scholarly journals Identification and Analysis of Key Genes Associated with Ulcerative Colitis by Integrated Bioinformatics Methods

2021 ◽  
Author(s):  
Xingyu Yu ◽  
Jinjie Li ◽  
Hongci Chen ◽  
Xingmeng Chen ◽  
Yu Xiang
Keyword(s):  
Ulcerative Colitis ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Chemokine Receptor ◽  
Cytokine Receptor ◽  
Receptor Type ◽  
Receptor Interaction ◽  
Potential Candidate ◽  
Hub Genes

Abstract Background: Ulcerative colitis (UC) is a prevalent inflammatory bowel disease of the colonic mucosa. The exact mechanism of the disease still remains unclear. Here we tried to explore new biomarkers and potential therapeutic targets in UC through adopting integrated bioinformatics tools.Results: By performing DEGs analysis, 59 upregulated and 39 downregulated DEGs were successfully identified from GSE3365, respectively. And they were mainly enriched in the terms of Cytokine-cytokine receptor interaction,Viral protein interaction with cytokine and cytokine receptor,Pantothenate and CoA biosynthesis,IL-17 signaling pathway and Chemokine signaling pathway. Based on the data of protein–protein interaction (PPI), the top 10 hub genes were ranked, including Growth-regulated alpha protein (CXCL1), C-C motif chemokine 2 (CCL2), C-X-C chemokine receptor type 1 (CXCR1), Low affinity immunoglobulin gamma Fc region receptor III-B (FCGR3B), C-X-C chemokine receptor type 2 (CXCR2), Prostaglandin G/H synthase 2 (PTGS2), Triggering receptor expressed on myeloid cells 1 (TREM1), Interleukin-1 receptor type 1 (IL1R1), fMet-Leu-Phe receptor (FPR1), and Band 3 anion transport protein (SLC4A1).What’s more, the results of correlation analysis demonstrated that there was a positive correlation between the 10 hub DEGs.Conclusion: Ten DEGs were identified as potential candidate diagnostic biomarkers for patients with UC in present study. However, further experiments are needed to confirm the functional pathways and hub genes associated with UC.

scholarly journals Identification of hub genes and pathways in psoriasis through bioinformatics and validation by RT-qPCR

2021 ◽  
Author(s):  
Ling Ai Zou ◽  
Qichao Jian
Keyword(s):  
Signaling Pathway ◽  
Quantitative Polymerase Chain Reaction ◽  
Cytokine Receptor ◽  
Receptor Interaction ◽  
Hacat Cells ◽  
Receptor Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Analyses ◽  
Receptor Signaling Pathway

Abstract Background Although several studies have attempted to investigate the aetiology and mechanism of psoriasis, the precise molecular mechanism remains unclear. Our study aimed to identify the hub genes and associated pathways that promote its pathogenesis in psoriasis, which would be helpful for the discovery of diagnostic and therapeutic markers. Methods GSE30999, GSE34248, GSE41662, and GSE50790 datasets were extracted from the Gene Expression Omnibus (GEO) database. The GEO profiles were integrated to obtain differentially expressed genes (DEGs) using the affy package in R software, with |logFC|> 1.5 and adjusted P < 0.05. The DEGs were utilised for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network analyses. Hub genes were identified using Cytoscape and enriched for analysis in www.bioinformatics.com.cn. These hub genes were validated in the four aforementioned datasets and M5-induced HaCaT cells using real-time quantitative polymerase chain reaction (RT-qPCR). Results A total of 359 DEGs were identified, which were mostly associated with responses to bacterium, defence responses to other organism, and antimicrobial humoral response. These DEGs were mostly enriched in the steroid hormone biosynthesis pathway, NOD-like receptor signaling pathway, and cytokine-cytokine receptor interaction. PPI network analysis indicated seven genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) as the probable hub genes of psoriasis; CXCL10 had a positive correlation with the other six hub genes. The chord plot results further supported the GO and KEGG analysis results of the 359 DEGs. Seven predicted hub genes were validated to be upregulated in four datasets and M5-induced HaCaT cells using RT-qPCR. Conclusions The pathogenesis of psoriasis may be associated with seven hub genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) and pathways, such as the NOD-like receptor signaling pathway and cytokine-cytokine receptor interaction. These hub genes, especially CXCL10, can be used as potential biomarkers in psoriasis.

scholarly journals Multi-Omics Integration and Network Analysis Reveal Potential Hub Genes and Genetic Mechanisms Regulating Bovine Mastitis

2022 ◽  
Vol 44 (1) ◽  
pp. 309-328
Author(s):  
Masoumeh Naserkheil ◽  
Farzad Ghafouri ◽  
Sonia Zakizadeh ◽  
Nasrollah Pirany ◽  
Zeinab Manzari ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Signaling Pathway ◽  
Protein Interaction ◽  
Association Studies ◽  
Bovine Mastitis ◽  
Receptor Interaction ◽  
Literature Mining ◽  
Genome Wide Association Studies ◽  
Hub Genes ◽  
Starting Point

Mastitis, inflammation of the mammary gland, is the most prevalent disease in dairy cattle that has a potential impact on profitability and animal welfare. Specifically designed multi-omics studies can be used to prioritize candidate genes and identify biomarkers and the molecular mechanisms underlying mastitis in dairy cattle. Hence, the present study aimed to explore the genetic basis of bovine mastitis by integrating microarray and RNA-Seq data containing healthy and mastitic samples in comparative transcriptome analysis with the results of published genome-wide association studies (GWAS) using a literature mining approach. The integration of different information sources resulted in the identification of 33 common and relevant genes associated with bovine mastitis. Among these, seven genes—CXCR1, HCK, IL1RN, MMP9, S100A9, GRO1, and SOCS3—were identified as the hub genes (highly connected genes) for mastitis susceptibility and resistance, and were subjected to protein-protein interaction (PPI) network and gene regulatory network construction. Gene ontology annotation and enrichment analysis revealed 23, 7, and 4 GO terms related to mastitis in the biological process, molecular function, and cellular component categories, respectively. Moreover, the main metabolic-signalling pathways responsible for the regulation of immune or inflammatory responses were significantly enriched in cytokine–cytokine-receptor interaction, the IL-17 signaling pathway, viral protein interaction with cytokines and cytokine receptors, and the chemokine signaling pathway. Consequently, the identification of these genes, pathways, and their respective functions could contribute to a better understanding of the genetics and mechanisms regulating mastitis and can be considered a starting point for future studies on bovine mastitis.

scholarly journals Identification of upregulated NF-κB inhibitor alpha and IRAK3 targeting lncRNA following intracranial aneurysm rupture-induced subarachnoid hemorrhage

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wei Leng ◽  
Dan Fan ◽  
Zhong Ren ◽  
Qiaoying Li
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Intracranial Aneurysm ◽  
Signaling Pathway ◽  
Aneurysm Rupture ◽  
Cytokine Receptor ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Regulatory Pathways ◽  
Ruptured Intracranial Aneurysm ◽  
Neurotrophin Signaling

Abstract Background This study was performed to identify genes and lncRNAs involved in the pathogenesis of subarachnoid hemorrhage (SAH) from ruptured intracranial aneurysm (RIA). Methods Microarray GSE36791 was downloaded from Gene Expression Omnibus (GEO) database followed by the identification of significantly different expressed RNAs (DERs, including lncRNA and mRNA) between patients with SAH and healthy individuals. Then, the functional analyses of DEmRNAs were conducted and weighted gene co-expression network analysis (WGCNA) was also performed to extract the modules associated with SAH. Following, the lncRNA-mRNA co-expression network was constructed and the gene set enrichment analysis (GSEA) was performed to screen key RNA biomarkers involved in the pathogenesis of SAH from RIA. We also verified the results in a bigger dataset GSE7337. Results Totally, 561 DERs, including 25 DElncRNAs and 536 DEmRNAs, were identified. Functional analysis revealed that the DEmRNAs were mainly associated with immune response-associated GO-BP terms and KEGG pathways. Moreover, there were 6 modules significantly positive-correlated with SAH. The lncRNA-mRNA co-expression network contained 2 lncRNAs (LINC00265 and LINC00937) and 169 mRNAs. The GSEA analysis showed that these two lncRNAs were associated with three pathways (cytokine-cytokine receptor interaction, neurotrophin signaling pathway, and apoptosis). Additionally, IRAK3 and NFKBIA involved in the neurotrophin signaling pathway and apoptosis while IL1R2, IL18RAP and IL18R1 was associated with cytokine-cytokine receptor interaction pathway. The expression levels of these genes have the same trend in GSE36791 and GSE7337. Conclusion LINC00265 and LINC00937 may be implicated with the pathogenesis of SAH from RIA. They were involved in three important regulatory pathways. 5 mRNAs played important roles in the three pathways.

