Key Prediction Genes of Nasopharyngeal Carcinoma:Screening Based on Systematic Bioinformatics and Validation by Cell Experiments / 中国医学科学院学报

Objective To screen out the potential prediction genes for nasopharyngeal carcinoma(NPC)from the gene microarray data of NPC samples and then verify the genes by cell experiments.Methods The NPC dataset was downloaded from Gene Expression Omnibus,and limma package was employed to screen out the differentially expressed genes.Weighted correlation network analysis package was used for weighted gene co-expression network analysis,and Venn diagram was drawn to find the common genes.The gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment were then performed for the common genes.The biomarkers for NPC were further explored by protein-protein interaction network,LASSO regression,and non-parametric tests.Real-time quantitative PCR and Western blotting were employed to determine the mRNA and protein levels of key predictors of NPC,so as to verify the screening results.Results There were 622 up-regulated genes and 351 down-regulated genes in the GSE12452 dataset.A total of 116 common genes were obtained by limma analysis and weighted gene co-expression network analysis.The common genes were mainly involved in the biological processes of cell proliferation and regulation and regulation of intercellular adhesion.They were mainly enriched in Rap1,Ras,and tumor necrosis factor signaling pathways.Six key genes were screened out,encoding angiopoietin-2(ANGPT2),dual oxidase 2(DUOX2),coagulation factor Ⅲ(F3),interleukin-15(IL-15),lipocalin-2,and retinoic acid receptor-related orphan receptor B(RORB).Real-time quantitative PCR and Western blotting showed that the NPC cells had up-regulated mRNA and protein levels of ANGPT2 and IL-15 and down-regulated mRNA and protein levels of DUOX2,F3,and RORB,which was consistent with the results predicted by bioinformatics.Conclusion ANGPT2,DUOX2,F3,IL-15 and RORB are potential predictive molecular markers and therapeutic targets for NPC,which may be involved in Rap1,Ras,tumor necrosis factor and other signaling pathways.

Gene expression profile in human cervical epithelial cells infected with Chlamydia trachomatis / 中华微生物学和免疫学杂志

Objective:To compare gene expression profiles in normal human cervical epithelial cells (HcerEpic) before and after Chlamydia trachomatis ( Ct) infection. Methods:HcerEpic cells that were pretreated with DEAE-D were infected with Ct serotype E standard strain and then cultured for 44 h. Uninfected HcerEpic cells were used as the control group. Total RNA was extracted from the cells in each group and reverse transcribed to construct a cDNA library. Differences in gene expression profiles between the two groups were analyzed by high-throughput sequencing and the representative genes were selected for verification by qPCR. Results:A total of 23 997 genes were detected, including 125 differentially expressed genes. Among the 125 genes, 119 were up-regulated and six were down-regulated. GO analysis showed that the differentially expressed genes were enriched in several biological processes including defense response to virus, typeⅠinterferon signaling pathway and cellular responses to typeⅠinterferons. KEGG enrichment analysis showed the differentially expressed genes were mainly enriched in the pathways related to virus infections, such as influenza A virus, herpes simplex virus, EB virus and HPV, and NOD-like receptor pathway.Conclusions:There were significant differences in transcriptome profiles of HcerEpic cells before and after Ct infection. The differentially expressed genes were mainly enriched in the interferon pathway, which was closely related to the antiviral processes in cells. qPCR verified the differentially expressed genes and the genes closely related to the interferon pathway, such as ISG15, IFIT2, OASL and UBE2L6.

Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis / 器官移植

Objective To identify M1 macrophage-related genes in rejection after kidney transplantation and construct a risk prediction model for renal allograft survival. Methods GSE36059 and GSE21374 datasets after kidney transplantation were downloaded from Gene Expression Omnibus (GEO) database. GSE36059 dataset included the samples from the recipients with rejection and stable allografts. Using this dataset, weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted to screen the M1 macrophage-related differentially expressed gene (M1-DEG). Then, GSE21374 dataset (including the follow-up data of graft loss) was divided into the training set and validation set according to a ratio of 7∶3. In the training set, a multivariate Cox's model was constructed using the variables screened by least absolute shrinkage and selection operator (LASSO), and the ability of this model to predict allograft survival was evaluated. CIBERSORT was employed to analyze the differences of infiltrated immune cells between the high-risk group and low-risk group, and the distribution of human leukocyte antigen (HLA)-related genes was analyzed between two groups. Gene set enrichment analysis (GSEA) was used to further clarify the biological process and pathway enrichment in the high-risk group. Finally, the database was employed to predict the microRNA (miRNA) interacting with the prognostic genes. Results In the GSE36059 dataset, 14 M1-DEG were screened. In the GSE21374 dataset, Toll-like receptor 8 (TLR8), Fc gamma receptor 1B (FCGR1B), BCL2 related protein A1 (BCL2A1), cathepsin S (CTSS), guanylate binding protein 2(GBP2) and caspase recruitment domain family member 16 (CARD16) were screened by LASSO-Cox regression analysis, and a multivariate Cox's model was constructed based on these 6 M1-DEG. The area under curve (AUC) of receiver operating characteristic of this model for predicting the 1- and 3-year graft survival was 0.918 and 0.877 in the training set, and 0.765 and 0.736 in the validation set, respectively. Immune cell infiltration analysis showed that the infiltration of rest and activated CD4+ memory T cells, γδT cells and M1 macrophages were increased in the high-risk group (all P < 0.05). The expression level of HLA I gene was up-regulated in the high-risk group. GSEA analysis suggested that immune response and graft rejection were enriched in the high-risk group. CTSS interacted with 8 miRNA, BCL2A1 and GBP2 interacted with 3 miRNA, and FCGR1B interacted with 1 miRNA. Conclusions The prognostic risk model based on 6 M1-DEG has high performance in predicting graft survival, which may provide evidence for early interventions for high-risk recipients.

Analysis of key genes and targeted protection methods affecting the survival of human islets / 器官移植

Objective To identify the key genes and targeted protection methods affecting the survival of human islets. Methods Using bioinformatics method, the gene expression profile (GSE53454) was selected through screening and comparison from Gene Expression Omnibus(GEO) database. GEO2R tool was employed to screen the differentially expressed gene(DEG) between the human islets exposed (exposure group) and non-exposed (non-exposure group) to interleukin (IL)-1β and interferon (IFN)-γ for 24, 48 and 72 h, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by DAVID. Protein-protein interaction (PPI) network was constructed by STRING and Cytoscape apps. Results A total of 69 up-regulated DEGs and 2 down-regulated DEGs were identified. GO analysis showed that during the biological process, DEGs were enriched in the aspects of virus defense and inflammatory response. In cellular components, DEGs were significantly enriched in extracellular space, outside plasma membrane and extracellular regions. Regarding molecular functions, DEGs were significantly enriched in chemokine activity and cytokine activity. KEGG analysis revealed that DEGs were mainly enriched in multiple signaling pathways, such as cytokine-cytokine receptor interaction, virus protein-cytokine and cytokine-receptor interaction, etc. Ten key genes (STAT1, CXCL10, IRF1, IL6, CXCL9, CCL5, CXCL11, ISG15, CD274, IFIT3) with high connectivity were selected by STRING analysis, all of which were significantly up-regulated in human islets exposed to IL-1β and IFN-γ. Six genes (STAT1, CXCL10, CXCL9, CXCL11, CCL5, IL6) were screened by KEGG enrichment analysis, mainly in Toll-like receptor signaling pathway. Conclusions STAT1, CXCL10, CXCL9, CXCL11, CCL5 and IL6 are the key genes affecting the survival of human islets, which are mainly enriched in Toll-like receptor signaling pathway and act as important targets for islet protection.

Bioinformatics analysis of gene expression profile of central nervous system primitive neuroectodermal tumors / 西安交通大学学报(医学版)

【Objective】 To analyze the gene expression profile of central nervous system primitive neuroectodermal tumors （CNS-PNETs） by bioinformatics methods so as to explore the possible pathogenesis of CNS-PNETs at the molecular level. 【Methods】 The gene expression profile of CNS-PNETs was downloaded from the GEO database， GSE35493 and GSE74195. The differentially expressed genes （DEGs） were screened by the online analysis tool of GEO2R and Venn software， DEGs were analyzed by using the online analysis tools of David database， such as Gene Ontology （GO） and pathway enrichment （KEGG）. The protein interaction network analysis （PPI） of CNS-PNETs was made by using STRING online analysis tool， Cytoscape software and its plug-in cytohubba to find the key genes. 【Results】 We obtained 262 DEGs， including 49 upregulated genes and 213 downregulated genes. The analysis of GO function and KEGG signal pathway enrichment showed that DEG was involved in DNA transcription and mitosis， cell division， synaptic signal transmission and other biological processes， and associated with cell cycle， tumor-related pathway， p53 signal pathway， synapsis-related signal pathway， cAMP signal pathway and calcium ion signal pathway. Ten key genes， namely， CDK1， CDC20， MAD2L1， KIF11， ASPM， TOP2A， TTK， NDC80， NUSAP1 and DLGAP5 were screened out by STRING analysis. 【Conclusion】 Ten key genes including CDK1 may play an important role in the initiation and progression of CNS-PNETs， providing new clues for exploring the pathogenesis of CNS-PNETs.

Serine protease inhibitor Hespintor’s potential mechanism based on integrated transcriptomics in inhibiting the growth of transplanted hepatoma in nude mice / 西安交通大学学报(医学版)

【Objective】 To investigate the relationship between differential expressions of lncRNAs and mRNAs and Hespintor’s possible anti-tumor mechanism using transcriptome sequencing technology. 【Methods】 First， a nude mouse model of human hepatoma tumors was established. The tumor tissue mass was collected after 4 weeks of group treatment. The cDNA libraries of tumor tissues were established in the Hespintor treatment group and the solvent control group， and transcriptome sequencing was performed. Based on RNA-seq data， the differentially expressed lncRNA and mRNA genes were screened， and the functional enrichment and interaction analysis were performed. The network module division approach was utilized to identify the target genes of Hespintor and build its regulatory network. 【Results】 The Hespintor treatment group yielded a high-confidence differentially expressed gene collection of 2 003 lncRNAs （DEGs lncRNA） and 1 038 mRNAs （DEGs mRNA）. Target mRNAs regulated by DEGs lncRNA were mainly enriched in PIP3 to activate the Akt signal， p53 signal pathway， FOXO-mediated transcription， and cell cycle， among other things. DEGs mRNA were primarily enriched in chemokine signaling pathways， extracellular matrix receptor interactions， complement， and coagulation cascades. Both of them were related in three ways： cell behavior， transcriptional regulation， and cell cycle. 【Conclusion】 The PI3K/Akt signaling pathway may play a key role in the inhibitory effect of Hespintor on tumor， inducing G1/S phase cell cycle arrest and apoptosis.

Screening Biomarkers of Sudden Coronary Death Based on mRNA Expression Profile of Rat Myocardial Tissues / 法医学杂志

OBJECTIVES@#To explore the differential expression of messenger RNA (mRNA) in myocardial tissues of rats with sudden coronary death (SCD), and to provide ideas for the forensic identification of SCD.@*METHODS@#The rat SCD model was established, and the transcriptome sequencing was performed by next-generation sequencing technology. Differentially expressed genes (DEGs) in myocardial tissues of SCD rats were screened by using the R package limma. A protein-protein interaction (PPI) network was constructed by using the STRING database and Cytoscape 3.8.2 on DEG, and hub genes were screened based on cytoHubba plug-in. Finally, the R package clusterProfiler was used to analyze the biological function and signal pathway enrichment of the selected DEG.@*RESULTS@#A total of 177 DEGs were associated with SCD and were mainly involved in the renin-angiotensin system and PI3K-Akt signaling pathway. The genes including angiotensinogen (AGT), complement component 4a (C4a), Fos proto-oncogene (FOS) and others played key roles in the development of SCD.@*CONCLUSIONS@#Genes such as AGT, C4a, FOS and other genes are expected to be potential biomarkers for forensic identification of SCD. The study based on mRNA expression profile can provide a reference for forensic identification of SCD.

Macrophage differential genes and their functions in silicosis based on single cell RNA sequencing and spatial transcriptome sequencing / 环境与职业医学

Background The rise of single cell RNA sequencing (scRNA-seq) and spatial transcriptome sequencing technologies has allowed for intensive study of lung diseases, but both have been poorly studied in silicosis. Objective To explore differentially expressed genes DEGs in silicosis macrophages by scRNA-seq combined with spatial transcriptome sequencing and analyze the potential diagnostic genes. Methods Male C57BL/6 mice (5-6 weeks old, 22-30 g) were randomly divided into 4 groups: normal saline (NS) group for 7 d, NS group for 56 d, SiO2 group for 7 d, and SiO2 group for 56 d, with 1 mouse in each group. A silicosis model was constructed by tracheal drip injection of SiO2 suspension (0.2 g·kg−1, 50 g·cm−2), and the control mice were given the same volume of NS. The right lung was removed for scRNA-seq and the left lung for spatial transcriptome sequencing on day 7 and day 56, respectively. Cell populations were captured using principal component analysis techniques and dimensionality reduction of uniform manifold approximation and projection. The Find Markers function in R language was applied to analyze the DEGs changes of macrophages in two groups of lung tissues, and the corresponding DEGs were subjected to Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes signaling pathway analysis, while STRING and CytoHubba plug-ins of Cytoscape software were applied to protein-protein interaction network analysis to screen out key (Hub) genes. Spatial transcriptome sequencing was used to explore the original location of Hub genes on lung tissue sections and their mapping in lung macrophages. Finally, the correlation of Hub gene expression levels in lung tissues of silicosis patients and mouse silicosis models was verified, the diagnostic efficacy of Hub gene using subject operating characteristic curves (ROC). In vitro experiments by applying cell viability assay were conducted to verify the changes in viability of mouse macrophages (RAW264.7) under SiO2 stimulation. Results The scRNA-seq revealed a total of 20 clusters captured and defined. The results of scRNA-seq and spatial transcriptome sequencing showed an increased number of macrophages in the lung tissue of the SiO2 group compared to the NS group and clustered in the focal areas. Among the 97 macrophage DEGs screened out, 75 were up-regulated genes, and mainly enriched in chemotaxis and migration of neutrophils, chemokine receptor binding, tumor necrosis factor signaling pathway, cytokine-cytokine receptor interaction pathway, and interleukin-17 signaling pathway; and 22 were down-regulated genes, and mainly enriched in late endosomes, peroxisome proliferator-activated receptors signaling pathway, and alcoholic liver disease signaling pathway. A total of 2 core modules and 3 Hub genes were screened out, including Ccl2, Ccl7, and Ptgs2. The scRNA-seq showed that they were expressed at elevated levels in the SiO2 group compared to the NS group and clustered in additional macrophages, and the spatial transcriptome sequencing showed that they clustered in inflammatory areas with nodular lesions. The CCL7 and PTGS2 expressions were increased in the lung tissue of SiO2 patients compared with the healthy subjects, and the areas under the working curve of the subjects were 0.850 and 0.786, respectively. The viability of RAW264.7 cells was enhanced under SiO2 stimulation at 3 h, 6 h, and 12 h compared to those without the stimulation (P<0.05). Conclusion Bioinformatics screening have identified 3 Hub genes (Ccl2, Ccl7, and Ptgs2)and 2 potential diagnostic genes (CCL7 and PTGS2) in the lung tissue of silicosis mice, which may be potential molecular markers of early-stage silicosis with implications for the development and prognosis of silicosis.

Bioinformatics analysis of differently expressed genes in osteoblastic sarcoma and screening of key genes / 中华肿瘤杂志

Objective: To screen the different expressed genes between osteosarcoma and normal osteoblasts, and find the key genes for the occurrence and development of osteosarcoma. Methods: The gene expression dataset GSE33382 of normal osteoblasts and osteosarcoma was obtained from Gene Expression Omnibus (GEO) database. The different expressed genes between normal osteoblasts and osteosarcoma were screened by limma package of R language, and the different expressed genes were analyzed by Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. The protein interaction network was constructed by the String database, and the network modules in the interaction network were screened by the molecular complex detection (MCODE) plug-in of Cytoscape software. The different expressed genes contained in the first three main modules screened by MCODE were analyzed by gene ontology (GO) using the BiNGO module of Cytoscape software. The MCC algorithm was used to screen the top 10 key genes in the protein interaction network. The gene expression and survival dataset GSE39055 of osteosarcoma was obtained from GEO database, and the survival analysis was performed by Kaplan-Meier method. The data of 48 patients with osteosarcoma treated in the First Affiliated Hospital of Fujian Medical University from January 2005 to December 2015 were selected for verification. The expression of STC2 protein in osteosarcoma was detected by immunohistochemical method, and the survival analysis was carried out combined with the clinical data of the patients. Results: A total of 874 different expressed genes were identified from GSE33382 dataset, including 402 down-regulated genes and 472 up-regulated genes. KEGG enrichment analysis showed that different expressed genes were mainly related to p53 signal pathway, glutathione metabolism, extracellular matrix receptor interaction, cell adhesion molecules, folate tolerance, and cell senescence. The top 10 key genes in the interaction network were GAS6, IL6, RCN1, MXRA8, STC2, EVA1A, PNPLA2, CYR61, SPARCL1 and FSTL3. STC2 was related to the survival rate of patients with osteosarcoma (P<0.05). The results showed that the expression of STC2 protein was related to tumor size and Enneking stage in 48 cases of osteosarcoma. The median survival time of 25 cases with STC2 high expression was 21.4 months, and that of 23 cases with STC2 low expression was 65.4 months. The survival rate of patients with high expression of STC2 was lower than that of patients with low expression of STC2 (P<0.05). Conclusions: Bioinformatics analysis can effectively screen the different expressed genes between osteosarcoma and normal osteoblasts. STC2 is one of the important predictors for the prognosis of osteosarcoma.

