The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing.

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Previous cancer studies faced reproducibility challenges due to analyzing mixed tumor tissues.
• Single-cell transcriptome analysis offers a more robust approach with lower variance compared to bulk tissue analysis.

Purpose of the Study:

• To identify discriminative transcripts between colorectal cancer (CRC) and normal epithelial cells using single-cell transcriptome data.
• To elucidate potential molecular mechanisms underlying CRC development.

Main Methods:

• Single-cell RNA sequencing was performed on 272 CRC epithelial cells and 160 normal epithelial cells.
• Advanced machine learning methods were employed to identify 342 discriminative transcripts.
• Functional enrichment analysis was conducted on the identified transcripts.

Main Results:

• 342 discriminative transcripts were identified, including key genes like LGALS4, PHGR1, and C15orf48.
• Upregulated transcripts in CRC cells were enriched in pathways such as Ribosome and p53 signaling.
• Downregulated transcripts were enriched in pathways including Mineral absorption and Oxidative phosphorylation.

Conclusions:

• Single-cell transcriptome analysis provides a robust method for identifying cancer-specific gene expression patterns.
• The identified discriminative transcripts and enriched pathways offer novel insights into colorectal cancer biology.
• This approach enhances the understanding of CRC mechanisms and aids in developing more reproducible cancer research.