Bufalin: A Systematic Review of Research Hotspots and Antitumor Mechanisms by Text Mining and Bioinformatics

2020 ◽  
Vol 48 (07) ◽  
pp. 1633-1650
Author(s):  
Xian Zhang ◽  
Xiaoxuan Zhao ◽  
Kaili Liu ◽  
Yuxuan Che ◽  
Xun Qiu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Mesh Terms ◽  
Go Enrichment ◽  
Pubmed Database ◽  
Pathways Analysis

Bufalin is an anticancer drug extract from traditional Chinese medicine. Several articles about bufalin have been published. However, the literature on bufalin has not yet been systematically studied. This study aimed to identify the study status and knowledge structures of bufalin and to summarize the antitumor mechanism. Data were retrieved and downloaded from the PubMed database. The softwares of BICOMB, gCLUTO, Ucinet 6.0, and NetDraw2.084 were used to analyze these publications. The bufalin related genes were recognized and tagged by ABNER software. Then these BF-related genes were performed by Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and protein-protein interaction (PPI) network analysis. A total of 474 papers met the search criteria from 2000 to 2019. By biclustering clustering analysis, the 50 high-frequency main MeSH terms/subheadings were classified into 5 clusters. The clusters of drug therapy and the mechanism of bufalin were hotspot topics. A total of 50 genes were identified as BF-related genes. PPI network analysis showed that inducing apoptosis was the main effect of bufalin, and apoptosis-related gene Caspase 3 was the most reported by people. Bufalin could inhibit the proliferation, invasion, and metastasis of cancer cells through multiple signaling pathways, such as PI3K/AKT, Hedgehog, MAPK/JNK, Wnt/[Formula: see text]-catenin, TGF-[Formula: see text]/Smad, Integrin signaling pathway, and NF-KB signaling pathway via KEGG analysis. Through the quantitative analysis of bufalin literature, we revealed the research status and hot spots in this field and provided some guidance for further research.

scholarly journals RNA seq analyses of chicken reveals biological pathways involved in acclimation into different geographical locations

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Himansu Kumar ◽  
Hyojun Choo ◽  
Asankadyr U. Iskender ◽  
Krishnamoorthy Srikanth ◽  
Hana Kim ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Climatic Conditions ◽  
Rna Seq ◽  
Kegg Pathways ◽  
Go Enrichment ◽  
Commercial Chicken ◽  
Pathways Analysis ◽  
Ppar Signaling ◽  
Transcriptome Expression

Abstract Transcriptome expression reflects genetic response in diverse conditions. In this study, RNA sequencing was utilized to profile multiple tissues such as liver, breast, caecum, and gizzard of Korean commercial chicken raised in Korea and Kyrgyzstan. We analyzed ten samples per tissue from each location to identify candidate genes which are involved in the adaptation of Korean commercial chicken to Kyrgyzstan. At false discovery rate (FDR) < 0.05 and fold change (FC) > 2, we found 315, 196, 167 and 198 genes in liver, breast, cecum, and gizzard respectively as differentially expressed between the two locations. GO enrichment analysis showed that these genes were highly enriched for cellular and metabolic processes, catalytic activity, and biological regulations. Similarly, KEGG pathways analysis indicated metabolic, PPAR signaling, FoxO, glycolysis/gluconeogenesis, biosynthesis, MAPK signaling, CAMs, citrate cycles pathways were differentially enriched. Enriched genes like TSKU, VTG1, SGK, CDK2 etc. in these pathways might be involved in acclimation of organisms into diverse climatic conditions. The qRT-PCR result also corroborated the RNA-Seq findings with R2 of 0.76, 0.80, 0.81, and 0.93 for liver, breast, caecum, and gizzard respectively. Our findings can improve the understanding of environmental acclimation process in chicken.

scholarly journals Network Pharmacological Analysis of Huanglian Jiedu Decoction for Anti- Atherosclerosis

2020 ◽  
Author(s):  
Kerui Wu ◽  
Lu Jiang ◽  
Lanlin Huang ◽  
Yaxing He ◽  
Xia Yan ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Signaling Pathway ◽  
Fluid Shear Stress ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Active Ingredients ◽  
Active Components ◽  
Protein Protein Interaction ◽  
Target Regulation

Abstract Objective: We aimed to predict the possible active components,key targets and pathways of Huanglian Jiedu Decoction(HLJDD) for anti-atherosclerosis. Methods: The TCMSP database was searched to obtain the active components and targets of HLJDD, the GeneCards and OMIM databases were searched to obtain related targets of atherosclerosis, and we obtain the intersection targets of them, which are the potential targets of HLJDD for anti-atherosclerosis.Application of Cytoscape 3.6.0 software to build a herbal-active ingredient-potential target regulation network.We perform protein-protein interaction(PPI) network analysis of potential targets through STRING 11.0 database and obtain the key targets,and the results of PPI network of key targets were visualized by Cytoscape3.6.0 software. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the key targets were performed using STRING11.0 database, and we finally constructed the possible pharmacological network of HLJDD for anti-atherosclerosis .Results: We finally obtained 14 key active ingredients of HLJDD, 65 key targets of anti-atherosclerosis, and 14 key active ingredients corresponded to 52 of these targets. These targets are mainly involved in biological processes such as reaction to organic substance, reaction to chemical stimulation,etc.They mainly involved in biological signaling pathways such as pathways in cancer,IL-17 signaling pathway,etc. Conclusion: HLJDD may act on 52 key targets such as PTGS2, HSP90AA1 and RELA through 14 key active ingredients, and influence the signaling pathways including fluid shear stress and atherosclerosis,PI3K-Akt signaling pathway,IL-17 signaling pathway,AGE-RAGE signaling pathway in diabetic complications,TNF signaling pathway,etc.Thus, it may play an anti-atherosclerosis role by inhibiting inflammatory reaction, oxidative stress and improving hemodynamics,etc.

scholarly journals Differentially expressed genes SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma

2020 ◽  
Author(s):  
Sheng Chang ◽  
Yang Cao
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Osteogenic Sarcoma ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Potential Biomarkers

Abstract Background: Osteosarcoma (osteogenic sarcoma, OS) is a primary cause of morbidity and mortality and is associated with poor prognosis in the field of orthopedic. Globally, rates of OS are highest among 15 to 25-year-old adolescent. However, the mechanism of gene regulation and signaling pathway is unknown. Material and Methods: GSE9508, including 34 OS samples and 5 non-malignant bone samples, was gained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were picked out by GEO2R online R soft tool. Furthermore, the protein-protein interaction (PPI) network between the DEGs was molded utilizing STRING online software. Afterward, PPI network of DEGs was constructed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out on DAVID online tool and visualized via cytoscape software. Subsequently, module analysis of PPI was performed by using MCODE app. What’s more, prognosis-related genes were screened by using online databases including GEPIA, UALCAN and cBioPortal databases. Results: Totally, 671 DEGs were picked out, including 501 up-regulated genes and 170 down-regulated genes. Moreover, 22 hub genes were identified to be significantly expressed in PPI network (16 up-regulated and 6 down-regulated). We found that spliceosome signaling pathway may provide a potential target in OS. Furthermore, on the basis of common crucial pathway, PRPF38A and SNRPC were closely associated with spliceosome. Conclusion: This study showed that SNRPC and PRPF38A are potential biomarkers candidates for osteosarcoma.

Analysis of sebaceous gland carcinoma associated genes using network analysis to identify potentially actionable genes

2022 ◽  
Vol 11 (6) ◽  
pp. 634-645
Author(s):  
Nimita Kant ◽  
Perumal Jayaraj ◽  
Chitra
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Cell Fate ◽  
Protein Interaction ◽  
Sebaceous Gland ◽  
Ppi Network ◽  
Sebaceous Gland Carcinoma ◽  
Protein Protein Interaction ◽  
Pubmed Database ◽  
Gland Carcinoma

Eyelid sebaceous gland carcinoma (SGC) is a rare but life-threatening condi-tion. However, there is limited computational research associated with un-derlying protein interactions specific to eyelid sebaceous gland carcinoma. The aim of our study is to identify and analyse the genes associated with eyelid sebaceous gland carcinoma using text mining and to develop a protein-protein interaction network to predict significant biological pathways using bioinformatics tool. Genes associated with eyelid sebaceous gland carcinoma were retrieved from the PubMed database using text mining with key terms ‘eyelid’, ‘sebaceous gland carcinoma’ and excluding the genes for ‘Muir-Torre Syndrome’. The interaction partners were identified using STRING. Cytoscape was used for visualization and analysis of the PPI network. Molec-ular complexes in the network were predicted using MCODE plug-in and ana-lyzed for gene ontology terms using DAVID. PubMed retrieval process identi-fied 79 genes related to eyelid sebaceous gland carcinoma. The PPI network associated with eyelid sebaceous gland carcinoma produced 79 nodes, 1768 edges. Network analysis using Cytoscape identified nine key genes and two molecular complexes to be enriched in the protein-protein interaction net-work. GO enrichment analysis identified biological processes cell fate com-mitment, Wnt signalling pathway, retinoic acid signalling and response to cytokines to be enriched in our network. Genes identified in the study might play a pivotal role in understanding the underlying molecular pathways in-volved in the development and progression of eyelid sebaceous gland carci-noma. Furthermore, it may aid in the identification of candidate biomarkers and therapeutic targets in the treatment of eyelid sebaceous gland carcino-ma.

