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 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.

Identification of differentially expressed genes, biological pathways and prognostic signature in bladder cancer

2021 ◽  
Author(s):  
Fucai Tang ◽  
Xiayan Qian ◽  
Zeguang Lu ◽  
Yongchang Lai ◽  
Zhibiao Li ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Biological Pathways ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Go Analysis ◽  
Ppi Networks ◽  
Kegg Pathway Analysis

Abstract Background Bladder cancer (BC) is one of the most common malignant cancer of urinary system in the worldwide. The purpose of the present study was to analysis differentially expressed genes (DEGs), biological pathways and prognostic significance BC by bioinformatics analysis. Methods The gene expression dataset GSE7476 and the mRNA Seq sequencing data were downloaded respectively from GEO and TCGA. A total of 220 DEGs were obtained in BC. GO analysis and KEGG pathway analysis were performed for up- and down-regulated DEGs. Then, a protein-protein interaction (PPI) networks and module were constructed by Cytoscape software. Survival analysis of hub genes was performed. Results The result of GO analysis revealed that the up-regulated DEGs were enriched mainly in sister chromatid segregation, while the down-regulated DEGs were enriched mainly in muscle contraction. The result of KEGG pathway analysis showed that up-regulated DEGs were enriched mainly in cell cycle, while down-regulated DEGs enriched in IL-17 signaling pathway. 41 hub gene and 3 crucial modules were identified in the PPI network. 15 genes significantly associated with patient prognosis in BC were obtained by Kaplan-Meier analysis. Conclusions In summary, the present study identified hub genes, crucial pathways and provide possible the molecular targets and prognostic biomarkers for targeted therapy and prognostic assessment of BC.

Transcriptional identification of differentially expressed genes during the prepupal–pupal transition in the oriental armyworm, Mythimna separata (Walker) (Lepidoptera: Noctuidae)

2021 ◽  
pp. 1-14
Author(s):  
Peirong Li ◽  
Xinru Li ◽  
Wei Wang ◽  
Xiaoling Tan ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Metabolic Pathways ◽  
Developmental Stages ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Mythimna Separata ◽  
Kegg Pathway Analysis ◽  
Oriental Armyworm ◽  
Lepidoptera Noctuidae

Abstract The oriental armyworm, Mythimna separata (Walker) is a serious pest of agriculture that does particular damage to Gramineae crops in Asia, Europe, and Oceania. Metamorphosis is a key developmental stage in insects, although the genes underlying the metamorphic transition in M. separata remain largely unknown. Here, we sequenced the transcriptomes of five stages; mature larvae (ML), wandering (W), and pupation (1, 5, and 10 days after pupation, designated P1, P5, and P10) to identify transition-associated genes. Four libraries were generated, with 22,884, 23,534, 26,643, and 33,238 differentially expressed genes (DEGs) for the ML-vs-W, W-vs-P1, P1-vs-P5, and P5-vs-P10, respectively. Gene ontology enrichment analysis of DEGs showed that genes regulating the biosynthesis of the membrane and integral components of the membrane, which includes the cuticular protein (CP), 20-hydroxyecdysone (20E), and juvenile hormone (JH) biosynthesis, were enriched. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that DEGs were enriched in the metabolic pathways. Of these DEGs, thirty CP, seventeen 20E, and seven JH genes were differentially expressed across the developmental stages. For transcriptome validation, ten CP, 20E, and JH-related genes were selected and verified by real-time PCR quantitative. Collectively, our results provided a basis for further studies of the molecular mechanism of metamorphosis in M. separata.

Integrated Bioinformatics and Machine Learning Algorithms Analyses Highlight Related Pathways and Genes Associated with Alzheimer's Disease

2021 ◽  
Vol 17 ◽  
Author(s):  
Hui Zhang ◽  
Qidong Liu ◽  
Xiaoru Sun ◽  
Yaru Xu ◽  
Yiling Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Analysis ◽  
Predictive Model ◽  
Differentially Expressed Genes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Differentially Expressed ◽  
Diagnosis And Treatment ◽  
Ppi Network ◽  
Key Genes

Background: The pathophysiology of Alzheimer's disease (AD) is still not fully studied. Objective: This study aimed to explore the differently expressed key genes in AD and build a predictive model of diagnosis and treatment. Methods: Gene expression data of the entorhinal cortex of AD, asymptomatic AD, and control samples from the GEO database were analyzed to explore the relevant pathways and key genes in the progression of AD. Differentially expressed genes between AD and the other two groups in the module were selected to identify biological mechanisms in AD through KEGG and PPI network analysis in Metascape. Furthermore, genes with a high connectivity degree by PPI network analysis were selected to build a predictive model using different machine learning algorithms. Besides, model performance was tested with five-fold cross-validation to select the best fitting model. Results: A total of 20 co-expression gene clusters were identified after the network was constructed. Module 1 (in black) and module 2 (in royal blue) were most positively and negatively correlated with AD, respectively. Total 565 genes in module 1 and 215 genes in module 2, respectively, overlapped in two differentially expressed genes lists. They were enriched in the G protein-coupled receptor signaling pathway, immune-related processes, and so on. 11 genes were screened by using lasso logistic regression, and they were considered to play an important role in predicting AD samples. The model built by the support vector machine algorithm with 11 genes showed the best performance. Conclusion: This result shed light on the diagnosis and treatment of AD.

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 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 Bioinformatics analysis of differentially expressed genes and their potentially interacting miRNAs in oral lichen planus

2021 ◽  
Author(s):  
Churen Zhang ◽  
Ruoran Sun
Keyword(s):  
Lichen Planus ◽  
Differentially Expressed Genes ◽  
Oral Mucosa ◽  
Oral Lichen Planus ◽  
Kegg Pathway ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Oral Lichen

Abstract Background. Among the diseases of oral mucosa, oral lichen planus (OLP) is characterized by chronic autoimmune/autoinflammation. However, the etiology and pathogenesis of OLP were still limited. The aim of this research was to identify the differentially expressed genes and their potentially interacted miRNAs in OLP to provide a possible explanation of the pathogenesis of OLP and therapeutic biomarkers.Methods. The OLP microarray dataset (GSE52130) was download from the Gene Expression Omnibus (GEO) database. R software was used to identify differentially expressed genes between the OLP samples and normal oral mucosa. Functional enrichment analysis of DEGs, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, were conducted. Protein–protein interaction (PPI) network analysis was performed in the STRING database. CytoHubba in the Cytoscape software was applied to determining the top 10 hub genes, whose relative miRNA was identified through RNA Interactome Database.Results. Overall, 627 DEGs was identified in OLP samples, including 351 highly expressed genes and 276 lowly expressed genes. GO analysis indicated that the epidermal differentiation was mostly enriched. For the KEGG pathway, the DEGs in OLP samples were mostly involved in Staphylococcus aureus infection, Estrogen signaling pathway, Serotonergic synapse and Histidine metabolism. Top 10 hub genes including LOR, LCE3D, LCE3E, LCE1B, LCE2B, SPRR2E, SPRR2G, LCE2A, RPTN and CDSN were identified from the PPI network. The miRNA (hsa-miR-98-5p) was regarded as the mostly possible miRNA involved in OLP.

scholarly journals Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

2019 ◽  
Author(s):  
ChenChen Yang ◽  
Aifeng Gong
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Mrna Levels ◽  
Pcr Assay ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1and MMP1 in GC tissues and cell lines, respectively.Results We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1.Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

scholarly journals Unraveling proteome changes and potential regulatory proteins of bovine follicular Granulosa cells by mass spectrometry and multi-omics analysis

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuning Hou ◽  
Qingling Hao ◽  
Zhiwei Zhu ◽  
Dongmei Xu ◽  
Wenzhong Liu ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Follicular Development ◽  
Regulatory Proteins ◽  
Differentially Expressed ◽  
Omics Data ◽  
Kegg Pathway Analysis ◽  
Omics Data Analysis

Abstract Background In previous study, we performed next-gene sequencing to investigate the differentially expressed transcripts of bovine follicular granulosa cells (GCs) at dominant follicle (DF) and subordinate follicle (SF) stages during first follicular wave. Present study is designed to further identify the key regulatory proteins and signaling pathways associated with follicular development using label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) and multi-omics data analysis approach. Methods DF and SF from three cattle were collected by daily ultrasonography. The GCs were isolated from each follicle, total proteins were digested by trypsin, and then proteomic analyzed via LC-MS/MS, respectively. Proteins identified were retrieved from Uniprot-COW fasta database, and differentially expressed proteins were used to functional enrichment and KEGG pathway analysis. Proteome data and transcriptome data obtained from previous studies were integrated. Results Total 3409 proteins were identified from 30,321 peptides (FDR ≤0.01) obtained from LC-MS/MS analysis and 259 of them were found to be differentially expressed at different stage of follicular development (fold Change > 2, P < 0.05). KEGG pathway analysis of proteome data revealed important signaling pathways associated with follicular development, multi-omics data analysis results showed 13 proteins were identified as being differentially expressed in DF versus SF. Conclusions This study represents the first investigation of transcriptome and proteome of bovine follicles and offers essential information for future investigation of DF and SF in cattle. It also will enrich the theory of animal follicular development.

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

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
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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