A Method for the Identification of Lactate Metabolism-related Prognostic Biomarkers and Its Validations in Non-small Cell Lung Cancer

Overview

Journal Sci Rep

Specialty Science

Date 2025 Feb 17

PMID 39962075

Authors

Weiyang Yang

Miao Gu

Yabin Zhang

Yunfan Zhang

Tao Liu

Di Wu

Juntao Deng

Min Liu

Youwei Zhang

Affiliations

Soon will be listed here.

Abstract

Lactate metabolism (LM) plays a crucial role in tumor progression and therapy resistance in non-small cell lung cancer (NSCLC). Several methods had been developed for NSCLC prognosis prediction based on lactate metabolism-related information. The existing methods for the construction of prognosis prediction models are mostly based on single models such as linear models, SVM, and decision trees. Prognosis biomarkers and prognosis prediction models based on this kind of methods often have limited prognostic performance. In this study, we proposed a novel methodology for constructing prognosis prediction model and identifying lactate-related prognostic biomarkers in NSCLC. We first screened for lactate metabolism-related malignant genes from the scRNA-Seq data of NSCLC malignant cells. We proposed a Cox elastic-net regression combined with genetic algorithm (GA-EnCox) to predict prognosis and optimize the selection of key biomarkers. We identified five key LM-related genes (LYPD3, KRT8, CCT6A, PSMB7, and HMGA1) that significantly correlated with patient prognosis in LUAD cohorts. The prognostic model constructed with these genes outperformed other currently popular models across multiple datasets, demonstrating stable predictive capability. Survival analysis based on bulk RNA-Seq data demonstrated that the low-risk group had significantly better overall survival compared to the high-risk group. Further analysis revealed that lactate metabolism-related prognosis risk might be associated with monocyte lineages such as macrophages and DC's infiltration and these prognosis biomarkers may indicate the therapeutic responses of immune checkpoint inhibitors for NSCLC patients. More importantly, we validated HMGA1 and KRT8 at protein level and their association with histologic grades, stages, and clinical outcomes in consistently treated in-house NSCLC cohorts. Finally, we experimentally validated one of the biomarkers, HMGA1, confirming its role in promoting malignant phenotypes of NSCLC. This study provides valuable insights into the role of lactate metabolism-related biomarkers and their impact on patient outcomes, it was expected to provide important reference value for prognosis assessment and personalized treatment decision of NSCLC patients.

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