scholarly journals Investigation of Potential Genetic Biomarkers and Molecular Mechanism of Ulcerative Colitis Utilizing Bioinformatics Analysis

2019 ◽  
Author(s):  
Jiaqi Zhang ◽  
Xue Wang ◽  
Lin Xu ◽  
Zedan Zhang ◽  
Fengyun Wang ◽  
...  
Keyword(s):  
Ulcerative Colitis ◽  
Gene Therapy ◽  
Inflammatory Response ◽  
Signaling Pathway ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Amyloid A ◽  
Chemokine Signaling ◽  
Chemokine Signaling Pathway ◽  
Potential Biomarkers

Abstract Objectives: To reveal the molecular mechanisms of ulcerative colitis (UC) and provide potential biomarkers for UC gene therapy. Methods: We downloaded the GSE87473 microarray dataset from the Gene Expression Omnibus (GEO) and identified the differentially expressed genes (DEGs) between UC samples and normal samples. Then ,a module partition analysis was performed based on a weighted gene co-expression network analysis (WGCNA),followed by pathway and functional enrichment analyses. Furthermore, we investigated the hub genes . At last, data validation was performed to ensure the reliability of the hub genes. Results: Between UC group and normal group, 988 DEGs were investigated . The DEGs were clustered into 5 modules using WGCNA. These DEGs were mainly enriched in functions such as the immune response, the inflammatory response and chemotaxis, and they were mainly enriched in KEGG pathways such as the cytokine-cytokine receptor interaction , chemokine signaling pathway, and complement and coagulation cascades. The hub genes, including dual oxidase maturation factor 2(DUOXA2), serum amyloid A (SAA) 1 and SAA2, TNFAIP3-interacting protein 3(TNIP3), C-X-C motif chemokine (CXCL1), solute carrier family 6 member 14(SLC6A14) and complement decay-accelerating factor (CD antigen CD55),were revealed as potential tissue biomarkers for UC diagnosis or treatment. Conclusions: This study provides supportive evidence that DUOXA2, A-SAA, TNIP3, CXCL1, SLC6A14 and CD55 might be used as potential biomarkers for tissue biopsy of UC, especially SLC6A14 and CD55, which may be new targets for UC gene therapy. Moreover, the DUOX2/DUOXA2, NF-κB /TNIP3 and CXCL1/CXCR2 pathways might play an important role in the progression of UC through the chemokine signaling pathway and inflammatory response.

scholarly journals Anticancer Effects of Emodin on HepG2 Cell: Evidence from Bioinformatic Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Rui-sheng Zhou ◽  
Xiong-Wen Wang ◽  
Qin-feng Sun ◽  
Zeng Jie Ye ◽  
Jian-wei Liu ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Hepg2 Cells ◽  
Molecular Mechanisms ◽  
Mapk Signaling ◽  
Ion Concentration ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Mrna Levels ◽  
Hub Genes ◽  
Positive Regulation

Hepatocellular carcinoma (HCC) is a primary cause of cancer-related death in the world. Despite the fact that there are many methods to treat HCC, the 5-year survival rate of HCC is still at a low level. Emodin can inhibit the growth of HCC cells invitroand invivo. However, the gene regulation of emodin in HCC has not been well studied. In our research, RNA sequencing technology was used to identify the differentially expressed genes (DEGs) in HepG2 cells induced by emodin. A total of 859 DEGs were identified, including 712 downregulated genes and 147 upregulated genes in HepG2 cells treated with emodin. We used DAVID for function and pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed using STRING, and Cytoscape was used for module analysis. The enriched functions and pathways of the DEGs include positive regulation of apoptotic process, structural molecule activity and lipopolysaccharide binding, protein digestion and absorption, ECM-receptor interaction, complement and coagulation cascades, and MAPK signaling pathway. 25 hub genes were identified and pathway analysis revealed that these genes were mainly enriched in neuropeptide signaling pathway, inflammatory response, and positive regulation of cytosolic calcium ion concentration. Survival analysis showed that LPAR6, C5, SSTR5, GPR68, and P2RY4 may be involved in the molecular mechanisms of emodin therapy for HCC. A quantitative real-time PCR (qRT-PCR) assay showed that the mRNA levels of LPAR6, C5, SSTR5, GPR68, and P2RY4 were significantly decreased in HepG2 cells treated with emodin. In conclusion, the identified DEGs and hub genes in the present study provide new clues for further researches on the molecular mechanisms of emodin.

