This research demonstrates a prognostic model for lung adenocarcinoma, predicting outcomes using macrophage mitophagy biomarkers and machine learning techniques.
Mitochondrial autophagy (mitophagy) in macrophages is crucial yet poorly understood within the lung adenocarcinoma (LUAD) tumor microenvironment. This study aimed to identify key macrophage mitophagy-related genes and develop a robust prognostic model for LUAD patients. By integrating single-cell transcriptomics with machine learning algorithms, including LASSO, SVM, and random forest, we identified TUBB6 and CAT as core prognostic genes. A novel risk model based on these genes (RiskScore = 0.225 × TUBB6 - 0.19 × CAT) was constructed and validated, demonstrating that patients in the high-risk group had significantly shorter overall survival (P < 0.001). The high-risk score also correlated with an altered immune microenvironment and increased sensitivity to chemotherapies like cisplatin and gemcitabine. Furthermore, in vitro experiments confirmed that macrophage-specific overexpression of TUBB6 significantly enhanced LUAD cell proliferation, migration, and invasion through secreted factors. Our findings establish a reliable, macrophage mitophagy-based prognostic model and highlight TUBB6 and CAT as novel biomarkers and potential therapeutic targets, offering new avenues for precision medicine in LUAD.
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Yang et al. (2025) studied this question.
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