DAP Kinase-Related Apoptosis-Inducing Protein Kinase 2 (DRAK2) Is a Key Regulator and Molecular Marker in Chronic Lymphocytic Leukemia.
Katarzyna SzoltysekCarmela CiardulloPeixun ZhouAnna WalaszczykElaine WillmoreVikki RandScott MarshallAndy HallChristine J HarrisonJeyanthy EswaranMeera SoundararajanPublished in: International journal of molecular sciences (2020)
Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in the Western World and it is characterized by a marked degree of clinical heterogeneity. An impaired balance between pro- and anti-apoptotic stimuli determines chemorefractoriness and outcome. The low proliferation rate of CLL cells indicates that one of the primary mechanisms involved in disease development may be an apoptotic failure. Here, we study the clinical and functional significance of DRAK2, a novel stress response kinase that plays a critical role in apoptosis, T-cell biology, and B-cell activation in CLL. We have analyzed CLL patient samples and showed that low expression levels of DRAK2 were significantly associated with unfavorable outcome in our CLL cohort. DRAK2 expression levels showed a positive correlation with the expression of DAPK1, and TGFBR1. Consistent with clinical data, the downregulation of DRAK2 in MEC-1 CLL cells strongly increased cell viability and proliferation. Further, our transcriptome data from MEC-1 cells highlighted MAPK, NF-κB, and Akt and as critical signaling hubs upon DRAK2 knockdown. Taken together, our results indicate DRAK2 as a novel marker of CLL survival that plays key regulatory roles in CLL prognosis.
Keyphrases
- chronic lymphocytic leukemia
- cell cycle arrest
- signaling pathway
- induced apoptosis
- cell death
- pi k akt
- oxidative stress
- endoplasmic reticulum stress
- poor prognosis
- protein kinase
- cell proliferation
- transcription factor
- electronic health record
- anti inflammatory
- single cell
- gene expression
- binding protein
- big data
- acute myeloid leukemia
- bone marrow
- dna methylation
- tyrosine kinase
- lps induced
- genome wide
- young adults
- machine learning
- nuclear factor
- artificial intelligence
- rna seq