scholarly journals Identification and Validation of Hub Genes Related to Meniscus Senescence Based on Gene Expression Profiling Analysis

2021 ◽  
Author(s):  
ming chen ◽  
Siqi Zhou ◽  
Huasong Shi ◽  
Hanwen Gu ◽  
Yinxian Wen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Meniscus Cells ◽  
Toxic Drugs ◽  
Profiling Analysis

Abstract Background: The incidence of meniscal injury is on the rise, partly due to the general aging of the population. The compositional change in the meniscus with aging would increase the tissue vulnerability of the meniscus, which would induce meniscus tearing. Here, we investigated the molecular mechanism of age-related meniscus degeneration with gene expression profiling analysis.Methods: The GSE45233 dataset, including 6 elderly meniscus samples and 6 younger meniscus samples, which were obtained from patients undergoing arthroscopic partial meniscectomy, was downloaded from the Gene Expression Omnibus (GEO) database for subsequent bioinformatics analysis. To screen the differential expression of mRNAs, identify the miRNAs targeting hub genes, and forecast the potentially toxic drugs, we completed a series of bioinformatics analyses, including functional and pathway enrichment analysis, protein-protein interaction network, hub genes screening, construction of a lncRNA–miRNA–mRNA network, and molecular docking of potential drugs. Furthermore, hub genes were examined in human senescent menisci, mouse senescent meniscus tissues and mouse meniscus cells stimulated by IL-1β.Results: In total, the most significant 4 hub genes (RRM2, AURKB, CDK1, and TIMP1), 5 miRNAs (hsa-miR-6810-5p, hsa-miR-4676-5p, hsa-miR-6877-5p, hsa-miR-8085, and hsa-miR-6133) that could regulate such 4 hub genes and potential toxic drugs (Cladribine, Danusertib, Barasertib, Riviciclib, and Dinaciclib) that may have a targeting effect on these genes, were finally identified. The functional enrichment results showed that hub genes were mainly concentrated in aging and regulation of the cell cycle process. Further pathways enrichment analysis of these miRNA revealed that these miRNAs were involved in the synthesis of glycosaminoglycans. The hub genes were decreased in meniscus cells in vitro and meniscus tissues in vivo, which indicated that hub genes were related to meniscus senescence.Conclusions: In a word, our current study would provide a basis for finding markers of the aging meniscus to a certain extent.

scholarly journals Identification And Validation of Pivotal Genes Related To Meniscus Senescence Based On Gene Expression Profiling Analysis And In Vivo And In Vitro Models Detection

2021 ◽  
Author(s):  
Ming Chen ◽  
Siqi Zhou ◽  
Huasong Shi ◽  
Hanwen Gu ◽  
Yinxian Wen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Hub Genes ◽  
Age Related ◽  
Meniscus Cells ◽  
Toxic Drugs ◽  
Profiling Analysis

Abstract Background: The compositional change in the meniscus with aging would increase the tissue vulnerability of the meniscus, which would induce meniscus tearing. Here, we investigated the molecular mechanism of age-related meniscus degeneration with gene expression profiling analysis, and validate pivotal genes in vivo and in vitro models.Methods: The GSE45233 dataset, including 6 elderly meniscus samples and 6 younger meniscus samples, was downloaded from the Gene Expression Omnibus (GEO) database. To screen the differential expression of mRNAs, identify the miRNAs targeting hub genes, and forecast the potentially toxic drugs, we completed a series of bioinformatics analyses, including functional and pathway enrichment, protein-protein interaction network, hub genes screening, construction of a lncRNA–miRNA–mRNA network, and molecular docking of potential drugs. Furthermore, crucial genes were examined in human senescent menisci, mouse senescent meniscus tissues and mouse meniscus cells stimulated by IL-1β.Results: In total, the most significant 4 hub genes (RRM2, AURKB, CDK1, and TIMP1), 5 miRNAs (hsa-miR-6810-5p, hsa-miR-4676-5p, hsa-miR-6877-5p, hsa-miR-8085, and hsa-miR-6133) that regulated such 4 hub genes, and potential toxic drugs (Cladribine, Danusertib, Barasertib, Riviciclib, and Dinaciclib) that had a targeting effect on these genes, were finally identified. Moreover, these hub genes were decreased in meniscus cells in vitro and meniscus tissues in vivo, which indicated that hub genes were related to meniscus senescence and could serve as potential biomarkers for age-related meniscus tearing.Conclusions: In short, the integrated analysis of gene expression profile, co-expression network, and models detection identified pivotal genes, which elucidated the possible molecular basis underlying the senescence meniscus and also provided prognosis clues for early-onset age-related meniscus tearing.

