scholarly journals POS0399 TMT-BASED QUANTITATIVE PROTEOMICS ANALYSIS OF SYNOVIAL FLUID-DERIVED EXOSOMES IN RHEUMATOID ARTHRITIS, AXIAL SPONDYLOARTHRITIS, GOUT AND OSTEOARTHRITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 428.3-429
Author(s):  
Y. Liu ◽  
Y. Huang ◽  
Q. Huang ◽  
Z. Huang ◽  
Z. Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Cluster Analysis ◽  
Synovial Fluid ◽  
Pathway Analysis ◽  
Quantitative Proteomics ◽  
Axial Spondyloarthritis ◽  
Kegg Pathway ◽  
Hierarchical Cluster ◽  
Kegg Pathway Analysis ◽  
Differential Proteins

Background:The pathogeneses of the joint diseases rheumatoid arthritis (RA), axial spondyloarthritis (axSpA), gout, and osteoarthritis (OA) are still not fully elucidated. Exosomes in synovial fluid (SF) has a critical role in the pathogenesis of arthritis. None of study has compared the proteomics of SF-derived exosomes in RA, axSpA, gout and OA.Objectives:To compare the proteomics of SF-derived exosomes in RA, axSpA, gout and OA based on tandem mass tags (TMT) labeled quantitative proteomics technique.Methods:SF-derived exosomes was isolated from RA, axSpA, gout and OA patients by the Exoquick kit combined ultracentrifugation method. TMT labeled quantitative proteomics technique was used to compare the proteomics of SF-derived exosomes. Volcano plot, hierarchical cluster, Gene Ontologies (GO), Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted.Results:A total of 1678 credible proteins were detected. With the cut off criteria of |log2 (fold-change)| ≥1.2 and p-value <0.05, 267 (140 up-regulated and 127 down-regulated)differential proteins were found in OA vs gout, 291 (179 and 112) in axSpA vs OA, 515 (109 and 406) in RA vs axSpA, 298 (191 and 107) in axSpA vs gout, 462 (160 and 302) in RA vs gout, 536 (170 and 366) in RA vs OA. GO analysis showed that the biological progress of differential proteins were mainly enriched in the “immune response”. Regarding the molecular function, the differential proteins mainly mediated “antigen binding”. GO analysis of the cellular components indicated that most proteins were annotated as “extracellular exosomes”. KEGG pathway analysis demonstrated differential proteins were significantly enriched in “complement and coagulation cascades”. The hierarchical cluster analysis of the differential proteins in the four groups showed that Lysozyme C and Keratin were more abundant in gout, Hemoglobin and Actin-related protein 2/3 complex subunit 3 in OA, Sodium/potassium-transporting ATPase subunit alpha-1 and Immunoglobulin heavy constant delta in axSpA, Pregnancy zone protein and Stromelysin-1 in RA.Conclusion:The protein profiles of SF-derived exosomes in RA, axSpA, gout and OA patients were different. The differential proteins were the potential biomarkers of RA, axSpA, gout and OA.References:[1]Cretu D, Diamandis E P, Chandran V. Delineating the synovial fluid proteome: recent advancements and ongoing challenges in biomarker research.[J]. Critical reviews in clinical laboratory sciences, 2013,50(2):51-63.[2]McArdle A J, Menikou S. What is proteomics?[J]. Archives of disease in childhood. Education and practice edition, 2020.Figure 1.The hierarchical cluster analysis of differential proteins in axSpA, OA, Gout and RA.Disclosure of Interests:None declared

scholarly journals Identification of Pathways and Key Genes in Venous Remodeling After Arteriovenous Fistula by Bioinformatics Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Kong Jie ◽  
Wang Feng ◽  
Zhao Boxiang ◽  
Gong Maofeng ◽  
Zhang Jianbin ◽  
...  
Keyword(s):  
Arteriovenous Fistula ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Function Analysis ◽  
Gene Set Enrichment Analysis ◽  
Control Group ◽  
Hub Genes ◽  
Molting Cycle ◽  
Kegg Pathway Analysis ◽  
First Choice

The arteriovenous fistula (AVF) is the first choice for vascular access for hemodialysis of renal failure patients. Venous remodeling after exposure to high fistula flow is important for AVF to mature but the mechanism underlying remodeling is still unknown. The objective of this study is to identify the molecular mechanisms that contribute to venous remodeling after AVF. To screen and identify the differentially expressed genes (DEGs) that may involve venous remodeling after AVF, we used bioinformatics to download the public microarray data (GSE39488) from the Gene Expression Omnibus (GEO) and screen for DEGs. We then performed gene ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA) for the functional annotation of DEGs. The protein-protein interaction (PPI) network was constructed and the hub genes were carried out. Finally, we harvested 12 normal vein samples and 12 AVF vein samples which were used to confirm the expressions of the hub genes by immunohistochemistry. A total of 45 DEGs were detected, including 32 upregulated and 13 downregulated DEGs. The biological process (BP) of the GO analysis were enriched in the extrinsic apoptotic signaling pathway, cGMP-mediated pathway signaling, and molting cycle. The KEGG pathway analysis showed that the upregulated DEGs were enriched in glycosaminoglycan biosynthesis and purine metabolism, while the downregulated DEGs were mainly enriched in pathways of glycosaminoglycan biosynthesis, antifolate resistance, and ABC transporters. The GSEA analysis result showed that the top three involved pathways were oxidative phosphorylation, TNFA signaling via NF-K B, and the inflammatory response. The PPI was constructed and the hub genes found through the method of DMNC showed that INHBA and NR4A2 might play an important role in venous remodeling after AVF. The integrated optical density (DOI) examined by immunohistochemistry staining showed that the expression of both INHBA and NR4A2 increased in AVF compared to the control group. Our research contributes to the understanding of the molecular mechanism of venous remodeling after exposure to high fistula flow, which may be useful in treating AVF failure.

scholarly journals Bioinformatic Analysis of Circular RNA-Associated ceRNA Network Associated with Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jiacheng Wu ◽  
Shui Liu ◽  
Yien Xiang ◽  
Xianzhi Qu ◽  
Yingjun Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Pathway Analysis ◽  
Target Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Qrt Pcr ◽  
Cerna Network ◽  
Kegg Pathway Analysis

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and is associated with a high mortality rate and poor treatment efficacy. In an attempt to investigate the mechanisms involved in the pathogenesis of HCC, bioinformatic analysis and validation by qRT-PCR were performed. Three circRNA GEO datasets and one miRNA GEO dataset were selected for this purpose. Upon combined biological prediction, a total of 11 differentially expressed circRNAs, 15 differentially expressed miRNAs, and 560 target genes were screened to construct a circRNA-related ceRNA network. GO analysis and KEGG pathway analysis were performed for the 560 target genes. To further screen key genes, a protein-protein interaction network of the target genes was constructed using STRING, and the genes and modules with higher degree were identified by MCODE and CytoHubba plugins of Cytoscape. Subsequently, a module was screened out and subjected to GO enrichment analysis and KEGG pathway analysis. This module included eight genes, which were further screened using TCGA. Finally, UBE2L3 was selected as a key gene and the hsa_circ_0009910–miR-1261–UBE2L3 regulatory axis was established. The relative expression of the regulatory axis members was confirmed by qRT-PCR in 30 pairs of samples, including HCC tissues and adjacent nontumor tissues. The results suggested that hsa_circ_0009910, which was upregulated in HCC tissues, participates in the pathogenesis of HCC by acting as a sponge of miR-1261 to regulate the expression of UBE2L3. Overall, this study provides support for the possible mechanisms of progression in HCC.

scholarly journals Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xia Chen ◽  
Ling Liao ◽  
Yuwei Li ◽  
Hengliu Huang ◽  
Qing Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Metabolic Process ◽  
Hub Genes ◽  
Kegg Pathway Analysis ◽  
B Virus

Background. The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods. The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results. A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion. Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

scholarly journals Identification of key candidate genes and biological pathways in bladder cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e6036 ◽  
Author(s):  
Xin Gao ◽  
Yinyi Chen ◽  
Mei Chen ◽  
Shunlan Wang ◽  
Xiaohong Wen ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Pathway Analysis ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Aurora Kinase ◽  
Smooth Muscle Contraction ◽  
Ppi Network ◽  
Tissue Samples ◽  
Kegg Pathway Analysis ◽  
Key Genes

Background Bladder cancer is a malignant tumor in the urinary system with high mortality and recurrence rates. However, the causes and recurrence mechanism of bladder cancer are not fully understood. In this study, we used integrated bioinformatics to screen for key genes associated with the development of bladder cancer and reveal their potential molecular mechanisms. Methods The GSE7476, GSE13507, GSE37815 and GSE65635 expression profiles were downloaded from the Gene Expression Omnibus database, and these datasets contain 304 tissue samples, including 81 normal bladder tissue samples and 223 bladder cancer samples. The RobustRankAggreg (RRA) method was utilized to integrate and analyze the four datasets to obtain integrated differentially expressed genes (DEGs), and the gene ontology (GO) functional annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis were performed. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. The OncoLnc online tool was utilized to analyze the relationship between the expression of hub genes and the prognosis of bladder cancer. Results In total, 343 DEGs, including 111 upregulated and 232 downregulated genes, were identified from the four datasets. GO analysis showed that the upregulated genes were mainly involved in mitotic nuclear division, the spindle and protein binding. The downregulated genes were mainly involved in cell adhesion, extracellular exosomes and calcium ion binding. The top five enriched pathways obtained in the KEGG pathway analysis were focal adhesion (FA), PI3K-Akt signaling pathway, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction and vascular smooth muscle contraction. The top 10 hub genes identified from the PPI network were vascular endothelial growth factor A (VEGFA), TOP2A, CCNB1, Cell division cycle 20 (CDC20), aurora kinase B, ACTA2, Aurora kinase A, UBE2C, CEP55 and CCNB2. Survival analysis revealed that the expression levels of ACTA2, CCNB1, CDC20 and VEGFA were related to the prognosis of patients with bladder cancer. In addition, a KEGG pathway analysis of the top 2 modules identified from the PPI network revealed that Module 1 mainly involved the cell cycle and oocyte meiosis, while the analysis in Module 2 mainly involved the complement and coagulation cascades, vascular smooth muscle contraction and FA. Conclusions This study identified key genes and pathways in bladder cancer, which will improve our understanding of the molecular mechanisms underlying the development and progression of bladder cancer. These key genes might be potential therapeutic targets and biomarkers for the treatment of bladder cancer.

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

scholarly journals FKBP-related ncRNA-mRNA axis in breast cancer

2020 ◽  
Author(s):  
Hanchu Xiong ◽  
Zihan Chen ◽  
Wenwen Zheng ◽  
Jing Sun ◽  
Qingshuang Fu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcription Factors ◽  
Differential Expression ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Go Annotation ◽  
Prognostic Analysis ◽  
Kegg Pathway Analysis

Abstract Background Breast cancer (BC) is a disease with morbidity ranking the first of women worldwidely. FK506-binding protein (FKBP) family has been demonstrated to possess various functions by interacting with different molecular targets in BC. However, a comprehensive ncRNA-mRNA regulatory axis of FKBP has not yet been reported. Methods FKBP related miRNAs were obtained from miRWalk database. Then, potential lncRNAs, transcription factors as well as mRNAs of screened differentially expressed miRNAs (DE-miRNAs) were analysed by using LncBase v.2, miRGen v3 and miRWalk database. Additionally, differential expression and prognostic analysis of lncRNAs were evaluated using TANRIC database. Next, GO annotation and KEGG pathway analysis were processed using DAVID database. Protein-Protein Interaction (PPI) network was established and hub genes were identified using STRING database. Finally, differential expression and prognostic analysis of hub genes were further conducted using UALCAN and bc-GenExMiner v4.2 database, respectively. Results Eleven DE-miRNAs, consisting of four FKBP4 related DE-miRNAs and seven FKBP5 related DE-miRNAs, were screened. 482 predicted lncRNAs were found for DE-miRNAs. Then, expression and prognostic results of nine of top twenty lncRNAs of BC were significantly identified. LINC00662 and LINC00963 expression were significantly associated with patients’ overall survival (OS). Then, nine potential upstream transcription factors were identified in motifs of DE-miRNAs. 320 target genes were identified for GO annotation and KEGG pathway analysis, which were mainly enriched in cysteine-type endopeptidase activity involved in apoptotic process. Construction and analysis in PPI network showed that RAB7A was selected as a hub gene with the toppest connectivity scores. Differential expression analysis of nine in top ten hub genes of BC were significantly identified. RAB7A and ARRB1 expression were significantly related with BC patients’ OS. Conclusions In current study, we firstly established a predicted FKBP-related ncRNA-mRNA regulatory network, thus exploring a comprehensive interpretation of molecular mechanisms and providing potential clues in seeking novel therapeutics for BC. In the future, much more experiments should be conducted to verify our findings.

scholarly journals Explore the mechanism of Shengma-Gegen drug pair on hepatitis B virus based on network pharmacology

2020 ◽  
Author(s):  
Guozhe Zhang ◽  
Chunmiao Chen ◽  
Yiyu Qin ◽  
Jianwei Ji
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Cellular Response ◽  
Kegg Pathway ◽  
Drug Pair ◽  
Kegg Pathway Analysis ◽  
Go Enrichment ◽  
B Virus ◽  
Ethanol Extracts

Abstract Background Sheng-Ma-Ge-Gen-Tang (SMGGT; 升麻葛根汤) is a famous prescription of traditional Chinese medicine used against measles of children for many hundreds of years. And its anti-hepatitis B virus (HBV) activity has been justified in clinical, however, the function substances and the mechanisms have not been studied yet. Methods The latent active compounds of Cimicifugae Rhizoma (Shengma)-Radix Puerariae (Gegen) drug pair were searched, and excavated their related targets. Then seek the targets of HBV through three network databases. The drug-disease targets of protein-protein interaction (PPI) data was carried out. The drug-disease targets were enriched by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Subsequently, anti-HepG2.2.15 cytotoxicity and anti-HBV experiments were performed on the aqueous and ethanol extracts of Shengma-Gegen in vitro to test and verify their anti-HBV activities. Results GO enrichment indicated biological processes (cellular response to lipid, cellular response to organic cyclic compound, etc.), cellular components (vesicle lumen, cytoplasmic vesicle lumen, etc.) and molecular function (kinase binding, steroid binding, etc.). Many cancer-related pathways were enriched in KEGG pathway analysis. Also got some virus and bacteria diseases. A KEGG-targets network showed that RAF1, CCND1, BCL2, and EGFR might be the core targets. Cytotoxicity of Shengma and Gegen to HepG2.2.15 are in proportion to their concentrations. Shengma and Gegen aqueous and ethanol extracts exhibited a curtain extant anti-HBV-DNA activity. Conclusions Shengma-Gegen showed not only anti-HBV activity but also anti-tumor and anti-viral activities, which need to be tested in the future.

scholarly journals Prognostic Implication of the Expression Level of PECAM-1 in Non-small Cell Lung Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Shuhui Cao ◽  
Yue Wang ◽  
Jingwen Li ◽  
Xuxinyi Ling ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Survival Analysis ◽  
Cell Motility ◽  
Pathway Analysis ◽  
Kinase Inhibitor ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Kegg Pathway Analysis ◽  
Low Expression

Background: Lung cancer is a malignant disease that threatens human health. Hence, it is crucial to identify effective prognostic factors and treatment targets. Single-cell RNA sequencing can quantify the expression profiles of transcripts in individual cells.Methods:GSE117570 profiles were downloaded from the Gene Expression Omnibus database. Key ligand-receptor genes in the tumor and the normal groups were screened to identify integrated differentially expressed genes (DEGs) from the GSE118370 and The Cancer Genome Atlas Lung Adenocarcinoma databases. DEGs associated with more ligand-receptor pairs were selected as candidate DEGs for Gene Ontology (GO) functional annotation, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and survival analysis. In addition, we conducted validation immunohistochemical experiments on postoperative specimens of 30 patients with lung cancer.Results: A total of 18 candidate DEGs were identified from the tumor and the normal groups. The analysis of the GO biological process revealed that these DEGs were mainly enriched in wound healing, in response to wounding, cell migration, cell motility, and regulation of cell motility, while the KEGG pathway analysis found that these DEGs were mainly enriched in proteoglycans in cancer, bladder cancer, malaria, tyrosine kinase inhibitor resistance in Epidermal Growth Factor Receptor (EGFR), and the ERBB signaling pathway. Survival analysis showed that a high, rather than a low, expression of platelet endothelial cell adhesion molecule-1 (PECAM-1) was associated with improved survival. Similarly, in postoperative patients with lung cancer, we found that the overall survival of the PECAM-1 high-expression group shows a better trend than the PECAM-1 low-expression group (p = 0.172).Conclusions: The candidate DEGs identified in this study may play some important roles in the occurrence and development of lung cancer, especially PECAM-1, which may present potential prognostic biomarkers for the outcome.

scholarly journals Extrachromosomal Circular DNAs are Common and Functional in Esophageal Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Zhenguo Sun ◽  
Na Ji ◽  
Renchang Zhao ◽  
Jinghui Liang ◽  
Jin Jiang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Pathway Analysis ◽  
Sanger Sequencing ◽  
Kegg Pathway ◽  
Length Distribution ◽  
Normal Epithelium ◽  
Kegg Pathway Analysis ◽  
Circular Dnas

