A comprehensive landscape of transcription profiles and data resources for human leukemia

Author:

Luo Mei1234ORCID,Miao Ya-Ru3,Ke Ya-Juan56,Guo An-Yuan13ORCID,Zhang Qiong1ORCID

Affiliation:

1. 1Research Center of Clinical Medicine, Affiliated Hospital of Nantong University, Nantong, China

2. 2Department of Dermatology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

3. 3Center for Artificial Intelligence Biology, Hubei Bioinformatics & Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China

4. 4National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People’s Republic of China

5. 5Dian Diagnostics Group Co, Ltd, Hangzhou, China

6. 6Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Hangzhou, China

Abstract

Abstract As a heterogeneous group of hematologic malignancies, leukemia has been widely studied at the transcriptome level. However, a comprehensive transcriptomic landscape and resources for different leukemia subtypes are lacking. Thus, in this study, we integrated the RNA sequencing data sets of >3000 samples from 14 leukemia subtypes and 53 related cell lines via a unified analysis pipeline. We depicted the corresponding transcriptomic landscape and developed a user-friendly data portal LeukemiaDB. LeukemiaDB was designed with 5 main modules: protein-coding gene, long noncoding RNA (lncRNA), circular RNA, alternative splicing, and fusion gene modules. In LeukemiaDB, users can search and browse the expression level, regulatory modules, and molecular information across leukemia subtypes or cell lines. In addition, a comprehensive analysis of data in LeukemiaDB demonstrates that (1) different leukemia subtypes or cell lines have similar expression distribution of the protein-coding gene and lncRNA; (2) some alternative splicing events are shared among nearly all leukemia subtypes, for example, MYL6 in A3SS, MYB in A5SS, HMBS in retained intron, GTPBP10 in mutually exclusive exons, and POLL in skipped exon; (3) some leukemia-specific protein-coding genes, for example, ABCA6, ARHGAP44, WNT3, and BLACE, and fusion genes, for example, BCR-ABL1 and KMT2A-AFF1 are involved in leukemogenesis; (4) some highly correlated regulatory modules were also identified in different leukemia subtypes, for example, the HOXA9 module in acute myeloid leukemia and the NOTCH1 module in T-cell acute lymphoblastic leukemia. In summary, the developed LeukemiaDB provides valuable insights into oncogenesis and progression of leukemia and, to the best of our knowledge, is the most comprehensive transcriptome resource of human leukemia available to the research community.

Publisher

American Society of Hematology

Subject

Hematology

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