Identification of biomarkers for esophageal squamous cell carcinoma based on bioinformatics / 预防医学

Objective @#To identify biomarkers for esophageal squamous cell carcinoma (ESCC) using bioinformatics tools, so as to provide insights into diagnosis and targeted therapy of ESCC. @*Methods@#The gene expression datasets GSE23400, GSE45670, GSE20347 and GSE17351 were screened and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) of ESCC were screened using the online tool GEO2R, and the common DEGs among the four datasets were determined using Venn diagram. Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID database, and protein-protein interaction (PPI) analysis was performed using the STRING database. The key modules were identified using molecular complex detection (MCODE) plugin in the Cytoscape software, and hub genes with the highest connectivity degree were identified using the CytoHubba plugin, and the gene expression was validated on the UALCAN platform. Survival analysis of hub genes was performed using the Kaplan-Meier plotter database.@*Results@#Totally 146 common DEGs were screened, including 102 up-regulated genes and 44 down-regulated genes. GO annotation analysis showed that the common DEGs were mainly enriched in biological processes of cell cycle, sister chromatid separation in the late mitotic phase and cell cycle regulation, enriched in cellular components of spindle and centrosome, and molecular functions of enzyme binding and ATP binding. KEGG pathway analysis showed that DEGs was significantly enriched in cell cycle, extracellular matrix (ECM)-receptor interactions and oocyte meiosis. A total of 10 hub genes were screened, and gene expression validation and survival analysis identified 7 genes associated with prognosis of ESCC, including CCNB1, CDK1, BUB1B, ZWINT, AURKA, MAD2L1 and MCM4, which were all highly expressed in ESCC specimens. @*Conclusion@#Seven hub genes of ESCC are identified based on bioinformatics, which may serve as biomarkers and therapeutic targets for ESCC.

Bioinformatics Analysis of Core Genes and Key Pathways in Myelodysplastic Syndrome / 中国实验血液学杂志

OBJECTIVE@#To screen differentially expressed gene (DEG) related to myelodysplastic syndrome (MDS) based on Gene Expression Omnibus (GEO) database, and explore the core genes and pathogenesis of MDS by analyzing the biological functions and related signaling pathways of DEG.@*METHODS@#The expression profiles of GSE4619, GSE19429, GSE58831 including MDS patients and normal controls were downloaded from GEO database. The gene expression analysis tool (GEO2R) of GEO database was used to screen DEG according to | log FC (fold change) |≥1 and P<0.01. David online database was used to annotate gene ontology function (GO). Metascape online database was used to enrich and analyze differential genes in Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction network (PPI) was constructed by using STRING database. CytoHubba and Mcode plug-ins of Cytoscape were used to analyze the key gene clusters and hub genes. R language was used to diagnose hub genes and draw the ROC curve. GSEA enrichment analysis was performed on GSE19429 according to the expression of LEF1.@*RESULTS@#A total of 74 co-DEG were identified, including 14 up-regulated genes and 60 down regulated genes. GO enrichment analysis indicated that BP of down regulated genes was mainly enriched in the transcription and regulation of RNA polymerase II promoter, negative regulation of cell proliferation, and immune response. CC of down regulated genes was mainly enriched in the nucleus, transcription factor complexes, and adhesion spots. MF was mainly enriched in protein binding, DNA binding, and β-catenin binding. KEGG pathway was enriched in primary immunodeficiency, Hippo signaling pathway, cAMP signaling pathway, transcriptional mis-regulation in cancer and hematopoietic cell lineage. BP of up-regulated genes was mainly enriched in type I interferon signaling pathway and viral response. CC was mainly enriched in cytoplasm. MF was mainly enriched in RNA binding. Ten hub genes and three important gene clusters were screened by STRING database and Cytoscape software. The functions of the three key gene clusters were closely related to immune regulation. ROC analysis showed that the hub genes had a good diagnostic significance for MDS. GSEA analysis indicated that LEF1 may affect the normal function of hematopoietic stem cells by regulating inflammatory reaction, which further revealed the pathogenesis of MDS.@*CONCLUSION@#Bioinformatics can effectively screen the core genes and key signaling pathways of MDS, which provides a new strategy for the diagnosis and treatment of MDS.

