Functional analysis of gene expression profiling-based prediction in bladder cancer

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.

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.

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.

We aimed to give a systematic hypothesis on the functions of exercise on circulating monocytes by identifying a discrete set of genes in circulating monocytes that were altered by exercise. The microarray expression profile of GSE51835 was downloaded from gene expression omnibus (GEO) database for the identification of differentially expressed genes (DEGs) using limma and affy packages in R language. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for DEGs, followed by the construction of co-expression network and protein-protein interaction (PPI) network. The top 10 nodes in PPI network were screened, and subnetwork was constructed for the key genes identification. Totally, 35 DEGs, including 2 upregulated genes and 33 downregulated genes, were identified. The enriched GO terms were mainly linked to immune response and defence response, and the enriched KEGG pathways were mainly associated with natural killer cell-mediated cytotoxicity and graft-versus-host disease. Dual-specificity phosphatase 2 (DUSP2) was identified as a key node in the co-expression network. In the PPI network, CD247 module (CD247), chemokine (C-X-C motif) receptor 4 (CXCR4), granzyme B (GZMB) and perforin 1 (PRF1) were identified as key nodes. An important interaction, GZMB/PRF1, was detected. Five key genes, including DUSP2, CD247, CXCR4, GZMB and PRF1, and an interaction of GZMB/PRF1, were significant factors in the immune processes of circulating monocytes, which might be regulated by brief exercises, leading to the enhancement of immune function.

The aim of the present study was to identify differentially expressed genes (DEGs) and their related functions and pathways of major burn injuries, and to prevent the occurrence of complications. The expression profiling of E-GEOD-37069 was downloaded from ArrayExpress Archive. The DEGs of major burn injuries were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) functional enrichment analysis were then performed for the DEGs. Based on the KEGG database, a pathway relationship network was constructed, and DEGs in significant GO terms and pathways were investigated. Gene signal network and gene co-expression network of these inserted DEGs were constructed. A total of 3,328 DEGs of major burn injuries were identified, including 1,337 up- and 1,991 downregulated DEGs. These DEGs were mainly enriched into various GO terms, including transcription, DNA-dependent, signal transduction and blood coagulation. Moreover, they were also enriched into different pathways, such as hematopoietic cell lineage, metabolic pathway and chemokine signaling pathway. The pathway relationship network was constructed with 72 nodes. The MAPK signaling pathway was the hub node. Based on the same gene symbol, 702 DEGs were obtained, identified in both GO terms and pathways. Finally, the gene signaling network and gene co-expression network were constructed with 391 and 128 nodes, respectively. These identified DEGs, including GNB2, LILRA2, ARRB2 and ARHGEF2, may be potential key genes involved in the treatment of major burn injuries and prevention of complications.

Transcriptomic analysis of the thyroid and ovarian stroma reveals key pathways and potential candidate genes associated with egg production in ducks

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.

BackgroundRNA-sequencing analysis is increasingly utilized to study gene expression in non-model organisms without sequenced genomes. Aethionema arabicum (Brassicaceae) exhibits seed dimorphism as a bet-hedging strategy – producing both a less dormant mucilaginous (M+) seed morph and a more dormant non-mucilaginous (NM) seed morph. Here, we compared de novo and reference-genome based transcriptome assemblies to investigate Ae. arabicum seed dimorphism and to evaluate the reference-free versus -dependent approach for identifying differentially expressed genes (DEGs).ResultsA de novo transcriptome assembly was generated using sequences from M+ and NM Ae. arabicum dry seed morphs. The transcripts of the de novo assembly contained 63.1% complete Benchmarking Universal Single-Copy Orthologs (BUSCO) compared to 90.9% for the transcripts of the reference genome. DEG detection used the strict consensus of three methods (DESeq2, edgeR and NOISeq). Only 37% of 1533 differentially expressed de novo assembled transcripts paired with 1876 genome-derived DEGs. Gene Ontology (GO) terms distinguished the seed morphs: the terms translation and nucleosome assembly were overrepresented in DEGs higher in abundance in M+ dry seeds, whereas terms related to mRNA processing and transcription were overrepresented in DEGs higher in abundance in NM dry seeds. DEGs amongst these GO terms included ribosomal proteins and histones (higher in M+), RNA polymerase II subunits and related transcription and elongation factors (higher in NM). Expression of the inferred DEGs and other genes associated with seed maturation (e.g. those encoding late embryogenesis abundant proteins and transcription factors regulating seed development and maturation such as ABI3, FUS3, LEC1 and WRI1 homologs) were put in context with Arabidopsis thaliana seed maturation and indicated that M+ seeds may desiccate and mature faster than NM. The 1901 transcriptomic DEG set GO-terms had almost 90% overlap with the 2191 genome-derived DEG GO-terms.ConclusionsWhilst there was only modest overlap of DEGs identified in reference-free versus -dependent approaches, the resulting GO analysis was concordant in both approaches. The identified differences in dry seed transcriptomes suggest mechanisms underpinning previously identified contrasts between morphology and germination behaviour of M+ and NM seeds.

