CanCellVar: A database for single-cell variants map in human cancer.

Numerous variants, including both single-nucleotide variants (SNVs) in DNA and A>G RNA edits in mRNA as essential drivers of cellular proliferation and tumorigenesis, are commonly associated with cancer progression and growth. Thus, mining and summarizing single-cell variants will provide a refined and higher-resolution view of cancer and further contribute to precision medicine. Here, we established a database, CanCellVar, which aims to provide and visualize the comprehensive atlas of single-cell variants in tumor microenvironment. The current CanCellVar identified ∼3 million variants (∼1.4 million SNVs and ∼1.4 million A>G RNA edits) involved in 2,754,531 cells of 5 major cell types across 37 cancer types. CanCellVar provides the basic annotation information as well as cellular and molecular function properties of variants. In addition, the clinical relevance of variants can be obtained including tumor grade, treatment, metastasis, and others. Several flexible tools were also developed to aid retrieval and to analyze cell-cell interactions, gene expression, cell-development trajectories, regulation, and molecular structure affected by variants. Collectively, CanCellVar will serve as a valuable resource for investigating the functions and characteristics of single-cell variations and their roles in human tumor evolution and treatment.

MicroProteinDB: A database to provide knowledge on sequences, structures and function of ncRNA-derived microproteins.

Omics-based technologies have revolutionized our comprehension of microproteins encoded by ncRNAs, revealing their abundant presence and pivotal roles within complex functional landscapes. Here, we developed MicroProteinDB (http://bio-bigdata.hrbmu.edu.cn/MicroProteinDB), which offers and visualizes the extensive knowledge to aid retrieval and analysis of computationally predicted and experimentally validated microproteins originating from various ncRNA types. Employing prediction algorithms grounded in diverse deep learning approaches, MicroProteinDB comprehensively documents the fundamental physicochemical properties, secondary and tertiary structures, interactions with functional proteins, family domains, and inter-species conservation of microproteins. With five major analytical modules, it will serve as a valuable knowledge for investigating ncRNA-derived microproteins.

A machine learning model identifies M3-like subtype in AML based on PML/RARα targets.

The typical genomic feature of acute myeloid leukemia (AML) M3 subtype is the fusion event of PML/RARα, and ATRA/ATO-based combination therapy is current standard treatment regimen for M3 subtype. Here, a machine-learning model based on expressions of PML/RARα targets was developed to identify M3 patients by analyzing 1228 AML patients. Our model exhibited high accuracy. To enable more non-M3 AML patients to potentially benefit from ATRA/ATO therapy, M3-like patients were further identified. We found that M3-like patients had strong GMP features, including the expression patterns of M3 subtype marker genes, the proportion of myeloid progenitor cells, and deconvolution of AML constituent cell populations. M3-like patients exhibited distinct genomic features, low immune activity and better clinical survival. The initiative identification of patients similar to M3 subtype may help to identify more patients that would benefit from ATO/ATRA treatment and deepen our understanding of the molecular mechanism of AML pathogenesis.