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Subtyping and risk model construction based on taurine metabolism-related genes for predicting prognosis and immune
Lei Wu1, Minzhe Li2, Jia Zhang3
1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
Objectives:
Lung adenocarcinoma (LUAD) remains a major cause of cancer-related deaths worldwide. While taurine has been shown to suppress tumor growth in laboratory models, its relationship with LUAD patient survival remains unclear. This study constructs a taurine metabolism-based prognostic model for LUAD to predict survival and immunotherapy response, supporting personalized treatment.
Methods:
Using transcriptomic and clinical data from public databases (TCGA and GEO), we identified prognostic genes linked to taurine metabolism in LUAD. Patients were classified into two molecular subtypes using clustering analysis. Key genes were verified experimentally (qRT-PCR), and a 9-gene risk model was developed using machine learning approaches. We then analyzed immune infiltration, tumor mutations, immunotherapy response, and drug sensitivity across subtypes and risk groups. Regulatory networks involving transcription factors and miRNAs were also constructed.
Results:
A 9-gene signature effectively stratified LUAD patients into high- and low-risk groups. High-risk patients showed activated tumor growth pathways and an immune-suppressive microenvironment, resulting in poorer survival and limited response to immunotherapy. In contrast, low-risk patients exhibited favorable outcomes and enhanced immune activity. We also identified STAT3/GATA2 and hsa-miR-98-5p as key regulatory factors.
Conclusions:
This study highlights the important role of taurine metabolism in LUAD progression and immunity. The proposed gene signature offers a new tool for prognosis prediction and may help guide personalized treatment strategies.
