Integration of Single-Cell Sequencing and Bulk Transcriptome Data Develops Prognostic Markers Based on PCLAF⁺ Stem-Like Tumor Cells Using Artificial Neural Network in Gastric Cancer

Stem-like tumor cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyze the role of stem-like tumor cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree.