scholarly journals Bioinformatics Analysis to Identify Key Genes and Pathways Associated with Sex Differences in Rheumatoid Arthritis

2021 ◽  
Author(s):  
Siwei Su ◽  
Wenjun Jiang ◽  
Xiaoying Wang ◽  
Sen Du ◽  
Lu Zhou ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Males And Females ◽  
Key Genes

Abstract ObjectiveThis study aims to explore the key genes and investigated the different signaling pathways of rheumatoid arthritis (RA) between males and females.Data and MethodsThe gene expression data of GSE55457, GSE55584, and GSE12021 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software. Then, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of DEGs were constructed by Cytoscape 3.6.0. ResultsA total of 416 upregulated DEGs and 336 downregulated DEGs were identified in males, and 744 upregulated DEGs and 309 downregulated DEGs were identified in females.IL6, MYC, EGFR, FOS and JUN were considered as hub genes in RA pathogenesis in males, while IL6, ALB, PTPRC, CXCL8 and CCR5 were considered as hub genes in RA pathogenesis in females. ConclusionIdentified DEG may be involved in the different mechanisms of RA disease progression between males and females, and they are treated as prognostic markers or therapeutic targets for males and females. The pathogenesis mechanism of RA is sex-dependent.

scholarly journals Analysis of differential gene expression between right-sided and left-sided colon cancer by bioinformatics analysis

2020 ◽  
Author(s):  
Qiangwei Chi ◽  
Shizuan Chen ◽  
Shaotang Li
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Bioinformatics Analysis ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Abstract Background Colon cancer is a common tumor of the digestive tract worldwide. Recent researches have revealed that colon cancer exhibits distinct differences in clinical and biological characteristics depending on the location of the tumor. However, the underlying genetic and molecular mechanism of the differences between right-sided colon cancer (RCC) and left-sided colon cancer (LCC) are not fully understood. This study aimed to identify molecular potential biomarkers and therapeutic targets for precise treatment of right-sided and left-sided colon cancer using bioinformatics analysis. Methods The gene microarray profile, named GSE44076, from the Gene Expression Omnibus (GEO) public database was downloaded and processed to then select differentially expressed genes (DEGs) on the base of two sample groups of RCC and LCC. Also, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein–protein interaction (PPI) network construction, module analysis, validation of hub genes, and survival analysis. Results Finally, we obtained 2259 DEGs between RCC and LCC, 1300 of which were upregulated in RCC and 945 of which were upregulated in LCC. The results of GO and KEGG analysis of the DEGs indicated that the biological functions of DEGs in RCC and LCC were significantly different. CTLA4, IL10, IL2RB, IFNG, NCAM1, EGFR, MYC, SRC, CUL3, and NCBP2 were identified from the PPI networks as the hub genes of RCC and LCC. Among the hub genes, the log-rank tests for overall survival (OS) and disease free survival (DFS) were applied. Moreover, all hub genes, except CUL3, had differential expression levels of miRNA between tumor group and normal group. Conclusion These hub genes and pathways identified based on bioinformatics analysis might conduce to explain the differences between RCC and LCC, and most of the hub genes were specific to the malignant tissues. Notably, these hub genes, especially the genes associated with immunotherapy such as CTLA4, might be potential specific targets or prognostic markers for precise treatment of colon cancer.

Prognostic Values and Prospective Pathway Signaling of miR-30a in Ovarian Cancer: A Study Based on Gene Expression Omnibus and Bioinformatics Analysis

2020 ◽  
Author(s):  
Weijia Lu ◽  
Yunyu Wu ◽  
CanXiong Lu ◽  
Ting Zhu ◽  
ZhongLu Ren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Target Genes ◽  
Ovarian Tissue ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Public Data