Abstract BackgroundEsophageal squamous cell carcinoma (ESCC) is the leading cause of cancer-related mortality worldwide. Extrachromosomal circular DNAs (eccDNAs), especially the more-spread smaller type, were rarely studied before. Recently, the distribution of eccDNAs was reported to be distinguished in different tumor cell lineage or different condition of lung cancer. However, there was no exploration of eccDNA distribution and function in ESCC. MethodsThe eccDNAs from 3 surgical matched ESCC tissues were extracted and amplified after removing linear DNA / mitochondrial circular DNA and rolling circle amplification. High-throughput eccDNA sequencing and subsequent bioinformatics analysis was performed to study the distribution pattern and the level of eccDNAs in these matched tissues. Gene ontology and KEGG pathway analysis based on the genes associated with the eccDNAs at differential level were performed. 5 up-regulated and 5 down-regulated candidate eccDNAs were selected to be validated by routine PCR, TOPO-TA Cloning and Sanger sequencing. The nucleotides flanking eccDNA junctions were analyzed to explore the mechanism of eccDNA formation.Results184557 eccDNAs were identified. The overall length distribution was from 33bp to 968842bp, with the peak at ~360bp. They were originated mainly from 5’-untranslated regions, 3’-untranslated region, and rarely from exons, introns, LINE or Alu repeat region. The distribution patterns of the eccDNAs, such as chromosome distribution, length distribution and genomic annotation, were similar between ESCC and matched normal epithelium. Nevertheless, 16031 eccDNAs were found to be at differential level between ESCC and matched normal epithelium, including 10126 up-regulated eccDNAs and 5905 down-regulated eccDNAs. GO analysis and KEGG pathway analysis showed enriched pathways in cancer, MAPK pathway, GTPase related activity and cytoskeleton function et al. PCR, TOPO-TA Cloning and Sanger sequencing validated the junctional sites of 5 up-regulated candidate eccDNAs and 4 other unexpected eccDNAs. Repeat nucleotide pattern between the position flanking the start site and that flanking the end site was detected.ConclusionThis study firstly demonstrated genome-wide presence of eccDNAs, explored the eccDNAs at differential level and revealed potential mechanism of eccDNAs in ESCC, thus suggesting potential clinical utility in ESCC and increasing our understanding about genome plasticity.

scholarly journals Comparative proteome analysis of the spinal dural arteriovenous fistula arterial draining vein with label-free quantitative proteomics

2020 ◽  
Author(s):  
Peixi Liu ◽  
Yuan Shi ◽  
Sichen Li ◽  
Yingjun Liu ◽  
Yingjie Zhou ◽  
...  
Keyword(s):  
Quantitative Proteomics ◽  
Dural Arteriovenous Fistula ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Differentially Expressed Proteins ◽  
Label Free ◽  
Spinal Dural Arteriovenous Fistula ◽  
Differentially Expressed Protein ◽  
Kegg Pathway Analysis ◽  
Go Terms

Abstract Background: Spinal dural arteriovenous fistula (SDAVF) is the most common spinal vascular shunt lesion. Although pathological changes in the SDAVF draining vein (SDAVF-DV) have been elucidated, protein changes remain enigmatic. We investigated protein changes in the SDAVF-DV.Methods: Three SDAVF-DV samples were collected, and superficial temporal artery (STA) and superficial temporal vein (STV) samples were used as controls. After quantification and enzymolysis of the proteins, label-free quantitative proteomics was performed, and the peptide mixture was fractionated and analysed by liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify the differentially expressed proteins. Bioinformatics analysis of the differentially expressed proteins was also performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) network analyses.Results: Compared with the STA, the SDAVF-DV had 195 upregulated proteins and 303 downregulated proteins. GO analysis showed that the most differential GO terms in each category were the adenylate cyclase-modulating G protein-coupled receptor signalling pathway, U6 snRNP and SH3 domain binding. KEGG pathway analysis showed that the most differentially expressed protein pathway was focal adhesion. Compared with the STV, the SDAVF-DV had 158 upregulated proteins and 362 downregulated proteins. GO analysis showed that the most differential GO terms in each category were lamellipodium assembly, U6 snRNP, and SH3 domain binding. KEGG pathway analysis showed that the most differentially expressed protein pathway was dilated cardiomyopathy. The PPI analysis revealed PPIs among the top 300 proteins.Conclusions: We demonstrated that the SDAVF-DV showed specific protein expression changes under long-period venous hypertension. The results of the present study will provide insights into the pathogenesis of SDAVF formation at the protein level. The proteomic results provide a scientific foundation for further study to explore the pathophysiological mechanism of SDAVF.

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