Deciphering the Underlying Mechanism of Eucommiae Cortex against Osteoporotic Fracture by Network Pharmacology

Background. Du Zhong (DZ), or Eucommiae Cortex, a traditional Chinese herbal medicine, has been used to treat osteoporosis. Although it has been reported that DZ can improve bone mass in ovariectomized rats, its pharmacological mechanisms in treating osteoporotic fractures (OPF) remain unclear. Methods. In this study, we used a network pharmacological manner to explore its potential complicated mechanism in treating OPF. We obtained DZ compounds from TCMSP and BATMAN-TCM databases and collected potential targets of these compounds through target fishing based on TCMSP and BATMAN-TCM databases. Next, we collected the OPF targets by using CTD, GeneCards, OMIM, HPO, and GenCLiP 3 databases. And then the overlapping genes between DZ potential targets and OPF targets were used to build up the protein-protein interaction (PPI) network and to analyze their interactions and find out the big hub genes in this network. Subsequently, clusterProfiler package in R language was utilized to conduct the enrichment of Gene Ontology biological process and KEGG pathways. Results. There were totally 93 active compounds and 916 related targets in DZ. After the enrichment analysis, we collected top 25 cellular biological processes and top 25 pathways based on the adjusted P value and found that the DZ anti-OPF effect was mainly associated with the regulation of ROS and inflammatory response. Furthermore, 64 hub genes in PPI network, such as MAPK1 (degree = 41), SRC (degree = 39), PIK3R1 (degree = 36), VEGFA (degree = 31), TP53 (degree = 29), EGFR (degree = 29), JUN (degree = 29), AGT (degree = 29), MAPK1, SRC, PIK3R1, VEGFA, and TP53, were considered as potential therapeutic targets, implying the underlying mechanisms of DZ acting on OPF. Conclusion. We investigated the possible therapeutic mechanisms of DZ from a systemic perspective. These key targets and pathways provided promising directions for the future research to reveal the exact regulating mechanisms of DZ in treating OPF.

Download Full-text

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Download Full-text

Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

10.21203/rs.3.rs-25246/v4 ◽

2020 ◽

Author(s):

Wenxing Su ◽

Yi Guan ◽

Biao Huang ◽

Juanjuan Wang ◽

Yuqian Wei ◽

...

Keyword(s):

Differentially Expressed Gene ◽

Primary Melanoma ◽

Enrichment Analysis ◽

Gene Interaction ◽

Underlying Mechanism ◽

Bioinformatic Analysis ◽

Pathway Enrichment Analysis ◽

Melanoma Metastasis ◽

Ppi Network ◽

Hub Genes

Abstract Background: Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. Methods: With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. Results: A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression. Conclusions: To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

Download Full-text

Uncovering Active Ingredients and Mechanisms of Spica Prunellae in the Treatment of Colon Adenocarcinoma: A Study Based on Network Pharmacology and Bioinformatics

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323999200730210536 ◽

2020 ◽

Vol 23 ◽

Author(s):

Yan Lei ◽

Hao Yuan ◽

Liyue Gai ◽

Xuelian Wu ◽

Zhixiao Luo

Keyword(s):

Target Genes ◽

Experimental Studies ◽

Colon Adenocarcinoma ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Network Pharmacology ◽

Therapeutic Effects ◽

Ppi Network ◽

Active Ingredients ◽

Hub Genes

Background: As a well-known herb used in the treatment of colon adenocarcinoma (COAD), Spica Prunellae (SP) shows favorable clinical effect and safety in China for many years, but its active ingredients and therapeutic mechanisms against COAD remain poorly understood. Therefore, this study aims to uncover active ingredients and mechanism of SP in the treatment of COAD using a combined approach of network pharmacology and bioinformatics. Methods: A comprehensive approach mainly comprised of target prediction, network construction, pathway and functional enrichment analysis, and hub genes verification was adopted in the current study. Results: We collected 102 compounds-related genes and 3549 differently expressed genes (DEGs) following treatment with SP, and 64 disease-drug target genes between them were recognized. In addition, a total of 25 active ingredients in SP were identified.Pathway and functional enrichment analyses suggested that the mechanisms of SP against COAD might be to induce apoptosis of colon cancer cells by regulating PI3K-Akt and TNF signaling pathway. Recognition of hub genes and core functional modules was performed by constructing protein-protein interaction (PPI) network, from whichTP53, MYC, MAPK8 and CASP3 were found as the hub target genes that might play an important part in therapy for COAD. Subsequently we further compared differential expression level and assessed prognostic value of these four hub genes. These result of verification suggested that SP exerted therapeutic effects against COAD via a PPI network involving TP53, MYC, MAPK8 and CASP3. Conclusion: In this study, active ingredients and mechanism of SPin the treatment of COAD were systematically dis-cussed, which providedthe foundation for further experimental studies and mightact to promote its appropriate clinical application.

Download Full-text

Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

BMC Cancer ◽

10.1186/s12885-020-07372-5 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Wenxing Su ◽

Yi Guan ◽

Biao Huang ◽

Juanjuan Wang ◽

Yuqian Wei ◽

...

Keyword(s):

Differentially Expressed Gene ◽

Primary Melanoma ◽

Enrichment Analysis ◽

Gene Interaction ◽

Underlying Mechanism ◽

Bioinformatic Analysis ◽

Pathway Enrichment Analysis ◽

Melanoma Metastasis ◽

Ppi Network ◽

Hub Genes

Abstract Background Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. Methods With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein-protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. Results A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression. Conclusions To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

Download Full-text

Uncovering the Mechanism of Ge-Gen-Qin-Lian Decoction for Treating Ulcerative Colitis Based on Network Pharmacology and Molecular Docking Verification

10.21203/rs.3.rs-74864/v1 ◽

2020 ◽

Author(s):

Lin Xu ◽

Jiaqi Zhang ◽

Zedan Zhang ◽

Yifan Wang ◽

Fengyun Wang ◽

...

Keyword(s):

Ulcerative Colitis ◽

Molecular Docking ◽

Kegg Pathway ◽

Interaction Network ◽

Enrichment Analysis ◽

Underlying Mechanism ◽

Network Pharmacology ◽

Active Ingredients ◽

Hub Genes ◽

Pathway Analyses

Abstract Background and objective: Ge-Gen-Qin-Lian Decoction (GGQLD), a traditional Chinese medicine (TCM) formula, has been widely used for ulcerative colitis (UC) in China while the pharmacological mechanisms still remain unclear. The present research was designed to clarify the underlying mechanism of GGQD against UC. Methods: In this research, a GGQLD-compound-target-UC (G-U) network was constructed based on public databases to clarify the relationship between active compounds in GGQLD and potential targets. GO and KEGG pathway enrichment analyses were performed to investigate biological functions associated with potential targets. A protein-protein interaction network was constructed to screen and evaluate hub genes and key active ingredients, another GO and KEGG pathway analyses were subsequently performed on hub genes. Molecular docking was used to verify the activities of binding between hub targets and ingredients. Results: Finally, 83 potential therapeutic targets and 118 correspond active ingredients were obtained by network pharmacology. GO and KEGG enrichment analysis revealed that GGQLD had an effect of anti-inflammation, antioxidation, and immunomodulatory. The effect of GGQLD on UC might be achieved by regulating the balance of cytokines (eg., IL6, TNF, IL1β, CXCL8, CCL2, IL10, IL4, IL2) in immune system and inflammation-related pathways, such as IL-17 pathway and Th17 cell differentiation pathway. Besides, molecular docking results demonstrated that the main active ingredients, quercetin, exhibited good affinity to hub targets. Conclusion: This research fully reflects the characteristics of multi-component and multi-target for GGQLD in the treatment of UC. Furthermore, the present study provided new insight into the mechanisms of GGQLD against UC.

Download Full-text

Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

BioMed Research International ◽

10.1155/2021/8849415 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Junwei Liu ◽

Fang Han ◽

Jianyi Ding ◽

Xiaodong Liang ◽

Jie Liu ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Enrichment Analysis ◽

Underlying Mechanism ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Functional Study ◽

Ppi Network ◽

Hub Genes ◽

Qrt Pcr

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

Download Full-text

Underlying Genes and Molecular Mechanism of Keloids Investigated by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-148691/v1 ◽

2021 ◽

Author(s):

chuying LI ◽

Mei-Tong Jin ◽

Yin-Li Luo ◽

Zhe-Hu Jin ◽

Long-Quan PI

Keyword(s):

Expression Profiles ◽

Normal Skin ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Underlying Mechanism ◽

Cholinergic Receptor ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes

Abstract Background: We aimed to identify the overlapping differentially expressed genes (DEGs) of keloids distinguished from normal scar and normal skin and relevant underlying mechanism using integrated bioinformatics methods.Methods: The expression profiles of 18 keloid samples, 7 normal skin and 5 normal scar, were obtained from the GSE7890, GSE44270, GSE92566, and GSE3189 datasets in the Gene Expression Omnibus database. DEGs were identified using the LIMMA package in R. Gene ontology (GO) functional enrichment analysis was performed using the R software. A DEG-associated protein–protein interaction (PPI) network was constructed using STRING and MCODE was used for module analysis of the PPI network. Moreover, the hub genes were verified by qRT-PCR. The predicted DEGs, their regulatory miRNA and TF regulation network was analyzed using miRnet. Results: A total of 978 common DEGs were identified in the keloid samples. Genes with more than 45 interaction degrees, including neuropeptide Y (NPY), opioid receptor mu 1 (OPRM1), cholinergic receptor muscarinic 2 (CHRM2), and proopiomelanocortin (POMC), were found in the PPI network. Hsa-miR-335 and Sp1 as upstream-regulators regulated CHRM2, NPY, and POMC. Functional enrichment analysis revealed that hub genes were commonly enriched in the “G protein-coupled receptor signaling pathway” GO_BP termConclusion: Taken together, CHRM2, NPY, POMC, and OPRM1 potentially have crucial roles in keloid disease. Furthermore, miR-335 and Sp1 are potential targets for preventing keloid formation.