scholarly journals Identification and Validation of Immune-Related Prognostic Genes in the Tumor Microenvironment of Colon Adenocarcinoma

2022 ◽  
Vol 12 ◽  
Author(s):  
Shenghua Pan ◽  
Tingting Tang ◽  
Yanke Wu ◽  
Liang Zhang ◽  
Zekai Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Kaplan Meier

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan–Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein–protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients’ overall survival (log rank p &lt; 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan–Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.

scholarly journals Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray

Biomolecules ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 282
Author(s):  
Alshabi ◽  
BasavarajVastrad ◽  
Shaikh ◽  
Vastrad
Keyword(s):  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Module ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Human Protein Atlas ◽  
Go Enrichment Analysis

: Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes—miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H) , which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes—miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA.

scholarly journals Multivariate Analysis of Potential Pleiotropic Genes For Breast, Ovarian And Cervical Cancers Using Gene-Based Association Analysis

2021 ◽  
Author(s):  
XiaoCan Jia ◽  
Nian Shi ◽  
Yu Feng ◽  
Huili Zhu ◽  
Yuping Wang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Association Studies ◽  
Statistical Significance ◽  
Enrichment Analysis ◽  
Biological Pathways ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Ppi Network ◽  
Cervical Cancers ◽  
Protein Protein Interaction

Abstract Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery in gynecological malignancies, the single nucleotide polymorphisms (SNPs) identified by this prevailing univariate approach only explain a small percentage of heredity. The extensive previous studies have repeatedly shown breast, ovarian and cervical cancers have common genetic mechanisms and the overlapping pathophysiological pathways. Novel multivariate analytical methods are necessary to identify shared pleiotropic genes. In this study, a total of 40,859 SNPs mapped in 11,597 gene regions were performed to identify potential common variants by using metaCCA and VEGAS2 analysis. Gene enrichment and protein-protein interaction (PPI) network analysis were used to explore potential biological pathways and connectivity. After metaCCA analysis, 4,203 SNPs (P<1.22×10−6) and 1,886 pleotropic gene (P<4.31×10−6) were identified. By screening the results of gene-based P-values, the existence of 3 confirmed pleiotropic genes and 16 novel genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one cancer in the VEGAS2 analysis were identified. The enrichment analysis showed the biological pathways of these genes were mainly enriched in 4 signaling pathways and 11 differentially expressed genes were found to encode interacting proteins in PPI network analysis. Altogether, we identified novel genetic variants of breast, ovarian and cervical cancers and provided evidence of biological functions which developed new insights for the diagnosis and treatment of these cancers.

scholarly journals Bioinformatic analysis identifies potential key genes of epilepsy

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0254326
Author(s):  
Yike Zhu ◽  
Dan Huang ◽  
Zhongyan Zhao ◽  
Chuansen Lu
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
P Value ◽  
Ppi Network ◽  
Functional Roles ◽  
Protein Protein Interaction

Background Epilepsy is one of the most common brain disorders worldwide. It is usually hard to be identified properly, and a third of patients are drug-resistant. Genes related to the progression and prognosis of epilepsy are particularly needed to be identified. Methods In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed. Results In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs. Conclusion This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.

scholarly journals Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9633
Author(s):  
Jie Meng ◽  
Rui Su ◽  
Yun Liao ◽  
Yanyan Li ◽  
Ling Li
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

scholarly journals Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Hanxi Wan ◽  
Xinwei Huang ◽  
Peilin Cong ◽  
Mengfan He ◽  
Aiwen Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Expression Levels ◽  
Protein Protein Interaction

Idiopathic pulmonary fibrosis (IPF) is a progressive disease whose etiology remains unknown. The purpose of this study was to explore hub genes and pathways related to IPF development and prognosis. Multiple gene expression datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis (WGCNA) was performed and differentially expressed genes (DEGs) identified to investigate Hub modules and genes correlated with IPF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were performed on selected key genes. In the PPI network and cytoHubba plugin, 11 hub genes were identified, including ASPN, CDH2, COL1A1, COL1A2, COL3A1, COL14A1, CTSK, MMP1, MMP7, POSTN, and SPP1. Correlation between hub genes was displayed and validated. Expression levels of hub genes were verified using quantitative real-time PCR (qRT-PCR). Dysregulated expression of these genes and their crosstalk might impact the development of IPF through modulating IPF-related biological processes and signaling pathways. Among these genes, expression levels of COL1A1, COL3A1, CTSK, MMP1, MMP7, POSTN, and SPP1 were positively correlated with IPF prognosis. The present study provides further insights into individualized treatment and prognosis for IPF.

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