Identification and Validation of Glomerulotubular Crosstalk Genes Mediating IgA Nephropathy by Integrated Bioinformatics

2021 ◽  
Author(s):  
Yawen Bai ◽  
Yajing Li ◽  
Yali Xi ◽  
Chunjie Ma
Keyword(s):  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Diagnostic Significance ◽  
Function Enrichment Analysis ◽  
Gene Modules ◽  
Key Genes ◽  
End Stage ◽  
Microarray Datasets ◽  
Geo Database ◽  
Function Enrichment

Abstract BackgroundIgA nephropathy (IgAN), which has been reported as the most prevalent glomerulonephritis globally, is the major contributor to end-stage renal illness. This bioinformatics study aimed to explore glomeruli-tubulointerstitial crosstalk genes and dysregulated pathways relating to the pathogenesis of IgAN. MethodsThe microarray datasets from the Gene Expression Omnibus (GEO) database were searched. Weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) of both glomeruli and tubulointerstitial were conducted individually. The co-expression gene modules of tubulointerstitial and glomeruli were compared via gene function enrichment analysis. Subsequently, the crosstalk co-expression network was constructed via the STRING database and key genes were mined from the crosstalk network. Results583 DEGs and eight modules were identified in glomeruli samples, while 272 DEGs and four modules were in tubulointerstitial samples. There were 119 overlapping DEGs of the two groups. Among the distinctive modules, four modules in glomeruli and one module in tubulointerstitial were positively associated with IgAN. While four modules in glomeruli and two modules in tubulointerstitial were negatively associated with IgAN. The top ten key genes screened by CytoHubba were ITGAM, ALB, TYROBP, ITGB2, CYBB, HCK, CSF1R, LAPTM5, FN1and CTSS. The above genes were all validated using another two datasets, and all of the key genes demonstrated possible diagnostic significance. Conclusionshe crosstalk genes confirmed in this study may provide novel insight into the pathogenesis of IgAN. Immune-related pathways are associated with both glomerular and tubulointerstitial injuries in IgAN. The glomerulotubular crosstalk might perform a role in the pathogenesis of IgAN.

scholarly journals mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
Yitong Zhang ◽  
Joseph Ta-Chien Tseng ◽  
I-Chia Lien ◽  
Fenglan Li ◽  
Wei Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Lung Adenocarcinoma ◽  
Tumor Necrosis Factor Receptor ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Clinical Stages ◽  
Key Genes ◽  
Geo Database

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

scholarly journals Identification of Potential Key Genes and Pathways in Enzalutamide-Resistant Prostate Cancer Cell Lines: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Long Zheng ◽  
Xiaojie Dou ◽  
Xiaodong Ma ◽  
Wei Qu ◽  
Xiaoshuang Tang
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cell Lines ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Key Genes ◽  
Geo Database

Enzalutamide (ENZ) has been approved for the treatment of advanced prostate cancer (PCa), but some patients develop ENZ resistance initially or after long-term administration. Although a few key genes have been discovered by previous efforts, the complete mechanisms of ENZ resistance remain unsolved. To further identify more potential key genes and pathways in the development of ENZ resistance, we employed the GSE104935 dataset, including 5 ENZ-resistant (ENZ-R) and 5 ENZ-sensitive (ENZ-S) PCa cell lines, from the Gene Expression Omnibus (GEO) database. Integrated bioinformatics analyses were conducted, such as analysis of differentially expressed genes (DEGs), Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) analysis, gene set enrichment analysis (GSEA), and survival analysis. From these, we identified 201 DEGs (93 upregulated and 108 downregulated) and 12 hub genes (AR, ACKR3, GPER1, CCR7, NMU, NDRG1, FKBP5, NKX3-1, GAL, LPAR3, F2RL1, and PTGFR) that are potentially associated with ENZ resistance. One upregulated pathway (hedgehog pathway) and seven downregulated pathways (pathways related to androgen response, p53, estrogen response, TNF-α, TGF-β, complement, and pancreas β cells) were identified as potential key pathways involved in the occurrence of ENZ resistance. Our findings may contribute to further understanding the molecular mechanisms of ENZ resistance and provide some clues for the prevention and treatment of ENZ resistance.

