scholarly journals Temporal Identification of Dysregulated Genes and Pathways in Clear Cell Renal Cell Carcinoma Based on Systematic Tracking of Disrupted Modules

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Shao-Mei Wang ◽  
Ze-Qiang Sun ◽  
Hong-Yun Li ◽  
Jin Wang ◽  
Qing-Yong Liu
Keyword(s):  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Glutathione Metabolism ◽  
Pathway Enrichment Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Bipartite Matching ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Metabolism Of Xenobiotics

Objective. The objective of this work is to identify dysregulated genes and pathways of ccRCC temporally according to systematic tracking of the dysregulated modules of reweighted Protein-Protein Interaction (PPI) networks.Methods. Firstly, normal and ccRCC PPI network were inferred and reweighted based on Pearson correlation coefficient (PCC). Then, we identified altered modules using maximum weight bipartite matching and ranked them in nonincreasing order. Finally, gene compositions of altered modules were analyzed, and pathways enrichment analyses of genes in altered modules were carried out based on Expression Analysis Systematic Explored (EASE) test.Results. We obtained 136, 576, 693, and 531 disrupted modules of ccRCC stages I, II, III, and IV, respectively. Gene composition analyses of altered modules revealed that there were 56 common genes (such asMAPK1,CCNA2, andGSTM3) existing in the four stages. Besides pathway enrichment analysis identified 5 common pathways (glutathione metabolism, cell cycle, alanine, aspartate, and glutamate metabolism, arginine and proline metabolism, and metabolism of xenobiotics by cytochrome P450) across stages I, II, III, and IV.Conclusions. We successfully identified dysregulated genes and pathways of ccRCC in different stages, and these might be potential biological markers and processes for treatment and etiology mechanism in ccRCC.

scholarly journals Identification of Key Genes and miRNAs in Osteosarcoma Patients with Chemoresistance by Bioinformatics Analysis

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Binbin Xie ◽  
Yiran Li ◽  
Rongjie Zhao ◽  
Yuzi Xu ◽  
Yuhui Wu ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Search Tool ◽  
Poor Outcomes

Chemoresistance is a significant factor associated with poor outcomes of osteosarcoma patients. The present study aims to identify Chemoresistance-regulated gene signatures and microRNAs (miRNAs) in Gene Expression Omnibus (GEO) database. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) included positive regulation of transcription, DNA-templated, tryptophan metabolism, and the like. Then differentially expressed genes (DEGs) were uploaded to Search Tool for the Retrieval of Interacting Genes (STRING) to construct protein-protein interaction (PPI) networks, and 9 hub genes were screened, such as fucosyltransferase 3 (Lewis blood group) (FUT3) whose expression in chemoresistant samples was high, but with a better prognosis in osteosarcoma patients. Furthermore, the connection between DEGs and differentially expressed miRNAs (DEMs) was explored. GEO2R was utilized to screen out DEGs and DEMs. A total of 668 DEGs and 5 DEMs were extracted from GSE7437 and GSE30934 differentiating samples of poor and good chemotherapy reaction patients. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used to perform GO and KEGG pathway enrichment analysis to identify potential pathways and functional annotations linked with osteosarcoma chemoresistance. The present study may provide a deeper understanding about regulatory genes of osteosarcoma chemoresistance and identify potential therapeutic targets for osteosarcoma.

scholarly journals Quantitative Phosphoproteomic Comparison of Lens Proteins in Highly Myopic Cataract and Age-Related Cataract

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Shaohua Zhang ◽  
Keke Zhang ◽  
Wenwen He ◽  
Yi Lu ◽  
Xiangjia Zhu
Keyword(s):  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Phosphoglycerate Kinase ◽  
Enrichment Analysis ◽  
Glutathione Metabolism ◽  
Pathway Enrichment Analysis ◽  
Phosphorylation Sites ◽  
Pathway Enrichment ◽  
Age Related ◽  
Go Enrichment

