 up-regulated genes and 582 down-regulated genes. GO showed that DEGs were involved in ribosome biogenesis, ribonucleoprotein complex biosynthesis and rRNA processing in terms of biological processes (BP). Cellular components (CC) included mitochondrial matrix, condensed chromosomes, chromosomal regions, etc. In terms of molecular functions (MF), DEGs participated in catalytic activity, acting on RNA, transferase activity, etc. KEGG showed that DEGs were mainly involved in nucleoplasmic transport, cell cycle, DNA replication, etc. PPI screened TOP10 hub genes: BUB1B, ASPM, KIF4A, CDC6, NCAPG, MCM7, MCM3, MCM5, MCM10, RFC4. Analysis of the Gliovis database showed that BUB1B, ASPM, KIF4A, CDC6, NCAPG, MCM3, MCM5, MCM10 and RFC4 were relatively up-regulated in recurrent glioma tissues and negatively correlated with survival. Conclusion BUB1B, ASPM, KIF4A, CDC6, NCAPG, MCM3, MCM5, MCM10 and RFC4, which are highly expressed in drug-resistant glioma cells and recurrent glioma tissues and negatively correlated with survival, are identified as hub genes for temozolomide resistance in glioma by comprehensive bioinformatics analysis. [ABSTRACT FROM AUTHOR]

Aim GEO database was used to explore the common pathogenesis of gout complicated with atherosclerosis (As). Methods The gene expression matrices of gout (GSE160170) and As (GSE100927) were downloaded from the GEO database, the differentially expressed genes (DEG) of gout and As were analyzed, and enrichment analysis was performed separately. After analyzing the common differentially expressed genes (CDEG), functional enrichment analysis, protein-protein interaction (PPI) network analysis, and hub genes (HG) identification were performed on them, and co-expression analysis and validation were performed on hub genes. Finally, the immune cell infiltration of gout and As was analyzed, and the correlation between hub genes and infiltrating immune cells (IIC) was explored. Results The GSE160170 dataset obtained 1 606 differentially expressed genes, while the GSE100927 dataset obtained 481 differentially expressed genes. The enrichment analysis of 22 differentially expressed genes showed that the regulation of cytokines may be the key mechanism of gout complicated with As. Six hub genes (CCR2, CD2, FCGR3A, FGD3, IL10RA, SIGLEC1) were identified using the CytoHubba plugin, and the validation results of these hub genes showed that they were still reliable. The co-expression network showed that these hub genes could affect the regulation of tumor necrosis factor superfamily cytokines. Immune cell infiltration analysis showed that the expression of NK cells in gout was significantly increased, and was significantly related to CCR2 gene. The expression of living fertilizer large cells in As was significantly increased, and was significantly related to CD2 gene. Conclusion The regulatory effect of tumor necrosis factor superfamily cytokines may be the core factor of gout complicated with As. [ABSTRACT FROM AUTHOR]

BackgroundAlcohol use disorder （AUD） is a type of chronic relapsing brain disorder. Genetic factors play an important role in the pathogenesis of AUD. And screening for molecular markers of AUD is of great significance for further elucidating the pathogenesis of the disease， discovering novel therapeutic targets and preventing relapse.ObjectiveTo explore relevant hub genes and potential signal pathways associated with the development of AUD through bioinformatics analysis， and to provide a new direction for the prevention and treatment of AUD.MethodsThe GSE161986 dataset was acquired from the Gene Expression Omnibus （GEO） database. The limma package in R was utilized to identify differentially expressed genes （DEGs）. Gene set enrichment analysis （GSEA） was carried out using the Database for Annotation， Visualization and Integrated Discovery （DAVID）. A protein–protein interaction （PPI） network of DEGs was assessed using the STRING database and visualized by Cytoscape software. Finally， hub genes were validated in GSE44456 dataset.ResultsA total of 114 DEGs were identified. GSEA revealed that these genes were mainly involved in the regulation of signal transduction， protein binding， membrane trafficking and MAPK signaling pathway. PPI network and validation study indicated that GAD1， TIMP1 and CD44 were potential hub genes involved in AUD.ConclusionAberrant expression of GAD1 and TIMP1 as well as MAPK signaling pathway may play a key role in the pathogenesis of AUD， and may serve as potential molecular targets for the diagnosis and treatment of AUD. ［Funded by "Flying Project" of Shanghai Mental Health Center （number， 2022-FX-01）］

Copyright of China Journal of Oral & Maxillofacial Surgery is the property of Shanghai Jiao Tong University, College of Stomatology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background Cardiovascular disease (CVD) is a common and frequently occurring disease, and the prevalence and mortality of which are increasing rapidly. Atherosclerosis (AS) is the pathological basis of ischemic CVD. Studies have shown that epicardial adipose tissue (EAT) promotes the progression of AS by secreting exosomes and bioactive substances, but the mechanism of action still needs to be further studied. Objective To perform a bioinformatics analysis of role of EAT in coronary artery disease (CAD) at cellular and molecular levels by identifying the differentially expressed genes (DEGs) in EAT to explore the status of immune cell infiltration, and to assess and verify whether EAT is derived from DEGs in exosomes in CAD patients. Methods We downloaded GSE64554 and GSE120774 datasets about EAT from the GEO database and performed a bioinformatics analysis using R language and related packages. We first used R language to screen the DEGs in EAT and CAD patients, then used GO/KEGG enrichment analysis to establish a protein interaction network to explore biological functions of the screened genes and transcription factors potentially involved in their regulation process. After that, we conducted a weighted gene co-expression network analysis (WGCNA) of EAT in GSE64554 dataset to obtain a gene module related to CAD phenotype, then crossed the hub genes in this module and DEGs in EAT to obtain the key common genes. We used Cibersort to characterize the immune cell infiltration in EAT. Then we obtained DEGs from blood exosomes of CAD patients and healthy controls included in the exoRbase database, crossed DEGs in EAT and blood exosomes to identify the common genes to be used as diagnostic and therapeutic markers for CAD, and their values were tested by qRT-PCR measurement of clinical samples. The selected genes were analyzed by GO/KEGG and Metascape enrichment analyses. Results A total of 1 511 DEGs in EAT of CAD patients were identified, including 956 with up-regulated expression and 555 with down-regulated expression. By crossing the DEGs in EAT and hub genes in modules associated with CAD closely, we identified DDX47, FEM1C, NOL11, SRP54, ABI1, PATL1, BNIP2, C1orf159, and CHCHD4 as key genes in the development of CAD. Immune cell infiltration analysis showed that the abundance of immature CD4+ T cells increased while expression abundance of resting dendritic cells decreased in EAT of CAD patients (P0.05), and decreased F2RmRNA expression level (P

Objective To screen differentially expressed genes (DEGs) related to the progression of osteoarthritis by bioinformatics method. Methods The GSE206848 dataset was downloaded from the gene expression omics database (GEO),analyzed by GEO2R online tool, and screened for DEGs. R software was used to draw volcano and heat maps, then GO functional enrichment analysis and KEGG pathway enrichment analysis were performed for DEGs. The DEGs data were imported into STRING database to construct protein-protein interaction (PPI) network, the hub genes were screened through 5 algorithms for intersection selection of the CytoHubba plugin. Results After GEO2R screening, 1 592 DEGs (545 upregulated genes and 1 047 downregulated genes) were obtained from the GSE206848 dataset. GO funtional enrichment analysis showed that the biological process of DEGs was mainly enriched in the regulation of DNA metabolism, the regulation of supramolecular fibrous tissue, and the negative regulation of organelle tissue. Cell localization mainly enriched cell margin, actin cytoskeleton and nuclear speckle. Molecular functions mainly enrich tubulin binding, protein serine/threonine kinase activity, and protein serine/threonine/tyrosine kinase activity. The enrichment analysis of KEGG pathway mainly involved PI3K Akt signaling pathway, actin cytoskeleton regulation, etc. After using the CytoHubba plugin to screen three hub genes (PIK3CA,EGFR,and SRC) . Conclusion In this study, three hub genes related to the progression of osteoarthritis were screened, namely EGFR,PIK3CA and SRC. [ABSTRACT FROM AUTHOR]

