Pathway Enrichment Analysis Of Differentially Expressed Genes Research Articles

Identification of candidate genes controlling chilling tolerance of rice in the cold region at the booting stage by BSA-Seq and RNA-Seq.

Rice is sensitive to low temperatures, specifically at the booting stage. Chilling tolerance of rice is a quantitative trait loci that is governed by multiple genes, and thus, its precise identification through the conventional methods is an arduous task. In this study, we investigated the candidate genes related to chilling tolerance at the booting stage of rice. The F2 population was derived from Longjing25 (chilling-tolerant) and Longjing11 (chilling-sensitive) cross. Two bulked segregant analysis pools were constructed. A 0.82 Mb region containing 98 annotated genes on chromosomes 6 and 9 was recognized as the candidate region associated with chilling tolerance of rice at the booting stage. Transcriptomic analysis of Longjing25 and Longjing11 revealed 50 differentially expressed genes (DEGs) on the candidate intervals. KEGG pathway enrichment analysis of DEGs was performed. Nine pathways were found to be enriched, which contained 10 DEGs. A total of four genes had different expression patterns or levels between Longjing25 and Longjing11. Four out of the 10 DEGs were considered as potential candidate genes for chilling tolerance. This study will assist in the cloning of the candidate genes responsible for chilling tolerance and molecular breeding of rice for the development of chilling-tolerant rice varieties.

In silico prediction and validation of potential therapeutic genes in pancreatic β-cells associated with type 2 diabetes.

Diabetes mellitus is becoming a major health burden worldwide. Pancreatic β-cell death is a characteristic of type 2 diabetes (T2D), but the underlying mechanisms of pancreatic β-cell death remain unknown. Therefore, the aim of the present study was to identify potential targets in the pancreatic islet of T2D. The GSE20966 dataset was obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by using the GEO2R tool. The Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes Pathway enrichment analysis of DEGs were further assessed using the Database for Annotation, Visualization and Integrated Discovery. Furthermore, protein-protein interaction (PPI) networks were constructed for the up- and downregulated genes using STRING databases and were then visualized with Cytoscape. The body weight, fasting blood glucose (FBG), pancreatic index and biochemistry parameters were measured in db/db mice. Moreover, the morphology of the pancreas was detected by hematoxylin and eosin staining, and hub genes were assessed using reverse transcription-quantitative PCR (RT-qPCR) and western blot analysis. In total, 570 DEGs were screened, including 376 upregulated and 194 downregulated genes, which were associated with ‘complement activation, classical pathway’, ‘proteolysis’, ‘complement activation’ and ‘pancreatic secretion pathway’. It was found that the body weight, FBG, alanine aminotransferase, aspartate aminotransferase, total cholesterol, triglycerides, blood urea nitrogen, creatinine, fasting serum insulin, glucagon and low-density lipoprotein cholesterol levels were significantly higher in db/db mice, while high-density lipoprotein cholesterol levels and the pancreatic index were significantly decreased. Furthermore, albumin, interleukin-8, CD44, C-C motif chemokine ligand 2, hepatocyte growth factor, cystic fibrosis transmembrane conductance regulator, histone cluster 1 H2B family member n, mitogen-activated protein kinase 11 and neurotrophic receptor tyrosine kinase 2 were identified as hub genes in PPI network. RT-qPCR and western blotting results demonstrated the same expression trend in hub genes as found by the bioinformatics analysis. Therefore, the present study identified a series of hub genes involved in the progression of pancreatic β-cell, which may help to develop effective therapeutic strategy for T2D.

Does single gene expression omnibus data mining analysis apply for only tumors and not mental illness? A preliminary study on bipolar disorder based on bioinformatics methodology.

Background:Bipolar disorder (BD), a common kind of mood disorder with frequent recurrence, high rates of additional comorbid conditions and poor compliance, has an unclear pathogenesis. The Gene Expression Omnibus (GEO) database is a gene expression database created and maintained by the National Center for Biotechnology Information. Researchers can download expression data online for bioinformatics analysis, especially for cancer research. However, there is little research on the use of such bioinformatics analysis methodologies for mental illness by downloading differential expression data from the GEO database.Methods:Publicly available data were downloaded from the GEO database (GSE12649, GSE5388 and GSE5389), and differentially expressed genes (DEGs) were extracted by using the online tool GEO2R. A Venn diagram was used to screen out common DEGs between postmortem brain tissues and normal tissues. Functional annotation and pathway enrichment analysis of DEGs were performed by using Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses, respectively. Furthermore, a protein-protein interaction network was constructed to identify hub genes.Results:A total of 289 DEGs were found, among which 5 of 10 hub genes [HSP90AA1, HSP90AB 1, UBE2N, UBE3A, and CUL1] were identified as susceptibility genes whose expression was downregulated. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that variations in these 5 hub genes were obviously enriched in protein folding, protein polyubiquitination, apoptotic process, protein binding, the ubiquitin-mediated proteolysis pathway, and protein processing in the endoplasmic reticulum pathway. These findings strongly suggested that HSP90AA1, UBE3A, and CUL 1, which had large areas under the curve in receiver operator curves (P < .05), were potential diagnostic markers for BD.Conclusion:Although there are 3 hub genes [HSP90AA1, UBE3A, and CUL 1] that are tightly correlated with the occurrence of BD, mainly based on routine bioinformatics methods for cancer-related disease, the feasibility of applying this single GEO bioinformatics approach for mental illness is questionable, given the significant differences between mental illness and cancer-related diseases.

Identification of Differentially Expressed Genes and Lipid Metabolism Signaling Pathways between Muscle and Fat Tissues in Broiler Chickens.

In this study, signaling pathways and key differentially expressed genes (DEGs) involved in lipid metabolism in muscle and fat tissues were investigated. Muscle and abdominal fat tissues were obtained from 35-day-old female broilers for RNA sequencing. DEGs between muscle and fat tissues were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of DEGs were performed. A total of 6130 DEGs were identified to be significantly enriched in 365 GO terms, most of which were involved in biological processes, cellular components, and molecular functions in muscle and fat tissues. Three important lipid signaling pathways (pyruvate metabolism, the insulin signaling pathway, and the adipocytokine signaling pathway) were identified among the fat and muscle tissues of broilers. The key common DEGs in these pathways included phosphoenolpyruvate carboxykinase 2 (PCK2), acetyl-CoA carboxylase 1 alpha and beta (ACACA and ACACB), and the mitogen-activated protein kinase (AMPK) gene family. Hence, our findings revealed the pathways and key genes and gene families involved in the regulation of fat deposition in the muscle and fat tissues of broilers.

Identification of differentially expressed and methylated genes associated with rheumatoid arthritis based on network

Rheumatoid arthritis (RA) is a multi-systemic inflammatory autoimmune disease involving peripheral joints, and the pathogenesis is not clear. Studies showed that DNA methylation and expression might also be involved in the pathogenesis of RA. This study integrated three expression profile datasets (GSE55235, GSE12021, and GSE55457) and one methylation profile dataset GSE111942 to elucidate the potential essential candidate genes and pathways in RA. Differentially expressed genes (DEGs) and differentially methylation genes (DMGs) were identified by R programming software, using Limma package and ChAMP package, respectively. DAVID performed gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Functional annotation and construction of a protein–protein interaction (PPI) network and the Molecular Complex Detection Algorithm (MCODE) were analysed by STRING and Cystoscope, respectively. Then the connection analysis of DEGs and DMGs was carried out, and further to analyse the relationship between methylation and gene expression, aiming to screen out the potential genes. In this study, 288 DEGs and 228 DMGs were identified, and the majority of DEGs were up-regulated. Enrichment analysis represented that DEGs mainly involved immune response and participated in the Cytokine–cytokine receptor interaction signal pathway. 282 nodes were identified from DEGs PPI network and MCODE, filtering the most significant 2 modules, 23 core node genes were identified and most of them are involved in the T cell receptor signalling pathway and chemokine-mediated signalling pathway. Cross-analysis revealed 4 genes [KNTC1 (cg 01277763), LRRC8D (cg 07600884), DHRS9 (cg 05961700), and UCP2 (cg 05205664)] that exhibited differential expression and methylation in RA simultaneously. Therefore, the four genes could be used as the target for RA.

Putative Genes and Pathways Involved in the Acne Treatment of Isotretinoin via Microarray Data Analyses.

Acne is the eighth most common disease worldwide. Disease burden of acne such as anxiety, reduced self-esteem, and facial scarring lowers the life quality of acne patients. Isotretinoin is the most potent treatment for moderate-severe acne. However, the adverse events of isotretinoin especially teratogenicity limit its use. This study aims at investigating the therapeutical mechanisms of isotretinoin using bioinformatics analysis. Differentially expressed genes (DEGs) were filtered from microarray datasets GSE10432, GSE10433, and GSE11792. Functional and pathway enrichment analyses of DEGs were performed. Protein–protein interaction (PPI) network and module analyses were also conducted based on DEGs. Using isotretinoin for 1 week, LCN2, PTGES, and GDF15 were upregulated and might mediate sebocytes apoptosis and thus decreased sebum production; CCL2 originated from activated TNF signaling pathway and S100A7 could be related with “acne-flare”. While treating with isotretinoin for 8 weeks, key genes were downregulated, including HMGCS1, HMGCR, FDFT1, MVD, IDI1, and FDPS, which may be associated with decreased sebum synthesis; HMGCS1, HMGCR, and FDFT1 also probably associated with apoptosis of sebocytes. There were only two common genes including ACSBG1 and BCAT2 which worked in both 1 week and 8 weeks and could associate with decreased sebum synthesis and apoptosis of sebocytes, respectively. These results indicate potential therapeutics and side effect mechanisms of isotretinoin in the acne treatment and provide a research direction to further investigate the therapeutic mechanism of isotretinoin and thus develop retinoid-like compounds with similar curative effect and without teratogenicity.

Cardiac Transcriptome Analysis Reveals Nr4a1 Mediated Glucose Metabolism Dysregulation in Response to High-Fat Diet

Obesity is associated with an increased risk of developing cardiovascular disease (CVD), with limited alterations in cardiac genomic characteristics known. Cardiac transcriptome analysis was conducted to profile gene signatures in high-fat diet (HFD)-induced obese mice. A total of 184 differentially expressed genes (DEGs) were identified between groups. Based on the gene ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs, the critical role of closely interlocked glucose metabolism was determined in HFD-induced cardiac remodeling DEGs, including Nr4a1, Fgf21, Slc2a3, Pck1, Gck, Hmgcs2, and Bpgm. Subsequently, the expression levels of these DEGs were evaluated in both the myocardium and palmitic acid (PA)-stimulated H9c2 cardiomyocytes using qPCR. Nr4a1 was highlighted according to its overexpression resulting from the HFD. Additionally, inhibition of Nr4a1 by siRNA reversed the PA-induced altered expression of glucose metabolism-related DEGs and hexokinase 2 (HK2), the rate-limiting enzyme in glycolysis, thus indicating that Nr4a1 could modulate glucose metabolism homeostasis by regulating the expression of key enzymes in glycolysis, which may subsequently influence cardiac function in obesity. Overall, we provide a comprehensive understanding of the myocardium transcript molecular framework influenced by HFD and propose Nr4a1 as a key glucose metabolism target in obesity-induced CVD.

Identification of Target Genes and Transcription Factors in Mice with LMNA-Related Dilated Cardiomyopathy by Integrated Bioinformatic Analyses.

