Identification of co-expression network correlated with different periods of adipogenic and osteogenic differentiation of BMSCs by weighted gene co-expression network analysis (WGCNA)

被引:16
|
作者
Liu, Yu [1 ]
Tingart, Markus [1 ]
Lecouturier, Sophie [1 ]
Li, Jianzhang [1 ]
Eschweiler, Joerg [1 ]
机构
[1] RWTH Aachen Univ Clin, Dept Orthopaed Surg, Pauwelsstr 30, D-52074 Aachen, Germany
关键词
Bioinformatics; BMSCs; WGCNA; Osteogenic differentiation; Adipogenic differentiation;
D O I
10.1186/s12864-021-07584-4
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background The differentiation of bone marrow mesenchymal stem cells is a complex and dynamic process. The gene expression pattern and mechanism of different periods of adipogenic and osteogenic differentiation remain unclear. Additionally, the interaction between these two lineage determination requires further exploration. Results Five modules that were most significantly associated with osteogenic or adipogenic differentiation of BMSCs were selected for further investigation. Biological terms (e.g. ribosome biogenesis, TNF-alpha signalling pathway, glucose import and fatty acid metabolism) along with hub transcription factors (e.g. PPARG and YY1) and hub miRNAs (e.g. hsa-mir-26b-5p) were enriched in different modules. The expression pattern of 6 hub genes, ADIPOQ, FABP4, SLC7A5, SELPLG, BIRC3, and KLHL30 was validated by RT-qPCR. Finally, cell staining experiments extended the findings of bioinformatics analysis. Conclusion This study identified the key genes, biological functions, and regulators of each time point of adipogenic and osteogenic differentiation of BMSCs and provided novel evidence and ideas for further research on the differentiation of BMSCs.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Identification of pancreatic cancer type related factors by Weighted Gene Co-Expression Network Analysis
    Wei Wang
    Haibo Xing
    Changxin Huang
    Hong Pan
    Da Li
    Medical Oncology, 2020, 37
  • [42] Identification of the Biomarkers and Pathological Process of Heterotopic Ossification: Weighted Gene Co-Expression Network Analysis
    Wang, Shuang
    Tian, Jun
    Wang, Jianzhong
    Liu, Sizhu
    Ke, Lianwei
    Shang, Chaojiang
    Yang, Jichun
    Wang, Lin
    FRONTIERS IN ENDOCRINOLOGY, 2020, 11
  • [43] Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis
    Yin, Xiaomao
    Wang, Jianfeng
    Zhang, Jin
    CANCER CELL INTERNATIONAL, 2018, 18
  • [44] Spectral analysis of gene co-expression network of Zebrafish
    Jalan, S.
    Ung, C. Y.
    Bhojwani, J.
    Li, B.
    Zhang, L.
    Lan, S. H.
    Gong, Z.
    EPL, 2012, 99 (04)
  • [45] Multiscale Embedded Gene Co-expression Network Analysis
    Song, Won-Min
    Zhang, Bin
    PLOS COMPUTATIONAL BIOLOGY, 2015, 11 (11)
  • [46] Parameterization of asymmetric sigmoid functions in weighted gene co-expression network analysis
    Karabekmez, Muhammed Erkan
    Yarici, Merve
    COMPUTATIONAL BIOLOGY AND CHEMISTRY, 2024, 108
  • [47] Novel biomarkers identified by weighted gene co-expression network analysis for atherosclerosis
    Ni, Jiajun
    Huang, Kaijian
    Xu, Jialin
    Lu, Qi
    Chen, Chu
    HERZ, 2024, 49 (03) : 198 - 209
  • [48] PyWGCNA: a Python']Python package for weighted gene co-expression network analysis
    Rezaie, Narges
    Reese, Farilie
    Mortazavi, Ali
    BIOINFORMATICS, 2023, 39 (07)
  • [49] Weighted gene co-expression network analysis for hub genes in colorectal cancer
    Xu, Zheng
    Wang, Jianing
    Wang, Guosheng
    PHARMACOLOGICAL REPORTS, 2024, 76 (01) : 140 - 153
  • [50] Analysis on Technology Convergence Mechanism Using Weighted Gene Co-expression Network
    Miao, Hong
    Wang, Yan
    Huang, Lucheng
    Wu, Feifei
    Li, Xin
    2018 PORTLAND INTERNATIONAL CONFERENCE ON MANAGEMENT OF ENGINEERING AND TECHNOLOGY (PICMET '18): MANAGING TECHNOLOGICAL ENTREPRENEURSHIP: THE ENGINE FOR ECONOMIC GROWTH, 2018,