Background Alcohol use disorder(AUD) is a type of chronic relapsing brain disorder. Genetic factors play an important role in the pathogenesis of AUD. And screening for molecular markers of AUD is of great significance for further elucidating the pathogenesis of the disease, discovering novel therapeutic targets and preventing relapse. Objective To explore relevant hub genes and potential signal pathways associated with the development of AUD through bioinformatics analysis, and to provide a new direction for the prevention and treatment of AUD. Methods The GSE161986 dataset was acquired from the Gene Expression Omnibus(GEO) database. The limma package in R was utilized to identify differentially expressed genes(DEGs). Gene set enrichment analysis(GSEA) was carried out using the Database for Annotation, Visualization and Integrated Discovery(DAVID). A protein–protein interaction(PPI) network of DEGs was assessed using the STRING database and visualized by Cytoscape software. Finally, hub genes were validated in GSE44456 dataset. Results A total of 114 DEGs were identified. GSEA revealed that these genes were mainly involved in the regulation of signal transduction, protein binding, membrane trafficking and MAPK signaling pathway. PPI network and validation study indicated that GAD1, TIMP1 and CD44 were potential hub genes involved in AUD. Conclusion Aberrant expression of GAD1 and TIMP1 as well as MAPK signaling pathway may play a key role in the pathogenesis of AUD, and may serve as potential molecular targets for the diagnosis and treatment of AUD. ［Funded by "Flying Project" of Shanghai Mental Health Center(number, 2022-FX-01). [ABSTRACT FROM AUTHOR]

Objective To explore the association of hub genes and immune infiltration with acne pathogenesis by integrating and analyzing multi-chip data. Methods Gene microarray datasets were obtained from the GEO database, and differentially expressed genes (DEGs) in the acne phenotype-related gene module were screened using the "limma" R package and weighted gene co-expression network analysis (WGCNA). Moreover, GO functional enrichment and KEGG pathway analysis were performed. STRING online database and Cytoscape software were used to construct protein interaction network and to screen hub genes. Analysis of immune cell infiltration and correlation of hub genes with immune cell infiltration were based on the CIBERSORT algorithm. Results A total of 154 DEGs were screened. The DEGs associated with signaling receptor binding, cytokine activity and chemokine receptor binding were mainly enriched in pathways such as viral protein interaction with cytokine and cytokine receptors, cytokine-cytokine receptor interactions, chemokine signaling, NF-κB signaling and IL-17 signaling. The PPI network was constructed with 139 nodes and 1 597 edges, identifying two hub genes (CCR5, CXCL8). Among the 22 infiltrating immune cells, lesional site exhibited more neutrophils, activated mast cells, activated dendritic cells and activated CD4 memory T cells, but fewer regulatory T cells, resting dendritic cells and resting mast cells, in comparison to normal controls. Conclusion Changes in hub genes and immune cell infiltration, as identified by bioinformatics methods, may play an important role in the pathogenesis of acne.

Tuber is the main economic organ of potato. It is of great significance to illustrate its formation and development mechanism for high yield breeding of potato. Although some related genes have been reported, there are still a large number of genes involved in tuber production which need to be further excavated and identified. With the continuous innovation and development of sequencing technology, transcriptome data has become more abundant and complicated. The method of screening genes related to specific traits, tissues or developmental stages using Weighted Gene Co-expression Network Analysis (WGCNA) has been widely used. In this study, to construct co-expression networks and screen co-expression modules related to tuber development, WGCNA was used to analyze transcriptome data of potato DM 1-3516 R44 (DM) material and RH89-039-16 (RH) material from 15 tissues and organs, respectively. GO and KEGG pathway enrichment were performed on the genes in the screening expression module. The top 10 genes in the module were taken as the hub genes, annotated by PGSC database and NCBI, and verified by qRT-PCR. 13 co-expression modules were obtained in both DM and RH materials, among which the Cray module in DM material analysis and the Black module in RH material analysis were significantly correlated with tubers by WGCNA. The analysis showed that the genes of the two modules were significantly enriched in related processes such as hormone metabolism, starch and sucrose metabolism, and cell formation. In addition, gene StGA2ox1, which had been proved to be related to tuber development, was noted in Cyan, and StGA2ox1 was correlated with 10 hub genes in the module in the co-expression analysis. The qRT-PCR results of the hub genes were basically consistent with the results of RNA-seq. In this study, two co-expression modules related to tuber development were identified by WGCNA, and several candidate genes related to tuber development were screened out, which laid a foundation for high yield potato breeding. [ABSTRACT FROM AUTHOR]

