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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

  • 0Department of Maxillofacial and Otorhinolaryngological Oncology, Tianjin Medical University Cancer Institute and Hospital, Key Laboratory of Basic and Translational Medicine on Head & Neck Cancer, Tianjin, Key Laboratory of Cancer Prevention and Therapy, Tianjin Cancer Institute, National Clinical Research Center of Cancer, Tianjin, PR China.
Annals of Medicine +

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April 3, 2025

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April 3, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study identifies key T cell-related genes (TCRGs) in laryngeal cancer using single-cell sequencing and machine learning. A novel TCRG classifier accurately predicts prognosis and immunotherapy response.

Area Of Science

  • Oncology
  • Immunology
  • Bioinformatics

Background

  • The clinical significance of T cell-related molecules in laryngeal cancer (LC) at single-cell resolution remains unclear.
  • Understanding these molecular players is crucial for advancing LC diagnostics and therapeutics.

Purpose Of The Study

  • To elucidate the role of T cell-related genes (TCRGs) in laryngeal cancer using single-cell RNA sequencing.
  • To develop and validate a predictive model for prognosis and immunotherapy response in LC based on TCRGs.

Main Methods

  • Single-cell RNA sequencing was performed on three LC tissues and adjacent normal tissues.
  • Ten machine learning techniques were employed to identify hub TCRGs from TCGA and GEO databases.
  • A TCRG classifier was developed and validated across multiple cohorts, analyzing its correlation with immunological properties.

Main Results

  • T cells are identified as key components of the LC tumor microenvironment, involved in differentiation and intercellular communication.
  • The developed TCRG classifier demonstrated excellent prognostic value (mean C-index 0.66) and served as an independent risk factor.
  • TCRGs showed significant associations with immune scores, cell infiltration, immune pathways, and predicted immunotherapy response (IPS, TCIC, TIDE, IMvigor210).

Conclusions

  • A TCRG classifier is a valuable tool for predicting laryngeal cancer patient prognosis.
  • This classifier can aid in guiding laryngeal function preservation strategies.
  • It identifies patients likely to respond to immunotherapy, potentially transforming therapeutic approaches.

Keywords: