Brief Exercises Affect Gene Expression in Circulating Monocytes.

The present study aimed to investigate differentially expressed genes (DEGs) in whole blood (WB) obtained from patients with lumbar disc prolapse (LDP) and healthy volunteers. A total of 8 patients with LDP and 8 healthy volunteers were recruited. An Agilent SurePrint G3 human gene expression microarray 8×60 K was used to perform the microarray analyses. R was employed to identify DEGs, which were then subjected to bioinformatics analysis, including a Gene Ontology (GO) analysis, Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis and protein-protein interaction (PPI) network analysis. DEGs in the degenerative annulus fibrosis (AF) and nucleus pulposus (NP) compared with non-degenerative tissues were also identified based on microarray data and the intersections of the three were assessed. Furthermore, reverse transcription-quantitative (RT-q)PCR was performed to confirm the aberrant expression levels of selected DEGs in the WB of all subjects. A total of 161 DEGs between LDP patients and the healthy controls were identified (128 upregulated and 33 downregulated). These DEGs were enriched in 293 biological process, 36 cellular component and 21 molecular function GO terms, as well as in 24 KEGG pathways. The PPI network contained 4 submodules, and Toll-like receptor 4 had the highest degree centrality. A total of 22 DEGs were common to the three groups of DEGs. The RT-qPCR assay confirmed that the expression levels of cytochrome P450 family 27 subfamily A member 1, superoxide dismutase 2, protein disulfide isomerase family A member 4, FKBP prolyl isomerase 11 and ectonucleotide pyrophosphatase/phosphodiesterase 4 were significantly different between the patient group and the volunteer group. In conclusion, several genes were identified as potential biomarkers in WB that should be further explored in future studies to determine their potential application in the clinical treatment and diagnosis of LDP, and the present bioinformatics analysis revealed several GO terms, KEGG pathways and submodules of the PPI network that may be involved in LDP, although the exact mechanisms remain elusive.

Anthracyclines are among the most effective and commonly used chemotherapeutic agents. However, the development of acquired anthracycline resistance is a major limitation to their clinical application. The aim of the present study was to identify differentially expressed genes (DEGs) and biological processes associated with the acquisition of anthracycline resistance in human breast cancer cells. We performed a meta-analysis of publically available microarray datasets containing data on stepwise-selected, anthracycline‑resistant breast cancer cell lines using the RankProd package in R. Additionally, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to analyze GO term enrichment and pathways, respectively. A protein-protein interaction (PPI) network was also generated using Cytoscape software. The meta-analysis yielded 413 DEGs related to anthracycline resistance in human breast cancer cells, and 374 of these were not involved in individual DEGs. GO analyses showed the 413 genes were enriched with terms such as 'response to steroid metabolic process', 'chemical stimulus', 'external stimulus', 'hormone stimulus', 'multicellular organismal process', and 'system development'. Pathway analysis revealed significant pathways including steroid hormone biosynthesis, cytokine-cytokine receptor interaction, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and arachidonic acid metabolism. The PPI network indicated that proteins encoded by TRIM29, VTN, CCNA1, and karyopherin α 5 (KPNA5) participated in a significant number of interactions. In conclusion, our meta-analysis provides a comprehensive view of gene expression patterns associated with acquired resistance to anthracycline in breast cancer cells, and constitutes the basis for additional functional studies.

Renal cell carcinoma (RCC) is the most common form of kidney cancer, caused by renal epithelial cells. RCC remains to be a challenging public health problem worldwide. Metastases that are resistant to radiotherapy and chemotherapy are the major cause of death from cancer. However, the underlying molecular mechanism regulating the metastasis of RCC is poorly known. Publicly available databases of RCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using GEO2R analysis, whereas the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Gene Set Enrichment Analysis (GSEA) and Metascape. Protein-protein interaction (PPI) network of DEGs was analyzed by STRING online database, and Cytoscape software was used for visualizing PPI network. Survival analysis of hub genes was conducted using GEPIA online database. The expression levels of hub genes were investigated from The Human Protein Atlas online database and GEPIA online database. Finally, the comparative toxicogenomics database (CTD; http://ctdbase.org) was used to identify hub genes associated with tumor or metastasis. We identified 229 DEGs comprising 135 downregulated genes and 94 upregulated genes. Functional analysis revealed that these DEGs were associates with cell recognition, regulation of immune, negative regulation of adaptive immune response, and other functions. And these DEGs mainly related to P53 signaling pathway, cytokine-cytokine receptor interaction, Natural killer cell mediated cytotoxicity, and other pathways are involved. Ten genes were identified as hub genes through module analyses in the PPI network. Finally, survival analysis of 10 hub genes was conducted, which showed that the MMP2 (matrix metallo peptidase 2), DCN, COL4A1, CASR (calcium sensing receptor), GPR4 (G protein-coupled receptor 4), UTS2 (urotensin 2), and LDLR (low density lipoprotein receptor) genes were significant for survival. In this study, the DEGs between RCC and metastatic RCC were analyzed, which assist us in systematically understanding the pathogeny underlying metastasis of RCC. The MMP2, DCN, COL4A1, CASR, GPR4, UTS2, and LDLR genes might be used as potential targets to improve diagnosis and immunotherapy biomarkers for RCC.

Identification of novel candidate genes in rosacea by bioinformatic methods

To identify novel clinically relevant genes in papillary thyroid carcinoma from public databases. Four original microarray datasets, GSE3678, GSE3467, GSE33630 and GSE58545, were downloaded. Differentially expressed genes (DEGs) were filtered from integrated data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein-protein interaction (PPI) network construction. The CentiScape pug-in was performed to scale degree. The genes at the top of the degree distribution (≥ 95% percentile) in the significantly perturbed networks were defined as central genes. UALCAN and The Cancer Genome Atlas Clinical Explorer were used to verify clinically relevant genes and perform survival analysis. 225 commonly changed DEGs (111 up-regulated and 114 down-regulated) were identified. The DEGs were classified into three groups by GO terms. KEGG pathway enrichment analysis showed DEGs mainly enriched in the PI3K-Akt signaling pathway, pathways in cancer, focal adhesion and proteoglycans in cancer. DEGs' protein-protein interaction (PPI) network complex was developed; six central genes (BCL2, CCND1, FN1, IRS1, COL1A1, CXCL12) were identified. Among them, BCL2, CCND1 and COL1A1 were identified as clinically relevant genes. BCL2, CCND1 and COL1A1 may be key genes for papillary thyroid carcinoma. Further molecular biological experiments are required to confirm the function of the identified genes.

The aim of the present study was to identify the differentially expressed genes (DEGs) in esophageal squamous-cellcarcinoma (ESCC) and provide potential therapeutic targets. The microarray dataset GSE20347 was downloaded from the Gene Expression Omnibus (GEO) database, and included 17 tissue samples and 13 normal adjacent tissue samples from patients with ESCC. A total of 22,277 DEGs were identified. A heat map for the DEGs was constructed with the Morpheus online tool and the top 200 genes (100 upregulated and 100 downregulated) were selected for further bioinformatics analysis, including analysis of gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, protein-protein interaction networks and Spearman's correlation tests. The results of the GO analysis indicated that the upregulated DEGs were most significantly enriched in membrane-bounded vesicles in the cellular component (CC) category, but were not significantly enriched in any GO terms of the categories biological process (BP) or molecular function (MF); furthermore, the downregulated DEGs were most significantly enriched in regulation of DNA metabolic processes, nucleotide binding and chromosomes in the categories BP, MF and CC, respectively. The KEGG analysis indicated that the downregulated DEGs were enriched in the regulation of cell cycle pathways. The top 10 hub proteins in the protein-protein interaction network were cyclin-dependent kinase 4, budding uninhibited by benzimidazoles 1, cyclin B2, heat shock protein 90AA1, aurora kinase A, H2A histone family member Z, replication factor C subunit 4, and minichromosome maintenance complex component 2, −4 and −7. These proteins are mainly involved in regulating tumor progression. The genes in the four top modules were mainly implicated in regulating cell cycle pathways. Secreted Ly-6/uPAR-related protein (SLURP) was the hub gene, and SLURP and its interacting genes were most enriched in the chromosomal part in the CC category, organelle organization in the BP category and protein binding in the MF category, and were involved in pathways including DNA replication, cell cycle and P53 signaling. The expression of SLURP-1 in fifteen patients with esophageal carcinoma was detected using quantitative polymerase chain reaction analysis, and the results indicated that SLURP-1 expression was significantly decreased in the tumor samples relative to that in normal adjacent tissues. These results suggest that several hub proteins and the hub gene SLURP-1 may serve as potential therapeutic targets, and that gene dysfunction may be involved in the tumorigenesis of ESCC.

Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development. Nonalcoholic steatohepatitis (NASH) is a leading cause of chronic liver disease worldwide and is characterized by steatosis, inflammation, and fibrosis. The molecular mechanisms underlying NASH development remain obscure. The nuclear receptor small heterodimer partner (Shp) plays a complex role in lipid metabolism and inflammation. Here, we sought to determine SHP's role in regulating steatosis and inflammation in NASH. Shp deletion in murine hepatocytes (ShpHep−/−) resulted in massive infiltration of macrophages and CD4+ T cells in the liver. ShpHep−/− mice developed reduced steatosis, but surprisingly increased hepatic inflammation and fibrosis after being fed a high-fat, -cholesterol, and -fructose (HFCF) diet. RNA-Seq analysis revealed that pathways involved in inflammation and fibrosis are significantly activated in the liver of ShpHep−/− mice fed a chow diet. After having been fed the HFCF diet, WT mice displayed up-regulated peroxisome proliferator-activated receptor γ (Pparg) signaling in the liver; however, this response was completely abolished in the ShpHep−/− mice. In contrast, livers of ShpHep−/− mice had consistent NF-κB activation. To further characterize the role of Shp specifically in the transition of steatosis to NASH, mice were fed the HFCF diet for 4 weeks, followed by Shp deletion. Surprisingly, Shp deletion after steatosis development exacerbated hepatic inflammation and fibrosis without affecting liver steatosis. Together, our results indicate that, depending on NASH stage, hepatic Shp plays an opposing role in steatosis and inflammation. Mechanistically, Shp deletion in hepatocytes activated NF-κB and impaired Pparg activation, leading to the dissociation of steatosis, inflammation, and fibrosis in NASH development.

The purpose of the current study was to explore the carboplatin-induced sequential changes in gene expression and screen out key genes, which were associated with effects of carboplatin on epithelial ovarian cancer (EOC). The microarray dataset GSE13525 was downloaded from the Gene Expression Omnibus database, including 6 EOC cell samples separately treated with carboplatin at 24, 30 and 36 h (case group), and 6 samples treated with phosphate-buffered saline at the same time points (control group). A total of 3 sets of differentially expressed genes (DEGs) were respectively identified in case samples at 24, 30 and 36 h compared with the control group via the Limma package, and separately recorded as DEG-24, DEG-30 and DEG-36. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the overlapped DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In addition, the survival curves were drawn to illustrate the association between the expression levels of certain critical genes and the prognosis of EOC. A total of 170, 605 and 1043 DEGs were separately obtained in DEG-24 DEG-30 and DEG-36, and 110 overlaps were identified. The overlaps were enriched in 77 GO terms and 3 KEGG pathways. A total of 152 pairs were involved in the PPI network, and the abnormal expression levels (high or low) of c-Jun and cyclin B1 (CCNB1) would reduce the survival time of patients with EOC. The study indicated that c-Jun and CCNB1 may be the prognostic biomarkers of EOC treated with carboplatin, and certain pathways (such as p53 signaling pathway, cell cycle and mitogen-activation protein kinase signaling pathway) may be involved in carboplatin-resistant EOC.

The aim of this study was to explore the anti-psoriatic effect and potential mechanism of Angelica polysaccharide (APS) on an in vitro HaCaT cell model. MTS assay was performed to determine whether APS has the ability to inhibit the proliferation of HaCaT cells. RNA-sequencing (RNA-seq) was performed to investigate the underlying mechanism of APS. Quantitative real-time PCR (qRT-PCR) was used to verify the accuracy of RNA-seq data. Our MTS assay results demonstrated that APS time- and concentration-dependently inhibits the proliferation of HaCaT cells. The anti-proliferation property of APS suggests that APS may have anti-psoriatic effect. In the RNA-seq part, comparison between the CK group (i.e., Control group) and ASP groups revealed dramatic differences [468 differentially expressed genes (DEGs) for CK group vs. ASP50 group; 563 DEGs for CK group vs. ASP100 group; 532 DEGs for CK group vs. ASP200 group]. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrich analysis performed on all DEGs failed to find any significant enriched GO terms or KEGG pathways to explain the anti-proliferativeeffect of APS. All DEGs were then classified into 20 expression profiles by trend analysis. Interestingly, cell proliferation-related GO terms were mostly dispersed in the profile 2 and 17. DEGs enriched in these terms were then analyzed. After literature retrieval, DEGs such as SERPINE1, SMAD6, CTGF, and TGF-β were suspected to closely relevant to the anti-proliferation effect of APS. qRT-PCR results showed similar expression trend with RNA-seq data for 8 out of 10 genes, indicating our sequence data are reliable.

The present study aimed to analyze the modification of gene expression in bladder cancer (BC) by identifying significant differentially expressed genes (DEGs) and functionally assess them using bioinformatics analysis. To achieve this, two microarray datasets, GSE24152 (which included 10 fresh tumor tissue samples from urothelial bladder carcinoma patients and 7 benign mucosa samples from the bladder), and GSE42089 (which included 10 tissues samples from urothelial cell carcinoma patients and 8 tissues samples from the normal bladder), were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) were screened between benign the mucosa and control groups in GSE24152 and GSE42089 datasets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analysis were performed on overlapping DEGs identified in GSE24152 and GSE42089. Protein-protein interaction (PPI) networks and sub-networks were then constructed to identify key genes and main pathways. GO terms analysis was also performed for the selected clusters. In total, 1,325 DEGs in GSE24152 and 647 DEGs in GSE42089 were screened, in which 619 common DEGs were identified. The DEGs were mainly enriched in pathways and GO terms associated with mitotic and chromosome assembly, including nucleosome assembly, spindle checkpoint and DNA replication. In the interaction network, progesterone receptor (PGR), MAF bZIP transcription factor G (MAFG), cell division cycle 6 (CDC6) and members of the minichromosome maintenance family (MCMs) were identified as key genes. Histones were also considered to be significant factors in BC. Nucleosome assembly and sequence-specific DNA binding were the most significant clustered GO terms. In conclusion, the DEGs, including PGR, MAFG, CDC6 and MCMs, and those encoding the core histone family were closely associated with the development of BC via pathways associated with mitotic and chromosome assembly.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Background We attempted to explore the molecular mechanism underlying PHY906 intervention of colon cancer. Methods The microarray data of tumors treated by PHY906 and PBS alone were downloaded from the public Gene Expression Omnibus database. The dataset was further analyzed for the differentially expressed genes (DEGs) and their related biological functions were analyzed, followed by function and pathways. Protein-protein interaction (PPI) network was constructed and the significant nodes were screened by network centralities and then the significant modules analysis. Besides, they were clustered and transcriptional factors (TFs) were predicted. Results The gene expression patterns changed induced by PHY906 treatment, including 414 upregulated and 337 downregulated DEGs. The biological process of response to steroid hormone stimulus and regulation of interferon-gamma production were significantly enriched by DEGs. Ezh2 (enhancer of zeste 2) was found to be the key node in PPI network. There are 12 significant TFs predicted for module 1 genes and 3 TFs for module 2 genes. Conclusions PHY906 treatment may function in protecting the epithelial barrier against tumor cell invasion by modulating IFN-γ level and mediating cancer cell death by activating the response to steroid hormone stimulus and activating the response to steroid hormone stimulus. E2f1, Hsfy2, and Nfyb may be therapeutic targets for colon cancer. PHY906 showed treatment efficacy in modulating cell apoptosis by intervening interferon-gamma production and response to steroid hormone stimulus. Ezh2 and its TFs such as E2f1, Hsfy2, and Nfyb may be the potential therapeutic targets for anticancer agents development.

