Comprehensive characterization of the molecular feature of T cells in laryngeal cancer: evidence from integrated single-cell and bulk RNA sequencing data using multiple machine learning approaches.

ABSTRACT:

Background

The clinical relevance of T cell-related molecules at single-cell resolution in laryngeal cancer (LC) has not been clarified.

Materials and methods

Three LC tissues and matching adjoining normal tissues from the hospital were used to perform 10X single-cell RNA sequencing. Hub T cell-related genes (TCRGs) were detected by applying ten machine learning (ML) techniques based TCGA and GEO databases, which were also utilized to create a prediction model (TCRG classifier) and a multicenter validation model. Lastly, we conducted a comprehensive analysis of the TCRG's correlation with immunological properties.

Results

The analysis of single-cell RNA-seq data revealed that T cells are the primary components of the tumor microenvironment (TME), are significantly inv

SUBMITTER: Cui J 

PROVIDER: S-EPMC11980214 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature