scholarly journals Bioinformatics identification of crucial genes and pathways associated with hepatocellular carcinoma

2018 ◽  
Vol 38 (6) ◽  
Author(s):  
Xueren Gao ◽  
Xixi Wang ◽  
Shulong Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Chemical Carcinogenesis ◽  
Degradation Pathway ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Retinol Metabolism

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death worldwide. Up to date, HCC pathogenesis has not been fully understood. The aim of the present study was to identify crucial genes and pathways associated with HCC by bioinformatics methods. The differentially expressed genes (DEGs) between 14 HCC tissues and corresponding non-cancerous tissues were identified using limma package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The protein–protein interaction (PPI) network of DEGs was constructed and visualized by STRING database and Cytoscape software, respectively. The crucial genes in PPI network were identified using a Cytoscape plugin, CytoNCA. Furthermore, the effect of the expression level of the crucial genes on HCC patient survival was analyzed by an interactive web-portal, UALCAN. A total of 870 DEGs including 237 up-regulated and 633 down-regulated genes were identified in HCC tissues. KEGG pathway analysis revealed that DEGs were mainly enriched in complement and coagulation cascades pathway, chemical carcinogenesis pathway, retinol metabolism pathway, fatty acid degradation pathway, and valine, leucine and isoleucine degradation pathway. PPI network analysis showed that CDK1, CCNB1, CCNB2, MAD2L1, ACACB, IGF1, TOP2A, and EHHADH were crucial genes. Survival analysis suggested that the high expression of CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A significantly decreased the survival probability of HCC patients. In conclusion, the identification of the above crucial genes and pathways will not only contribute to elucidating the pathogenesis of HCC, but also provide prognostic markers and therapeutic targets for HCC.

scholarly journals Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Suthanthiram Backiyarani ◽  
Rajendran Sasikala ◽  
Simeon Sharmiladevi ◽  
Subbaraya Uma
Keyword(s):  
Candidate Genes ◽  
Protein Interaction ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Auxin Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

scholarly journals Identification of Key miRNAs in the Treatment of Dabrafenib-Resistant Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guangyu Gao ◽  
Zhen Yao ◽  
Jiaofeng Shen ◽  
Yulong Liu
Keyword(s):  
Drug Resistance ◽  
Molecular Mechanism ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Obvious Association

Dabrafenib resistance is a significant problem in melanoma, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism of drug resistance of dabrafenib and to explore the key genes and pathways that mediate drug resistance in melanoma. GSE117666 was downloaded from the Gene Expression Omnibus (GEO) database and 492 melanoma statistics were also downloaded from The Cancer Genome Atlas (TCGA) database. Besides, differentially expressed miRNAs (DEMs) were identified by taking advantage of the R software and GEO2R. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) and FunRich was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to identify potential pathways and functional annotations linked with melanoma chemoresistance. 9 DEMs and 872 mRNAs were selected after filtering. Then, target genes were uploaded to Metascape to construct protein-protein interaction (PPI) network. Also, 6 hub mRNAs were screened after performing the PPI network. Furthermore, a total of 4 out of 9 miRNAs had an obvious association with the survival rate ( P < 0.05 ) and showed a good power of risk prediction model of over survival. The present research may provide a deeper understanding of regulatory genes of dabrafenib resistance in melanoma.

System Pharmacological Mechanism and Compatibility Laws of Jianghuang from Traditional Chinese Medicine In Blood-Regulating Formulae

2020 ◽  
Author(s):  
Zhiqiang Liu ◽  
Bolong Wang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Kegg Pathway ◽  
Inflammatory Diseases ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Pharmacological Effects ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Pharmacological Mechanism