scholarly journals Integrated Bioinformatics Analysis of Master Regulators in Anaplastic Thyroid Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Zongfu Pan ◽  
Lu Li ◽  
Qilu Fang ◽  
Yangyang Qian ◽  
Yiwen Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Thyroid Carcinoma ◽  
Signaling Pathway ◽  
Regulatory Network ◽  
Anaplastic Thyroid Carcinoma ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Master Regulators

Anaplastic thyroid carcinoma (ATC) is one of the most aggressive and rapidly lethal tumors. However, limited advances have been made to prolong the survival and to reduce the mortality over the last decades. Therefore, identifying the master regulators underlying ATC progression is desperately needed. In our present study, three datasets including GSE33630, GSE29265, and GSE65144 were retrieved from Gene Expression Omnibus with a total of 32 ATC samples and 78 normal thyroid tissues. A total of 1804 consistently changed differentially expressed genes (DEGs) were identified from three datasets. KEGG pathways enrichment suggested that upregulated DEGs were mainly enriched in ECM-receptor interaction, cell cycle, PI3K-Akt signaling pathway, focal adhesion, and p53 signaling pathway. Furthermore, key gene modules in PPI network were identified by Cytoscape plugin MCODE and they were mainly associated with DNA replication, cell cycle process, collagen fibril organization, and regulation of leukocyte migration. Additionally, TOP2A, CDK1, CCNB1, VEGFA, BIRC5, MAPK1, CCNA2, MAD2L1, CDC20, and BUB1 were identified as hub genes of the PPI network. Interestingly, module analysis showed that 8 out of 10 hub genes participated in Module 1 network and more than 70% genes of Module 2 consisted of collagen family members. Notably, transcription factors (TFs) regulatory network analysis indicated that E2F7, FOXM1, and NFYB were master regulators of Module 1, while CREB3L1 was the master regulator of Module 2. Experimental validation showed that CREB3L1, E2F7, and FOXM1 were significantly upregulated in ATC tissue and cell line when compared with normal thyroid group. In conclusion, the TFs regulatory network provided a more detail molecular mechanism underlying ATC occurrence and progression. TFs including E2F7, FOXM1, CREB3L1, and NFYB were likely to be master regulators of ATC progression, suggesting their potential role as molecular therapeutic targets in ATC treatment.

scholarly journals Biodata Mining of Differentially Expressed Genes between Acute Myocardial Infarction and Unstable Angina Based on Integrated Bioinformatics

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Siyu Guo ◽  
Zhihong Huang ◽  
Xinkui Liu ◽  
Jingyuan Zhang ◽  
Peizhi Ye ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Unstable Angina ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Trp Channels ◽  
Cytokine Receptor ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Coronary Syndrome

Acute coronary syndrome (ACS) is a complex syndrome of clinical symptoms. In order to accurately diagnose the type of disease in ACS patients, this study is aimed at exploring the differentially expressed genes (DEGs) and biological pathways between acute myocardial infarction (AMI) and unstable angina (UA). The GSE29111 and GSE60993 datasets containing microarray data from AMI and UA patients were downloaded from the Gene Expression Omnibus (GEO) database. DEG analysis of these 2 datasets is performed using the “limma” package in R software. DEGs were also analyzed using protein-protein interaction (PPI), Molecular Complex Detection (MCODE) algorithm, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Correlation analysis and “cytoHubba” were used to analyze the hub genes. A total of 286 DEGs were obtained from GSE29111 and GSE60993, including 132 upregulated genes and 154 downregulated genes. Subsequent comprehensive analysis identified 20 key genes that may be related to the occurrence and development of AMI and UA and were involved in the inflammatory response, interaction of neuroactive ligand-receptor, calcium signaling pathway, inflammatory mediator regulation of TRP channels, viral protein interaction with cytokine and cytokine receptor, human cytomegalovirus infection, and cytokine-cytokine receptor interaction pathway. The integrated bioinformatical analysis could improve our understanding of DEGs between AMI and UA. The results of this study might provide a new perspective and reference for the early diagnosis and treatment of ACS.

scholarly journals Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases

2021 ◽  
Author(s):  
Qiangqiang Zheng ◽  
Shihui Min ◽  
Qinghua Zhou
Keyword(s):  
Signaling Pathway ◽  
Clinical Features ◽  
Enrichment Analysis ◽  
Expression Level ◽  
Normal Lung ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Viral Carcinogenesis ◽  
Relationship Of

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The current study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan-Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5,930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM-receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression level of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression level of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD, and may be potential therapeutic targets for LUAD.

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