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

Area of Science:

• Genomics
• Cancer Biology
• Bioinformatics

Background:

• Single-nucleotide variants (SNVs) and RNA edits drive cancer progression.
• Understanding these variants at a single-cell level is crucial for precision medicine.

Purpose of the Study:

• To establish CanCellVar, a comprehensive database and visualization tool for single-cell variants within the tumor microenvironment.
• To provide a high-resolution view of cancer by cataloging cellular and molecular variant information.

Main Methods:

• Collected and analyzed approximately 3 million variants (SNVs and A>G RNA edits) from over 2.7 million cells across 37 cancer types.
• Integrated basic annotation, cellular/molecular functions, and clinical relevance (grade, treatment, metastasis) for each variant.
• Developed tools for variant retrieval, cell-cell interaction analysis, gene expression, and trajectory analysis.

Main Results:

• CanCellVar contains ~1.4 million SNVs and ~1.4 million A>G RNA edits from 5 major cell types in 37 cancer types.
• The database links variants to crucial clinical information and cellular functions.
• Integrated analysis tools facilitate deeper investigation into variant impact.

Conclusions:

• CanCellVar serves as a valuable resource for studying single-cell variations in human tumors.
• The database supports research into variant functions, tumor evolution, and therapeutic strategies.
• Facilitates a refined understanding of cancer through single-cell variant analysis.