Bioinformatics Analysis of Microarray Data in Myelodysplastic Syndrome Based on Gene Expression Omnibus Database / 中国实验血液学杂志

OBJECTIVE@#To identify the key genes and explore mechanisms in the development of myelodysplastic syndrome (MDS) by bioinformatics analysis.@*METHODS@#Two cohorts profile datasets of MDS were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed gene (DEG) was screened by GEO2R, functional annotation of DEG was gained from GO database, gene ontology (GO) enrichment analysis was performed via Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and key genes were screened by Matthews correlation coefficient (MCC) based on STRING database.@*RESULTS@#There were 112 DEGs identified, including 85 up-regulated genes and 27 down-regulated genes. GO enrichment analysis showed that biological processes were mainly enriched in immune response, etc, cellular component in cell membrane, etc, and molecular function in protein binding, etc. KEGG signaling pathway analysis showed that main gene enrichment pathways were primary immunodeficiency, hematopoietic cell lineage, B cell receptor signaling pathway, Hippo signaling pathway, and asthma. Three significant modules were screened by Cytoscape software MCODE plug-in, while 10 key node genes (CD19, CD79A, CD79B, EBF1, VPREB1, IRF4, BLNK, RAG1, POU2AF1, IRF8) in protein-protein interaction (PPI) network were screened based on STRING database.@*CONCLUSION@#These screened key genes and signaling pathways are helpful to better understand molecular mechanism of MDS, and provide theoretical basis for clinical targeted therapy.

Bioinformatics analysis of differentially expressed genes associated with chronic schistosomiasis japonica-induced hepatic fibrosis / 中国血吸虫病防治杂志

Objective To screen differentially expressed genes (DEGs) associated with chronic schistosomiasis japonica-induced hepatic fibrosis and analyze their functions. Methods The dataset of gene expression profiles of patients with chronic schistosomiasis japonica-induced hepatic fibrosis was downloaded from the Gene Expression Omnibus (GEO) database, and DEGs were screened using R package. The biological functions of DEGs were characterized using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. In addition, the protein-protein interaction (PPI) network of DEGs was created to screen the hub genes. Results A total of 62 DEGs were identified, including 12 down-regulated genes and 50 up-regulated genes. GO enrichment analysis showed that DEGs were mainly enriched in 116 biological processes, including fatty acid, sulfur compound, acyl-coenzyme A and thioester metabolism; 19 cellular components, including mitochondrial matrix, outer mitochondrial membrane and organelle outer membrane; and 7 molecular functions, including insulin-like growth factor binding and oxidoreductase activity. KEGG pathway enrichment analysis that the DEGs were significantly enriched in phosphatidylinositol-3-kinase/serine/threonine protein kinase (PI3K/Akt), mitogen-activated protein kinase (MAPK), calcium metabolism and cyclic adenosine monophosphate (cAMP) signaling. PPI network analysis identified six hub genes involved in the development of chronic schistosomiasis japonica-induced hepatic fibrosis, including ACACA, ACSL1, GPAM, THRSP, PLIN1 and DGAT2, and ACSL1, ACACA and PLIN1 were the top 3 hub genes. Conclusions ACSL1, ACACA and PLIN1 may be the hub genes associated with the development of chronic schistosomiasis japonica-induced hepatic fibrosis, and abnormal lipid metabolism mediated by these DEGs may play an important role in the development of chronic schistosomiasis japonica-induced hepatic fibrosis.

Screening and identification the hub genes of cardiac hypertrophy in mice / 解剖学报

Objective To screen and identify the hub genes closely related to cardiac hypertrophy by using bioinformaticsmethod and biological experiments. Methods The chip data related to cardiac hypertrophy in mice were downloaded from the Gene Expression Omnibus (GEO) database, and the GE02R online tool was adopted to screen for differentially expressed genes; DAVID 6.7, String 11.0 and Cytoscape 3.7. 0 softwares were used to analyze differentially expressed genes; Kunming mice were randomly divided into a normal saline group (n = 6) and an angiotensin II (Ang II) group (n = 6) to establish a cardiac hypertrophy model, the expression of hub gene in Kunming mouse model of cardiac hypertrophy induced by Ang II was detected by Real-time PCR method. Results A total of 202 common differentially expressed genes and 12 hub genes were selected; the Real-time PCR result demonstrated that decorin(Dcn), HADHA and heat shock protein (HSP) 90αA 1 were significantly down-regulated in the Angli group. Conclusion The selected hub genes can influence the development of cardiac hypertrophy in Kunming mice through extracellular matrix and transforming growth factor β(TGF-β).

