Identification of Vital Hub Genes and Potential Molecular Pathways of Dermatomyositis by Bioinformatics Analysis.
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ABSTRACT:
SUBMITTER: Ouyang X
PROVIDER: S-EPMC8464434 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Glioblastoma (GBM) is the most common type of malignant brain tumor, and is associated with poor patient prognosis. A comprehensive understanding of the molecular mechanism underlying GBM may help to guide the identification of novel diagnoses and treatment targets. The gene expression profile of the GSE4290 GBM dataset was analyzed in order to identify differentially expressed genes (DEGs). Enriched pathways were identified through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses. A protein-protein interaction network was constructed in order to identify hub genes and for module analysis. Expression and survival analyses were conducted in order to screen and validate critical genes. A total of 1,801 DEGs were recorded, including 620 upregulated and 1,181 downregulated genes. Upregulated DEGs were enriched in the terms 'mitotic cell cycle process', 'mitotic cell cycle' and 'cell cycle process'. Downregulated genes were enriched in 'transsynaptic signaling', 'anterograde transsynaptic signaling' and 'synaptic signaling'. A total of 15 hub genes, which displayed a high degree of connectivity, were selected. These genes included vascular endothelial growth factor A, cyclin-dependent kinase 1 (CDK1), cell-division cycle protein 20 (CDC20), aurora kinase A (AURKA), and budding uninhibited by benzimidazoles 1 (BUB1). The identified DEGs and hub genes may help guide investigations on the mechanisms underlying the development and progression of GBM. CDK1, CDC20, AURKA and BUB1, which are involved in cell cycle pathways, may be potential targets in the diagnosis and therapy of GBM.
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Project description:ObjectiveGastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC.MethodsThe expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan-Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples.ResultsA total of three mRNA expression profiles (GSE13911, GSE79973 and GSE19826) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan-Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted.ConclusionIn conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.
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Project description:Acute-on-chronic liver failure (ACLF) is a critical and refractory disease and a hepatic disorder accompanied by immune dysfunction. Thus, it is essential to explore key immune-related genes of ACLF and investigate its mechanisms. We used two public datasets (GSE142255 and GSE168048) to perform various bioinformatics analyses, including WGCNA, CIBERSORT, and GSEA. We also constructed an ACLF immune-related protein-protein interaction (PPI) network to obtain hub differentially expressed genes (DEGs) and predict corresponding miRNAs. Finally, an ACLF rat model was established to verify the results. A total of 388 DEGs were identified in ACLF, including 162 upregulated and 226 downregulated genes. The enrichment analyses revealed that these DEGs were mainly involved in inflammatory-immune responses and biosynthetic metabolic pathways. Twenty-eight gene modules were obtained using WGCNA and the coral1 and darkseagreen4 modules were highly correlated with M1 macrophage polarization. As a result, 10 hub genes and 2 miRNAs were identified to be significantly altered in ACLF. The bioinformatics analyses of the two datasets presented valuable insights into the pathogenesis and screening of hub genes of ACLF. These results might contribute to a better understanding of the potential molecular mechanisms of ACLF. Finally, further studies are required to validate our current findings.
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Project description:Bladder cancer (BC) is the most common malignant tumor of urinary tract system. The aim of this study was to investigate the genetic signatures of bladder cancer (BC) and identify its potential molecular mechanisms. The gene expression profiles of GSE3167 (50 samples, including 41BC and 9 non-cancerous urothelial cells) was downloaded from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) were performed to identify enriched pathways, and a protein-protein interaction (PPI) network was used to identify hub genes and for module analysis. Moreover, we conducted expression and survival analyses to screen and validate hub genes. In total, 1528 DEGs were identified in bladder cancer (BC), including 1212 up-regulated genes and 316 down-regulated genes. Up-regulated differentially expressed genes (DEGs) were significantly enriched in negative regulation of macromolecule metabolic process, macromolecule catabolic process, proteolysis and regulation of cell death, while the down-regulated differentially expressed genes (DEGs) were mainly involved in cell surface receptor linked signal transduction, ion transport, cell-cell signaling and defense response. The top 10 hub genes with the highest degrees were selected from the PPI network. These genes included HSP90AA1, MYH11, MYL9, CNN1, ACTC1, RAN, ENO1, HNRNPC, ACTG2 and YWHAZ. From sub-networks, we found these genes were involved in the proteasome, pathways in cancer and cell cycle. Hence, the identified DEGs and hub genes may be beneficial to elucidate the mechanisms underlying BC.
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Project description:Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.
| S-EPMC8292096 | biostudies-literature
Project description:BACKGROUND Sepsis-induced myopathy (SIM) is a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study aimed to use bioinformatics analysis to identify hub genes and molecular pathways involved in SIM, to identify potential diagnostic or therapeutic biomarkers. MATERIAL AND METHODS The Gene Expression Omnibus (GEO) database was used to acquire the GSE13205 expression profile. The differentially expressed genes (DEGs) in cases of SIM and healthy controls, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the limma R/Bioconductor software package and clusterProfiler package in R, respectively. The protein-protein interaction (PPI) network data of DEGs was retrieved using the STRING database and analyzed using the Molecular Complex Detection (MCODE) Cytoscape software plugin. RESULTS A total of 196 DEGs were obtained in SIM samples compared with healthy samples, including 93 upregulated genes. The DEGs were significantly upregulated in mineral absorption, and the interleukin-17 (IL-17) signaling pathway and 103 down-regulated genes were associated with control of the bile secretion signaling pathway. A protein-protein interaction (PPI) network was constructed with 106 nodes and 192 edges. The top two important clusters were selected from the PPI by MCODE analysis. There were 16 hub genes with a high degree of connectivity in the PPI network that were selected, including heme oxygenase 1 (HMOX1), nicotinamide adenine dinucleotide phosphate quinone dehydrogenase 1 (NQO1), and metallothionein (MT)-1E. CONCLUSIONS Bioinformatics network analysis identified key hub genes and molecular mechanisms in SIM.
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Project description:Parkinson's disease (PD) is the second most common neurodegenerative disease, with significant socioeconomic burdens. One of the crucial pathological features of PD is the loss of dopaminergic neurons in the substantia nigra (SN). However, the exact pathogenesis remains unknown. Moreover, therapies to prevent neurodegenerative progress are still being explored. We performed bioinformatics analysis to identify candidate genes and molecular pathogenesis in the SN of patients with PD. We analyzed the expression profiles, GSE49036 and GSE7621, which included 31 SN tissues in PD samples and 17 SN tissues in healthy control samples, and identified 86 common differentially expressed genes (DEGs). Then, GO and KEGG pathway analyses of the identified DEGs were performed to understand the biological processes and significant pathways of PD. Subsequently, a protein-protein interaction network was established, with 15 hub genes and four key modules which were screened in this network. The expression profiles, GSE8397 and GSE42966, were used to verify these hub genes. We demonstrated a decrease in the expression levels of 14 hub genes in the SN tissues of PD samples. Our results indicated that, among the 14 hub genes, DRD2, SLC18A2, and SLC6A3 may participate in the pathogenesis of PD by influencing the function of the dopaminergic synapse. CACNA1E, KCNJ6, and KCNB1 may affect the function of the dopaminergic synapse by regulating ion transmembrane transport. Moreover, we identified eight microRNAs (miRNAs) that can regulate the hub genes and 339 transcription factors (TFs) targeting these hub genes and miRNAs. Subsequently, we established an mTF-miRNA-gene-gTF regulatory network. Together, the identification of DEGs, hub genes, miRNAs, and TFs could provide better insights into the pathogenesis of PD and contribute to the diagnosis and therapies.
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Project description:OBJECTIVE: We aim to explore the detailed molecular mechanisms of membrane nephropathy (MN) related genes by bioinformatics analysis. METHODS: Two microarray datasets (GSE108109 and GSE104948) with glomerular gene expression data from 65 MN patients and 9 healthy donors were obtained from the Gene Expression Omnibus (GEO) database. After processing the raw data, DEGs screening was conducted using the LIMMA (linear model for microarray data) package and Gene set enrichment analysis (GSEA) was performed with GSEA software (v. 3.0), followed by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The protein-protein interaction (PPI) network analysis was carried out to determine the hub genes, by applying the maximal clique centrality (MCC) method, which was visualized by Cytoscape. Finally, utilizing the Nephroseq v5 online platform, we analyzed subgroups associated with hub genes. The findings were further validated by immunohistochemistry (IHC) staining in renal tissues from MN or control patients. RESULTS: A sum of 370 DEGs (188 up-regulated genes, 182 down-regulated genes) and 20 hub genes were ascertained. GO and KEGG enrichment analysis demonstrated that DEGs of MN were preponderantly associated with cell damage and complement cascade-related immune responses. Combined with literature data and hub gene-related MN subset analysis, CTSS, ITGB2, and HCK may play important roles in the pathological process of MN. CONCLUSION: This study identified novel hub genes in MN using bioinformatics. We found that some hub genes such as CTSS, ITGB2, and HCK might contribute to MN immunopathological process, providing new insights for further study of the molecular mechanisms underlying glomerular injury of MN.
| S-EPMC9263269 | biostudies-literature
Project description:BackgroundHepatocellular carcinoma (HCC), the main type of liver cancer in human, is one of the most prevalent and deadly malignancies in the world. The present study aimed to identify hub genes and key biological pathways by integrated bioinformatics analysis.MethodsA bioinformatics pipeline based on gene co-expression network (GCN) analysis was built to analyze the gene expression profile of HCC. Firstly, differentially expressed genes (DEGs) were identified and a GCN was constructed with Pearson correlation analysis. Then, the gene modules were identified with 3 different community detection algorithms, and the correlation analysis between gene modules and clinical indicators was performed. Moreover, we used the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct a protein protein interaction (PPI) network of the key gene module, and we identified the hub genes using nine topology analysis algorithms based on this PPI network. Further, we used the Oncomine analysis, survival analysis, GEO data set and random forest algorithm to verify the important roles of hub genes in HCC. Lastly, we explored the methylation changes of hub genes using another GEO data (GSE73003).ResultsFirstly, among the expression profiles, 4,130 up-regulated genes and 471 down-regulated genes were identified. Next, the multi-level algorithm which had the highest modularity divided the GCN into nine gene modules. Also, a key gene module (m1) was identified. The biological processes of GO enrichment of m1 mainly included the processes of mitosis and meiosis and the functions of catalytic and exodeoxyribonuclease activity. Besides, these genes were enriched in the cell cycle and mitotic pathway. Furthermore, we identified 11 hub genes, MCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 which played key roles in HCC. The results of multiple verification methods indicated that the 11 hub genes had highly diagnostic efficiencies to distinguish tumors from normal tissues. Lastly, the methylation changes of gene CDC20, TOP2A, TK1, FEN1 in HCC samples had statistical significance (P-value < 0.05).ConclusionMCM3, TRMT6, AURKA, CDC20, TOP2A, ECT2, TK1, MCM2, FEN1, NCAPD2 and KPNA2 could be potential biomarkers or therapeutic targets for HCC. Meanwhile, the metabolic pathway, the cell cycle and mitotic pathway might played vital roles in the progression of HCC.
| S-EPMC7821758 | biostudies-literature
Project description:BackgroundMyocardial infarction (MI) is a common cause of death worldwide. It is characterized by coronary artery occlusion that causes ischemia and hypoxia of myocardial cells, leading to irreversible myocardial damage.Materials and methodsTo explore potential targets for treatment of MI, we reorganized and analyzed two microarray datasets (GSE4648 and GSE775). The GEO2R tool was used to screen for differentially expressed genes (DEGs) between infarcted and normal myocardium. We used the Database for Annotation, Visualization and Integrated Discovery (DAVID) to perform Gene Ontology functional annotation analysis (GO analysis) and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analysis (KEGG analysis). We examined protein-protein interactions to characterize the relationship between differentially expressed genes, and we screened potential hub genes according to the degree of connection. PCR and Western blotting were used to identify the core genes.ResultsAt different times of infarction, a total of 35 genes showed upregulation at all times; however, none of the genes showed downregulation at all 3 times. Similarly, 10 hub genes with high degrees of connectivity were identified. In vivo and in vitro experiments suggested that expression levels of MMP-9 increased at various times after myocardial infarction and that expression increased in a variety of cells simultaneously.ConclusionExpression levels of MMP-9 increase throughout the course of acute myocardial infarction, and this expression has both positive and negative sides. Further studies are needed to explore the role of MMP-9 in MI treatment. The potential values of Il6, Spp1, Ptgs2, Serpine1, Plaur, Cxcl5, Lgals3, Serpinb2, and Cd14 are also worth exploring.
| S-EPMC7718865 | biostudies-literature