scholarly journals Identification of Key Deregulated RNA-Binding Proteins in Pancreatic Cancer by Meta-Analysis and Prediction of Their Role as Modulators of Oncogenesis

2021 ◽  
Vol 9 ◽  
Author(s):  
Moumita Mukherjee ◽  
Srikanta Goswami
Keyword(s):  
Pancreatic Cancer ◽  
Cell Migration ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment

RNA-binding proteins (RBPs) play a significant role in multiple cellular processes with their deregulations strongly associated with cancer. However, there are not adequate evidences regarding global alteration and functions of RBPs in pancreatic cancer, interrogated in a systematic manner. In this study, we have prepared an exhaustive list of RBPs from multiple sources, downloaded gene expression microarray data from a total of 241 pancreatic tumors and 124 normal pancreatic tissues, performed a meta-analysis, and obtained differentially expressed RBPs (DE-RBPs) using the Limma package of R Bioconductor. The results were validated in microarray datasets and the Cancer Genome Atlas (TCGA) RNA sequencing dataset for pancreatic adenocarcinoma (PAAD). Pathway enrichment analysis was performed using DE-RBPs, and we also constructed the protein–protein interaction (PPI) network to detect key modules and hub-RBPs. Coding and noncoding targets for top altered and hub RBPs were identified, and altered pathways modulated by these targets were also investigated. Our meta-analysis identified 45 upregulated and 15 downregulated RBPs as differentially expressed in pancreatic cancer, and pathway enrichment analysis demonstrated their important contribution in tumor development. As a result of PPI network analysis, 26 hub RBPs were detected and coding and noncoding targets for all these RBPs were categorized. Functional exploration characterized the pathways related to epithelial-to-mesenchymal transition (EMT), cell migration, and metastasis to emerge as major pathways interfered by the targets of these RBPs. Our study identified a unique meta-signature of 26 hub-RBPs to primarily modulate pancreatic tumor cell migration and metastasis in pancreatic cancer. IGF2BP3, ISG20, NIP7, PRDX1, RCC2, RUVBL1, SNRPD1, PAIP2B, and SIDT2 were found to play the most prominent role in the regulation of EMT in the process. The findings not only contribute to understand the biology of RBPs in pancreatic cancer but also to evaluate their candidature as possible therapeutic targets.

scholarly journals Is There a Relationship Between Psoriasis and Hepatitis C?: a Meta-analysis and Bioinformatics Investigation

2021 ◽  
Author(s):  
Yong Liu ◽  
Sheng Nan Cui ◽  
Meng Yao Duan ◽  
Zhi Li Dou ◽  
Yi Zhen Li ◽  
...  
Keyword(s):  
Hepatitis C ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Case Control ◽  
Cochrane Library ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Overlapping Genes ◽  
Cross Sectional ◽  
Pathway Enrichment

Abstract Background: The relationship between psoriasis and hepatitis C was previously controversial, so our purpose is to investigate this connection.Methods: We conducted a systematic review of the case-control, cross-sectional and cohort studies examining the association between psoriasis and hepatitis C in PubMed, EMBASE and Cochrane library databases and investigated the overlapping genes between psoriasis targets and hepatitis C targets using bioinformatics analysis. Based on overlapping genes and hub nodes, we also constructed the protein-protein interaction (PPI) network and module respectively, followed by the pathway enrichment analysis. Results: We included 11 publications that reported a total of 11 studies (8 cross-sectional and 3 case-control). The case–control and cross-sectional studies included 25,047 psoriasis patients and 4,091,631 controls in total. Psoriasis was associated with a significant increase of prevalent hepatitis C (OR 1.72; 95% confidence interval [CI] (1.17-2.52)). A total of 389 significant genes were common to both hepatitis C and psoriasis, which mainly involved IL6, TNF, IL10, ALB, STAT3 and CXCL8. The module and pathway enrichment analyses showed that the common genes had the potential to influence varieties of biological pathways, including the inflammatory response, cytokine activity, cytokine-cytokine receptor interaction, Toll-like receptor signaling pathway, which play an important role in the pathogenesis of hepatitis C and psoriasis.Conclusion: Patients with psoriasis display increased prevalence of hepatitis C and the basic related mechanisms between hepatitis C and psoriasis had been preliminarily clarified.

Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

2020 ◽  
Author(s):  
Liancheng Zhu ◽  
Mingzi Tan ◽  
Haoya Xu ◽  
Bei Lin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Coexpression Analysis ◽  
Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

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 Mechanism of quercetin therapeutic targets for Alzheimer disease and type 2 diabetes mellitus

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Guoxiu Zu ◽  
Keyun Sun ◽  
Ling Li ◽  
Xiuli Zu ◽  
Tao Han ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment

AbstractQuercetin has demonstrated antioxidant, anti-inflammatory, hypoglycemic, and hypolipidemic activities, suggesting therapeutic potential against type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD). In this study, potential molecular targets of quercetin were first identified using the Swiss Target Prediction platform and pathogenic targets of T2DM and AD were identified using online Mendelian inheritance in man (OMIM), DisGeNET, TTD, DrugBank, and GeneCards databases. The 95 targets shared among quercetin, T2DM, and AD were used to establish a protein–protein interaction (PPI) network, top 25 core genes, and protein functional modules using MCODE. Metascape was then used for gene ontology and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. A protein functional module with best score was obtained from the PPI network using CytoHubba, and 6 high-probability quercetin targets (AKT1, JUN, MAPK, TNF, VEGFA, and EGFR) were confirmed by docking simulations. Molecular dynamics simulation was carried out according to the molecular docking results. KEGG pathway enrichment analysis suggested that the major shared mechanisms for T2DM and AD include “AGE-RAGE signaling pathway in diabetic complications,” “pathways in cancer,” and “MAPK signaling pathway” (the key pathway). We speculate that quercetin may have therapeutic applications in T2DM and AD by targeting MAPK signaling, providing a theoretical foundation for future clinical research.

scholarly journals Screening of Osteoarthritis-Related Genes and Function Determination Based on Bioinformatics Tools

2021 ◽  
Author(s):  
Zheng Fu ◽  
Weiqian Jiang ◽  
Wenlong Yan ◽  
Fei Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Experimental Studies ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Early Diagnostic

Abstract Background:Osteoarthritis(OA), commonly seen in the middle-aged and elderly population, imposes a heavy burden on patients from the clinical, humanistic and economic aspects. Our work aims at the discovery of early diagnostic and therapeutic targets for OA and new candidate biomarkers for experimental studies on OA via bioinformatics analysis.Methods:The dataset GSE114007 was downloaded from GEO to identify differentially expressed genes(DEGs) in R using 3 different algorithms. Overlapping DEGs were subject to GO and KEGG pathway enrichment analysis and functional annotation. Following the identification of DEGs, a protein-protein interaction(PPI) network was established and imported into Cytoscape to screen for hubgenes. The expression of each hubgene was verified in two other datasets and create miRNA-mRNA regulatory networks.Results:174 upregulated genes and 117 downregulated genes were identified among the overlapping DEGs. According to the results of GO enrichment analysis,MF enrichment was basically found in ECM degradation and collagen breakdown; enrichment was also present in the development, ossification, and differentiation of cells. The KEGG pathway enrichment analysis suggested significant enrichment in such pathways as PI3K-AKT, P53, TNF, and FoxO. 23 hubgenes were obtained from the PPI network, and 11 genes were identified as DEGs through verification. 8 genes were used for the establishment of miRNA-mRNA regulatory networks.Conclusion:OA-related genes, proteins, pathways and miRNAs that were identified through bioinformatics analysis may provide a reference for the discovery of early diagnostic and therapeutic targets for OA, as well as candidate biomarkers for experimental studies on OA.

scholarly journals Integrative genomics analysis of various omics data and networks identify risk genes and variants vulnerable to childhood-onset asthma

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiuqing Ma ◽  
Peilan Wang ◽  
Guobing Xu ◽  
Fang Yu ◽  
Yunlong Ma
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Integrative Genomics ◽  
Ppi Network ◽  
Childhood Onset ◽  
Pathway Enrichment ◽  
Asthma Risk ◽  
Genome Wide

Abstract Background Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown. Methods In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma. Results Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group. Conclusions We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

scholarly journals Multi‐omics analysis reveals the interaction between the complement system and the coagulation cascade in the development of endometriosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liang Yu ◽  
Huaji Shen ◽  
Xiaohan Ren ◽  
Anqi Wang ◽  
Shu Zhu ◽  
...  
Keyword(s):  
Complement System ◽  
Quantitative Polymerase Chain Reaction ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Coagulation Cascade ◽  
Coagulation System ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment

AbstractEndometriosis (EMS) is a disease that shows immune dysfunction and chronic inflammation characteristics, suggesting a role of complement system in its pathophysiology. To find out the hub genes and pathways involved in the pathogenesis of EMs, three raw microarray datasets were recruited from the Gene Expression Omnibus database (GEO). Then, a series of bioinformatics technologies including gene ontology (GO), Hallmark pathway enrichment, protein–protein interaction (PPI) network and gene co-expression correlation analysis were performed to identify hub genes. The hub genes were further verified by the Real-time quantitative polymerase chain reaction (RT-PCR) and Western Blot (WB). We identified 129 differentially expressed genes (DEGs) in EMs, of which 78 were up-regulated and 51 were down-regulated. Through GO functional enrichment analysis, we found that the DEGs are mainly enriched in cell adhesion, extracellular matrix remodeling, chemokine regulation, angiogenesis regulation, epithelial cell proliferation, et al. In Hallmark pathway enrichment analysis, coagulation pathway showed great significance and the terms in which included the central complement factors. Moreover, the genes were dominating in PPI network. Combined co-expression analysis with experimental verification, we found that the up-regulated expression of complement (C1S, C1QA, C1R, and C3) was positively related to tissue factor (TF) in EMs. In this study, we discovered the over expression complement and the positive correlation between complement and TF in EMs, which suggested that interaction of complement and coagulation system may play a role within the pathophysiology of EMS.

scholarly journals Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Farhadian ◽  
Seyed Abbas Rafat ◽  
Bahman Panahi ◽  
Christopher Mayack
Keyword(s):  
Network Analysis ◽  
Regulatory Networks ◽  
Kegg Pathway ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Phenotypic Traits ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Dependent Protein

AbstractThe exponential growth in knowledge has resulted in a better understanding of the lactation process in a wide variety of animals. However, the underlying genetic mechanisms are not yet clearly known. In order to identify the mechanisms involved in the lactation process, various mehods, including meta-analysis, weighted gene co-express network analysis (WGCNA), hub genes identification, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment at before peak (BP), peak (P), and after peak (AP) stages of the lactation processes have been employed. A total of 104, 85, and 26 differentially expressed genes were identified based on PB vs. P, BP vs. AP, and P vs. AP comparisons, respectively. GO and KEGG pathway enrichment analysis revealed that DEGs were significantly enriched in the “ubiquitin-dependent ERAD” and the “chaperone cofactor-dependent protein refolding” in BP vs. P and P vs. P, respectively. WGCNA identified five significant functional modules related to the lactation process. Moreover, GJA1, AP2A2, and NPAS3 were defined as hub genes in the identified modules, highlighting the importance of their regulatory impacts on the lactation process. The findings of this study provide new insights into the complex regulatory networks of the lactation process at three distinct stages, while suggesting several candidate genes that may be useful for future animal breeding programs. Furthermore, this study supports the notion that in combination with a meta-analysis, the WGCNA represents an opportunity to achieve a higher resolution analysis that can better predict the most important functional genes that might provide a more robust bio-signature for phenotypic traits, thus providing more suitable biomarker candidates for future studies.

scholarly journals Mechanism of Quercetin Therapeutic Targets for Alzheimer Disease and type 2 Diabetes Mellitus

2021 ◽  
Author(s):  
Guoxiu Zu ◽  
Keyun Sun ◽  
Ling Li ◽  
Xiuli Zu ◽  
Tao Han ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment

Abstract Quercetin has demonstrated antioxidant, anti-inflammatory, hypoglycemic, and hypolipidemic activities, suggesting therapeutic potential against type 2 diabetes mellitus (T2DM) and Alzheimer’s disease (AD). In this study, potential molecular targets of quercetin were first identified using the Swiss Target Prediction platform and pathogenic targets of T2DM and AD were identified using Online Mendelian Inheritance in Man (OMIM), DisGeNET, TTD, DrugBank, and GeneCards databases. The 95 targets shared among quercetin, T2DM, and AD were used to establish a protein–protein interaction (PPI) network, top 25 core genes, and protein functional modules using MCODE. Metascape was then used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. A protein functional module with best score was obtained from the PPI network using CytoHubba, and 6 high-probability quercetin targets (AKT1, JUN, MAPK, TNF, VEGFA, and EGFR) were confirmed by docking simulations. KEGG pathway enrichment analysis suggested that the major shared mechanisms for T2DM and AD include “AGE-RAGE signaling pathway in diabetic complications,” “pathways in cancer,” and “MAPK signaling pathway” (the key pathway). We speculate that quercetin may have therapeutic applications in T2DM and AD by targeting MAPK signaling, providing a theoretical foundation for future clinical research.

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