Bioinformatics analysis and experimental validation of differentially expressed genes in mouse articular chondrocytes treated with IL-1β using microarray data

被引:6
|
作者
Liang, Fan [1 ]
Peng, Le [1 ]
Ma, Yong-Gang [1 ]
Hu, Wei [1 ]
Zhang, Wei-Bing [1 ]
Deng, Ming [1 ]
Li, Ya-Ming [1 ]
机构
[1] Wuhan Univ, Renmin Hosp, Dept Orthoped, 238 Jiefang Rd, Wuhan 430060, Hubei, Peoples R China
关键词
osteoarthritis; bioinformatics analysis; differentially expressed genes; KNEE OSTEOARTHRITIS; CARTILAGE; PROTECTS; DEGRADATION; APOPTOSIS; PAIN;
D O I
10.3892/etm.2021.10928
中图分类号
R-3 [医学研究方法]; R3 [基础医学];
学科分类号
1001 ;
摘要
Osteoarthritis (OA) is the most prevalent chronic degenerative disease that affects the health of the elderly. The present study aimed to identify significant genes involved in OA via bioinformatics analysis. A gene expression dataset (GSE104793) was downloaded from the Gene Expression Omnibus. Bioinformatics analysis was then performed in order to identify differentially expressed genes (DEGs) between untreated chondrocytes and chondrocytes cultured with interleukin-1 beta (IL-1 beta) for 24 h. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using Metascape. A protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes. Gene set enrichment analysis (GSEA) was performed using GSEA software. Furthermore, chondrocytes were extracted and treated with IL-1 beta (10 ng/ml) for 24 h, and reverse-transcription quantitative PCR was used to confirm differential expression of hub genes. Patient samples were also collected to verify the bioinformatic analysis results. Based on the cut-off criteria used for determination of the DEGs, a total of 844 DEGs, including 498 upregulated and 346 downregulated DEGs, were identified. The DEGs were mainly enriched in the GO terms and KEGG pathways 'inflammatory response', 'negative regulation of cell proliferation', 'ossification', 'taxis', 'blood vessel morphogenesis', 'extracellular structure organization', 'mitotic cell cycle process' and 'TNF signaling pathway'. The majority of the PCR results, namely the differential expression of kininogen 2, complement C3, cyclin B1, cell division cycle 20, cyclin A2, 1-phosphatidylinositol 4-kinase, BUB1 mitotic checkpoint serine/threonine kinase, kinesin family member 11, cyclin B2 and BUB1 mitotic checkpoint serine/threonine kinase B were consistent with the bioinformatics results. Collectively, the present observations provided a regulation network of IL-1 beta-stimulated chondrocytes, which may provide potential targets of OA therapy.
引用
收藏
页数:10
相关论文
共 50 条
  • [21] Microarray data analysis to identify differentially expressed genes and biological pathways associated with asthma
    Qi, Shanshan
    Liu, Guanghui
    Dong, Xiang
    Huang, Nan
    Li, Wenjing
    Chen, Hao
    EXPERIMENTAL AND THERAPEUTIC MEDICINE, 2018, 16 (03) : 1613 - 1620
  • [22] Effects of polysulfated glycosaminoglycan and triamcinolone acetonid on the production of proteinases and their inhibitors by IL-1α treated articular chondrocytes
    Sadowski, T
    Steinmeyer, J
    BIOCHEMICAL PHARMACOLOGY, 2002, 64 (02) : 217 - 227
  • [23] Identification and verification of characteristic differentially expressed ferroptosis-related genes in osteosarcoma using bioinformatics analysis
    Hu, Jianhua
    Yang, Xi
    Ren, Jing
    Zhong, Shixiao
    Fan, Qianbo
    Li, Weichao
    TOXICOLOGY MECHANISMS AND METHODS, 2023, 33 (09) : 781 - 795
  • [24] Identification of key pathways and differentially expressed genes in bronchopulmonary dysplasia using bioinformatics analysis
    Yan, Weiheng
    Jiang, Miaomiao
    Zheng, Jun
    BIOTECHNOLOGY LETTERS, 2020, 42 (12) : 2569 - 2580
  • [25] Identification of key pathways and differentially expressed genes in bronchopulmonary dysplasia using bioinformatics analysis
    Weiheng Yan
    Miaomiao Jiang
    Jun Zheng
    Biotechnology Letters, 2020, 42 : 2569 - 2580
  • [26] Identifying Differentially Expressed Genes for Time-course Microarray Data through Functional Data Analysis
    Chen K.
    Wang J.-L.
    Statistics in Biosciences, 2010, 2 (2) : 95 - 119
  • [27] Identification of the differentially expressed genes associated with familial combined hyperlipidemia using bioinformatics analysis
    Luo, Xiaoli
    Yu, Changqing
    Fu, Chunjiang
    Shi, Weibin
    Wang, Xukai
    Zeng, Chunyu
    Wang, Hongyong
    MOLECULAR MEDICINE REPORTS, 2015, 11 (06) : 4032 - 4038
  • [28] Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis (vol 13, 1206768, 2023)
    Altaf, Reem
    Ilyas, Umair
    Ma, Anmei
    Shi, Meiqi
    FRONTIERS IN ONCOLOGY, 2024, 14
  • [29] Identification of differentially expressed genes between mucinous adenocarcinoma and other adenocarcinoma of colorectal cancer using bioinformatics analysis
    Zhang, Xue
    Zuo, Jing
    Wang, Long
    Han, Jing
    Feng, Li
    Wang, Yudong
    Fan, Zhisong
    JOURNAL OF INTERNATIONAL MEDICAL RESEARCH, 2020, 48 (08)
  • [30] A Hyaluronan-binding Peptide (P15-1) Reduces Inflammatory and Catabolic Events in IL-1β-treated Human Articular Chondrocytes
    Shortt, Claire
    Luyt, Leonard G.
    Turley, Eva A.
    Cowman, Mary K.
    Kirsch, Thorsten
    SCIENTIFIC REPORTS, 2020, 10 (01)