scholarly journals ATRT-09. IDENTIFICATION OF HUB GENES IN ATYPICAL TERATOID/RHABDOID TUMORS BY MULTIPLE-MICROARRAY ANALYSIS

2020 ◽  
Vol 22 (Supplement_3) ◽  
pp. iii277-iii277
Author(s):  
Wei Liu ◽  
Yi Chai ◽  
Junhua Wang ◽  
Yuqi Zhang
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Malignant Neoplasms ◽  
Pathway Enrichment Analysis ◽  
Normal Brain ◽  
Hub Genes ◽  
Atypical Teratoid ◽  
Atypical Teratoid Rhabdoid Tumors ◽  
Rhabdoid Tumors

Abstract BACKGROUND Atypical teratoid/rhabdoid tumors (ATRT) are rare, highly malignant neoplasms arising in infants and young children. However, the biological basis of ATRTs remains poorly understood. In the present study, we employed integrated bioinformatics to investigate the hub genes and potential molecular mechanism in ATRT. METHODS Three microarray datasets, GSE35943, GSE6635 and GSE86574, were downloaded from Gene Expression Omnibus (GEO) which contained a total of 79 samples including 32 normal brain tissue samples and 47 ATRT samples. The RobustRankAggreg method was employed to integrate the results of these gene expression datasets to obtain differentially expressed genes (DEGs). The GO function and KEGG pathway enrichment analysis were conducted at the Enrichr database. The hub genes were screened according to the degree using Cytoscape software. Finally, transcription factor (TF) of hub genes were obtained by the NetworkAnalyst algorithm. RESULTS A total of 297 DEGs, consisting of 94 downregulated DEGs and 103 upregulated DEGs were identified. Functional enrichment analysis revealed that these genes were associated with cell cycle, p53 signaling pathway and DNA replication. Protein-protein interaction (PPI) network analysis revealed that CDK1, CCNA2, BUB1B, CDC20, KIF11, KIF20A, KIF2C, NCAPG, NDC80, NUSAP1, PBK, RRM2, TPX2, TOP2A and TTK were hub genes and these genes could be regulated by MYC, SOX2 and KDM5B according to the results of TF analysis. CONCLUSIONS Our study will improve the understanding of the molecular mechanisms and provide novel therapeutic targets for ATRT.

scholarly journals The Carcinogenome Project: In-vitro Gene Expression Profiling of Chemical Perturbations to Predict Long-Term Carcinogenicity

2018 ◽  
Author(s):  
Amy Li ◽  
Xiaodong Lu ◽  
Ted Natoli ◽  
Joshua Bittker ◽  
Nisha Sipes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Pathway Enrichment ◽  
Link Type ◽  
Chemical Perturbations

AbstractBackground: Most chemicals in commerce have not been evaluated for their carcinogenic potential. The current de-facto gold-standard approach to carcinogen testing adopts the two-year rodent bioassay, a time consuming and costly procedure. Alternative approaches, such as high-throughput in-vitro assays, show promise in addressing the limitations in carcinogen screening.Objectives: We developed a screening process for predicting chemical carcinogenicity and genotoxicity and characterizing modes of actions (MoAs) using in-vitro gene expression assays.Methods: We generated a large toxicogenomics resource comprising ~6,000 expression profiles corresponding to 330 chemicals profiled in HepG2 cells at multiple doses and in replicates. Predictive models of carcinogenicity were built using a Random Forest classifier. Differential pathway enrichment analysis was performed to identify pathways associated with carcinogen exposure. Signatures of carcinogenicity and genotoxicity were compared with external data sources including Drugmatrix and the Connectivity Map.Results: Among profiles with sufficient bioactivity, our classifiers achieved 72.2% AUC for predicting carcinogenicity and 82.3% AUC for predicting genotoxicity. Our analysis showed that chemical bioactivity, as measured by the strength and reproducibility of the transcriptional response, is not significantly associated with long-term carcinogenicity, as evidenced by the many carcinogenic chemicals that did not elicit substantial changes in gene expression at doses up to 40 μM. However, sufficiently high transcriptional bioactivity is necessary for a chemical to be used for prediction of carcinogenicity. Pathway enrichment analysis revealed several pathways consistent with literature review of pathways that drive cancer, including DNA damage and DNA repair. These data are available for download via https://clue.io/CRCGN_ABC, and a web portal for interactive query and visualization of the data and results is accessible at https://carcinogenome.org.Conclusions: We demonstrated a short-term in-vitro screening approach using gene expression profiling to predict long-term carcinogenicity and infer MoAs of chemical perturbations.

