Screening for Key Pathways Associated with the Development of Osteoporosis by Bioinformatics Analysis.
Ontology highlight
ABSTRACT:
SUBMITTER: Liu Y
PROVIDER: S-EPMC5390640 | biostudies-literature | 2017
REPOSITORIES: biostudies-literature
Publications
Screening for Key Pathways Associated with the Development of Osteoporosis by Bioinformatics Analysis.
Liu Yanqing Y Wang Yueqiu Y Zhang Yanxia Y Liu Zhiyong Z Xiang Hongfei H Peng Xianbo X Chen Bohua B Jia Guyou G
BioMed research international 20170330
<i>Objectives.</i> We aimed to find the key pathways associated with the development of osteoporosis. <i>Methods.</i> We downloaded expression profile data of GSE35959 and analyzed the differentially expressed genes (DEGs) in 3 comparison groups (old_op versus middle, old_op versus old, and old_op versus senescent). KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses were carried out. Besides, Venn diagram analysis and gene functional interaction (FI) network analysis were ...[more]
Similar Datasets
Project description:The aim of the present study was to identify potential key genes associated with the progression and prognosis of colorectal cancer (CRC). Differentially expressed genes (DEGs) between CRC and normal samples were screened by integrated analysis of gene expression profile datasets, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to identify the biological role of DEGs. In addition, a protein‑protein interaction network and survival analysis were used to identify the key genes. The profiles of GSE9348, GSE22598 and GSE113513 were downloaded from the GEO database. A total of 405 common DEGs were identified, including 236 down‑ and 169 upregulated. GO analysis revealed that the downregulated DEGs were mainly enriched in 'detoxification of copper ion' [biological process, (BP)], 'oxidoreductase activity, acting on CH‑OH group of donors, NAD or NADP as acceptor' [molecular function, (MF)] and 'brush border' [cellular component, (CC)]. Upregulated DEGs were mainly involved in 'nuclear division' (BP), 'snoRNA binding' (MF) and 'nucleolar part' (CC). KEGG pathway analysis revealed that DEGs were mainly involved in 'mineral absorption', 'nitrogen metabolism', 'cell cycle' and 'caffeine metabolism'. A PPI network was constructed with 268 nodes and 1,027 edges. The top one module was selected, and it was revealed that module‑related genes were mainly enriched in the GO terms 'sister chromatid segregation' (BP), 'chemokine activity' (MF), and 'condensed chromosome (CC)'. The KEGG pathway was mainly enriched in 'cell cycle', 'progesterone‑mediated oocyte maturation', 'chemokine signaling pathway', 'IL‑17 signaling pathway', 'legionellosis', and 'rheumatoid arthritis'. DNA topoisomerase II‑α (TOP2A), mitotic arrest deficient 2 like 1 (MAD2L1), cyclin B1 (CCNB1), checkpoint kinase 1 (CHEK1), cell division cycle 6 (CDC6) and ubiquitin conjugating enzyme E2 C (UBE2C) were indicated as hub genes. Furthermore, survival analysis revealed that TOP2A, MAD2L1, CDC6 and CHEK1 may serve as prognostic biomarkers in CRC. The present study provided insights into the molecular mechanism of CRC that may be useful in further investigations.
| S-EPMC6625394 | biostudies-literature
Project description:The purpose of this study was to identify the hub genes and biological pathways of nasopharyngeal carcinoma (NPC) through bioinformatics analysis and potential new therapeutic targets. In this study, three datasets were downloaded from the Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) between NPC and normal tissues were analyzed using the GEO2R online tool. Volcano and heat maps of the DEGs were visualized using the hiplot database. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the upregulated and downregulated DEGs were performed using the DAVID database. Finally, we established a protein-protein interaction (PPI) network using the STRING database and showed the differential expression of hub genes between the normal and tumor tissues. In all, 109,371,221 upregulated DEGs and 139,226,520 downregulated DEGs were obtained in datasets GSE40290, GSE61218, and GSE53819, respectively, and 18 common differential genes, named co-DEGs, were screened in the three datasets. The most abundant biological GO terms of the co-DEGs were inflammatory response et al. The KEGG pathway enrichment analysis showed that co-DEGs mainly participated in the interleukin (IL)-17 signaling pathway et al. Finally, we identified four hub genes using PPI analysis and observed that three of them were highly expressed in tumor tissues. In this study, the hub genes of NPC, such as PTGS2, and pathways such as IL-17 signaling, were identified through bioinformatics analysis, which may be potential new therapeutic targets for NPC.
| S-EPMC9779079 | biostudies-literature
Project description:Osteoporosis (OP) is a systemic bone disease caused by various factors, including, the decrease of bone density and quality, the destruction of bone microstructure, and the increase of bone fragility. It is a disease with a high incidence in a large proportion of the world's elderly population. However, osteoporosis lacks obvious symptoms and sensitive biomarkers. Therefore, it is extremely urgent to discover and identify disease-related biomarkers for early clinical diagnosis and effective intervention for osteoporosis. In our study, the Linear Models for Microarray Data (LIMMA) tool was used to screen differential expressed genes from transcriptome sequencing data of OP blood samples downloaded from the GEO database, and cluster Profiler was used for enriching analysis of differently expressed genes. In order to analyzed the relevance of gene modules, clinical symptoms, and the most related module setting genes associated with disease progression, we adapted Weighted Gene Co-expression Network Analysis (WGCNA) to screen and analyze the related pathways and relevant molecules. We used the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database to construct protein interaction network of key modules, and Cytoscape software was used to complete network visualization and screen of core genes in the network. Various plug-in algorithms of cytoHubba were used to identify key genes of OP. Finally, correlation analysis and single-gene gene probe concentration analysis (GSEA) analysis were performed for each core gene. Results of a total of 8 key genes that were closely related to the occurrence and development of OP were screened out, which provided a brand-new idea for the clinical diagnosis and early prevention of OP. Quantitative real-time PCR (qRT-PCR) was performed for validation, the expression levels of CUL1, PTEN and STAT1 genes in the OS group were significantly higher than in the non-OS groups. Receiver operating characteristic analysis demonstrated that CUL1, PTEN and STAT1 displayed considerable diagnostic accuracy for OS.
| S-EPMC9999471 | biostudies-literature
Project description:BackgroundThis study aimed to identify biological markers for diabetic nephropathy (DN) and explore their underlying mechanisms.MethodsFour datasets, GSE30528, GSE47183, GSE104948, and GSE96804, were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the "limma" package, and the "RobustRankAggreg" package was used to screen the overlapping DEGs. The hub genes were identified using cytoHubba of Cytoscape. Logistic regression analysis was used to further analyse the hub genes, followed by receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes. Correlation analysis and enrichment analysis of the hub genes were performed to identify the potential functions of the hub genes involved in DN.ResultsIn total, 55 DEGs, including 38 upregulated and 17 downregulated genes, were identified from the three datasets. Four hub genes (FN1, CD44, C1QB, and C1QA) were screened out by the "UpSetR" package, and FN1 was identified as a key gene for DN by logistic regression analysis. Correlation analysis and enrichment analysis showed that FN1 was positively correlated with four genes (COL6A3, COL1A2, THBS2, and CD44) and with the development of DN through the extracellular matrix (ECM)-receptor interaction pathway.ConclusionsWe identified four candidate genes: FN1, C1QA, C1QB, and CD44. On further investigating the biological functions of FN1, we showed that FN1 was positively correlated with THBS2, COL1A2, COL6A3, and CD44 and involved in the development of DN through the ECM-receptor interaction pathway. THBS2, COL1A2, COL6A3, and CD44 may be novel biomarkers and target therapeutic candidates for DN.
| S-EPMC9340545 | biostudies-literature
Project description:The aim of the present study was to identify the key genes associated with osteosarcoma (OS) using a bioinformatics approach. Microarray data (GSE36004) was downloaded from the Gene Expression Omnibus database, including 19 OS cell lines and 6 normal controls. Differentially expressed genes (DEGs) in the OS cell lines were identified using the Limma package, and differentially methylated regions were screened with methyAnalysis in R. Copy number analysis was performed and genes with copy number gains/losses were further screened using DNAcopy and cghMCR packages. Functional enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery online tool, and protein-protein interactions were identified based on information obtained from the Search Tool for the Retrieval of Interacting Genes database. A total of 47 downregulated genes were screened in hyper-methylated regions, including the fragment crystallizable (Fc) region of immunoglobulin E, high affinity I, receptor for; γ polypeptide (FCER1G), leptin (LEP) and feline Gardner-Rasheed sarcoma viral oncogene homolog (FGR). In addition, a total of 17 upregulated genes, including the TPase family, AAA domain containing 2 (ATAD2) and cyclin-dependent kinase 4 (CDK4), exhibited copy number gains, while 5 downregulated genes, including Rho GTPase activating protein 9 (ARHGAP9) and major histocompatibility complex, class II, DO α (HLA-DOA), exhibited copy number losses. These results indicate that hyper-methylation of FCER1G, LEP, and FGR may serve a crucial function in the development of OS. In addition, copy number alterations of these DEGs, including ATAD2, CDK4, ARHGAP9 and HLA-DOA, may also contribute to OS progression. These DEGs may be candidate targets for the diagnosis and treatment of this disease.
