scholarly journals A Comprehensive Understanding of the Anticancer Mechanisms of FDY2004 Against Cervical Cancer Based on Network Pharmacology

2021 ◽  
Vol 16 (3) ◽  
pp. 1934578X2110043
Author(s):  
Ho-Sung Lee ◽  
In-Hee Lee ◽  
Kyungrae Kang ◽  
Sang-In Park ◽  
Minho Jung ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Cellular Functions ◽  
Papillomavirus Infection ◽  
Pharmacological Targets ◽  
Systemic Level ◽  
Viral Carcinogenesis ◽  
Persicae Semen ◽  
Phytochemical Components

Herbal drugs are continuously being developed and used as effective therapeutics for various cancers, such as cervical cancer (CC); however, their mechanisms of action at a systemic level have not been explored fully. To study such mechanisms, we conducted a network pharmacological investigation of the anti-CC mechanisms of FDY2004, an herbal drug consisting of Moutan Radicis Cortex, Persicae Semen , and Rhei Radix et Rhizoma. We found that FDY2004 inhibited the viability of human CC cells. By performing pharmacokinetic evaluation and network analysis of the phytochemical components of FDY2004, we identified 29 bioactive components and their 116 CC-associated pharmacological targets. Gene ontology enrichment analysis showed that the modulation of cellular functions, such as apoptosis, growth, proliferation, and survival, might be mediated through the FDY2004 targets. The therapeutic targets were also key components of CC-associated oncogenic and tumor-suppressive pathways, including PI3K-Akt, human papillomavirus infection, IL-17, MAPK, TNF, focal adhesion, and viral carcinogenesis pathways. In conclusion, our data present a comprehensive insight for the mechanisms of the anti-CC properties of FDY2004.

scholarly journals Identification of Key Genes and Pathways associated with Chemotherapy and Radiotherapy Sensitivity in Locally Advanced Cervical Cancer

2020 ◽  
Author(s):  
Zhenhua Zhang ◽  
Yao Zhang ◽  
Yongshun Ma ◽  
Shixing Xiang ◽  
Jing Shen ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Progenitor Cells ◽  
Locally Advanced ◽  
Enrichment Analysis ◽  
R Package ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Locally Advanced Cervical Cancer ◽  
Papillomavirus Infection ◽  
Key Genes

Abstract Objectives：Currently, the standard treatment approach for locally advanced cervical cancer (LACC) is concurrent chemoradiotherapy (CCRT). However, resistance to radiotherapy and chemotherapy often leads to treatment failure. Intrinsic resistance is often a decisive factor in treatment response. Thus, it is urgent to identify the key genes and pathways associated with CCRT sensitivity in LACC.Materials and Methods：We searched the Gene Expression Omnibus (GEO) database for patients with LACC and analyzed differentially expressed genes (DEGs) between responders and non-responders. Gene Ontology (GO) enrichment analysis of DEGs were performed using DAVID tools. And Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were conducted using R package cluster Profiler. The Weighted Gene Co-Expression Network Analysis (WGCNA) from R package WGCNA was used for the identification of highly correlated gene modules. A protein-protein interaction (PPI) network was constructed using STRING and 20 hub genes were selected. The expression levels of these 20 genes were was analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA 2) databases. Furthermore, single-cell transcriptome sequencing was used to elucidate the cell type composition of the cervix sample and analyze the expression levels of 10 hub genes in cells.Results: Compared with non-responders, 580 genes were significantly up-regulated. The up-regulated genes were mainly related to Human papillomavirus infection, focal adhesion, and ECM-receptor interaction signaling pathway. We screened 10 hub genes (COL1A1, COL6A1, COL6A2, LAMA4, COL6A3, LAMC1, HSPG2, ITGA9, CTGF, PDGFRB) and further studied them. Compared with healthy people, these 10 genes were low expressed in cervical cancer patients, and their mRNA levels were positively correlated. Receiver Operator Characteristic curve (ROC) analysis indicated that 10 hub genes could differentiate responders from non-responders. Furthermore, we showed that COL1A1, COL6A1, COL6A2 were highly expressed after radiotherapy or chemoradiotherapy. By analyzing the single-cell sequence, we found that the main cell types in cervical tissue include Fibroblasts, Smooth muscle cells, Tissue stem cells, Endothelial cells, Progenitor cells, Epithelial cells, T cells, Basal cells, Macrophages, and Mast cells. And COL1A1, COL6A1, COL6A2, COL6A3, CTGF, PDGFRB were highly expressed in Progenitor cells.Conclusions: In summary, COL1A1, COL6A1, COL6A2, LAMA4, COL6A3, LAMC1, HSPG2, ITGA9, CTGF, and PDGFRB might serve as therapeutic targets to enhance the therapeutic effect of CCRT in the treatment of LACC. These genes were involved in Human papillomavirus infection, focal adhesion, and ECM-receptor interaction signaling pathway. And they were involved in various biological processes, including cell adhesion and extracellular matrix organizations. Besides, COL1A1, COL6A1, COL6A2, COL6A3, CTGF, and PDGFRB were highly expressed in Progenitor cells.

scholarly journals Identification of Key Genes and Pathways Associated with Chemotherapy and Radiotherapy Sensitivity in Locally Advanced Cervical Cancer

2020 ◽  
Author(s):  
Zhenhua Zhang ◽  
Yao Zhang ◽  
Yongshun Ma ◽  
Shixing Xiang ◽  
Jing Shen ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Progenitor Cells ◽  
Locally Advanced ◽  
Enrichment Analysis ◽  
R Package ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Locally Advanced Cervical Cancer ◽  
Papillomavirus Infection ◽  
Key Genes

Abstract Objectives: Currently, the standard treatment approach for locally advanced cervical cancer (LACC) is concurrent chemoradiotherapy (CCRT). However, resistance to radiotherapy and chemotherapy often leads to treatment failure. Intrinsic resistance is often a decisive factor in treatment response. Thus, it is urgent to identify the key genes and pathways associated with CCRT sensitivity in LACC.Materials and Methods: We searched the Gene Expression Omnibus (GEO) database for patients with LACC and analyzed differentially expressed genes (DEGs) between responders and non-responders. Gene Ontology (GO) enrichment analysis of DEGs were performed using DAVID tools. And Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were conducted using R package cluster Profiler. The Weighted Gene Co-Expression Network Analysis (WGCNA) from R package WGCNA was used for the identification of highly correlated gene modules. A protein-protein interaction (PPI) network was constructed using STRING and 20 hub genes were selected. The expression levels of these 20 genes were was analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA 2) databases. Furthermore, single-cell transcriptome sequencing was used to elucidate the cell type composition of the cervix sample and analyze the expression levels of 10 hub genes in cells.Results: Compared with non-responders, 580 genes were significantly up-regulated. The up-regulated genes were mainly related to Human papillomavirus infection, focal adhesion, and ECM-receptor interaction signaling pathway. We screened 10 hub genes (COL1A1, COL6A1, COL6A2, LAMA4, COL6A3, LAMC1, HSPG2, ITGA9, CTGF, PDGFRB) and further studied them. Compared with healthy people, these 10 genes were low expressed in cervical cancer patients, and their mRNA levels were positively correlated. Receiver Operator Characteristic curve (ROC) analysis indicated that 10 hub genes could differentiate responders from non-responders. Furthermore, we showed that COL1A1, COL6A1, COL6A2 were highly expressed after radiotherapy or chemoradiotherapy. By analyzing the single-cell sequence, we found that the main cell types in cervical tissue include Fibroblasts, Smooth muscle cells, Tissue stem cells, Endothelial cells, Progenitor cells, Epithelial cells, T cells, Basal cells, Macrophages, and Mast cells. And COL1A1, COL6A1, COL6A2, COL6A3, CTGF, PDGFRB were highly expressed in Progenitor cells.Conclusions: In summary, COL1A1, COL6A1, COL6A2, LAMA4, COL6A3, LAMC1, HSPG2, ITGA9, CTGF, and PDGFRB might serve as therapeutic targets to enhance the therapeutic effect of CCRT in the treatment of LACC. These genes were involved in Human papillomavirus infection, focal adhesion, and ECM-receptor interaction signaling pathway. And they were involved in various biological processes, including cell adhesion and extracellular matrix organizations. Besides, COL1A1, COL6A1, COL6A2, COL6A3, CTGF, and PDGFRB were highly expressed in Progenitor cells.

Systematic Investigation of Quercetin for Treating Cardiovascular Disease Based on Network Pharmacology

2019 ◽  
Vol 22 (6) ◽  
pp. 411-420 ◽  
Author(s):  
Xian-Jun Wu ◽  
Xin-Bin Zhou ◽  
Chen Chen ◽  
Wei Mao
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Pathway Enrichment ◽  
Compound Target

Aim and Objective: Cardiovascular disease is a serious threat to human health because of its high mortality and morbidity rates. At present, there is no effective treatment. In Southeast Asia, traditional Chinese medicine is widely used in the treatment of cardiovascular diseases. Quercetin is a flavonoid extract of Ginkgo biloba leaves. Basic experiments and clinical studies have shown that quercetin has a significant effect on the treatment of cardiovascular diseases. However, its precise mechanism is still unclear. Therefore, it is necessary to exploit the network pharmacological potential effects of quercetin on cardiovascular disease. Materials and Methods: In the present study, a novel network pharmacology strategy based on pharmacokinetic filtering, target fishing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, compound-target-pathway network structured was performed to explore the anti- cardiovascular disease mechanism of quercetin. Results:: The outcomes showed that quercetin possesses favorable pharmacokinetic profiles, which have interactions with 47 cardiovascular disease-related targets and 12 KEGG signaling pathways to provide potential synergistic therapeutic effects. Following the construction of Compound-Target-Pathway (C-T-P) network, and the network topological feature calculation, we obtained top 10 core genes in this network which were AKT1, IL1B, TNF, IL6, JUN, CCL2, FOS, VEGFA, CXCL8, and ICAM1. KEGG pathway enrichment analysis. These indicated that quercetin produced the therapeutic effects against cardiovascular disease by systemically and holistically regulating many signaling pathways, including Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, MAPK signaling pathway, IL-17 signaling pathway and PI3K-Akt signaling pathway.

Exploring the Potential of Capsaicin Against Cancer Metastasis Based on TGF-β Signaling Modulation Through Module-based Network Pharmacology Approach

2020 ◽  
Vol 17 (5) ◽  
pp. 647-660 ◽  
Author(s):  
Shivananda Kandagalla ◽  
Sharath Belenahalli Shekarappa ◽  
Gollapalli Pavan ◽  
Umme Hani ◽  
Manjunatha Hanumanthappa
Keyword(s):  
Amino Acid ◽  
Amino Acid Composition ◽  
Signaling Pathway ◽  
Acid Composition ◽  
Protein Interaction ◽  
Cancer Metastasis ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Mode Analysis ◽  
Network Pharmacology

Background: Capsaicin is an active alkaloid /principal component of red pepper responsible for the pungency of chili pepper. Capsaicin by changing the intracellular redox homeostasis regulate a variety of signaling pathways ultimately producing a divergent cellular outcome. Several reports showed the potential of capsaicin against cancer metastasis, however unexplored molecular mechanism is still an active part of the research. Several growth factors have a critical role during cancer metastasis among them TGF- β signaling play a vital role. Methods: The present study aimed at analyzing capsaicin modulation of TGF-β signaling using network pharmacology approach. The chemical and protein interaction data of capsaicin was curated and abstracted using STITCH4.0, PubChem and ChEMBL database. Further, the compiled data set was subjected to the pathway and functional enrichment analysis using Protein Analysis THrough Evolutionary Relationship (PANTHER) and, Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Meanwhile, the pattern of amino acid composition across the capsaicin targets was analyzed using the EMBOSS Pepstat tool. Capsaicin targets involved in TGF- β were identified and their Protein-Protein Interaction (PPI) network constructed using STRING v10 and Cytoscape (v 3.2.1). From the above-constructed network, the clusters were mined using the MCODE clustering algorithm and finally binding affinity of capsaicin with its targets involved in TGF-β signaling pathway was analyzed using Autodock Vina. Results: The analysis explored capsaicin targets and, their associated functional and pathway annotations. Besides, the analysis also provides a detailed distinct pattern of amino acid composition across the capsaicin targets. The capsaicin targets described as MAPK14, JUN, SMAD3, MAPK3, MAPK1 and MYC involved in TGF-β signaling pathway through pathway enrichment analysis. The binding mode analysis of capsaicin with its targets has shown high affinity with MAPK3, MAPK1, JUN and MYC. Conclusion: The study explores the potential of capsaicin as a potent modulator of TGF-β signaling pathway during cancer metastasis and proposes new methodology and mechanism of action of capsaicin against TGF- β signaling pathway.