Download Full-text

Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

10.21203/rs.3.rs-25246/v3 ◽

2020 ◽

Author(s):

wenxing su ◽

yi guan ◽

biao huang ◽

juanjuan wang ◽

yuqian wei ◽

...

Keyword(s):

Differentially Expressed Gene ◽

Primary Melanoma ◽

Enrichment Analysis ◽

Gene Interaction ◽

Underlying Mechanism ◽

Bioinformatic Analysis ◽

Pathway Enrichment Analysis ◽

Melanoma Metastasis ◽

Ppi Network ◽

Hub Genes

Download Full-text

Study on potential differentially expressed genes in stroke by bioinformatics analysis

Neurological Sciences ◽

10.1007/s10072-021-05470-1 ◽

2021 ◽

Author(s):

Xitong Yang ◽

Pengyu Wang ◽

Shanquan Yan ◽

Guangming Wang

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Normal Control ◽

Enrichment Analysis ◽

Normal Control Group ◽

Control Group ◽

Ppi Network ◽

Hub Genes ◽

Pathway Enrichment ◽

Key Genes

AbstractStroke is a sudden cerebrovascular circulatory disorder with high morbidity, disability, mortality, and recurrence rate, but its pathogenesis and key genes are still unclear. In this study, bioinformatics was used to deeply analyze the pathogenesis of stroke and related key genes, so as to study the potential pathogenesis of stroke and provide guidance for clinical treatment. Gene Expression profiles of GSE58294 and GSE16561 were obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IS and normal control group. The different expression genes (DEGs) between IS and normal control group were screened with the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), the function and pathway enrichment analysis of DEGS were performed. Then, a protein–protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. Cytoscape with CytoHubba were used to identify the hub genes. Finally, NetworkAnalyst was used to construct the targeted microRNAs (miRNAs) of the hub genes. A total of 85 DEGs were screened out in this study, including 65 upward genes and 20 downward genes. In addition, 3 KEGG pathways, cytokine − cytokine receptor interaction, hematopoietic cell lineage, B cell receptor signaling pathway, were significantly enriched using a database for labeling, visualization, and synthetic discovery. In combination with the results of the PPI network and CytoHubba, 10 hub genes including CEACAM8, CD19, MMP9, ARG1, CKAP4, CCR7, MGAM, CD79A, CD79B, and CLEC4D were selected. Combined with DEG-miRNAs visualization, 5 miRNAs, including hsa-mir-146a-5p, hsa-mir-7-5p, hsa-mir-335-5p, and hsa-mir-27a- 3p, were predicted as possibly the key miRNAs. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of ischemic stroke, and provide a new strategy for clinical therapy.

Download Full-text

Studies on the Mechanism of Gegen Qinlian Decoction in Treating Diabetes Mellitus Based on Network Pharmacology

Natural Product Communications ◽

10.1177/1934578x20982138 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1934578X2098213

Author(s):

Xiaodong Deng ◽

Yuhua Liang ◽

Jianmei Hu ◽

Yuhui Yang

Keyword(s):

Diabetes Mellitus ◽

Molecular Docking ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Network Pharmacology ◽

Hub Genes ◽

Topological Parameters

Diabetes mellitus (DM) is a chronic disease that is very common and seriously threatens patient health. Gegen Qinlian decoction (GQD) has long been applied clinically, but its mechanism in pharmacology has not been extensively and systematically studied. A GQD protein interaction network and diabetes protein interaction network were constructed based on the methods of system biology. Functional module analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Gene Ontology (GO) enrichment analysis were carried out on the 2 networks. The hub nodes were filtered by comparative analysis. The topological parameters, interactions, and biological functions of the 2 networks were analyzed in multiple ways. By applying GEO-based external datasets to verify the results of our analysis that the Gene Set Enrichment Analysis (GSEA) displayed metabolic pathways in which hub genes played roles in regulating different expression states. Molecular docking is used to verify the effective components that can be combined with hub nodes. By comparing the 2 networks, 24 hub targets were filtered. There were 7 complex relationships between the networks. The results showed 4 topological parameters of the 24 selected hub targets that were much higher than the median values, suggesting that these hub targets show specific involvement in the network. The hub genes were verified in the GEO database, and these genes were closely related to the biological processes involved in glucose metabolism. Molecular docking results showed that 5,7,2', 6'-tetrahydroxyflavone, magnograndiolide, gancaonin I, isoglycyrol, gancaonin A, worenine, and glyzaglabrin produced the strongest binding effect with 10 hub nodes. This compound–target mode of interaction may be the main mechanism of action of GQD. This study reflected the synergistic characteristics of multiple targets and multiple pathways of traditional Chinese medicine and discussed the mechanism of GQD in the treatment of DM at the molecular pharmacological level.

Download Full-text