| S-EPMC5588164 | biostudies-literature
Project description:BACKGROUND RUNXl plays a key regulatory role in the process of hematopoiesis and is a common target for multiple chromosomal translocations in human acute leukemia. Mutations of RUNX1 gene can lead to acute leukemia and affect the prognosis of AML patients. We aimed to identify pivotal genes and pathways involved in RUNX1-mutated patients of with acute myeloid leukemia (AML) and to explore possible molecular markers for novel therapeutic targets of the disease. MATERIAL AND METHODS The RNA sequencing datasets of 151 cases of AML were obtained from the Cancer Genome Atlas database. Differentially expressed genes (DEGs) were identified using edgeR of the R platform. PPI (protein-protein interaction) network clustering modules were analyzed with ClusterONE, and the KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses for modules were performed. RESULTS A total of 379 genes were identified as DEGs. The KEGG enrichment analysis of DEGs showed significantly enriched pathways in cancer, extracellular matrix (ECM)-receptor interaction pathway, and cyclic adenosine monophosphate (cAMP) signaling pathway. The top 10 genes ranked by degree were PRKACG, ANKRD7, RNFL7, ROPN11, TEX14, PRMT8, OTOA, CFAP99, NRXN1, and DMRT1, which were identified as hub genes from the protein-protein interaction network (PPI). Statistical analysis revealed that RUNX1-mutated patients with AML had a shorter median survival time (MST) with poor clinical outcome and an increased risk of death when compared with those without RUNX1 mutations. CONCLUSIONS DEGs and pathways identified in the present study will help understand the molecular mechanisms underlying RUNX1 mutations in AML and develop effective therapeutic strategies for RUNX1-mutation AML.
| S-EPMC6186152 | biostudies-other
Project description:BACKGROUND Essential thrombocythemia (ET) is a form of chronic myeloproliferative neoplasm (MPN), and thrombosis is an important complication. This study aimed to use bioinformatics analysis to identify differentially expressed genes (DEGs) in ET associated thrombosis. MATERIAL AND METHODS Two datasets were identified from the Gene Expression Omnibus (GEO) database to investigate the expression profiles in ET. The GSE103176 dataset included 24 patients with ET and 15 healthy individuals with samples from CD34+ bone marrow cells. The GSE54644 dataset included 47 patients with ET and 11 healthy individuals with samples from peripheral neutrophils. GEO2R was used to screen DEGs, followed by over-representation analysis. Protein-protein interaction (PPI) network analysis and module analysis were performed using the STRING database and Cytoscape software. Hub genes were identified using the cytoHubba Cytoscape plugin, and maximal clique centrality (MCC) was identified. The MCODE Cytoscape plugin was used to identify network clusters, or highly interconnected regions. RESULTS There were 586 and 392 DEGs identified from the GSE103176 and GSE54644 datasets, respectively. The upregulated DEGs for CD34+ cells were predominantly enriched for granulocyte activation or related pathways for biological process (BP), and secretory vesicle for the cellular component (CC). The top hub genes within CD34+ cells included CXCL1, CAMP, HP, MMP8, PTX3, ORM1, LYZ, LTF, PGLYRP1, and OLFM4. CONCLUSIONS Bioinformatics analysis identified DEGs and hub genes that interacted with CD34+ cells and neutrophils that may predict an increased risk of thrombosis in patients with ET. These preliminary findings should be validated using next-generation sequencing (NGS) and clinical studies.
| S-EPMC6911306 | biostudies-literature
Project description:Alzheimer disease (AD) is a progressive neurodegenerative disease, the etiology of which remains largely unknown. Accumulating evidence indicates that elevated manganese (Mn) in brain exerts toxic effects on neurons and contributes to AD development. Thus, we aimed to explore the gene and pathway variations through analysis of high through-put data in this process.To screen the differentially expressed genes (DEGs) that may play critical roles in Mn-induced AD, public microarray data regarding Mn-treated neurocytes versus controls (GSE70845), and AD versus controls (GSE48350), were downloaded and the DEGs were screened out, respectively. The intersection of the DEGs of each datasets was obtained by using Venn analysis. Then, gene ontology (GO) function analysis and KEGG pathway analysis were carried out. For screening hub genes, protein-protein interaction network was constructed. At last, DEGs were analyzed in Connectivity Map (CMAP) for identification of small molecules that overcome Mn-induced neurotoxicity or AD development.The intersection of the DEGs obtained 140 upregulated and 267 downregulated genes. The top 5 items of biological processes of GO analysis were taxis, chemotaxis, cell-cell signaling, regulation of cellular physiological process, and response to wounding. The top 5 items of KEGG pathway analysis were cytokine-cytokine receptor interaction, apoptosis, oxidative phosphorylation, Toll-like receptor signaling pathway, and insulin signaling pathway. Afterwards, several hub genes such as INSR, VEGFA, PRKACB, DLG4, and BCL2 that might play key roles in Mn-induced AD were further screened out. Interestingly, tyrphostin AG-825, an inhibitor of tyrosine phosphorylation, was predicted to be a potential agent for overcoming Mn-induced neurotoxicity or AD development.The present study provided a novel insight into the molecular mechanisms of Mn-induced neurotoxicity or AD development and screened out several small molecular candidates that might be critical for Mn neurotoxicity prevention and Mn-induced AD treatment.
| S-EPMC6392515 | biostudies-literature
Project description:The current study aimed to explore the mechanisms associated with classic Hodgkin lymphoma (cHL) to identify novel diagnostic and therapeutic targets. The GES12453 microarray dataset was downloaded from the Gene Expression Omnibus database; the differentially expressed genes (DEGs) between cHL samples and normal B cell samples by were identified using the limma package. Gene ontology (GO) and pathway enrichment analysis of DEGs gene were performed. Furthermore, construction and analysis of protein‑protein interaction (PPI) network was performed, and co‑expression modules of DEGs were produced. A total of 450 DEGs were identified, comprising 216 upregulated and 234 downregulated genes in cHL compared with normal B cell samples. The DEGs were enriched in biological processes associated with immune response. The upregulated genes were mainly associated with the pathway of transcriptional misregulation in cancer, while downregulated genes were associated with B cell receptor signaling. PPI network analysis demonstrated that IL6 had the highest connectivity degree. Interleukin‑6 (IL6) and signal transducer and activator of transcription 1 (STAT1) were demonstrated to be involved with the response to cytokine GO term in co‑expression module 1. Spleen tyrosine kinase (SYK), B‑cell linker protein (BLNK), CD79B, phospholipase C γ2 (PLCG2) were enriched in the B cell receptor signaling pathway in module 2. Matrix metallopeptidase 9 (MMP9), protein tyrosine phosphatase receptor type C had the highest connectivity degrees in module 3 and module 4, respectively. The results suggested that DEGs, including IL6, STAT1, MMP9, SYK, BLNK, PLCG2 and CD79B, and the pathways of B cell receptor signaling, Epstein‑Barr virus infection and transcriptional misregulation in cancer have strong potential to be useful as targets for diagnosis or treatment of cHL.
| S-EPMC5647037 | biostudies-literature
Project description:BackgroundLong non-coding RNAs (lncRNAs) are important regulators in ankylosing spondylitis (AS). Few studies have examined the lncRNA-RNA binding protein (RBP) interaction in AS. This study performed bioinformatics analysis and clinical verification to identify key lncRNAs and propose their RBP interaction.MethodsThree GEO datasets of AS were analyzed by differential expression analysis. The differentially expressed lncRNAs between the AS and control groups were screened out, and the intersecting lncRNAs were regarded as target lncRNAs. Functional was performed to identify target lncRNAs by enrichment analysis, co-expressed RNA analysis, and lncRNA-RBP interaction analysis. Finally, this study analyzed the differential expression level and clinical value of lncRNAs between the AS and control groups.ResultsLinc00304, linc00926, and MIAT were differentially expressed and upregulated. Enrichment analysis indicated that the key KEGG terms were the T-cell receptor signaling pathway and B-cell receptor signaling pathway. The key molecular function term was protein binding, and the key biological process term was adaptive immune response. In qRT-PCR results, 44 samples were validated. linc00304 expression was positively correlated with bath ankylosing spondylitis disease activity index (BASDAI), bath ankylosing spondylitis functional index (BASFI), erythrocyte sedimentation rate (ESR), and c-reactive protein (CRP). linc00926 expression was only positively correlated with ESR, whereas MIAT expression was positively correlated with BASFI, ESR, and CRP. Logistic regression revealed that linc00304, ESR, and CRP were the independent risk factors for BASDAI activation. The area under the curve (AUC) of serum linc00304 level in the diagnosis of AS was 0.687 (cutoff value: 0.413, specificity: 0.423, sensitivity: 0.900). AUC of linc00926 was 0.664 (cutoff value: 0.299, sensitivity: 0.882, specificity: 0.417). AUC of MIAT was 0.623 (cutoff value: 0.432, specificity: 0.443, sensitivity: 0.890) (all P <0.05).ConclusionOverall, this study uncovered three novel lncRNAs, which were upregulated in AS, and proposed a new lncRNA-RBP-mRNA interaction that might regulate adaptive immune response.
| S-EPMC9642369 | biostudies-literature