BackgroundDilated cardiomyopathy (DCM), which is characterized by enlarged ventricular dimensions and systolic dysfunction, is the most common type of cardiomyopathy. Mutations in the LMNA gene are reported in approximately 10% of familial DCM cases. However, the mechanism of LMNA mutations in human DCM remains unclear.Material/MethodsWe used the GSE36502 and GSE123916 datasets to obtain gene expression profiles from LMNA-related DCM mice and to identify differentially expressed genes (DEGs). Crucial function and pathway enrichment analyses of DEGs were performed. Protein–protein interaction (PPI) network analysis was carried out to identify the top 10 hub genes, which were validated using reverse transcription-polymerase chain reaction (RT-PCR) to find target genes. Weighted gene co-expression network analysis (WGCNA) was used to explore the module relevant to external traits of LMNA-related DCM mice. Transcription factors (TFs) for the selected genes were analyzed using NetworkAnalyst.ResultsA total of 156 common DEGs (co-DEGs) were identified, including 80 up-regulated and 76 down-regulated genes. The enriched biological functions and pathways were oxidative stress, regulation of apoptosis, regulation of fibrosis, and MAPK pathways. Five target genes (Timp1, Hmox1, Spp1, Atf3, and Adipoq) were verified after RT-PCR. Most co-DEGs were discovered to be related to the development of external traits. Three TFs (ELF1, ETS1, and NRF1) showed close interactions with the hub genes.ConclusionsOur study used integrated bioinformatic analyses and revealed some important genes in mice with LMNA-related DCM, which could provide novel insights into the mechanism underlying human LMNA-related DCM.

Integrated Analysis of Hub Genes and Pathways In Esophageal Carcinoma Based on NCBI's Gene Expression Omnibus (GEO) Database: A Bioinformatics Analysis.

BackgroundEsophageal carcinoma (ESCA) is a health challenge with poor prognosis and limited treatment options. Our aim is to screen for hub genes and pathways associated with ESCA pathology as diagnostic or therapeutic targets.Material/MethodsWe downloaded 2 ESCA-related datasets from the Gene Expression Omnibus (GEO) database. Subsequently, differentially expressed genes (DEGs) of ESCA were determined by statistical analysis. Both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using online analytic tools. Network analysis was employed to construct a protein-protein interaction (PPI) network and to filter hub genes. We evaluated the expression level and impact of hub genes on survival of ESCA patients using the OncoLoc webserver.ResultsA total of 210 DEGs were identified. The GO analysis showed that the DEGs were enriched in cell division. The KEGG pathway analysis showed DEGs that were enriched in cell cycle regulation, known cancer pathways, the PI3K-Akt signaling pathway, and the cGMP-PKG signaling pathway. The top 10 hub genes were markedly upregulated in ESCA tissue compared with normal esophageal tissue. Moreover, the expression level of the hub genes was different at different pathological stages of ESCA. Further prognostic analysis identified that the top 10 hub genes were related to late survival of ESCA patients, while exhibiting few associations with early survival time.ConclusionsThe signaling pathways involving the DEGs probably represent the pathological mechanism underlying ESCA. The hub genes were associated with survival of ESCA patients, and as such have the potential to serve as diagnostic indicators and therapeutic targets.

Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA-mRNA network associated with lupus nephritis.

Lupus nephritis (LN) is one of the serious complications of systemic lupus erythematosus. The aim of this study was to identify core genes and pathways involved in the pathogenesis of LN. We screened differentially expressed genes (DEGs) in LN patients using mRNA expression profile data from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DEGs was performed utilizing the Database for annotation, Visualization and Integrated Discovery. Target genes with differentially expressed miRNAs (DEMIs) were predicted using the miRTarBase database, and the intersection between these target genes and DEGs was selected to be studied further. In total, 107 common DEGs (CDEGs) were identified from the Tub_LN group and Glom_LN group, and 66 DEMIs were identified. Fifty-three hub genes and two significant modules were identified from the protein-protein interaction (PPI) network, and a miRNA-mRNA network was constructed. The CDEGs, module genes in the PPI network and genes intersecting with the CDEGs and target genes of DEMIs were all associated with the PI3K-Akt signalling pathway. In summary, this study reveals some crucial genes and pathways potentially involving in the pathogenesis of LN. These findings provide a new insight for the research and treatment of LN.

