Transcriptome analysis of differentially expressed genes and pathways associated with mitoxantrone treatment prostate cancer

The global physiological function of specifically expressed genes of mitoxantrone (MTX)‐resistant prostate cancer (PCa) is unclear. In this study, gene expression pattern from microarray data was investigated for identifying differentially expressed genes (DEGs) in MTX‐resistant PCa xenografts. Human PCa cell lines DU145 and PC3 were cultured in vitro and xenografted into severe combined immunodeficiency (SCID) mice, treated with MTX intragastrically, three times a week until all mice relapsed. Gene expression profiles of the xenografts from castrated mice were performed with Affymetrix human whole genomic oligonucleotide microarray. The Cytoscape software was used to investigate the relationship between proteins and the signalling transduction network. A total of 355 overlapping genes were differentially expressed in MTX‐resistant DU145R and PC3R xenografts. Of these, 16 genes were selected to be validated by quantitative real‐time PCR (qRT‐PCR) in these xenografts, and further tested in a set of formalin‐fixed, paraffin‐embedded and optimal cutting temperature (OCT) clinical tumour samples. Functional and pathway enrichment analyses revealed that these DEGs were closely related to cellular activity, androgen synthesis, DNA damage and repair, also involved in the ERK/MAPK, PI3K/serine‐threonine protein kinase, also known as protein kinase B, PKB (AKT) and apoptosis signalling pathways. This exploratory analysis provides information about potential candidate genes and may bring new insights into the molecular cascade involvement in MTX‐resistant PCa.     

Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the common joints disorder in the world. Although they have showed the analogous clinical manifestation and overlapping cellular and molecular foundation, the pathogenesis of RA and OA were different. The pathophysiologic mechanisms of arthritis in RA and OA have not been investigated thoroughly. Thus, the aim of study is to identify the potential crucial genes and pathways associated with RA and OA and further analyze the molecular mechanisms implicated in genesis. First, we compared gene expression profiles in synovial tissue between RA and OA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene Expression Series (GSE) 1919, GSE55235, and GSE36700 were downloaded from the GEO database, including 20 patients of OA and 21 patients of RA. Differentially expressed genes (DEGs) including “CXCL13,” “CD247,” “CCL5,” “GZMB,” “IGKC,” “IL7R,” “UBD///GABBR1,” “ADAMDEC1,” “BTC,” “AIM2,” “SHANK2,” “CCL18,” “LAMP3,” “CR1,” and “IL32.” Second, Gene Ontology analyses revealed that DEGs were significantly enriched in integral component of extracellular space, extracellular region, and plasma membrane in the molecular function group. Signaling pathway analyses indicated that DEGs had common pathways in chemokine signaling pathway, cytokine-cytokine receptor interaction, and cytosolic DNA-sensing pathway. Third, DEGs showed the complex DEGs protein-protein interaction network with the Coexpression of 83.22%, Shared protein domains of 8.40%, Colocalization of 4.76%, Predicted of 2.87%, and Genetic interactions of 0.75%. In conclusion, the novel DEGs and pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms of RA.     

Key genes and pathways in tumor-educated dendritic cells by bioinformatical analysis

Specific tumor microenvironment signaling might prevent the maturation of dendritic cells (DCs) with tolerogenic and immunosuppressive potential accounting for antigen-specific unresponsiveness in the lymphoid organs and in the periphery. In the present study, dendritic cells treated with LLC lung cancer cell or 4T1 breast cancer cell culture supernatants significantly down-regulated the expression of co-stimulatory molecules MHC-II, CD40, CD80, but up-regulated the inhibitory molecule PD-L1/L2, VISTA, and increased the messengerRNA levels of interleukin (IL)-6, arginase I, and IL-10, but decreased tumor necrosis factor-α and IL-12a. RNA was isolated from the dendritic cells with or without tumor supernatant stimulation and RNA sequencing was done. Then the differential expression genes were sorted, the candidate genes were analyzed and pathway enrichment analysis was done, and the associated protein–protein interaction network (PPI) was established. After integrated bioinformatical analysis, 405 (279 up-regulated and 126 down-regulated) consistently differential expression genes were identified. Using gene ontology and pathway analysis, it was found that differential expression genes were mainly enriched in the immune response, cell–cell interaction, hemostasis, and cell surface interactions with the vascular wall. The PPI data demonstrated that 236 nodes were classified with 1072 edges, and the most remarkable three modules involved 53 central node genes associated with cell survival, cell-substrate adhesion, chemotaxis, migration, immune response, and complement receptor mediated signaling pathway. These findings revealed the immune status of dendritic cells in the tumor environment.     

ChIP‐Seq of ERα and RNA polymerase II defines genes differentially responding to ligands

We used ChIP‐Seq to map ERα‐binding sites and to profile changes in RNA polymerase II (RNAPII) occupancy in MCF‐7 cells in response to estradiol (E2), tamoxifen or fulvestrant. We identify 10 205 high confidence ERα‐binding sites in response to E2 of which 68% contain an estrogen response element (ERE) and only 7% contain a FOXA1 motif. Remarkably, 596 genes change significantly in RNAPII occupancy (59% up and 41% down) already after 1 h of E2 exposure. Although promoter proximal enrichment of RNAPII (PPEP) occurs frequently in MCF‐7 cells (17%), it is only observed on a minority of E2‐regulated genes (4%). Tamoxifen and fulvestrant partially reduce ERα DNA binding and prevent RNAPII loading on the promoter and coding body on E2‐upregulated genes. Both ligands act differently on E2‐downregulated genes: tamoxifen acts as an agonist thus downregulating these genes, whereas fulvestrant antagonizes E2‐induced repression and often increases RNAPII occupancy. Furthermore, our data identify genes preferentially regulated by tamoxifen but not by E2 or fulvestrant. Thus (partial) antagonist loaded ERα acts mechanistically different on E2‐activated and E2‐repressed genes.     