Gene expression profiling of the bone trabecula in patients with osteonecrosis of the femoral head by RNA sequencing

2019 ◽  
Vol 166 (6) ◽  
pp. 475-484
Author(s):  
Haobo Bai ◽  
Tingmei Chen ◽  
Qian Lu ◽  
Weiwen Zhu ◽  
Jian Zhang
Keyword(s):  
Gene Expression ◽  
Femoral Head ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Enrichment Analysis ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Bone Trabecula ◽  
Mrna Profiling ◽  
Normal Bone

Abstract Early diagnosis and treatment of osteonecrosis of the femoral head (ONFH) is challenging. Bone trabecula play a vital role in the severity and progression of ONFH. In the present study, the investigators used gene expression profiling of bone trabecula to investigate gene alterations in ONFH patients. Osteonecrotic bone trabecula (ONBT) such as necrosis, fibrosis, and lacuna were confirmed by histological examination in the patients. The adjacent ‘normal’ bone trabecula (ANBT) did not show any pathological changes. Gene sequencing data revealed that although ANBT showed no significant histological changes, alteration of mRNA profiling in ANBT was observed, similar to that in ONBT. Our results indicated that the alteration of mRNA profiling in ANBT may cause normal bone tissue to develop into necrotic bone. RNA-seq data indicated that 2,297 differentially abundant mRNAs were found in the ONBT group (1,032 upregulated and 1,265 downregulated) and 1,523 differentially abundant mRNAs in the ANBT group (744 upregulated and 799 downregulated) compared with the healthy control group. Gene ontology (GO) enrichment analysis suggested that fatty acid metabolism and degradation were the main zones enriched with differentially expressed genes (DEG). Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis indicated that peroxisome proliferator-activated receptor γ (PPAR-γ) pathway was the most significantly regulated pathway. Lipocalin-2 (LCN2), an osteoblast-enriched secreted protein, was significantly decreased in ONBT suggesting that downregulation of LCN2 might affect lipid metabolism and lead to hyperlipidemia, and thus promote pathogenesis of ONFH.

scholarly journals Identification and validation of pivotal genes related to age-related meniscus degeneration based on gene expression profiling analysis and in vivo and in vitro models detection

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ming Chen ◽  
Siqi Zhou ◽  
Huasong Shi ◽  
Hanwen Gu ◽  
Yinxian Wen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
In Vitro Models ◽  
Hub Genes ◽  
Age Related ◽  
Meniscus Degeneration ◽  
Meniscus Cells ◽  
Profiling Analysis

Abstract Background The componential and structural change in the meniscus with aging would increase the tissue vulnerability of the meniscus, which would induce meniscus tearing. Here, we investigated the molecular mechanism of age-related meniscus degeneration with gene expression profiling analysis, and validate pivotal genes in vivo and in vitro models. Methods The GSE45233 dataset, including 6 elderly meniscus samples and 6 younger meniscus samples, was downloaded from the Gene Expression Omnibus (GEO) database. To screen the differential expression of mRNAs and identify the miRNAs targeting hub genes, we completed a series of bioinformatics analyses, including functional and pathway enrichment, protein–protein interaction network, hub genes screening, and construction of a lncRNA–miRNA–mRNA network. Furthermore, crucial genes were examined in human senescent menisci, mouse senescent meniscus tissues and mouse meniscus cells stimulated by IL-1β. Results In total, the most significant 4 hub genes (RRM2, AURKB, CDK1, and TIMP1) and 5 miRNAs (hsa-miR-6810-5p, hsa-miR-4676-5p, hsa-miR-6877-5p, hsa-miR-8085, and hsa-miR-6133) that regulated such 4 hub genes, were finally identified. Moreover, these hub genes were decreased in meniscus cells in vitro and meniscus tissues in vivo, which indicated that hub genes were related to meniscus senescence and could serve as potential biomarkers for age-related meniscus tearing. Conclusions In short, the integrated analysis of gene expression profile, co-expression network, and models detection identified pivotal genes, which elucidated the possible molecular basis underlying the senescence meniscus and also provided prognosis clues for early-onset age-related meniscus tearing.