Microarray Analysis for Differentially Expressed Genes Between Stromal and Epithelial Cells in Development and Metastasis of Invasive Breast Cancer.

Both epithelium and stroma are involved in breast cancer invasion and metastasis. This study aimed at identifying the roles of the stroma in breast cancer tumorigenesis and metastasis. Gene expression profiling GSE10797 was downloaded from the Gene Expression Omnibus database, and it included 28-paired stroma and epithelium breast tissue samples from invasive breast cancer patients and 10 paired normal breast tissue samples. Differentially expressed genes (DEGs) between breast cancer and normal breast tissue samples were identified by using the limma package followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to seek the potential functions of DEGs. Moreover, a protein-protein interaction network was constructed based on the String database, and modules were selected through the BioNet tool. Further, functional annotations of DEGs were carried out by using tumor suppressor gene and tumor associated gene databases. Ultimately, KEGG pathway enrichment analysis for DEGs in modules was performed. A total of 38 and 156 DEGs were identified from normal invasive stromal cells and epithelial cells, respectively. DEGs in stromal and epithelial cells were significantly enriched in extracellular matrix (ECM)- and cell proliferation-related functions. COL1A2, a hub node in the stromal module, was mainly enriched in ECM-receptor interaction and focal adhesion pathways. JUN, a hub node in the epithelium module, was significantly enriched in cancer and ErbB signaling pathways. COL1A2, COL1A1, COL3A1, JUN, and FN1 might be vital for tumorigenesis and metastasis of invasive breast cancer. These genes might be potential therapeutic targets for breast cancer treatment.

Identification of potential novel biomarkers and therapeutic targets involved in human atrial fibrillation based on bioinformatics analysis.

Atrial fibrillation (AF) is the most common arrhythmia. However, exact molecular mechanism of AF remains unclear. Our study aimed to identify underlying biomarkers and pathways involved in AF based on bioinformatics analysis. The GSE79768 human heart tissue dataset was obtained from the Gene Expression Omnibus (GEO) database. A total of 26 heart tissue samples including 14 AF atrium heart tissue samples and 12 sinus rhythm heart tissue samples were used to identify the differentially expressed genes (DEGs). The functional enrichment analysis, protein‑protein interaction network, and miRNA-targeted gene regulatory network analysis were performed. A total o f 260 DEGs were identified in the AF and sinus rhythm groups, including 150 up‑regulated and 110 down‑regulated genes. Functional and pathway enrichment analyses of DEGs indicated that they were mainly involved in inflammatory response, immune response, and receptor‑mediated endocytosis. In addition, CXCR4, CXCR2, C3, CXCL11, CCR2, AGTR2, CXCL1, and others were the hub nodes in the protein‑protein interaction network and module analysis revealed that these hub nodes were also significantly enriched in the inflammatory response, cytokine‑cytokine receptor interaction, chemokine signaling, and neuro‑active ligand‑receptor interaction pathways. Furthermore, miRNA‑targeted regulatory network analysis showed that 58 miRNA were involved in 61 regulatory relationships including 9 up‑regulated and 5 down‑regulated genes. This study identified a series of key genes, including CXCR4, CXCR2, CXCL11, CCR2, LRRK2, IL1B, C3, CXCL1, and important miRNAs such as miR‑3123, miR‑548g‑3p, and miR‑9‑5p, along with pathways that were most closely related to human AF. Our results may provide a novel molecular mechanism and potential therapeutic targets for AF.

KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining.