Abstract Background: Jianghuang (JH) is a popular ingredient in blood-regulating traditional Chinese Medicine (TCM) that could be effective for the treatment of various diseases. We demonstrate the compatibility laws and system pharmacological mechanisms of the key formula containing JH by leveraging data mining of bioinformatics databases.Material/Methods: The compatibility laws of blood-regulating formulae containing JH from the Chinese Traditional Medicine Formula Dictionary were analyzed using a generalized rule induction (GRI) algorithm implemented. The putative target gene and miRNA were retrieved via a combination of the Arrowsmith knowledge discovery tool and FunRich 3.1.3. System pharmacological mechanisms are traced by their protein-protein interaction (PPI) network, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted using Uniprot, the Human Protein Atlas (HPA), STRING 11.0, and KOBAS 3.0.Results: We found that the JH-CX-DG formula (Ligusticum chuanxiong-Angelica sinensis) could represent a key formula containing JH in blood-regulating TCM formulae. The JH-CX-DG formula was observed to directly target AKT, TLR4, caspase-3, PI3K, mTOR, p38 MAPK, VEGF, iNOS, Nrf2, BDNF, NF-κB, Bcl-2, and Bax 13 targets and regulate targets through 13 miRNA. The PPI network and KEGG pathway enrichment analysis showed that the JH-CX-DG formula possess potential pharmacological effects including anti-inflammatory, improving microcirculation, and anti-tumor through the regulation of multiple pathways including PI3K/Akt, MAPK, Toll-like receptor, T cell receptor, EGFR, VEGFR, Apoptosis, HIF-1 (p < 0.05).Conclusion: The JH-CX-DG formula can exert beneficial pharmacological effects through multi-target and multi-pathway interactions. It can be effectively administered for the treatment of inflammatory diseases, microcirculation disorders, cardiovascular disease, and cancer. We found a new effective drug formula through analyzing the compatibility law and systemic pharmacological mechanism of JH. Our study provides a theoretical basis and directions for subsequent research on the JH-CX-DG formula.

scholarly journals In Silico Integration Approach Reveals Key MicroRNAs and Their Target Genes in Follicular Thyroid Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Shengqing Hu ◽  
Yunfei Liao ◽  
Juan Zheng ◽  
Luoning Gou ◽  
Anita Regmi ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Target Genes ◽  
Follicular Thyroid Carcinoma ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Biological Targets ◽  
Protein Protein Interaction ◽  
Bioinformatic Tools ◽  
The Common

To better understand the molecular mechanism for the pathogenesis of follicular thyroid carcinoma (FTC), this study aimed at identifying key miRNAs and their target genes associated with FTC, as well as analyzing their interactions. Based on the gene microarray data GSE82208 and microRNA dataset GSE62054, the differentially expressed genes (DEGs) and microRNAs (DEMs) were obtained using R and SAM software. The common DEMs from R and SAM were fed to three different bioinformatic tools, TargetScan, miRDB, and miRTarBase, respectively, to predict their biological targets. With DEGs intersected with target genes of DEMs, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed through the DAVID database. Then a protein-protein interaction (PPI) network was constructed by STRING. Finally, the module analysis for PPI network was performed by MCODE and BiNGO. A total of nine DEMs were identified, and their function might work through regulating hub genes in the PPI network especially KIT and EGFR. KEGG analysis showed that intersection genes were enriched in the PI3K-Akt signaling pathway and microRNAs in cancer. In conclusion, the study of miRNA-mRNA network would offer molecular support for differential diagnosis between malignant FTC and benign FTA, providing new insights into the potential targets for follicular thyroid carcinoma diagnosis and treatment.

scholarly journals Analysis of the Molecular Mechanisms of the Effects of Prunella vulgaris against Subacute Thyroiditis Based on Network Pharmacology

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Xin Shen ◽  
Rui Yang ◽  
Jianpeng An ◽  
Xia Zhong
Keyword(s):  
Signaling Pathways ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Subacute Thyroiditis ◽  
Network Pharmacology ◽  
Pharmacokinetic Parameters ◽  
Pathway Enrichment Analysis ◽  
Prunella Vulgaris ◽  
Protein Protein Interaction

Prunella vulgaris (PV) has a long history of application in traditional Chinese and Western medicine as a remedy for the treatment of subacute thyroiditis (SAT). This study applied network pharmacology to elucidate the mechanism of the effects of PV against SAT. Components of the potential therapeutic targets of PV and SAT-related targets were retrieved from databases. To construct a protein-protein interaction (PPI) network, the intersection of SAT-related targets and PV-related targets was input into the STRING platform. Gene ontology (GO) analysis and KEGG pathway enrichment analysis were carried out using the DAVID database. Networks were constructed by Cytoscape for visualization. The results showed that a total of 11 compounds were identified according to the pharmacokinetic parameters of ADME. A total of 126 PV-related targets and 2207 SAT-related targets were collected, and 83 overlapping targets were subsequently obtained. The results of the KEGG pathway and compound-target-pathway (C-T-P) network analysis suggested that the anti-SAT effect of PV mainly occurs through quercetin, luteolin, kaempferol, and beta-sitosterol and is most closely associated with their regulation of inflammation and apoptosis by targeting the PIK3CG, MAPK1, MAPK14, TNF, and PTGS2 proteins and the PI3K-Akt and TNF signaling pathways. The study demonstrated that quercetin, luteolin, kaempferol, and beta-sitosterol in PV may play a major role in the treatment of SAT, which was associated with the regulation of inflammation and apoptosis, by targeting the PI3K-Akt and TNF signaling pathways.

