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Intramucosal Inoculation of Squamous Cell Carcinoma Cells in Mice for Tumor Immune Profiling and Treatment Response Assessment
Published on: April 22, 2019
Identification of S1PR4 as an immune modulator for favorable prognosis in HNSCC through machine learning
Chenshen Huang1,2, Fengshuo Zhu3,4, Hao Zhang5
1Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Abstract:
G protein-coupled receptors (GPCRs) are the largest family of membrane proteins and play a critical role as pharmacological targets. An improved understanding of GPCRs' involvement in tumor microenvironment may provide new perspectives for cancer therapy. This study used machine learning to classify head and neck squamous cell carcinoma (HNSCC) patients into two GPCR-based subtypes. Notably, these subtypes showed significant differences in prognosis, gene expression, and immune microenvironment, particularly CD8+ T cell infiltration. S1PR4 emerged as a key regulator distinguishing the subtypes, positively correlated with CD8+ T cell proportion and cytotoxicity in HNSCC. It was predominantly expressed in CX3CR1+CD8+ T cells among T cells. Upregulation of S1PR4 enhanced T cell function during CAR-T cell therapy, suggesting its potential in cancer immunotherapy. These findings highlight S1PR4 as an immune modulator for favorable prognosis in HNSCC, and offer a potential GPCR-targeted therapeutic option for HNSCC treatment.
Insights
Machine learning identified two G protein-coupled receptor (GPCR)-based subtypes in head and neck squamous cell carcinoma (HNSCC). The S1PR4 gene distinguished these subtypes, showing potential for targeted cancer immunotherapy.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- G protein-coupled receptors (GPCRs) are crucial pharmacological targets, with their role in the tumor microenvironment offering new cancer therapy avenues.
- Understanding GPCRs in head and neck squamous cell carcinoma (HNSCC) is vital for developing novel treatment strategies.
Purpose of the Study:
- To classify HNSCC patients into distinct GPCR-based subtypes using machine learning.
- To investigate the prognostic, gene expression, and immune microenvironment differences between these subtypes.
- To identify key GPCRs involved in HNSCC immune modulation.
Main Methods:
- Machine learning algorithms were employed to stratify HNSCC patients into two GPCR-based subtypes.
- Comparative analysis of prognosis, gene expression profiles, and immune cell infiltration (especially CD8+ T cells) was performed.
- The expression and function of key regulatory genes, such as S1PR4, were investigated.
Main Results:
- Two distinct GPCR-based HNSCC subtypes were identified, exhibiting significant differences in patient prognosis and immune microenvironment.
- S1PR4 was identified as a key regulator, positively correlating with CD8+ T cell infiltration and cytotoxicity.
- S1PR4 expression was prominent in CX3CR1+CD8+ T cells, and its upregulation enhanced T cell function in CAR-T therapy models.
Conclusions:
- S1PR4 acts as an immune modulator associated with a favorable prognosis in HNSCC.
- Targeting GPCRs, specifically S1PR4, presents a promising therapeutic strategy for HNSCC immunotherapy.
- These findings offer new insights into GPCR-driven mechanisms within the HNSCC tumor microenvironment.