Background:The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC.Methods:The gene expression profiling (GSE86945, GSE86946 and GSE102088) data were obtained from Gene Expression Omnibus Datasets, DEGs were identified by using R software, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools, and the protein-protein interaction (PPI) network of the DEGs was constructed by the STRING database and visualized by Cytoscape software. Finally, the survival value of hub DEGs in breast cancer patients were performed by the Kaplan–Meier plotter online tool.Results:A total of 2998 DEGs were identified between TNBC and health breast tissue, including 411 up-regulated DEGs and 2587 down-regulated DEGs. GO analysis results showed that down-regulated DEGs were enriched in gene expression (BP), extracellular exosome (CC), and nucleic acid binding, and up-regulated were enriched in chromatin assembly (BP), nucleosome (CC), and DNA binding (MF). KEGG pathway results showed that DEGs were mainly enriched in Pathways in cancer and Systemic lupus erythematosus and so on. Top 10 hub genes were picked out from PPI network by connective degree, and 7 of top 10 hub genes were significantly related with adverse overall survival in breast cancer patients (P < .05). Further analysis found that only EGFR had a significant association with the prognosis of triple-negative breast cancer (P < .05).Conclusions:Our study showed that DEGs were enriched in pathways in cancer, top 10 DEGs belong to up-regulated DEGs, and 7 gene connected with poor prognosis in breast cancer, including HSP90AA1, SRC, HSPA8, ESR1, ACTB, PPP2CA, and RPL4. These can provide some guidance for our research on the diagnosis and prognosis of TNBC, and further research is needed to evaluate their value in the targeted therapy of TNBC.

RNA sequencing analysis of monocrotaline-induced PAH reveals dysregulated chemokine and neuroactive ligand receptor pathways.

Pulmonary arterial hypertension (PAH) is a serious disease characterized by elevated pulmonary artery pressure, inflammatory cell infiltration and pulmonary vascular remodeling. However, little is known about the pathogenic mechanisms underlying the disease onset and progression. RNA sequencing (RNA-seq) was used to identify the transcriptional profiling in control and rats injected with monocrotaline (MCT) for 1, 2, 3 and 4 weeks. A total of 23200 transcripts and 280, 1342, 908 and 3155 differentially expressed genes (DEGs) were identified at the end of week 1, 2, 3 and 4, of which Svop was the common top 10 DEGs over the course of PAH progression. Functional enrichment analysis of DEGs showed inflammatory/immune response occurred in the early stage of PAH development. KEGG pathway enrichment analysis of DEGs showed that cytokine-cytokine receptor interaction and neuroactive ligand-receptor interaction were in the initiation and progression of PAH. Further analysis revealed impaired expression of cholinergic receptors, adrenergic receptors including alpha1, beta1 and beta2 receptor, and dysregulated expression of γ-aminobutyric acid receptors. In summary, the dysregulated inflammation/immunity and neuroactive ligand receptor signaling pathways may be involved in the onset and progression of PAH.

Transcriptome-based discovery of genes and networks related to RSC3Q-mediated resistance to Soybean mosaic virus in soybean

Abstract Soybean mosaic virus (SMV) is a worldwide disease of soybean (Glycine max (L.) Merr.) that can cause serious reduction in yield and seed quality. Soybean cv. Qihuang-1 is an important source of resistance to SMV in China, carrying a resistance gene (RSC3Q) against SMV strain SC3. In order to discover genes and networks regulated by RSC3Q-mediated resistance in Qihuang-1, we analysed transcriptome data of a pair of near-isogenic lines, R (RSC3Q) and S (rSC3Q), from the cross Qihuang-1 × Nannong 1138-2 (rSC3Q), after SC3 inoculation. Many differentially expressed genes (DEGs) were identified in the R and S lines at 6, 20 and 48 h post-inoculation. Based on pathway-enrichment analysis of DEGs, three genes encoding calmodulin-like protein (Glyma03g28650, Glyma19g31395 and Glyma11g33790) with downregulated expression in the S line were identified in the plant–pathogen interaction pathway at 6 h post-inoculation. Analyses by quantitative real-time PCR were performed to verify that these three genes were not beneficial for SMV infection. Our results also revealed a complex plant-hormone signal network in RSC3Q-mediated resistance during the early stage of SMV infection. Expression of jasmonic acid repressor genes (TIFY/JAZ) and abscisic acid-induced genes (PP2C3a) was upregulated in the R line but not the S line. More DEGs related to indole-3-acetic acid were found in the R line than the S line, and no salicylic acid-related DEGs were identified. These results suggest that suppression of jasmonic acid or promotion of abscisic acid is important for RSC3Q-mediated resistance against SC3, and that salicylic acid may not act as a main regulator of RSC3Q-mediated resistance during early stages of SC3 infection. Growth and development were greatly affected through RSC3Q-mediated resistance responses after SC3 infection. Our understanding would be enhanced by identification of factors associated with RSC3Q that help to trigger the resistance response.