Purpose. To investigate and compare the lens phosphoproteomes in patients with highly myopic cataract (HMC) or age-related cataract (ARC). Methods. In this study, we undertook a comparative phosphoproteome analysis of the lenses from patients with HMC or ARC. Intact lenses from ARC and HMC patients were separated into the cortex and nucleus. After protein digestion, the phosphopeptides were quantitatively analyzed with TiO2 enrichment and liquid chromatography-mass spectrometry. The potential functions of different phosphopeptides were assessed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Results. In total, 522 phosphorylation sites in 164 phosphoproteins were identified. The number of phosphorylation sites was significantly higher in the cortex than in the nucleus, in both ARC and HMC lenses. The differentially phosphorylated peptides in the lens cortex and nucleus in HMC eyes were significantly involved in the glutathione metabolism pathway. The KEGG pathway enrichment analysis indicated that the differences in phosphosignaling mediators between the ARC and HMC lenses were associated with glycolysis and the level of phosphorylated phosphoglycerate kinase 1 was lower in HMC lenses than in ARC lenses. Conclusions. We provide an overview of the differential phosphoproteomes of HMC and ARC lenses that can be used to clarify the molecular mechanisms underlying their different phenotypes.

scholarly journals Immunological Analysis and Differential Genes Screening of Venous Thromboembolism

2020 ◽  
Author(s):  
Li-Na Gao ◽  
Qiang Li ◽  
Jian-Qin Xie ◽  
Wan-Xia Yang ◽  
Chong-Ge You
Keyword(s):  
Venous Thromboembolism ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
R Software ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
New Ideas ◽  
Gene Expression Profile Data ◽  
Differential Genes

Abstract Purpose: To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods: The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results: Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions: Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

scholarly journals A bioinformatics analysis to identify novel biomarkers for prognosis of pulmonary tuberculosis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Yahong Sun ◽  
Gang Chen ◽  
Zhihao Liu ◽  
Lina Yu ◽  
Yan Shang
Keyword(s):  
Pulmonary Tuberculosis ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Clusters ◽  
Pathway Enrichment Analysis ◽  
Go Annotation ◽  
Pathway Enrichment ◽  
Ppi Networks ◽  
Novel Biomarkers ◽  
Geo Database

Abstract Background Due to the fact that pulmonary tuberculosis (PTB) is a highly infectious respiratory disease characterized by high herd susceptibility and hard to be treated, this study aimed to search novel effective biomarkers to improve the prognosis and treatment of PTB patients. Methods Firstly, bioinformatics analysis was performed to identify PTB-related differentially expressed genes (DEGs) from GEO database, which were then subjected to GO annotation and KEGG pathway enrichment analysis to initially describe their functions. Afterwards, clustering analysis was conducted to identify PTB-related gene clusters and relevant PPI networks were established using the STRING database. Results Based on the further differential and clustering analyses, 10 DEGs decreased during PTB development were identified and considered as candidate hub genes. Besides, we retrospectively analyzed some relevant studies and found that 7 genes (CCL20, PTGS2, ICAM1, TIMP1, MMP9, CXCL8 and IL6) presented an intimate correlation with PTB development and had the potential serving as biomarkers. Conclusions Overall, this study provides a theoretical basis for research on novel biomarkers of PTB, and helps to estimate PTB prognosis as well as probe into targeted molecular treatment.

scholarly journals Immunological analysis and differential genes screening of venous thromboembolism

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Li-Na Gao ◽  
Qiang Li ◽  
Jian-Qin Xie ◽  
Wan-Xia Yang ◽  
Chong-Ge You
Keyword(s):  
Venous Thromboembolism ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
R Software ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
New Ideas ◽  
Gene Expression Profile Data ◽  
Differential Genes

Abstract Purpose To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

scholarly journals Identification of Potential Markers for Differentiating Epithelial Ovarian Cancer from Ovarian Low Malignant Potential Tumors through Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Wende Hao ◽  
Hongyu Zhao ◽  
Zhefeng Li ◽  
Jie Li ◽  
Jiahao Guo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Kegg Pathway ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Low Malignant Potential ◽  
Pearson Correlation Analysis ◽  
Analysis Survival ◽  
Core Genes

Abstract Background: Epithelial ovarian cancer (EOC) is one of the most deadly female malignancies and is often diagnosed in advanced stages. In contrast, ovarian low malignant potential (LMP) tumors with favorable prognosis are intermediate between benign and malignant tumors. However, the current accuracy in distinguishing these diseases is unsatisfactory, leading to delays or unnecessary treatments. Therefore, unveiling the molecular differences between LMP and EOC and identifying useful molecular markers may increase the accuracy of diagnosis and also provide a rational basis for the development of new therapeutic and preventive strategies for EOC. Methods: In this study, three microarray data (GSE9899, GSE57477 and GSE27651) were integrated to explore the differentially expressed genes (DEGs) between LMP and EOC samples. Then, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis of these DEGs. Furthermore, 5 core genes were identified by protein–protein interaction (PPI) network analysis, receiver operating characteristic (ROC) analysis, survival and Pearson correlation analysis. Meanwhile, we also identified the potential function of these 5 genes in EOC through KEGG pathway enrichment analysis. Finally, chemical-core gene network construction was performed to identify the potential drugs or risk factors for EOC.Results: A total of 234 DEGs were successfully screened, including 81 upregulated genes and 153 downregulated genes. KEGG-pathway analysis indicated that the upregulated DEGs were mainly enriched in Cell cycle and Oocyte meiosis, whereas the downregulated DEGs were enriched in Huntington's disease. As for GO analysis, the upregulated DEGs were mainly associated with Protein binding, Nucleoplasm and Nucleus, whereas the downregulated DEGs were highly enriched in Cilium, Microtubule, and Motile cilium. In addition, 5 core genes (CCNB1, KIF20A, ASPM, AURKA, and KIF23) were identified through protein–protein interaction (PPI) network analysis, ROC analysis, survival and Pearson correlation analysis, which show better diagnostic efficiency and higher prognostic value for EOC. Furthermore, we identified the potential function of these 5 genes in EOC through KEGG pathway enrichment analysis and found that all 5 core genes were enriched in “DNA replication”, “Mismatch repair”, “Fanconi anemia pathway”, “Cell cycle”, “Homologous recombination” and “Nucleotide excision repair”, and “DNA replication” was the key player in them all. Finally, NetworkAnalyst was used to identify top 15 chemicals that link with the 5 core genes. Among them, 11 chemicals were potential drugs and 4 chemicals were risk factors for EOC.Conclusions: Based on an integrated analysis, we identified potential biomarkers, risk factors and drugs for EOC, which may open a new direction for EOC diagnosis, condition appraisal, prevention and treatment in future.

scholarly journals Identification of Constituents and Exploring the Mechanism for Toutongning Capsule in the Treatment of Migraine

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Xia Du ◽  
Zhibiao Di ◽  
Yang Liu ◽  
Wenbing Zhi ◽  
Yuan Liu ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Multiple Components ◽  
Search Tool ◽  
Q Exactive ◽  
Drug Similarity ◽  
Target Fishing

Toutongning capsule (TTNC) is an effective and safe traditional Chinese medicine used in the treatment of migraine. In this present study, a multiscale strategy was used to systematically investigate the mechanism of TTNC in treating migraine, which contained UPLC-UESI-Q Exactive Focus network pharmacology and experimental verification. First, 88 compounds were identified by the UPLC-UESI-Q Exactive Focus method for TTNC. Then, the target fishing for these compounds was performed by means of an efficient drug similarity search tool. Third, a series of network pharmacology experiments were performed to predict the key compounds, targets, and pathways. They were protein-protein interaction (PPI), KEGG pathway enrichment analysis, and herbs-compounds-targets-pathways (H-C-T-P) network construction. As a result, 18 potential key compounds, 20 potential key targets, and 6 potential signaling pathways were obtained for TTNC in treatment with migraine. Finally, molecular docking and experimental were carried out to verify the key targets. In short, the results showed that TTNC is able to treat migraine through multiple components, multiple targets, and multiple pathways. This work may provide a theoretical basis for further research on the molecular mechanism of TTNC in the treatment of migraine.

scholarly journals Construction of a circRNA-miRNA-mRNA network based on competitive endogenous RNA reveals the key pathways and central genes of osteosarcoma in vivo