Abstract Objective MicroRNAs (MiRNAs) is considered to play an important role in the occurrence and development of ovarian cancer(OC). Although miRNAs has been widely recognized in ovarian cancer, the role of hsa-miR-30a-5p (miR-30a) in OC has not been fully elucidated. Methods Through the analysis of public data sets in Gene Expression Omnibus (GEO) database and literature review, the significance of miR-30a expression in OC is evaluated. Three mRNA datasets of OC and normal ovarian tissue, GSE14407, GSE18520 and GSE36668, were downloaded from GEO to find the differentially expressed gene (DEG). Then the target genes of hsa-miR-30a-5p were predicted by miRWALK3.0 and TargetScan. Then, the gene overlap between DEG and the predicted target genes of miR-30a in OC was analyzed by Gene Ontology (GO) enrichment analysis. Protein-protein interaction (PPI) network was constructed by STRING and Cytoscape, and the effect of HUB gene on the prognosis of OC was analyzed. Results A common pattern of up-regulation of miR-30a in OC was found. A total of 225 DEG, were identified, both OC-related and miR-30a-related. Many DEG are enriched in the interactions of intracellular matrix tissue, ion binding and biological process regulation. Among the 10 major Hub genes analyzed by PPI, five Hub genes were significantly related to the overall poor survival of OC patients, in which the low expression of ESR1 ,MAPK10, Tp53 and the high expression of YKT ,NSF were related to poor prognosis of OC.

scholarly journals Bioinformatics analysis of common key genes and pathways of intracranial, abdominal, and thoracic aneurysms

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Siwei Bi ◽  
Ruiqi Liu ◽  
Linfeng He ◽  
Jingyi Li ◽  
Jun Gu
Keyword(s):  
Aortic Aneurysm ◽  
Enrichment Analysis ◽  
Cytosolic Calcium ◽  
Gene Expression Omnibus ◽  
Ion Concentration ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Thoracic Aneurysms ◽  
Calcium Ion Concentration

Abstract Background Aneurysm is a severe and fatal disease. This study aims to comprehensively identify the highly conservative co-expression modules and hub genes in the abdominal aortic aneurysm (AAA), thoracic aortic aneurysm (TAA) and intracranial aneurysm (ICA) and facilitate the discovery of pathogenesis for aneurysm. Methods GSE57691, GSE122897, and GSE5180 microarray datasets were downloaded from the Gene Expression Omnibus database. We selected highly conservative modules using weighted gene co‑expression network analysis before performing the Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway and Reactome enrichment analysis. The protein–protein interaction (PPI) network and the miRNA-hub genes network were constructed. Furtherly, we validated the preservation of hub genes in three other datasets. Results Two modules with 193 genes and 159 genes were identified as well preserved in AAA, TAA, and ICA. The enrichment analysis identified that these genes were involved in several biological processes such as positive regulation of cytosolic calcium ion concentration, hemostasis, and regulation of secretion by cells. Ten highly connected PPI networks were constructed, and 55 hub genes were identified. In the miRNA-hub genes network, CCR7 was the most connected gene, followed by TNF and CXCR4. The most connected miRNAs were hsa-mir-26b-5p and hsa-mir-335-5p. The hub gene module was proved to be preserved in all three datasets. Conclusions Our study highlighted and validated two highly conservative co-expression modules and miRNA-hub genes network in three kinds of aneurysms, which may promote understanding of the aneurysm and provide potential therapeutic targets and biomarkers of aneurysm.

scholarly journals Prognostic values and prospective pathway signaling of miR-126 in non-small cell lung cancer: a study based on gene expression omnibus and bioinformatics analysis

2020 ◽  
Author(s):  
Zichen Jiao ◽  
Ao Yu ◽  
Xiaofeng He ◽  
Yulong Xuan ◽  
He Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Hub Genes ◽  
Biological Regulation ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Go Enrichment ◽  
Nsclc Patients

Abstract Objective MiRNAs are considered to be crucial for NSCLC’s initiation and development. MiRNAs have been widely identified in NSCLC. However, the role of miR-126 in NSCLC has not been fully explained.Methods miR-126 Expression in NSCLC was evaluated by analyzing the common data sets in Gene Expression Omnibus(GEO) database and reviewing former thesis papers. Three mRNA datasets, GSE18842, GSE19804 and GSE101929, from GEO to indentify the differentially expressed genes (DEG). We prognosed the target genes of hsa-miR-126-5p using TargetScan and analyzed the gene overlap between the target genes of miR-126 and DEG in NSCLC. Subsequently, we analyzed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We used STRING and Cytoscape to construct a protein-protein interaction (PPI) network, and analyzed the influence of HUB gene on the prognosis of NSCLC.Results A common pattern of mir-126 downregulation in NSCLC was identified in the literature review. A total of 187 DEGs were identified, both NSCLC-related and miR-126-related. Many DEGs are extendedly enriched in cell membranes, signal receptor binding, and biological regulation. Among the 10 main Hub genes analyzed by PPI, 4 HUB genes (NCAP-G,MELK,KIAA0101,TPX2) were obviously related to the poor recuperation of NSCLC patients. When these genes highly expressed, survival rate of NSCLC patients was low. Furthermore, we identified the recessive miR-126-related genes that may be involved in NSCLC, such as TPX2, HMMR, and ANLN through network analysis.Conclusion this study suggests that mir-126 is radical for the biological processing of NSCLC.

Screening and Interaction Analysis of Key Genes in miR-542-3p Over- Expressed Osteosarcoma Cells by Bioinformatics

2020 ◽  
Vol 23 (5) ◽  
pp. 411-418
Author(s):  
Zhongqiu Li ◽  
Peng Zhang ◽  
Feifei Feng ◽  
Qiao Zhang
Keyword(s):  
Gene Expression ◽  
Interaction Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Cancer Genome Atlas ◽  
Key Genes ◽  
Gene Expression Profile Data

Background: Osteosarcoma is one of the most serious primary malignant bone tumors that threaten the lives of children and adolescents. However, the mechanism underlying and how to prevent or treat the disease have not been well understood. Aims & Objective: This aim of the present study was to identify the key genes and explore novel insights into the molecular mechanism of miR-542-3p over-expressed Osteosarcoma. Materials & Methods: Gene expression profile data GDS5367 was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened using GEO2R, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID database. And protein-protein interaction (PPI) network was constructed by the STRING database. In addition, the most highly connected module was screened by plugin MCODE and hub genes by plugin CytoHubba. Furthermore, UALCAN and The Cancer Genome Atlas were performed for survival analysis. Result: In total, 1421 DEGs were identified, including 598 genes were up-regulated and 823 genes were down-regulated. GO analysis showed that DEGs were classified into three groups and DEGs mainly enriched in Steroid biosynthesis, Ubiquitin mediated proteolysis and p53 signaling pathway. Six hub genes (UBA52, RNF114, UBE2H, TRIP12, HNRNPC, and PTBP1) may be key genes with the progression of osteosarcoma. Conclusion: The results could better understand the mechanism of osteosarcoma, which may facilitate a novel insight into treatment targets.

scholarly journals Identification and Characterization of Biomarkers and Their Role in Opioid Addiction by Integrated Bioinformatics Analysis

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiuning Zhang ◽  
Hailei Yu ◽  
Rui Bai ◽  
Chunling Ma
Keyword(s):  
Gene Expression ◽  
Sequence Similarity ◽  
Gene Expression Omnibus ◽  
Opioid Addiction ◽  
Local Alignment ◽  
Calculation Methods ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Difficult Challenge

Although numerous studies have confirmed that the mechanisms of opiate addiction include genetic and epigenetic aspects, the results of such studies are inconsistent. Here, we downloaded gene expression profiling information, GSE87823, from the Gene Expression Omnibus database. Samples from males between ages 19 and 35 were selected for analysis of differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses were used to analyze the pathways associated with the DEGs. We further constructed protein-protein interaction (PPI) networks using the STRING database and used 10 different calculation methods to validate the hub genes. Finally, we utilized the Basic Local Alignment Search Tool (BLAST) to identify the DEG with the highest sequence similarity in mouse and detected the change in expression of the hub genes in this animal model using RT-qPCR. We identified three key genes, ADCY9, PECAM1, and IL4. ADCY9 expression decreased in the nucleus accumbens of opioid-addicted mice compared with control mice, which was consistent with the change seen in humans. The importance and originality of this study are provided by two aspects. Firstly, we used a variety of calculation methods to obtain hub genes; secondly, we exploited homology analysis to solve the difficult challenge that addiction-related experiments cannot be carried out in patients or healthy individuals. In short, this study not only explores potential biomarkers and therapeutic targets of opioid addiction but also provides new ideas for subsequent research on opioid addiction.

scholarly journals Prognostic values and prospective pathway signaling of MicroRNA-182 in ovarian cancer: a study based on gene expression omnibus (GEO) and bioinformatics analysis

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yaowei Li ◽  
Li Li
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated. Methods We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed. Results A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC. Conclusions Our study suggests that miR-182 is essential for the biological progression of OC.

scholarly journals Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

2020 ◽  
Author(s):  
Ming Cao ◽  
Chen Shen ◽  
Jie Zhu ◽  
YuHai Wang
Keyword(s):  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Benign Meningioma ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

scholarly journals Bioinformatics-Based Research on Key Genes and Pathways of Intervertebral Disc Degeneration

Cartilage ◽  
2020 ◽  
pp. 194760352097324
Author(s):  
Qi Yan ◽  
Quan Xiao ◽  
Jun Ge ◽  
Cenhao Wu ◽  
Yingjie Wang ◽  
...  
Keyword(s):  
Intervertebral Disc ◽  
Disc Degeneration ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Network Analyses ◽  
Ppi Networks ◽  
Limma Package ◽  
Key Genes ◽  
New Treatment

Objective To find out the pathways and key genes and to reveal disc degeneration pathogenesis based on bioinformatic analyses. Design The GSE70362 dataset was downloaded from the GEO (Gene Expression Omnibus) database. Differentially expressed genes (DEGs) between the patients having disc degeneration and healthy controls were screened by Limma package in R language. Critical genes were identified by adopting gene ontologies (GOs), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Results We identified 112 DEGs, including 60 genes which were upregulated and 52 that were downregulated. Analyses, such as GO and KEGG demonstrated that the DEGs got enriched in 4 biological processes and 2 signaling pathways, mainly related to disc degeneration. The PPI network analyses identified 5 key proteins, CCND1 (cyclin D1), GATA3, TNFSF11, LEF1, and DKK1 (Dickkopf related protein 1). Conclusion In this study, the DEGs and pathways determined promoted us understand the disc degeneration mechanisms. Also, the study may contribute novel biomarkers for the diagnosis and prevention of disc degeneration, and seek new treatment methods to repair and even regenerate degenerative intervertebral disc.

scholarly journals Identification of novel biomarkers and candidate small molecule drugs in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis of high-throughput data

2020 ◽  
Vol 40 (12) ◽  
Author(s):  
Bin Zuo ◽  
JunFeng Zhu ◽  
Fei Xiao ◽  
ChengLong Wang ◽  
Yun Shen ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Small Molecule ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Chemokine Signaling ◽  
Limma Package ◽  
Chemokine Signaling Pathway

Abstract Background: Rheumatoid arthritis (RA) and osteoarthritis (OA) are two major types of joint diseases. The present study aimed to identify hub genes involved in the pathogenesis and further explore the potential treatment targets of RA and OA. Methods: The gene expression profile of GSE12021 was downloaded from Gene Expression Omnibus (GEO). Total 31 samples (12 RA, 10 OA and 9 NC samples) were used. The differentially expressed genes (DEGs) in RA versus NC, OA versus NC and RA versus OA groups were screened using limma package. We also verified the DEGs in GSE55235 and GSE100786. Functional annotation and protein–protein interaction (PPI) network construction of OA- and RA-specific DEGs were performed. Finally, the candidate small molecules as potential drugs to treat RA and OA were predicted in CMap database. Results: 165 up-regulated and 163 down-regulated DEGs between RA and NC samples, 73 up-regulated and 293 down-regulated DEGs between OA and NC samples, 92 up-regulated and 98 down-regulated DEGs between RA and OA samples were identified. Immune response and TNF signaling pathway were significantly enriched pathways for RA- and OA-specific DEGs, respectively. The hub genes were mainly associated with ‘Primary immunodeficiency’ (RA vs. NC group), ‘Ribosome’ (OA vs. NC group), and ‘Chemokine signaling pathway’ (RA vs. OA group). Arecoline and Cefamandole were the most promising small molecule to reverse the RA and OA gene expression. Conclusion: Our findings suggest new insights into the underlying pathogenesis of RA and OA, which may improve the diagnosis and treatment of these intractable chronic diseases.

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