Chromophobe renal cell carcinoma (chRCC), the third most common histological subtype of RCC, comprises 5–7% of all RCC cases. The aim of the present study was to identify potential biomarkers for chRCC and to examine the underlying mechanisms. A total of 4 profile datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed with the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed to predict hub genes. Hub gene expression within chRCC across multiple datasets, as well as overall survival, were investigated by utilizing the Oncomine platform and UALCAN dataset, separately. A total of 266 DEGs (88 upregulated genes and 168 downregulated genes) were identified from 4 profile datasets. Integrating the results from the PPI network, Oncomine platform and survival analysis, CFTR was screened as a key factor in the prognosis of chRCC. GO and KEGG analysis revealed that 266 DEGs were mainly enriched in 17 terms and 9 pathways. The present study identified key genes and potential molecular mechanisms underlying the development of chRCC, and CFTR may be a potential prognostic biomarker and novel therapeutic target for chRCC.

Transcriptome analysis reveals key genes and signalling pathways related to residual feed intake in meat-type ducks.

BackgroundLiver fibrosis is a transitional stage from hepatitis to cirrhosis, and hepatitis B virus (HBV) is the most common cause of liver disease. Transcriptome sequencing technology and bioinformatics analysis are increasingly being used to screen diagnostic targets for liver fibrosis.MethodsThe GSE171294 dataset of HBV‐induced liver fibrosis tissue and normal tissue was obtained from the Gene Expression Omnibus (GEO) public database and used to screen for differentially expressed mRNAs using R software. mRNAs with |log fold change| >1 and p < 0.05 were considered to be differentially expressed. A heat map was drawn to visualize the expression patterns of the differentially expressed mRNAs. To screen for candidate target mRNAs, the differentially expressed mRNAs were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis. Finally, a protein–protein interaction (PPI) network was constructed to analyze the relationships between the differentially expressed mRNAs.ResultsA total of 243 differentially expressed mRNAs were identified (p < 0.05); 129 were up‐regulated and 114 were down‐regulated. The up‐regulated and down‐regulated mRNAs were significantly enriched in 16 and 8 KEGG pathways, respectively. The enriched KEGG pathways included Salmonella infection, Protein processing in the endoplasmic reticulum, IL‐17 signaling pathway, and Aldosterone synthesis and secretion. The enriched GO terms were related mainly to cell proliferation, apoptosis, endoplasmic reticulum complex assembly, and myosin synthesis. The PPI network contained 161 nodes and 120 pairs of interactions. The top 10 key nodes were CAV1, CD4, NR3C1, PDIA3, EZR, IRF4, SOX9, HSP90AB1, CD40, and SEC13.ConclusionsBioinformatics analysis of the transcriptome sequencing data in the GSE171294 dataset identified CD4, NR3C1, and EZR and other genes at key nodes as new targets for the treatment of liver fibrosis caused by HBV. These results provide new insights for HBV‐induced liver fibrosis research and clinical treatment.