scholarly journals Network pharmacology and molecular docking study on the active ingredients of qidengmingmu capsule for the treatment of diabetic retinopathy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mingxu Zhang ◽  
Jiawei Yang ◽  
Xiulan Zhao ◽  
Ying Zhao ◽  
Siquan Zhu
Keyword(s):  
Diabetic Retinopathy ◽  
Molecular Docking ◽  
Molecular Mechanisms ◽  
Hypoxia Inducible Factor ◽  
Enrichment Analysis ◽  
Docking Study ◽  
Network Pharmacology ◽  
Biological Processes ◽  
Active Ingredients ◽  
Molecular Docking Study

AbstractDiabetic retinopathy (DR) is a leading cause of irreversible blindness globally. Qidengmingmu Capsule (QC) is a Chinese patent medicine used to treat DR, but the molecular mechanism of the treatment remains unknown. In this study, we identified and validated potential molecular mechanisms involved in the treatment of DR with QC via network pharmacology and molecular docking methods. The results of Ingredient-DR Target Network showed that 134 common targets and 20 active ingredients of QC were involved. According to the results of enrichment analysis, 2307 biological processes and 40 pathways were related to the treatment effects. Most of these processes and pathways were important for cell survival and were associated with many key factors in DR, such as vascular endothelial growth factor-A (VEGFA), hypoxia-inducible factor-1A (HIF-1Α), and tumor necrosis factor-α (TNFα). Based on the results of the PPI network and KEGG enrichment analyses, we selected AKT1, HIF-1α, VEGFA, TNFα and their corresponding active ingredients for molecular docking. According to the molecular docking results, several key targets of DR (including AKT1, HIF-1α, VEGFA, and TNFα) can form stable bonds with the corresponding active ingredients of QC. In conclusion, through network pharmacology methods, we found that potential biological mechanisms involved in the alleviation of DR by QC are related to multiple biological processes and signaling pathways. The molecular docking results also provide us with sound directions for further experiments.

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

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.

scholarly journals Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

2021 ◽  
Vol 22 (5) ◽  
pp. 2442
Author(s):  
Qun Wang ◽  
Aurelia Vattai ◽  
Theresa Vilsmaier ◽  
Till Kaltofen ◽  
Alexander Steger ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Clinical Data ◽  
Regulatory Network ◽  
Univariate Analysis ◽  
Predictive Biomarkers ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Functional Enrichment ◽  
Wilcoxon Test

Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.

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

BMC Cancer ◽  
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.

scholarly journals miRNA Mediated Noise Making of 3′UTR Mutations in Cancer

Genes ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 545 ◽  
Author(s):  
Wei Wu ◽  
Lingxiang Wu ◽  
Mengyan Zhu ◽  
Ziyu Wang ◽  
Min Wu ◽  
...  
Keyword(s):  
Somatic Mutations ◽  
Target Genes ◽  
Expression Profiles ◽  
Tumor Development ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Messenger Rnas ◽  
Cellular Functions ◽  
Mrna Binding

Somatic mutations in 3′-untranslated regions (3′UTR) do not alter amino acids and are considered to be silent in cancers. We found that such mutations can promote tumor progression by altering microRNA (miRNA) targeting efficiency and consequently affecting miRNA–mRNA interactions. We identified 67,159 somatic mutations located in the 3′UTRs of messenger RNAs (mRNAs) which can alter miRNA–mRNA interactions (functional somatic mutations, funcMutations), and 69.3% of these funcMutations (the degree of energy change > 12 kcal/mol) were identified to significantly promote loss of miRNA-mRNA binding. By integrating mRNA expression profiles of 21 cancer types, we found that the expression of target genes was positively correlated with the loss of absolute affinity level and negatively correlated with the gain of absolute affinity level. Functional enrichment analysis revealed that genes carrying funcMutations were significantly enriched in the MAPK and WNT signaling pathways, and analysis of regulatory modules identified eighteen miRNA modules involved with similar cellular functions. Our findings elucidate a complex relationship between miRNA, mRNA, and mutations, and suggest that 3′UTR mutations may play an important role in tumor development.

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