2021 ◽  
Author(s):  
Zhihao Chen ◽  
Xi Wang ◽  
Liubing Li ◽  
Mingxiao Han ◽  
Min Wang ◽  
...  
Keyword(s):  
Protein Modification ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Micro Rna ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Go Annotation ◽  
Pathway Enrichment ◽  
Ppi Networks

Abstract Circular RNAs (circRNAs) play important roles in a variety of pathological functions. However, the potential functions and detailed mechanisms of circRNAs in osteosarcoma (OS) have not been fully elucidated. In this study, the circRNA, micro RNA (miRNA), and messenger RNA (mRNA) expression profile of human OS was investigated based on the raw microarray data GSE140256, GSE65071 and GSE16088 in Gene Expression Omnibus (GEO) datasets, and seven differentially-expressed circRNAs (DEcircRNAs), 166 differentially-expressed miRNAs (DEmiRNAs), and 175 differentially-expressed mRNAs (DEmRNAs) were identified in total. FunRich was employed to analyze the differentially-expressed transcription factors on the basis of identified DEmiRNAs. In addition, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to further study biological functions of the DEmRNAs. Interestingly, post-translational protein modification, collagen-containing extracellular matrix, and single-stranded DNA binding were the most significant pathways enriched for DEmRNAs in GO annotation analysis. Meanwhile, in KEGG pathway enrichment analysis, complement and coagulation cascades, RNA transport and drug metabolism − other enzymes were the most significantly enriched pathways of DEmRNAs in OS. We constructed circRNA-miRNA-mRNA and protein–protein interaction (PPI) networks that may be associated with pathological processes of OS. Finally, we also revealed the pattern of tumor-infiltrating immune cells in OS and further explored the ceRNA networks we constructed in which we found that COL1A1 and RAN were significantly correlated with overall survival in patients with osteosarcoma (p < 0.05). To our knowledge, this study provides the first profile analysis of DEcircRNAs, DEmiRNAs, and DEmRNAs with OS in vivo and reveals a novel idea for understanding the pathogenesis of OS.

scholarly journals KRT8 and KRT19, associated with EMT, are hypomethylated and overexpressed in lung adenocarcinoma and link to unfavorable prognosis

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Wenlong Wang ◽  
Junhong He ◽  
Hongda Lu ◽  
Qingzhi Kong ◽  
Shengyou Lin
Keyword(s):  
Lung Adenocarcinoma ◽  
Epithelial To Mesenchymal Transition ◽  
Methylation Status ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Cxcl12 Expression ◽  
Mesenchymal Transition ◽  
Gene Set ◽  
Protein Protein Interaction ◽  
Ppi Networks

Abstract Background: Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer. To date, the prognosis of patients with LUAD remains dismal. Methods: Three datasets were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained. FunRich was used to perform pathway enrichment analysis. Protein–protein interaction (PPI) networks were established and hub genes were obtained by Cytoscape software. GEPIA was utilized to conduct correlation and survival analysis. Upstream miRNAs of DEGs were predicted via miRNet database, and methylation status of promoters of DEGs was determined through UALCAN database. Results: A total of 375 DEGs, including 105 and 270 up-regulated and down-regulated genes in LUAD, were commonly appeared in three datasets. These DEGs were significantly enriched in mesenchymal-to-epithelial transition (MET) and epithelial-to-mesenchymal transition (EMT). About 8 up-regulated and 5 down-regulated DEGs were commonly appeared in EMT/MET-related gene set and the top 50 hub gene set. Among the 13 genes, increased expression of KRT8 and KRT19 indicated unfavorable prognosis whereas high expression of DCN and CXCL12 suggested favorable prognosis in LUAD. Correlation analysis showed that KRT8 (DCN) expression was linked to KRT19 (CXCL12) expression. Further analysis displayed that KRT8 and KRT19 could jointly forecast poor prognosis in LUAD. About 42 and 2 potential miRNAs were predicted to target KRT8 and KRT19, respectively. Moreover, methylation level analysis demonstrated that KRT8 and KRT19 were significantly hypomethylated in LUAD compared with normal controls. Conclusions: All these findings suggest that KRT8 and KRT19 are hypomethylated and overexpressed in LUAD and associated with unfavorable prognosis.

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