Cell Cycle Regulation and DNA Damage Response Networks in Diffuse- and Intestinal-Type Gastric Cancer.
Shihori TanabeSabina QuaderRyuichi OnoHoracio CabralKazuhiko AoyagiAkihiko HiroseHiroshi YokozakiHiroki SasakiPublished in: Cancers (2021)
Dynamic regulation in molecular networks including cell cycle regulation and DNA damage response play an important role in cancer. To reveal the feature of cancer malignancy, gene expression and network regulation were profiled in diffuse- and intestinal-type gastric cancer (GC). The results of the network analysis with Ingenuity Pathway Analysis (IPA) showed that the activation states of several canonical pathways related to cell cycle regulation were altered. The G1/S checkpoint regulation pathway was activated in diffuse-type GC compared to intestinal-type GC, while canonical pathways of the cell cycle control of chromosomal replication, and the cyclin and cell cycle regulation, were activated in intestinal-type GC compared to diffuse-type GC. A canonical pathway on the role of BRCA1 in the DNA damage response was activated in intestinal-type GC compared to diffuse-type GC, where gene expression of BRCA1, which is related to G1/S phase transition, was upregulated in intestinal-type GC compared to diffuse-type GC. Several microRNAs (miRNAs), such as mir-10, mir-17, mir-19, mir-194, mir-224, mir-25, mir-34, mir-451 and mir-605, were identified to have direct relationships in the G1/S cell cycle checkpoint regulation pathway. Additionally, cell cycle regulation may be altered in epithelial-mesenchymal transition (EMT) conditions. The alterations in the activation states of the pathways related to cell cycle regulation in diffuse- and intestinal-type GC highlighted the significance of cell cycle regulation in EMT.
Keyphrases
- cell cycle
- cell proliferation
- long non coding rna
- dna damage response
- gene expression
- epithelial mesenchymal transition
- long noncoding rna
- pi k akt
- low grade
- dna methylation
- gas chromatography
- squamous cell carcinoma
- dna repair
- machine learning
- network analysis
- dna damage
- young adults
- deep learning
- cell death
- high grade
- high resolution
- single cell
- copy number
- solid phase extraction