scholarly journals Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042199727
Author(s):  
Xinyu Wang ◽  
Jiaojiao Yang ◽  
Xueren Gao
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Gene Expression Omnibus ◽  
Poor Overall Survival ◽  
Key Genes ◽  
Low Expression

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.

scholarly journals Integrative Analysis of Three Novel Competing Endogenous RNA Biomarkers with a Prognostic Value in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Juan Tan ◽  
Weimin Wang ◽  
Bin Song ◽  
Yingjian Song ◽  
Zili Meng
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Function Enrichment Analysis ◽  
Pathway Function ◽  
Function Enrichment

Increasing evidence has shown competitive endogenous RNAs (ceRNAs) play key roles in numerous cancers. Nevertheless, the ceRNA network that can predict the prognosis of lung adenocarcinoma (LUAD) is still lacking. The aim of the present study was to identify the prognostic value of key ceRNAs in lung tumorigenesis. Differentially expressed (DE) RNAs were identified between LUAD and adjacent normal samples by limma package in R using The Cancer Genome Atlas database (TCGA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway function enrichment analysis was performed using the clusterProfiler package in R. Subsequently, the LUAD ceRNA network was established in three steps based on ceRNA hypothesis. Hub RNAs were identified using degree analysis methods based on Cytoscape plugin cytoHubba. Multivariate Cox regression analysis was implemented to calculate the risk score using the candidate ceRNAs and overall survival information. The survival differences between the high-risk and low-risk ceRNA groups were determined by the Kaplan-Meier and log-rank test using survival and survminer package in R. A total of 2,989 mRNAs, 185 lncRNAs, and 153 miRNAs were identified. GO and KEGG pathway function enrichment analysis showed that DE mRNAs were mainly associated with “sister chromatid segregation,” “regulation of angiogenesis,” “cell adhesion molecules (CAMs),” “cell cycle,” and “ECM-receptor interaction.” LUAD-related ceRNA network was constructed, which comprised of 54 nodes and 78 edges. Top ten hub RNAs (hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-340-5p, hsa-miR-377-3p, hsa-miR-21-5p, hsa-miR-326, SNHG1, RALGPS2, and PITX2) were identified according to their degree. Kaplan-Meier survival analyses demonstrated that hsa-miR-21-5p and RALGPS2 had a significant prognostic value. Finally, we found that a high risk of three novel ceRNA interactions (SNHG1-hsa-miR-21-5p-RALGPS2, SNHG1-hsa-miR-326-RALGPS2, and SNHG1-hsa-miR-377-3p-RALGPS2) was positively associated with worse prognosis. Three novel ceRNAs (SNHG1-hsa-miR-21-5p-RALGPS2, SNHG1-hsa-miR-326-RALGPS2, and SNHG1-hsa-miR-377-3p-RALGPS2) might be potential biomarkers for the prognosis and treatment of LUAD.

scholarly journals Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Mechanisms of Atherosclerotic Cerebral Infarction

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Benzhuo Zhang ◽  
Wei Huang ◽  
Mingquan Yi ◽  
Chunxu Xing
Keyword(s):  
Network Analysis ◽  
Cerebral Infarction ◽  
Antigen Processing ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Neutrophil Activation ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Geo Database

Atherosclerotic cerebral infarction (ACI) seriously threatens the health of the senile patients, and the strategies are urgent for the diagnosis and treatment of ACI. This study investigated the mRNA profiling of the patients with ischemic stroke and atherosclerosis via excavating the datasets in the GEO database and attempted to reveal the biomarkers and molecular mechanism of ACI. In this study, GES16561 and GES100927 were obtained from Gene Expression Omnibus (GEO) database, and the related differentially expressed genes (DEGs) were analyzed with R language. Furthermore, the DEGs were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Besides, the protein-protein interaction (PPI) network of DEGs was analyzed by STRING database and Cytoscape. The results showed that 133 downregulated DEGs and 234 upregulated DEGs were found in GES16561, 25 downregulated DEGs and 104 upregulated DEGs were found in GSE100927, and 6 common genes were found in GES16561 and GES100927. GO enrichment analysis showed that the functional models of the common genes were involved in neutrophil activation, neutrophil degranulation, neutrophil activation, and immune response. KEGG enrichment analysis showed that the DEGs in both GSE100927 and GSE16561 were connected with the pathways including Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Phagosome, Antigen processing and presentation, and Staphylococcus aureus infection. The PPI network analysis showed that 9 common DEGs were found in GSE100927 and GSE16561, and a cluster with 6 nodes and 12 edges was also identified by PPI network analysis. In conclusion, this study suggested that FCGR3A and MAPK pathways were connected with ACI.

scholarly journals Identification of Key Genes and Competitive Endogenous RNA Network Construction in Osteonecrosis of the Femoral Head by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Yang Dinglong ◽  
Chen Shuai ◽  
Chen Yujing ◽  
Wang Beiyang ◽  
Zhang Guohao ◽  
...  
Keyword(s):  
Femoral Head ◽  
Progressive Collapse ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Gene Expression Omnibus ◽  
Vital Role ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Key Genes ◽  
Normal Controls

Abstract Background: Despite cumulative evidence shows osteonecrosis of the femoral head (ONFH) could result in the progressive collapse of the femoral head. The pathogenesis of ONFH remains unclear. Early ONFH is difficult to diagnose due to the lack of effective biomarkers. Method: In Gene Expression Omnibus (GEO) database, we searched the Microarray datasets for serum (GSE123568) in ONFH and normal controls to identify differentially expressed genes (DEGs) by R software. The enrichment analyses were performed to enrich pathways of DEGs. Protein–protein interaction (PPI), miRNA-mRNA co-expression, ceRNA networks were constructed using Cytoscape to identity top15 hub genes, target miRNAs of hub genes and potential regulatory pathways. Furthermore, hub genes validated in GSE74089 with high diagnostic value for ONFH were selected as key genes. The Human Protein Atlas (HPA) and Bgee Database were used to find out the subcellular and tissue distribution of key genes.Results: A total of 568 DEGs were identified between 30 ONFH samples and 10 normal controls. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis showed that DEGs are mostly enriched in innate immune responses, thrombosis and signal transduction. Fifteen hub genes were identified by PPI network using Cytoscape. The 15 hub genes were almost all positively correlated with each other. The expression of PLEK (P＜0.001), TLR2 (P＜0.05), and TREM1 (P＜0.001) were validated in dataset GSE74089 and they had high diagnostic value (AUC＞0.8) for ONFH. MALAT1-miR-146b-5p-TLR2, MALAT1-miR-664b-3p-PLEK, NORAD-miR-106b-5p-TLR2, and MSMO1-miR-106b-5p-TLR2 might be potential RNA regulatory pathways in the disease progression of ONFH. PLEK mainly expressed in nucleus, TREM1 in dictyosome, and TLR2 in nucleoplasm and mitochondria.Conclusions: In this study, we found that PLEK, TLR2, and TREM1 might be potential biomarkers in diagnostic and play a vital role in the progression of ONFH.

scholarly journals Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

2019 ◽  
Author(s):  
ChenChen Yang ◽  
Aifeng Gong
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Mrna Levels ◽  
Pcr Assay ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis.Methods Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1and MMP1 in GC tissues and cell lines, respectively.Results We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1.Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines.Conclusion In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

scholarly journals Identification of Key Genes in Gastric Cancer by Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xinyu Chong ◽  
Rui Peng ◽  
Yan Sun ◽  
Luyu Zhang ◽  
Zheng Zhang
Keyword(s):  
Gastric Cancer ◽  
Topological Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Key Genes ◽  
Secreted Phosphoprotein 1 ◽  
Centrality Analysis

Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.

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.

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