ArticleInternational journal of molecular medicine2017
Identification of key genes associated with Schmid-type metaphyseal chondrodysplasia based on microarray data.
Article in International journal of molecular medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- A Collagen10a1 mutation disrupts cell polarity in a medaka model for metaphyseal chondrodysplasia type Schmid.iScience · 2024Article
- Application of weighted gene co-expression network analysis to identify novel key genes in diabetic nephropathy.Journal of diabetes investigation · 2022Article
- Identification of two novel COL10A1 heterozygous mutations in two Chinese pedigrees with Schmid-type metaphyseal chondrodysplasia.BMC medical genetics · 2019Article
- A Venezuelan Case of Schmid-Type Metaphyseal Chondrodysplasia with a Novel Mutation inMolecular syndromology · 2019Article
- Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis.Oncology letters · 2018Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to gain a better understanding of the molecular circuitry of Schmid-type metaphyseal chondrodysplasia (SMCD), and to identify more potential genes associated with the pathogenesis of SMCD. Microarray data from GSE72261 were downloaded from the NCBI GEO database, including collagen X p.Asn617Lys knock-in mutation (ColXN617K), ablated XBP1 activity (Xbp1CartΔEx2), compound mutant (C/X), and wild-type (WT) specimens. Differentially expressed genes (DEGs) were screened in Xbp1 vs. WT, Col vs. WT and CX vs. WT, respectively. Pathway enrichment analysis of these DEGs was performed. Transcription factors (TFs) of the overlapping DEGs were identified. Weighted correlation network analysis (WGCNA) was performed to find modules of DEGs with high correlations, followed by gene function analysis and a protein-protein interaction network construction. In total, 481, 1,530 and 1,214 DEGs were identified in Xbp1 vs. WT, Col vs. WT and CX vs. WT, respectively. These DEGs were enriched in different pathways, such as extracellular matrix (ECM)-receptor interaction and metabolism-related pathways. A total of 7 TFs were found to regulate 19 common upregulated genes, and 4 TFs were identified to regulate 21 common downregulated genes. Two significant gene co-expression modules were enriched and DEGs in the 2 modules were mainly enriched in different biological processes, such as ribosome biogenesis. Moreover, Kras (downregulated), Col5a1 (upregulated) and Furin (upregulated) were both identified in the regulatory networks and protein-protein interaction (PPI) network. On the whole, our findings indicate that the Kras, Col5a1 and Furin genes may play essential roles in the molecular mechanisms of SMCD, which warrants further investigation.
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