The landscape of extrachromosomal circular DNA (eccDNA) in the normal hematopoiesis and leukemia evolution.
Tiansheng ZengWenhui HuangLongzhen CuiPei ZhuQing LinWenjuan ZhangJunyi LiCong DengZhihua WuZeyong HuangZhiyong ZhangTingting QianWei XieMin XiaoYingyu ChenWenjie ShiPublished in: Cell death discovery (2022)
Elevated extrachromosomal circular DNA (eccDNA) has been reported to accelerate tumor pathogenesis. Although the eccDNA profiles of other tumors have been established, the landscape of the eccDNA of acute myeloid leukemia (AML) has not been revealed. Our study first depicted the eccDNA profile of normal hematopoiesis and AML evolution by exploiting the ATAC-seq and RNA-seq data from nine healthy donors and 12 AML patients, which contained a total of 137 cell samples and 96 RNA-seq samples (including 16 blood cell types of the normal hematopoietic and AML hierarchies). We found the number of eccDNAs generally increased with the evolution of normal hematopoiesis and AML. The ecDNAs and ring chromosomes were found to reappear both in normal hematopoiesis and AML cells. Furthermore, we compared the eccDNAs of AML with normal cells. There were almost 300 AML-specific genes, including the known oncogenes NRAS, MCL1, EVI1, GATA2, WT1, and PAK1. And the ecDNA (chr11: 58668376-58826008) occurred in five out of 17 AML evolution-related cells, which was associated with the high expression of the GLYATL1 gene and the high expressed GLYATL1 was a poor prognostic factor. In conclusion, the eccDNA profiles of normal hematopoiesis and AML evolution were depicted and the recurrent eccDNAs we revealed might be utilized in the treatment of AML as biomarkers.
Keyphrases
- acute myeloid leukemia
- single cell
- rna seq
- allogeneic hematopoietic stem cell transplantation
- prognostic factors
- induced apoptosis
- genome wide
- cell cycle arrest
- stem cells
- transcription factor
- artificial intelligence
- patient reported outcomes
- cell death
- acute lymphoblastic leukemia
- ejection fraction
- endoplasmic reticulum stress
- dna methylation
- big data
- cell proliferation
- machine learning
- binding protein
- electronic health record
- circulating tumor cells
- nucleic acid