Translational cancer research · 2026
A pathomics model for predicting lactate-related prognostic risk groups and their biological mechanisms in lung adenocarcinoma.
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Zhou Lingling, Liu Wen, Pan Ye, Lei Wan, Yan Lingfei, Zou Jingfang, Qian Kejian, Wang Fei
Аннотация
BACKGROUND: Lung adenocarcinoma (LUAD) exhibits considerable heterogeneity in postoperative survival, underscoring the need for robust prognostic biomarkers. Given the emerging role of lactate metabolism in cancer progression, this study aimed to develop a pathomics model for prognostic stratification by predicting lactate-related prognostic risk groups.
METHODS: Using data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project, we performed non-negative matrix factorization to stratify patients based on lactate-related genes. A lactate-related prognostic model was constructed via least absolute shrinkage and selection operator (LASSO)-Cox regression. Patients were then randomly split into training and validation sets to develop and evaluate an XGBoost-based pathomics model for prognostic risk groups prediction. Gene set variation analysis (GSVA), immune cell infiltration analysis, and mutational landscape analysis, were further conducted to explore the underlying mechanisms.
RESULTS: A four-gene lactate-related prognostic signature (GAPDH, PGK1, SOCS3, and CXCL9) was constructed. The pathomics model achieved an area under the receiver operating characteristic curve (AUC) of 0.825 [95% confidence interval (CI): 0.773-0.877] in the training set and 0.700 (95% CI: 0.592-0.808) in the validation set for predicting lactate-related prognostic risk groups. The calibration curve indicated excellent agreement between the pathomics model's predictions and actual observations. Moreover, the high-pathomics score (PS) phenotype, characterized by elevated M2 macrophage infiltration and a higher TP53 mutation rate, exhibited significant enrichment of tumor-related pathways.
CONCLUSIONS: We developed a pathomics model for predicting lactate-related prognostic risk groups in LUAD. The model showed moderate predictive performance, suggesting potential utility for prognostic stratification and treatment planning, though further external validation is needed.
METHODS: Using data from The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project, we performed non-negative matrix factorization to stratify patients based on lactate-related genes. A lactate-related prognostic model was constructed via least absolute shrinkage and selection operator (LASSO)-Cox regression. Patients were then randomly split into training and validation sets to develop and evaluate an XGBoost-based pathomics model for prognostic risk groups prediction. Gene set variation analysis (GSVA), immune cell infiltration analysis, and mutational landscape analysis, were further conducted to explore the underlying mechanisms.
RESULTS: A four-gene lactate-related prognostic signature (GAPDH, PGK1, SOCS3, and CXCL9) was constructed. The pathomics model achieved an area under the receiver operating characteristic curve (AUC) of 0.825 [95% confidence interval (CI): 0.773-0.877] in the training set and 0.700 (95% CI: 0.592-0.808) in the validation set for predicting lactate-related prognostic risk groups. The calibration curve indicated excellent agreement between the pathomics model's predictions and actual observations. Moreover, the high-pathomics score (PS) phenotype, characterized by elevated M2 macrophage infiltration and a higher TP53 mutation rate, exhibited significant enrichment of tumor-related pathways.
CONCLUSIONS: We developed a pathomics model for predicting lactate-related prognostic risk groups in LUAD. The model showed moderate predictive performance, suggesting potential utility for prognostic stratification and treatment planning, though further external validation is needed.