Dataset Information

Identification of Key Genes and Pathways Associated with Sex Differences in Osteoarthritis Based on Bioinformatics Analysis.

ABSTRACT: Sex differences have been suggested to play critical roles in the pathophysiology of osteoarthritis (OA), resulting in sex-specific prevalence and incidence. However, their roles in the development of OA remain largely unknown. The aim of this study was to screen out key genes and pathways mediating biological differences between OA females after menopause and OA males. First, the gene expression data of GSE36700 and GSE55457 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between sexes were identified using R software, respectively. The overlapping DEGs were obtained. Then, protein-protein interactive (PPI) network was constructed to further analyze interactions between the overlapping DEGs. Finally, enrichment analyses were separately performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes tools. In our results, a total of 278 overlapping DEGs were identified between OA females after menopause and OA males, including 219 upregulated and 59 downregulated genes. In the PPI network, seven hub genes were identified, including EGF, ERBB2, CDC42, PIK3R2, LCK, CBL, and STAT1. Functional enrichment analysis revealed that these genes were mainly enriched in PI3K-Akt signaling pathway, osteoclast differentiation, and focal adhesion. In conclusion, the results in the current study suggest that pathways of PI3K-Akt, osteoclast differentiation, and focal adhesion may play important roles in the development of OA females after menopause. EGFR, ERBB2, CDC42, and STAT1 may be key genes related to OA progression in postmenopausal women and may be promising therapeutic targets for OA.

SUBMITTER: Wang S

PROVIDER: S-EPMC6925789 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of Key Genes and Pathways Associated with Sex Differences in Osteoarthritis Based on Bioinformatics Analysis.

Wang Shiying S Wang Huanmei H Liu Wei W Wei Biaofang B

BioMed research international 20191206

Sex differences have been suggested to play critical roles in the pathophysiology of osteoarthritis (OA), resulting in sex-specific prevalence and incidence. However, their roles in the development of OA remain largely unknown. The aim of this study was to screen out key genes and pathways mediating biological differences between OA females after menopause and OA males. First, the gene expression data of GSE36700 and GSE55457 were downloaded from the Gene Expression Omnibus database. Differentia ...[more]

PMID: 31886203

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Project description:Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the diagnosis and treatment of CRC. We downloaded the colorectal cancer datasets from the Gene Expression Omnibus (GEO) database site. We used GEO2R to screen for differentially expressed genes (DEGs) of which those with a fold change >1 were considered as up-regulated and those with a fold change <-1 were considered as down-regulated on the basis of a P < .05. "Gene ontology (GO)" and "Kyoto Encyclopedia of Genes and Genomes (KEGG)" data were analyzed by the "DAVID" software. The online search tool "STRING" was used to search for interacting genes or proteins and we used Cytoscape (v3.8.0) to generate a PPI network map and to identify key genes. Finally, survival analysis and stage mapping of key genes were performed using "GEPIA" with the aim of elucidating their potential impact on CRC. Our study revealed 120 intersecting genes of which 55 were up- and 65 were downregulated, respectively. GO analysis revealed that these genes were involved in cell proliferation, exosome secretion, G2/M transition, cytosol, protein binding, and protein kinase activity. KEGG pathway analysis showed that these genes were involved in cell cycle and mineral absorption. The Cytoscape PPI map showed 17 nodes and 262 edges, and 10 hub genes were identified by top 10 degrees. Survival analysis demonstrated that the AURKA, CCNB1, and CCNA2 genes were strongly associated with the survival rate of CRC patients. In addition, CCNB1, CCNA2, CDK1, CKS2, MAD2L1, and DLGAP5 could be correlated to pathological CRC staging. In this research, we identified key genes that may explain the molecular mechanism of occurrence and progression of CRC but may also contribute to an improvement in the clinical staging and prognosis of CRC patients.

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Project description:BackgroundEsophageal adenocarcinoma (EAC) is an aggressive malignancy and accounts for the majority of cancer-related death worldwide. It is often diagnosed at an advanced stage and entails a poor prognosis for those afflicted. The mechanisms of its pathogenesis and progress remain unclear and require urgent elucidation. This study aimed to identify specific genes and potential pathways associated with the progression and prognosis of EAC using bioinformatics analyses.MethodsEAC microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs) using bioinformatics analysis. The DEGs in TCGA were then analyzed to construct a co-expression network by weighted correlation network analysis (WGCNA), and module-clinical trait relationships were analyzed to explore the genes that associated with clinicopathological parameters of EAC. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were performed for the cancer-related genes, and a DEG-based protein-protein interaction (PPI) network was used to extract hub genes through Cytoscape plugins. The consensus survival analysis for EAC (OSeac) was performed to identify the prognosis-related genes. The immune infiltration was evaluated by tumor immune estimation resource (TIMER) algorithms, and a risk score prognostic model was established using univariate, multivariate Cox proportional hazards regression, and lasso regression analysis.ResultsUltimately, 190 cancer-related DEGs were identified, 6 of which were found to play vital roles in the progression of EAC, including ACTA2, BGN, CALD1, COL1A1, COL4A1, and DCN. The risk score prognostic model consisted of 6 other genes that had an important impact on the prognosis of EAC, including CLDN3, EPB41L4A, ESM1, MT1X, PAQR5, and PLAU. The area under the curve of the prognostic model for predicting the survival of patients at 1, 2, and 3 years was 0.707, 0.702, and 0.726, respectively.ConclusionsThis study identified several genes with the potential to become useful targets for the diagnosis and treatment of EAC. The 6-gene-related risk score prognostic model and nomogram based on these genes may be a reliable tool for predicting the prognosis of patients with EAC.

Dataset Information

Identification of Key Genes and Pathways Associated with Sex Differences in Osteoarthritis Based on Bioinformatics Analysis.

Publications

Identification of Key Genes and Pathways Associated with Sex Differences in Osteoarthritis Based on Bioinformatics Analysis.

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