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Updated: May 6, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Comprehensive characterization of the molecular feature of T cells in laryngeal cancer: evidence from integrated
Jie Cui1, Yangpeng Ou2, Kai Yue1
1Department 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.
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 involved in cell differentiation pathways, and play a considerable role in intercellular communication. Based on 10 ML approaches, TCRG classifier were identified to develop and validate. The TCRG classifier exhibited excellent prognostic values with a mean C-index of 0.66 in six cohorts, serving as an independent risk factor (p < 0.01). Additionally, the TCRG exhibited a significant relationship with immune score, immune cell infiltration, immune-associated pathways, immune checkpoint inhibitors, human leukocyte antigen, and immunogenicity. Lastly, IPS, TCIC, TIDE, and IMvigor210 cohort analysis illustrated that the immunotherapy response may be accurately predicted using TCRG.
Conclusion:
A TCRG classifier is an excellent resource for predicting a patient's prognosis, potentially guiding the preservation of laryngeal function, and identifying patients who may have a positive response to immunotherapy, which might have profound effects on therapeutic practice.