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer in adults. The aim of this study is to identify the biomarkers and potential molecular mechanisms of ccRCC. Three gene expression profiles and two miRNA expression profiles were downloaded from GEO database. A total of 330 up-regulated differentially expressed genes (DEGs), 545 down-regulated DEGs, 26 up-regulated differentially expressed miRNAs (DEMs) and 11 down-regulated DEMs were identified by GEO2R. The gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by KOBAS software. The results showed that GO terms of the up-regulated DEGs were mostly enriched in response to stimulus at BP level, cell periphery at CC level and binding at MF level, while the GO terms of down-regulated DEGs were enriched in single-organism process at BP level, extracellular exosome at CC level and catalytic activity at MF level. As for KEGG pathways, HIF-1 signaling pathway, focal adhesion, PI3K-Akt signaling pathway and metabolic pathways were significantly enriched. Then, protein-protein interaction (PPI) network and miRNA-gene network were constructed and analyzed by Cytoscape. A total of eight DEGs were identified as biomarkers, including VEGFA, PPARA, CCND1, FLT1, CXCL12, FN1, DCN and ERBB4. Expression validation and survival analysis were performed by GEPIA and OncoLnc, respectively. Four biomarkers were verified by quantitative real-time PCR (qPCR) in 786-O cell line and HK-2 cell line. All four genes had the same expression trend as predicted. Our study provides a series of biomarkers and molecular mechanisms for the deeper research of ccRCC.

BackgroundThe submandibular glands, as major salivary glands, participate in rumen digestion in goats. Sialic acid, lysozyme, immunoglobulin A (IgA), lactoferrin and other biologically active substances secreted in the submandibular glands were reported in succession, which suggests that the submandibular gland may have immune functions in addition to participating in digestion. The aim of this study was to map the expression profile of differentially expressed genes (DEGs) at three different stages by transcriptome sequencing, screen immune-related genes and pathways by bioinformatics methods, and predict the immune function of submandibular glands at different developmental stages.MethodsNine submandibular gland tissue samples were collected from groups of 1-month-old kids, 12-month-old adolescent goats and 24-month-old adult goats (3 samples from each group), and high-throughput transcriptome sequencing was conducted on these samples. The DEGs among the three stages were screened and analysed. Key genes and signalling pathways were selected via protein-protein interaction (PPI) network analysis.ResultsThe results revealed 2,706, 2,525 and 52 DEGs between 1-month-old and 12-month-old goats, between 1-month-old and 24-month-old goats, and between 12-month-old and 24-month-old goats, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that most of the DEGs were enriched in immune- related GO terms and pathways. Based on functional enrichment analysis and network analysis, 10 genes (PTPRC, CD28, SELL, LCP2, MYC, LCK, ZAP70, ITGB2, SYK and CCR7), two signalling pathways (the T cell receptor signalling pathway and the NF-κβ signalling pathway) and eight GO terms (T cell receptor signalling pathway, neutrophil mediated immunity, B cell mediated immunity, regulation of alpha-beta T cell activation, positive regulation of T cell proliferation, regulation of leukocyte differentiation, positive regulation of antigen receptor-mediated signalling pathway, positive regulation of lymphocyte proliferation) that may play key roles in the immune functions of the goat submandibular glands at different developmental stages were identified. Moreover, we found that eight antibacterial peptide-encoding genes were downregulated in the tuberculosis and salivary secretion pathways, while all immunoglobulins were upregulated in 10 immune system pathways. These findings indicate that the submandibular glands may be important immunological organs during the growth process of goats and that the immune function of these glands gradually weakens with age up to 12 months but remains relatively stable after 12 months of age. Overall, this study will improve our understanding of transcriptional regulation related to goat submandibular gland immune function.

Hyperinflammation is related to the development of COVID-19. Resveratrol is considered an anti-inflammatory and antiviral agent. Herein, we used a network pharmacological approach and bioinformatic gene analysis to explore the pharmacological mechanism of Resveratrol in COVID-19 therapy. Potential targets of Resveratrol were obtained from public databases. SARS-CoV-2 differentially expressed genes (DEGs) were screened out via bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE147507, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis; then, protein-protein interaction network was constructed. The common targets, GO terms, and KEGG pathways of Resveratrol targets and SARS-CoV-2 DEGs were confirmed. KEGG Mapper queried the location of common targets in the key pathways. A notable overlap of the GO terms and KEGG pathways between Resveratrol targets and SARS-CoV-2 DEGs was revealed. The shared targets between Resveratrol targets and SARS-CoV-2 mainly involved the IL-17 signaling pathway, NF-kappa B signaling pathway, and TNF signaling pathway. Our study uncovered that Resveratrol is a promising therapeutic candidate for COVID-19 and we also revealed the probable key targets and pathways involved. Ultimately, we bring forward new insights and encourage more studies on Resveratol to benefit COVID-19 patients.

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.

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.

Identification of novel candidate genes in rosacea by bioinformatic methods

To explore the potential therapeutic targets of early‑stage non-small cell lung cancer (NSCLC), gene microarray analysis was conducted. The microarray data of NSCLC in stage IA, IB, IIA, and IIB (GSE50081), were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in IB vs. IA, IIA vs. IB, IIB vs. IIA were screened out via R. ToppGene Suite was used to get the enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The GeneCoDis3 database and Cytoscape software were used to construct the transcriptional regulatory network. In total, 25, 17 and 14 DEGs were identified in IB vs. IA, IIA vs. IB, IIB vs. IIA of NSCLC, respectively. Some GO terms and pathways (e.g., extracellular space, alveolar lamellar body, bioactivation via cytochrome P450 pathway) were found significantly enriched in DEGs. Genes S100P, ALOX15B, CCL11, NLRP2, SERPINA3, FoxO4 and hsa-miR-491 may play important roles in the development of early-stage NSCLC. Thus, by bioinformatics analysis the key genes and biological processes involving in the development of early-stage NSCLC could be established, providing more potential references for the therapeutic targets.