scholarly journals Screening and identification of MSX1 for predicting progestin resistance in endometrial cancer

2020 ◽  
Author(s):  
Linlin Yang ◽  
Yunxia Cui ◽  
Ting Huang ◽  
Xiao Sun ◽  
Yudong Wang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Endometrial Cancer ◽  
Endometrial Carcinoma ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Hub Genes

Abstract Background: Progestin resistance is a critical obstacle for endometrial conservative therapy. Therefore, the studies to acquire a more comprehensive understanding of the mechanisms and specific biomarkers to predict progestin resistance are very important. However, the pivotal roles of essential molecules of progestin resistance are still unexplored. Methods: We downloaded GSE121367 with gene expression profiles of medroxyprogesterone acetate (MPA) resistant and sensitive cell lines from the GEO database. The “limma” R language package was applied to identify differentially expressed genes (DEGs). Gene ontology and pathway enrichment analysis was performed through the database of DAVID. Meanwhile, we conducted GSEA analysis to identify pathway enrichments. Protein–protein interaction construction of top genes was conducted to screen hub genes by STRING and visualized in Cytoscape. A high connectivity degree of hub genes were picked out to perform the differential expression, methylation validation and overall survival analysis in the Gene Expression Profiling Interactive Analysis database, Human Protein Atlas database and Kaplan–Meier plotter online tool, respectively. In addition, microRNAs and upstream transcription factors of hub genes were predicted by miRTarBase and Network Analyst database. Results: A total number of 3282 differentially expressed genes were identified. Functional enrichment analysis demonstrated that these genes were mostly enriched in negative regulation of DNA binding, chronic inflammatory response and cell adhesion molecules pathway. We screened out ten hub genes including CDH1, JAG1, PTGES, EPCAM, CNTNAP2, TBX1, MSX1, KRT19, OAS1 and DAB2 among different groups. The genomic alteration rates of hub genes were low based on the current uterine corpus endometrial carcinoma sample sets. Their relevant microRNA and transcription factor were detected and has-miR-335-5p, has-miR-124-3p, MAZ and TFDP1 were the most prominent. The methylation status of CDH1, JAG1, EPCAM and MSX1 were decreased, corresponding to their high protein expression in endometrial cancers, which also indicated better overall survival. The homeobox protein of MSX1 showed significantly tissue specificity. Conclusions: Our study identified ten hub genes associated with progestin resistance of endometrial cancer and screened out the gene of MSX1 which promised to be the specific indicator. This would shed new light on the underlying biological marker to overcome the progestin resistance of endometrial cancer. Keywords : Bioinformatic analysis, Progestin resistance, Endometrial carcinoma, MSX1

Preparation of Salmonella enterica Serovar Typhi Genomic DNA Microarrays for Gene Expression Profiling Analysis*

2009 ◽  
Vol 2009 (2) ◽  
pp. 206-212 ◽  
Author(s):  
Xiu-Mei SHENG ◽  
Xin-Xiang HUANG ◽  
Ling-Xiang MAO ◽  
Chao-Wang ZHU ◽  
Shun-Gao XU ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Salmonella Enterica ◽  
Genomic Dna ◽  
Dna Microarrays ◽  
Profiling Analysis

scholarly journals HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Zhixin Wu ◽  
Yinxian Wen ◽  
Guanlan Fan ◽  
Hangyuan He ◽  
Siqi Zhou ◽  
...  
Keyword(s):  
Femoral Head ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Diagnostic Value ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Overlapping Genes ◽  
Hub Genes ◽  
Diagnostic Biomarkers ◽  
Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

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