Gene expression profiles and related immune-inflammatory factors in the cerebral arteries in mouse models of subarachnoid haemorrhage

The study aimed to identify key genes involved in cerebral vasospasm (CVS) after subarachnoid haemorrhage (SAH). GSE46696 of basilar arteries of SAH mice and normal controls were downloaded from the Gene Expression Omnibus (GEO). Integrated microarray analysis was performed to identify differentially expressed genes (DEGs). GO and KEGG pathway enrichment analyses of DEGs were performed with ClueGO. The protein–protein interaction (PPI) networks were constructed using Cytoscape software. A total of 4103 DEGs were identified; among them, 254 DEGs (63 up-regulated genes and 191 down-regulated genes) showed significant differences at p < 0.05. GO analysis showed that the identified DEGs were over-represented in 16 GO terms. KEGG pathway analysis showed that pathways in immune inflammation were significantly enriched pathways for DEGs. DEGs with relatively frequent interactions after CVS secondary to SAH included MIKI, Cmpk2, TIr3, Psmb9, Ddx58, Lgals9, Ifi44, Stat2, Rsad2, Oas2, Usp18, H28, Irf7 and als3bp. Multiple genes were involved in the regulation of the immune response in the pathogenesis of SAH, including Ripk3, Ifih1, IL10, Reg3g, SIc11a1, NF-κB, Tlr7, Parp9, Rab7b, Dhx58, Gpx2, Zbp1, Aim2, Rsad2, Lgals9, TLR4, Adar, Zc3hav1, KIrk1, Irf7, IL-1β, Trafd1, Ddx58 and Trim5. Our findings revealed the gene expression profiles of the cerebral arteries in SAH mouse models, and speculated that DHx58 gene plays an important role in the immune response through regulating inflammatory cytokines expression, which may be a potential target in the treatment of CVS after SAH. Our finding provided new clues for understanding the mechanism of SAH.

Identification of key genes and multiple molecular pathways of metastatic process in prostate cancer.

BackgroundCancer metastasis is well known as the most adverse outcome and the major cause of mortality in cancer patients, including prostate cancer (PCa). There are no credible predictors, to this day, that can reflect the metastatic ability of localized PCa. In the present study, we firstly identified the differentially expressed genes (DEGs) and molecular pathways involved in the metastaic process of PCa by comparing gene expressions of metastaic PCa with localized PCa directly, with the purpose of identifying potential markers or therapeutic targets.MethodsThe gene expression profiles (GSE6919 and GSE32269) were downloaded from the Gene Expression Omnibus database, which contained 141 tissue samples, including 87 primary localized PCa samples and 54 metastaic PCa samples. After data processing, DEGs were identified by R language using the Student’s t-test adjusted via the Beniamini–Hochberg method. Subsequently, the gene ontology functional and pathway enrichment analyses of DEGs were performed and the protein–protein interaction network was constructed. Hub genes were identified using the plug-in cytoHubba in Cytoscape software by MCC and degree. Furthermore, validation and prognostic significance analysis of the hub genes were performed by UALCAN and gene expression profiling interactive analysis (GEPIA).ResultsA total of 90 DEGs were identified between localized and metastaic PCa, which consisted of 47 upregulated and 43 downregulated genes. The enriched functions and pathways of the DEGs include catabolic process, cell cycle, response to steroid hormone, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. A total of 10 genes were identified as hub genes and biological process analysis of hub genes showed that cell cycle phase, cell division, and mitotic cell cycle process were mainly enriched. The expression of hub genes were confirmed in metastaic PCa when compared with localized PCa tissues by The Cancer Genome Atlas database. Moreover, the disease-free survival analysis of hub genes revealed that these genes may play an important role in invasion, progression or recurrence. Therefore, these hub genes might be the key genes contributed to tumor progression or metastasis in PCa and provide candidate therapeutic targets for PCa.ConclusionsThe present study identified some DEGs between localized and metastaic PCa tissue samples. These key genes might be potential therapeutic targets and biomarkers for the metastaic process of PCa.

CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis.

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein–protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

Integrated Analysis of DNA Copy Number Changes and Gene Expression Identifies Key Genes in Gastric Cancer.

This study was aimed at identifying differentially expressed genes (DEGs) with copy number changes in gastric cancer (GC) pathogenesis. Microarray data GSE33429, including array-based comparative genomic hybridization and gene expression profiles, were obtained. DEGs were screened between GC and adjacent noncancerous tissues. Genes located at Minimum Common Regions (MCRs) were identified, and overlapped genes between DEGs and genes with amplification or deletion were identified. Gene Ontology function and pathway enrichment analysis of DEGs were performed. A protein-protein interaction network for DEGs was built, and significant modules were mined from the network. Functional annotation of genes in modules was also performed. A total of 677 up- and 583 downregulated DEGs were identified, including 37 overexpressed genes located at gained MCRs and 28 downregulated genes located at deleted MCRs. In significant modules, upregulated genes with amplification, including DSN1 (MIS12 kinetochore complex component), MAPRE1 (microtubule-associated protein, RP/EB family, member 1), TPX2 (microtubule-associated), UBE2C (ubiquitin-conjugating enzyme E2C), and MYBL2 (v-myb avian myeloblastosis viral oncogene homolog-like 2), were associated with cell cycle, but downregulated genes with deletion, including UGT2B15 (UDP glucuronosyltransferase 2 family, polypeptide B15), UGT2B17 (UDP glucuronosyltransferase 2 family, polypeptide B17), ADH1B (alcohol dehydrogenase 1B), and ADH1A (alcohol dehydrogenase 1A), were related to metabolism. The identified genes DSN1, MAPRE1, TPX2, UBE2C, and MYBL2 located at gained MCRs and UGT2B15, UGT2B17, ADH1B, and ADH1A located at deleted MCRs may play an important role in GC progression through regulating cell cycle and metabolism.

Transcriptome Analysis of Cordyceps militaris Reveals Genes Associated With Carotenoid Synthesis and Identification of the Function of the Cmtns Gene.

Cordyceps militaris, a valuable edible and medicinal fungus, has attracted increasing attention because of its various bioactive ingredients. However, the biosynthetic pathway of C. militaris carotenoids is still unknown due to lack of transcriptome information. To uncover genes related to the biosynthesis of C. militaris carotenoids, the transcriptomes of mycelia CM10_D cultured under dark conditions and mycelia CM10_L cultured under light exposure conditions were sequenced. Compared with mycelia CM10_D, 866 up-regulated genes and 856 down-regulated genes were found in mycelia CM10_L. Gene ontology (GO) analysis of differentially expressed genes (DEGs) indicated that DEGs were mainly classified into the “metabolic process,” “membrane,” and “catalytic activity” terms. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs suggested that DEGs were mainly enriched in “metabolic pathways,” “MAPK signaling pathway-yeast,” and “biosynthesis of secondary metabolites.” In addition, the carotenoid content of the Cmtns gene deletion mutant (ΔCmtns) was significantly lower than that of the wild-type C. militaris CM10, while the carotenoid content of the complementary strain (ΔCmtns-c) of the Cmtns gene was not significantly different from that of C. militaris CM10, suggesting that the Cmtns gene significantly affected the biosynthesis of carotenoids in C. militaris. These results potentially pave the way for revealing the biosynthetic pathway of carotenoids and improving carotenoids production in C. militaris.