Objective To investigate the differentially expressed genes (DEGs) related to the occurrence and development of osteoporosis (OP) and to screen the potential drug targets. Methods The microarray data concerns osteoporosis were obtained from GEO database and DEGs were identified with R statistical software. GO and KEGG enrichment analysis, protein-protein interaction (PPI) network analysis, and selection of Hub genes were conducted. Results A total of 569 DEGs were screened out. Among them, 7 genes were up-regulated and 562 genes were down-regulated. At the same time, differential gene GO analysis was mainly enriched in processes such as pre-mRNA intronic binding, nuclear body, histone modification, and mRNA 3’-end processing. KEGG analysis was mainly involved the ubiquitin mediated proteolysis signal pathway (hsa04120). PPI network analysis showed 517 nodes and 363 edges. Ten Hub genes, including TCEB1, CUL2, KBTBD6, KBTBD7, ASB8, KLHL42, ASB5, FBXO11, ANAPC10, and CDC23, were acquired using Cytoscape software. These genes were all down-regulated in mesenchymal cells of the femoral head of OP patients. Conclusion The top 10 Hub genes might help us to understand the pathophysiology of OP, and provide therapeutic targets for the development of drugs. Meanwhile, it might provide some new ideas for funding creative scientific hypotheses of OP. [ABSTRACT FROM AUTHOR]

Objective To screen out the differentially expressed genes（DEGs）of polymyositis（PM）by using bioinformatics method. Methods PM gene expression profile GSE128470 was downloaded from GEO database. We screened the DEGs of PM by using GEO2R online analysis tool. DAVID online website was used to perform Gene Ontology（GO）enrichment analysis and Kyoto Encyclopedia of Genes and Genomes（KEGG）signal pathway analysis of DEGs. The STRING database was used to predict the protein-protein interaction network of DEGs, and the plug-ins MOCOD and CytoHubba in the Cytoscape 3. 8. 2 software were used to screen out the core modules and hub genes. Results A total of 366 DEGs were obtained from PM gene expression profile GSE128470, of which, 331 were up-regulated and 35 were down-regulated. GO enrichment analysis showed that the main functions of DEGs involved interferon γ-mediated signaling pathways, immune responses, components on the lumen side of the endoplasmic reticulum, extracellular space, peptide antigen binding, MHC class Ⅱ receptor activity, etc. KEGG signal pathway analysis showed that the main functions of DEGs involved antigen processing and presentation, phagosomes, allograft rejection, cell adhesion molecules, interactions between cytokines and cytokine receptors, and Toll-like receptor signaling pathways. The top ten DEGs in the protein-protein interaction network were used as key genes, including PTPRC, STAT1, CD44, VCAM1, ICAM1, IRF8, IRF1, ITGB2, TYROBP, and HLA-DRB1. Conclusions In this study, 366 DEGs are screened out from the muscle tissues of PM patients. These DEGs are mainly involved in antigen processing and presentation, immune response, phagosome, cell adhesion molecules and other signal pathways. PTPRC, STAT1, CD44, VCAM1, ICAM1, IRF8, IRF1, ITGB2, TYROBP and HLA-DRB1 are the hub genes of PM. These key genes may become molecular markers for early diagnosis, targeted therapy and prognosis evaluation of PM. [ABSTRACT FROM AUTHOR]