Identification of potential therapeutic agents for ER-negative breast cancer using bioinformatics analysis / 西安交通大学学报(医学版)

【Objective】 To explore the key genes and potential therapeutic drugs for ER-negative breast cancer by bioinformatics. 【Methods】 The gene expression profile of breast cancer (GSE22219) was downloaded from the Gene Expression Omnibus (GEO). Principal components analysis (PCA) of GSE22219, and analyses of differentially expressed genes (DEGs) between the ER-negative and ER-positive subjects and Gene Ontology (GO) analysis were performed by R software. We analyzed The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Protein-Protein Interaction (PPI) network using STRING. The hub genes were identified using Cytoscape and analyzed using online programs. Drugbank analysis was used to find small molecular compounds as potential therapeutic agents to target the DEGs. 【Results】 We detect 69 DEGs and 8 hub genes between the ER-negative and ER-positive subjects. We found the most significant KEGG pathway of DEGs was aldosterone-regulated sodium reabsorption. The Gene Ontology (GO) analysis indicated that the most significantly enriched in prostate gland morphogenesis. Totally 21 small molecular compounds were identified as potential therapeutic agents for ER-negative breast cancer. 【Conclusion】 The bioinformatical analysis combined with drug database can help us find potential therapeutic agents to treat diseases. This method is a new paradigm which can guide future research on drugs.

Bioinformatics analysis of differentially expressed genes in non-small cell lung cancer / 西安交通大学学报(医学版)

【Objective】 To analyze the data of non-small cell lung cancer (NSCLC) gene chip using the bioinformatics method, screen differential expression genes (DEGs), and explore the biomarkers related to the prognosis of NSCLC so as to provide a new target for the treatment of NSCLC. 【Methods】 The NSCLC gene chip data were downloaded from the GEO database and the common DEGs in the two datasets were screened by GEO2R tool and FunRich3.1.3 software. The DAVID database was used in GO analysis and KEGG analysis of the DEGs. The protein-protein interaction (PPI) network was constructed using the STRING database; Cytoscape 3.8.0 software was used to select the top 20 hub genes. Then Kaplan-Meier plotter was used to analyze the prognosis of the identified hub genes, and multiple external databases were used to verify the expressions of the hub genes and their relationship with prognosis. 【Results】 A total of 159 intersect DEGs were screened from the two datasets. A total of 20 hub genes were identified via PPI network. Survival analysis and validation results from multiple external databases showed that SPP1 was highly expressed in NSCLC tumor tissues and was significantly correlated with the patients’ poor prognosis (P<0.05). The subgroup analysis showed that SPP1 might cause the poor prognosis by affecting lymph node metastasis. 【Conclusion】 SPP1 may be a biomarker for evaluating the prognosis of NSCLC patients, providing a new idea for the targeted therapy of NSCLC.

Analysis of sepsis-related genes through weighted gene co-expression network / 中华危重病急救医学

Objective:To identify the Key genes in the development of sepsis through weighted gene co-expression network analysis (WGCNA).Methods:The gene expression dataset GSE154918 was downloaded from the public database Gene Expression Omnibus (GEO) database, which containes data from 105 microarrays of 40 control cases, 12 cases of asymptomatic infection, 39 cases of sepsis, and 14 cases of follow-up sepsis. The R software was used to screen out differentially expressed genes (DEG) in sepsis, and the distributed access view integrated database (DAVID), search tool for retrieval of interacting neighbouring genes (STRING) and visualization software Cytoscape were used to perform gene function and pathway enrichment analysis, Protein-protein interaction (PPI) network analysis and key gene analysis to screen out the key genes in the development of sepsis.Results:Forty-six candidate genes were obtained by WGCNA and combined with DEG expression analysis, and these 46 genes were analyzed by gene ontology (GO) and Kyoto City Encyclopedia of Genes and Genomes (KEGG) pathway enrichment to obtain gene functions and involved signaling pathways. The PPI network was further constructed using the STRING database, and 5 key genes were selected by the PPI network visualization software Cytoscape, including the mast cell expressed membrane protein 1 gene (MCEMP1), the S100 calcium-binding protein A12 gene (S100A12), the adipokine resistance factor gene (RETN), the c-type lectin structural domain family 4 member gene (CLEC4D), and peroxisome proliferator-activated receptor gene (PPARG), and differential expression analysis of each of these 5 genes showed that the expression levels of the above 5 genes were significantly upregulated in sepsis patients compared with healthy controls.Conclusion:In this study, 5 key genes related to sepsis were screened by constructing WGCNA method, which may be potential candidate targets related to sepsis diagnosis and treatment.

