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.

ABSTRACT:

Background

Gastric cancer stem cells (GCSCs) are important tumour cells involved in tumourigenesis and gastric cancer development. However, their clinical value remains unclear due to the limitations of the available technologies. This study aims to explore the clinical significance of GCSCs, their connection to the tumour microenvironment, and their underlying molecular mechanisms.

Methods

Stem-like tumour cells were identified by mining single-cell transcriptomic data from multiple samples. Integrated analysis of single-cell and bulk transcriptome data was performed to analyse the role of stem-like tumour cells in predicting clinical outcomes by introducing the intermediate variable mRNA stemness degree (SD). Consensus clustering analysis was performed to develop an SD-rel

SUBMITTER: Shi Y 

PROVIDER: S-EPMC11547969 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature