ArticleMolecular medicine reports2018
Bioinformatics analysis to screen key genes implicated in the differentiation of induced pluripotent stem cells to hepatocytes.
Article in Molecular medicine reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 6 citations in OpenAlex.
- Unveiling Immune-related feature genes for Alzheimer's disease based on machine learning.Frontiers in immunology · 2024Article
- Screening of key biomarkers and immune infiltration in Pulmonary Arterial Hypertension via integrated bioinformatics analysis.Bioengineered · 2021Article
- Screening of Hub Genes Associated with Pulmonary Arterial Hypertension by Integrated Bioinformatic Analysis.BioMed research international · 2021Article
- Analysis of differentially expressed genes among human hair follicle-derived iPSCs, induced hepatocyte-like cells, and primary hepatocytes.Stem cell research & therapy · 2018Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the lack of potential organs, hepatocellular transplantation has been considered for treating end-stage liver disease. Induced pluripotent stem cells (iPSCs) are reverted from somatic cells and are able to differentiate into hepatocytes. The present study aimed to investigate the mechanisms underlying iPSC differentiation to hepatocytes. GSE66076 was downloaded from the Gene Expression Omnibus; this database includes data from 3 undifferentiated (T0), 3 definitive endoderm (T5), and 3 early hepatocyte (T24) samples across hepatic‑directed differentiation of iPSCs. Differentially expressed genes (DEGs) between T0 and T5 or T24 samples were identified using the linear models for microarray data package in Bioconductor, and enrichment analyses were performed. Using the weighted correlation network analysis package in R, clusters were identified for the merged DEGs. Cytoscape was used to construct protein‑protein interaction (PPI) networks for DEGs identified to belong to significant clusters. Using the ReactomeFI plugin in Cytoscape, functional interaction (FI) networks were constructed for the common genes. A total of 433 and 1,342 DEGs were identified in the T5 and T24 samples respectively, compared with the T0 samples. Blue and turquoise clusters were identified as significant gene clusters. In the PPI network for DEGs in the blue cluster, the key node fibroblast growth factor 2 (FGF2) could interact with bone morphogenetic protein 2 (BMP2). Cyclin‑dependent kinase 1 (CDK1) was demonstrated to have the highest degree (degree=71) in the PPI network for DEGs in the turquoise cluster. Enrichment analysis for the common genes, including hepatocyte nuclear factor 4α (HNF4A) and epidermal growth factor (EGF), in the FI network indicated that EGF and FGF2 were enriched in the Ras and Rap1 signaling pathways. The present results suggest that FGF2, BMP2, CDK1, HNF4A and EGF may participate in the differentiation of iPSCs into hepatocytes.
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