Objective: To explore the hub genes associated with the pathogenesis and healing of diabetic foot ulcer (DFU) and their biological functions through bioinformatics analysis of transcriptome sequencing data., Methods: The transcriptome sequencing datasets of DFU were selected from Gene Expression Omnibus (GEO) database, and the data were regrouped and normalized for bioinformatics analysis. The skin transcriptome sequencing datasets of DFU patients were compared with those of normal controls and the transcriptome sequencing datasets of skin from ulcerous wound edge of DFU patients were compared with those of non-ulcerous skin of DFU patients so that differentially expressed genes were identified, pathway enrichment and protein-to-protein interaction (PPI) analyses were performed, hub genes were found through nodal analysis, and receiver operating characteristic (ROC) curve was applied to a testing dataset to validate the diagnostic efficiency of the hub genes related to DFU. The intersecting genes from the two sets of analyses were again subjected to pathway enrichment and PPI analyses to screen for hub genes associated with DFU wound healing. What's more, gene set enrichment analysis (GSEA) was carried out on relevant samples to probe for the possible functions and pathway of non-significant genes in DFU., Results: A total of 620 up-regulated differentially expressed genes and 196 down-regulated differentially expressed genes were identified in the training dataset which compared DFU patients with non-diabetic patients. The functions of these genes were enriched in the metabolism of terpenoids and polyketides, signaling molecules and interaction, phospholipase D signaling pathway, propanoate metabolism, PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, pyrimidine metabolism, IL-17 signaling pathway, Rap1 signaling pathway, etc. A total of 10 hub genes were identified with the PPI network. Among them, BGN 's value of the area under the curve of ROC analysis was 0.714 and CCND 1's was 0.712. In the sequencing analysis of ulcerous wound edge of DFU patients and non-ulcerous skin of DFU patients, 4072 up-regulated genes and 911 down-regulated genes were identified, of which, 372 genes were also detected in the differentially expressed genes of DFU. The functions of these differentially expressed genes were enriched in phospholipase D signaling pathway, xenobiotics biodegradation and energy metabolism, glutathione metabolism, pyrimidine metabolism, ErbB signaling pathway, melanin production, etc. A total of 7 hub genes were identified from PPI network. In GSEA analysis, pathways including pentose and glucuronate interconversions and homologous recombination, nicotinate and nicotinamide metabolism, neuroactive ligand receptor interaction, maturity-onset diabetes of the young, butanoate metabolism, lysine degradation, pantothenate and coenzyme A biosynthesis, riboflavin metabolism, steroid hormone biosynthesis, and valine, leucine and isoleucine degradation showed significant expression differences between DFU patients and normal controls., Conclusion: Bioinformatics analysis results suggest that BGN and CCND 1 are potential biomarkers for predicting DFU; CXCL 12, TLR 4, JAK 2, PPARA , UBC , DCN , KDR , and ARNTL are the hub genes of DFU, while CXCL 8, CXCL 12, TXN , SLIT 3, KRT 14, KIT , and NEO 1 are the hub genes related to wound healing of DFU., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

Objective: To investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules., Methods: We downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells., Results: Ten gene co-expression modules were constructed based on the correlations of gene expression, in which the brown ( r =0.66, P < 0.001) and turquoise modules ( r =-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells ( P < 0.01)., Conclusion: The brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.

Objective: To identify the key genes involved in the transformation of hepatitis B virus (HBV) into hepatocellular carcinoma (HCC) and explore the underlying molecular mechanisms., Methods: We analyzed the mRNA microarray data of 119 HBV-related HCC tissues and 252 HBV-related non-tumor tissues in GSE55092, GSE84044 and GSE121248 from the GEO database, and the "sva" R package was used to remove the batch effects. Integration analysis was performed to identify the differentially expressed genes (DEGs) in HBV-related liver cancer and liver tissues with HBV infection. The significant DEGs were functionally annotated using GO and KEGG analyses, and the most important modules and hub genes were explored with STRING analysis. Kaplan-Meier and Oncomine databases were used to verify the HCC gene expression data in the TCGA database to explore the correlations of the hub genes with the occurrence, progression and prognosis of HCC. We also examined the expressions of the hub genes in 17 pairs of surgical specimens of HCC and adjacent tissues using RT-qPCR., Results: We identified a total of 121 DEGs and 3 genetic markers in HCC ( P < 0.01). These DEGs included cyclin1 (CDK1), cyclin B1 (CCNB1), and nuclear division cycle 80 (NDC80), which participated in cell cycle, pyrimidine metabolism and DNA replication and were highly correlated ( P < 0.05). Analysis of the UALCAN database confirmed high expressions of these 3 genes in HCC tissues, which were correlated with a low survival rate of the patients, as shown by Kaplan-Meier analysis of the prognostic data from the UALCAN database. CDK1, CCNB1 and NDC80 were all correlated with the clinical grading of HCC ( P < 0.05). The results of RT-qPCR on the surgical specimens verified significantly higher expressions of CDK1, CCNB1 and NDC80 mRNA in HCC tissues than in the adjacent tissues., Conclusion: CDK1, CCNB1 and NDC80 genes can be used as prognostic markers of HBV-related HCC and may serve as potential targets in preclinical studies and clinical treatment of HCC.