scholarly journals CCDC114, DNAI2 and TOP2A involves in the effects of tibolone treatment on postmenopausal endometrium

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yanhua Lv ◽  
Yanqing Liu ◽  
Yueqiang Wang ◽  
Fanrong Kong ◽  
Qiuxiang Pang ◽  
...  
Keyword(s):  
Postmenopausal Women ◽  
Molecular Mechanisms ◽  
Ion Homeostasis ◽  
Enrichment Analysis ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Target Interaction ◽  
Protein Protein Interaction ◽  
Movement Function

Abstract Background This study aimed to explore the molecular mechanisms of tibolone treatment in postmenopausal women. Methods The gene set enrichment profile, GSE12446, which includes 9 human endometrial samples from postmenopausal women treated with tibolone (tibolone group) and 9 control samples (control group), was downloaded from GEO database for analysis. Differentially expressed genes (DEGs) in tibolone vs. control groups were identified and then used for function and pathway enrichment analysis. Protein–protein interaction (PPI) network and module analyses were also performed. Finally, drug–target interaction was predicted for genes in modules, and then were validated in Pubmed. Results A total of 238 up-regulated DEGs and 72 down-regulated DEGs were identified. These DEGs were mainly enriched in various biological processed and pathways, such as cilium movement (e.g., CCDC114 and DNAI2), calcium ion homeostasis, regulation of hormone levels and complement/coagulation cascades. PPI network contained 368 interactions and 166 genes, of which IGF1, DNALI1, CCDC114, TOP2A, DNAH5 and DNAI2 were the hue genes. A total of 96 drug–gene interactions were obtained, including 94 drugs and eight genes. TOP2A and HTR2B were found to be targets of 28 drugs and 38 drugs, respectively. Among the 94 obtained drugs, only 12 drugs were reported in studies, of which 7 drugs (e.g., epirubicin) were found to target TOP2A. Conclusions CCDC114 and DNAI2 might play important roles in tibolone-treated postmenopausal women via cilium movement function. TOP2A might be a crucial target of tibolone in endometrium of postmenopausal women.

scholarly journals A Network Pharmacology Approach to Uncover the Potential Mechanism of Yinchensini Decoction

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Guoming Chen ◽  
Chuyao Huang ◽  
Yunyun Liu ◽  
Tengyu Chen ◽  
Ruilan Huang ◽  
...  
Keyword(s):  
Rna Polymerase Ii ◽  
Enrichment Analysis ◽  
Drug Binding ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Regulation Of Transcription ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
System Pharmacology

Objective. To predict and explore the potential mechanism of Yinchensini decoction (YCSND) based on systemic pharmacology. Method. TCMSP database was searched for the active constituents and related target proteins of YCSND. Cytoscape 3.5.1 was used to construct the active ingredient-target interaction of YCSND and network topology analysis, with STRING online database for protein-protein interaction (PPI) network construction and analysis; and collection from the UniProt database of target protein gene name, with the DAVID database for the gene ontology (GO) functional analysis, KEGG pathway enrichment analysis mechanism and targets of YCSND. Results. The results indicate the core compounds of YCSND, namely, kaempferol, 7-Methoxy-2-methyl isoflavone, and formononetin. And its core targets are prostaglandin G/H synthase 2, estrogen receptor, Calmodulin, heat shock protein HSP 90, etc. PPI network analysis shows that the key components of the active ingredients of YCSND are JUN, TP53, MARK1, RELA, MYC, and so on. The results of the GO analysis demonstrate that extracellular space, cytosol, and plasma membrane are the main cellular components of YCSND. Its molecular functions are mainly acting on enzyme binding, protein heterodimerization activity, and drug binding. The biological process of YCSND is focused on response to drug, positive regulation of transcription from RNA polymerase II promoter, the response to ethanol, etc. KEGG results suggest that the pathways, including pathways in cancer, hepatitis B, and pancreatic cancer, play a key role in YCSND. Conclusion. YCSND exerts its drug effect through various signaling pathways and acts on kinds of targets. By system pharmacology, the potential role of drugs and the mechanism of action can be well predicted.

scholarly journals Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11375
Author(s):  
Youzheng Xu ◽  
Yixin Xu ◽  
Chun Wang ◽  
Baoguo Xia ◽  
Qingling Mu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Strong Predictor ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Poor Overall Survival ◽  
Protein Protein Interaction ◽  
Serous Cystadenocarcinoma ◽  
Kaplan Meier ◽  
Limma Package

Background Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently Methods Gene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival. Results Eighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival. Conclusion Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment.

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 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.

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