Key genes in malignant transformation of lung epithelial cells induced by hexavalent chromium based on GEO database / 环境与职业医学

Background Hexavalent chromium [Cr(VI)] can induce malignant transformation of lung epithelial cells, but its molecular mechanism is still unclear. Objective This study aims to explore the key genes of Cr(VI)-induced malignant transformation of lung epithelial cells and the mechanism of the transformation by bioinformatics analysis. Methods High-throughput gene expression profile data related to Cr(VI)-induced toxic effect was downloaded from the Gene Expression Omnibus(GEO) database, and the co-expressed genes were obtained by the intersection of differentially expressed genes in each dataset. DAVID 6.8 was used to analyze the function enrichment of gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathways of the selected differential expression genes. STRING, and Cytoscape 3.8.2 were applied to construct and visualize the protein-protein interaction network. The expressions of Hub genes in lung tumor were obtained by GEPIA2. Results A total of 234 differentially expressed genes were screened out from the GSE24025 and GSE36684 datasets, among which 99 genes were up-regulated while 135 genes were down-regulated. The results of GO and KEGG analyse were mainly concentrated in cell adhesion, negative regulation of cell proliferation, and transcription disorders. A rotein-protein interaction network was generated by STRING database and Cytoscape software. Four functional modules with high scores and 6 Hub genes were finally retrieved. The expression trend of FBLN1 in lung cancer subtypes was consistent with the results of transcriptome screening. Conclusion Cr(VI) exposure causes the differential expression of multiple genes in lung epithelial cells, involving cell morphology, movement, survival fate, phenotype function and signal pathway related to cancer development. FBLN1 may be the critical gene related to malignant cytopathy.

Screening of molecular markers in breast cancer based on GEO database / 中国肿瘤生物治疗杂志

@# Objective: To investigate the differentially expressed genes (DEGs) associated with the occurrence and development of breast cancer and to screen the molecular markers for breast cancer by bioinformatic analysis. Methods: Three breast cancer microarray datasets were downloaded from Gene Expression Omnibus (GEO) database. GEO2R was used to identify DEGs. The differentially co-expressed genes in the three datasets were screened by Venn diagram. GO function enrichment analysis and KEGG signal pathway analysis were performed using DAVID. The protein-protein interaction (PPI) network of DEGs was constructed using STRING. The most important modules in the PPI network were analyzed using Molecular Complex Detection (MCODE), and the genes with degree≥10 were identified as Hub genes. Hierarchical clustering analysis of hub genes was conducted using UCSC Cancer Genomics Brower. The survival curve and the co-expression network of hub genes were constructed using cBioPortal. Results: A total of 65 DEGs were screened from the three data sets. Eight hub genes, CTNNB1, CDKN1A, CXCR4, RUNX3, CASP8, TNFRSF10B, CFLAR and NRG1, were finally obtained, which exerted important roles in cell adhesion, proliferation and apoptosis regulation etc. Clustering analysis showed that the differential expression levels of CTNNB1, CFLAR, NRG1 and CXCR4 were associated with the occurrence of breast cancer. The overall survival analysis indicated that the patients with elevated CDKN1Aexpression had significantly shorter overall survival time (P<0.01). Conclusion: The hub genes identified in the present study can be used as molecular markers for breast cancer, providing candidate targets for diagnosis, treatment and prognostic prediction of breast cancer.

Screening of molecular markers in gastric cancer based on GEO database / 中国肿瘤生物治疗杂志

@#[Abstract] Objective: Bioinformatics combined with Gene Expression Omnibus (GEO) was used to screen key genes involved in the development of gastric cancer in order to obtain molecular markers for diagnosis, target selection and prognosis prediction of gastric cancer. Methods: The chip data sets related to gastric cancer (GC) from the GEO database were downloaded, and differentially expressed genes (DEG) were screened. Functional enrichment analysis on DEG was performed, and protein-protein interaction network (PPI) was constructed to screen key genes. Then, co-expression networks were further constructed, and survival curves were drawn and hierarchical clustering analysis was performed. Results: A total of 261 GC-related DEGs were selected, and 14 key genes were obtained through analysis, which were PLOD1, PLOD3, COL1A1, COL1A2, COL2A1, COL3A1, COL4A1, COL4A2, COL8A1, COL12A1, COL15A1, ITGA2, LUM and SERPINH1. Key genes are mainly involved in biological processes such as generation of collagen fiber tissues, extracellular matrix tissues, extracellular structure tissues, skin morphogenesis, collagen biosynthesis and vascular development. Survival curve analysis showed that the change in the expression of COL3A1 (P=0.0241) significantly reduced the overall survival rate of patients with gastric cancer; the change in the expression of ITGA2 (P=0.0679) also showed a correlation with the reduction of disease-free survival in gastric cancer patients. Compared with normal gastric tissues, hierarchical cluster analysis showed that the expressions of genes PLOD1, PLOD3, COL3A1, ITGA2, COL1A2, COL1A1, COL4A1, LUM, COL12A1, SERPINH1 and COL8A1 in GC tissues were up-regulated. Conclusion: The key genes obtained after screening can be used as potential molecular markers for early diagnosis, treatment target selection and prognosis judgment of gastric cancer, which provide reference for subsequent research.