LINC01605 Is a Novel Target of Mutant p53 in Breast and Ovarian Cancer Cell Lines.
Michela CoanMartina TosoLaura CesarattoIlenia RigoSilvia BorgnaAnna Dalla PietàLuigi ZandonàLorenzo IuriAntonella ZucchettoCarla PiazzaGustavo BaldassarreRiccardo SpizzoMilena Sabrina NicolosoPublished in: International journal of molecular sciences (2023)
TP53 is the most frequently mutated gene in human cancers. Most TP53 genomic alterations are missense mutations, which cause a loss of its tumour suppressor functions while providing mutant p53 (mut_p53) with oncogenic features (gain-of-function). Loss of p53 tumour suppressor functions alters the transcription of both protein-coding and non-protein-coding genes. Gain-of-function of mut_p53 triggers modification in gene expression as well; however, the impact of mut_p53 on the transcription of the non-protein-coding genes and whether these non-protein-coding genes affect oncogenic properties of cancer cell lines are not fully explored. In this study, we suggested that LINC01605 (also known as lincDUSP ) is a long non-coding RNA regulated by mut_p53 and proved that mut_p53 directly regulates LINC01605 by binding to an enhancer region downstream of the LINC01605 locus. We also showed that the loss or downregulation of LINC01605 impairs cell migration in a breast cancer cell line. Eventually, by performing a combined analysis of RNA-seq data generated in mut_TP53 -silenced and LINC01605 knockout cells, we showed that LINC01605 and mut_p53 share common gene pathways. Overall, our findings underline the importance of ncRNAs in the mut_p53 network in breast and ovarian cancer cell lines and in particular the importance of LINC01605 in mut_p53 pro-migratory pathways.
Keyphrases
- long non coding rna
- poor prognosis
- long noncoding rna
- cell proliferation
- genome wide
- gene expression
- rna seq
- transcription factor
- genome wide identification
- cell migration
- protein protein
- binding protein
- copy number
- single cell
- endothelial cells
- induced apoptosis
- small molecule
- signaling pathway
- genome wide analysis
- cell death
- bioinformatics analysis
- machine learning
- autism spectrum disorder
- big data
- cell cycle arrest