Identification of Key Candidate Proteins and Pathways Associated with Temozolomide Resistance in Glioblastoma Based on Subcellular Proteomics and Bioinformatical Analysis

TMZ resistance remains one of the main reasons why treatment of glioblastoma (GBM) fails. In order to investigate the underlying proteins and pathways associated with TMZ resistance, we conducted a cytoplasmic proteome research of U87 cells treated with TMZ for 1 week, followed by differentially expressed proteins (DEPs) screening, KEGG pathway analysis, protein–protein interaction (PPI) network construction, and validation of key candidate proteins in TCGA dataset. A total of 161 DEPs including 65 upregulated proteins and 96 downregulated proteins were identified. Upregulated DEPs were mainly related to regulation in actin cytoskeleton, focal adhesion, and phagosome and PI3K-AKT signaling pathways which were consistent with our previous studies. Further, the most significant module consisted of 28 downregulated proteins that were filtered from the PPI network, and 9 proteins (DHX9, HNRNPR, RPL3, HNRNPA3, SF1, DDX5, EIF5B, BTF3, and RPL8) among them were identified as the key candidate proteins, which were significantly associated with prognosis of GBM patients and mainly involved in ribosome and spliceosome pathway. Taking the above into consideration, we firstly identified candidate proteins and pathways associated with TMZ resistance in GBM using proteomics and bioinformatic analysis, and these proteins could be potential biomarkers for prevention or prediction of TMZ resistance in the future.

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Gene network in pulmonary tuberculosis based on bioinformatic analysis

10.21203/rs.2.22571/v1 ◽

2020 ◽

Author(s):

Lili Li ◽

Jian Lv ◽

Yuan He ◽

Zhihua Wang

Keyword(s):

Pulmonary Tuberculosis ◽

Chemokine Receptors ◽

Gene Network ◽

Kegg Pathway ◽

Bioinformatic Analysis ◽

Core Gene ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Causal Genes

Abstract Background: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. Methods: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. Results: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. Conclusions: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

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Gene network in pulmonary tuberculosis based on bioinformatic analysis

10.21203/rs.2.22571/v2 ◽

2020 ◽

Author(s):

Lili Li ◽

Jian Lv ◽

Yuan He ◽

Zhihua Wang

Keyword(s):

Pulmonary Tuberculosis ◽

Chemokine Receptors ◽

Gene Network ◽

Kegg Pathway ◽

Bioinformatic Analysis ◽

Core Gene ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Causal Genes

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Weighted gene co-expression network analysis to define pivotal modules and genes in diabetic heart failure

Bioscience Reports ◽

10.1042/bsr20200507 ◽

2020 ◽

Vol 40 (7) ◽

Author(s):

Weiwei Liang ◽

FangFang Sun

Keyword(s):

Heart Failure ◽

Kegg Pathway ◽

Diabetic Heart ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Light Yellow ◽

Go Enrichment ◽

Key Genes ◽

Functional Analyses

Abstract This research was carried out to reveal specific hub genes involved in diabetic heart failure, as well as remarkable pathways that hub genes locate. The GSE26887 dataset from the GEO website was downloaded. The gene co-expression network was generated and central modules were analyzed to identify key genes using the WGCNA method. Functional analyses were conducted on genes of the clinical interest modules via Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene ontology (GO) enrichment, associated with protein–protein interaction (PPI) network construction in a sequence. Centrality parameters of the PPI network were determined using the CentiScape plugin in Cytoscape. Key genes, defined as genes in the ≥95% percentile of the degree distribution of significantly perturbed networks, were identified. Twenty gene co-expression modules were detected by WGCNA analysis. The module marked in light yellow exhibited the most significant association with diabetes (P=0.08). Genes involved in this module were primarily located in immune response, plasma membrane and receptor binding, as shown by the GO analysis. These genes were primarily assembled in endocytosis and phagosomes for KEGG pathway enrichment. Three key genes, STK39, HLA-DPB1 and RAB5C, which may be key genes for diabetic heart failure, were identified. To our knowledge, our study is the first to have constructed the co-expression network involved in diabetic heart failure using the WGCNA method. The results of the present study have provided better understanding the molecular mechanism of diabetic heart failure.

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Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/9602016 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Huijing Zhu ◽

Xin Zhu ◽

Yuhong Liu ◽

Fusong Jiang ◽

Miao Chen ◽

...

Keyword(s):

Gene Expression ◽

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Candidate Genes ◽

Signaling Pathway ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Ppi Network ◽

Kegg Pathway Analysis

Objective. The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods. The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results. A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.

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

BioMed Research International ◽

10.1155/2019/8308694 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 1

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.

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Identification of key candidate genes and biological pathways in bladder cancer

PeerJ ◽

10.7717/peerj.6036 ◽

2018 ◽

Vol 6 ◽

pp. e6036 ◽

Cited By ~ 22

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.

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

PeerJ ◽

10.7717/peerj.11203 ◽

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.

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FKBP-related ncRNA-mRNA axis in breast cancer

10.21203/rs.3.rs-16732/v1 ◽

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.

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

10.21203/rs.2.19789/v1 ◽

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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Mechanisms of Berberine for the Treatment of Atherosclerosis Based on Network Pharmacology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/3568756 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Xuejiao Xie ◽

Xingyu Ma ◽

Siyu Zeng ◽

Wansi Tang ◽

Liucheng Xiao ◽

...

Keyword(s):

Signaling Pathway ◽

Pathway Analysis ◽

Kegg Pathway ◽

Functional Enrichment ◽

Network Pharmacology ◽

Foam Cell Formation ◽

Ppi Network ◽

The Core ◽

Ppi Networks ◽

Kegg Pathway Analysis

Atherosclerosis is a common metabolic disease characterized by lipid metabolic disorder. The processes of atherosclerosis include endothelial dysfunction, new endothelial layer formation, lipid sediment, foam cell formation, plaque formation, and plaque burst. Owing to the adverse effects of first-line medications, it is urgent to discover new medications to deal with atherosclerosis. Berberine is one of the most promising natural products derived from traditional Chinese medicine. However, the panoramic mechanism of berberine against atherosclerosis has not been discovered clearly. In this study, we used network pharmacology to investigate the interaction between berberine and atherosclerosis. We identified potential targets related to berberine and atherosclerosis from several databases. A total of 31 and 331 putative targets for berberine and atherosclerosis were identified, respectively. Then, we constructed berberine and atherosclerosis targets with PPI data. Berberine targets network with PPI data had 3204 nodes and 79437 edges. Atherosclerosis targets network with PPI data had 5451 nodes and 130891 edges. Furthermore, we merged the two PPI networks and obtained the core PPI network from the merged PPI network. The core PPI network had 132 nodes and 3339 edges. At last, we performed functional enrichment analyses including GO and KEGG pathway analysis in David database. GO analysis indicated that the biological processes were correlated with G1/S transition of mitotic cells cycle. KEGG pathway analysis found that the pathways directly associated with berberine against atherosclerosis were cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine inhibited inflammation and cell proliferation in the treatment of atherosclerosis. Our study provided a valid theoretical foundation for future research.

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