Pathologic evolution-related Gene Analysis based on both single-cell and bulk transcriptomics in Colorectal Cancer

Area of Science:

• Genomics and Bioinformatics
• Cancer Research
• Molecular Biology

Background:

• Colorectal cancer (CRC) patient outcomes vary significantly based on pathologic stage.
• The molecular signatures driving CRC pathologic evolution and prognosis remain incompletely understood.
• Identifying genes associated with cancer progression is crucial for improving patient stratification and treatment strategies.

Purpose of the Study:

• To identify genes associated with pathologic evolution in colorectal cancer (CRC) using single-cell and bulk transcriptomics.
• To develop and validate a novel cell infiltration classifier for predicting CRC prognosis.
• To define key marker genes that characterize the evolutionary trajectory of CRC.

Main Methods:

• Utilized a CRC single-cell transcriptomic dataset (GSE81861) and The Cancer Genome Atlas (TCGA) data for training.
• Employed graph-based clustering and pseudotime analysis to identify cell populations and construct evolutionary trajectories.
• Developed a cell infiltration classifier based on identified hub genes and validated its predictive performance in independent CRC cohorts.

Main Results:

• Epithelial and T cells were identified as key cell types associated with CRC pathologic stages.
• HOXC5, HOXC8, and BMP5 were identified as significant marker genes in the CRC pathologic evolution process.
• The developed cell infiltration classifier demonstrated high accuracy in predicting CRC pathologic stages and patient prognosis.

Conclusions:

• Pathologic evolution-related genes, including HOXC6, HOXC8, and BMP5, were identified in colorectal cancer.
• A novel cell infiltration classifier was proposed for forecasting CRC prognosis.
• The classifier effectively predicts patient outcomes based on pathologic stage-related hub genes.