Adaptive thresholds to detect differentially expressed genes in microarray data

To detect changes in gene expression data from microarrays, a fixed threshold for fold difference is used widely. However, it is not always guaranteed that a threshold value which is appropriate for highly expressed genes is suitable for lowly expressed genes. In this study, aiming at detecting truly differentially expressed genes from a wide expression range, we proposed an adaptive threshold method (AT). The adaptive thresholds, which have different values for different expression levels, are calculated based on two measurements under the same condition. The sensitivity, specificity and false discovery rate (FDR) of AT were investigated by simulations. The sensitivity and specificity under various noise conditions were greater than 89.7% and 99.32%, respectively. The FDR was smaller than 0.27. These results demonstrated the reliability of the method.     

Identification of differentially expressed genes related to metabolic syndrome induced with high-fat diet in E3 rats

Understanding the genes differentially expressing in aberrant organs of metabolic syndrome (MetS) facilitates the uncovering of molecular mechanisms and the identification of novel therapeutic targets for the disease. This study aimed to identify differentially expressed genes related to MetS in livers of E3 rats with high-fat-diet-induced metabolic syndrome (HFD-MetS). E3 rats were fed with high-fat diet for 24 weeks to induce MetS. Then, suppression subtractive hybridization (SSH) technology was used to identify the genes differentially expressed between HFD-MetS and control E3 rat livers. Twenty positive recombinant clones were chosen randomly from forward subtractive library and sent to sequence. BLAST analysis in GenBank database was used to determine the property of each cDNA fragment. In total, 11 annotated genes, 3 ESTs, and 2 novel gene fragments were identified by SSH technology. The expression of four genes (Alb, Pip4k2a, Scd1, and Tf) known to be associated with MetS and other five genes (Eif1, Rnase4, Rps12, Rup2, and Tmsb4) unknown to be relevant to MetS was significantly up-regulated in the livers of HFD-MetS E3 rats compared with control rats using real-time quantitative PCR (RT-qPCR). By analyzing the correlations between the expression of these nine genes and serum concentrations of TG, Tch, HDL-C, and LDL-C, we found that there were significant positive correlations between TG and the expression of five genes (Alb, Eif1, Pip4k2a, Rps12, and Tmsb4x), Tch and three genes (Rnase4, Scd1, and Tmsb4x), and LDL-C and two genes (Rnase4 and Scd1), as well there were significant negative correlations between HDL-C and the expression of three genes (Rup2, Scd1, and Tf). This study provides important clues for unraveling the molecular mechanisms of MetS.     

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

斑马鱼急性无机砷暴露后肝脏差异表达基因的生物信息学分析

砷是一种致癌物,是心血管、外周血管疾病、神经疾病、糖尿病和各种癌症的致病因素。目的:利用GO数据库和KEGG数据库等生物信息学方法对GEO数据库数据中的差异表达基因进行评价。利用生物信息学分析软件对差异基因进行功能富集、功能注释分析和生存分析。利用Cytoscape上的蛋白-蛋白相互作用网络(Protein-protein interaction network, PPI)软件对179个差异基因进行筛选和分析。结果发现126个基因作用于蛋白靶点,其中有10个基因为关键基因分别为:PSMB3、HSP701、HSPE1、STIP1、HSPD1、HSP70、DNAJB1B、HSP90AA1.1、HSPA9H和TCP1。核心基因主要作用于内质网中的蛋白质加工通路。这可能会为砷对肝脏损伤的潜在生物标志物和生物学机制提供新的思路。     

Identification of differentially expressed genes and the role of PDK4 in CD14+ monocytes of coronary artery disease

Background. Coronary artery disease (CAD) is a chronic inflammatory disease caused by development of atherosclerosis (AS), which is the leading cause of mortality and disability. Our study aimed to identify the differentially expressed genes (DEGs) in CD14⁺ monocytes from CAD patients compared with those from non-CAD controls, which might pave the way to diagnosis and treatment for CAD. Methods. The RNA-sequencing (RNA-seq) was performed by BGISEQ-500, followed by analyzing with R package to screening DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed by R package. In addition, we validated the results of RNA-seq using real-time quantitative polymerase chain reaction (RT-qPCR). Furthermore, we explored the function of selected ten genes in LDL-treated CD14⁺ monocytes by RT-qPCR. Results. a total of 2897 DEGs were identified, including 753 up- and 2144 down-regulated genes in CD14⁺ monocytes from CAD patients. These DEGs were mainly enriched in plasma membrane and cell periphery of cell component, immune system process of biological process, NF-κB signaling pathway, cell adhesion molecules signaling pathway and cytokine–cytokine receptor interaction signaling pathway. In LDL-treated CD14⁺ monocytes, the mRNA expression of pyruvate dehydrogenase kinase 4 (PDK4) was significantly up-regulated. Conclusion. In the present study, we suggested that PDK4 might play a role in progression of CAD. The study will provide some pieces of evidence to investigate the role and mechanism of key genes in the pathogenesis of CAD.