Objective: To investigate the significance of stemness-related genes in the diagnosis and treatment of ovarian cancer., Methods: Key modules and genes were identified with weighted gene co-expression network analysis (WGCNA). The signal pathways of high expression of key genes were analyzed by gene set enrichment analysis (GSEA) and single cell sequencing data. The chemosensitivity of ovarian cancer to chemotherapy drugs was estimated with pRRophetic. Flow cytometry was used to examine the expression of CD44 + CD117 + in SKOV3 cells and cancer stem cells. The expression of key genes in ovarian cancer stem cells was confirmed by qRT-PCR. The core genes were identified by GeneMANIA analysis., Results: According to the WGCNA results, 15 key genes were identified at the transcription level, all being highly expressed in many kinds of tumors. They were involved in the cell cycle, DNA repair, E2 target and G2M checkpoint pathway, and had significant correlation with chemosensitivity. The proportion of CD44 + CD117 + cells in SKOV3 cells and ovarian cancer stem cells were (1.20±0.34)% and (37.17±1.80)% respectively, with statistically significant difference ( P <0.05). qRT-PCR confirmed that seven key genes ( BUB 1, CDC 20, CCNB 2, DLGAP 5, KIF 4 A , NEK 2, NUSAP 1) in the WGCNA results were highly expressed in ovarian cancer stem cells, and BUB 1 might have played a core role., Conclusion: Seven hub genes, especially BUB 1, were identified by constructing gene co-expression network, which may become potential biomarkers of ovarian cancer gene., (Copyright© by Editorial Board of Journal of Sichuan University (Medical Sciences).)

The aim of this paper was to analyze the microarray data between ulcerative colitis(UC) patients and healthy people by bioinformatics technology, screen the differentially expressed genes of UC, and predict the potential Chinese medicines for UC. The GSE36807 gene expression profile was downloaded from the gene expression database(GEO) and the differentially expressed(both up-regulated and down-regulated) genes(DEGs) were analyzed by using R language software. The core genes in the DEGs were obtained by using String database, Cytoscape software and its plug-in analysis, and the gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG) were used to analyze the core genes. Moreover, the core genes and the medical ontology information retrieval platform(Coremine Medical) were mapped to each other to screen the traditional Chinese medicines and its active ingredients for treating UC. A total of 648 DEGs were screened, including 397 up-regulated genes and 251 down-regulated genes. Up-regulation of DEGs yielded 15 core genes including CXCL8, IL1 B, MMP9, CXCL1, CXCL10, CXCL9, CXCL2, CXCL5, TIMP1, CXCL11, STAT1,LCN2, IL1 RN, MMP1 and IDO1. Their biological processes and pathways were mainly enriched in interleukins, chemokine ligands and cytokines, chemokine-mediated signaling pathways, and were closely related to inflammatory responses, defense responses, cell chemotaxis, secretory granules, IL17 signaling pathways, Toll-like receptor signaling pathway, NOD-like receptor signaling pathway, and TNF signaling pathway. Potential Chinese medicines for the treatment of UC include Curcumae Longae Rhizoma, Coptidis Rhizoma, Scutellariae Radix, Dendrobii Caulis, Sanguisorbae Radix, Phellodendri Chinensis Cortex, Bletillae Rhizoma and Atractylodis Rhizoma. The analysis of DEGs and core genes could promote our understanding on pathogenesis of UC. This study provides potential gene targets and research ideas for the development of new drugs of Chinese medicine intervention for UC.