March 3, 2026Open Access

Development and validation of a radiomics model for lactate metabolism genes-based stratification and prognostic prediction in head and neck squamous cell carcinoma

Key Points

The lactate classification score significantly correlates with overall survival in head and neck squamous cell carcinoma patients.
Among 204 lactate-related genes, 24 differentially expressed genes were identified as critical for prognostic assessment.
A support vector machine model developed from extracted radiomic features achieved notable AUC values in predicting lactate-based stratification.
High radiomics scores correlate with poor outcomes and indicate an immunosuppressive microenvironment in HNSCC cases.

Abstract

Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy characterized by altered lactate metabolism, where traditional prognostic indicators are insufficient for precision medicine. This study aimed to construct an enhanced CT radiomics model integrated with lactate metabolism gene-related (LMGR) genomic signatures for HNSCC prognosis using TCGA and TCIA databases. A cohort of 399 HNSCC patients was analyzed. Analysis of 204 lactate-related genes identified 24 differentially expressed LMGR genes (DELMGR). Univariate Cox regression revealed that among these, PKLR, IL19, and CXCL9 exhibited protective effects (HR = 0.932, 0.885, and 0.931, respectively). A lactate classification score (LCS) was derived from the analysis of these three genes, demonstrating a significant correlation with overall survival (OS) in both univariate (HR = 1.807, 95 % CI: 1.346-2.424, P < 0.001) and multivariate assessments (HR = 1.772, 95 % CI: 1.296-2.424, P < 0.001). From enhanced CT images, 2060 radiomic features were extracted. Subsequently, after feature selection using mRMR and RFE algorithms, a support vector machine (SVM) model was built to predict LCS, which generated a radiomics score (RS). The model demonstrated AUC values of 0.773 and 0.760 in the training and validation datasets, respectively. The RS distribution significantly differed between lactate subtypes in the training cohort (P < 0.001), with specifically higher RS in the high-risk LCS group. High RS was associated with poor OS (HR = 3.582, 95 % CI: 1.240-10.348, P = 0.018) and was correlated with clinical features such as the perineural invasion and the margin status. Mechanistic analysis indicated that the high RS group was enriched in an immunosuppressive microenvironment and was associated with fatty acid metabolism pathways. This enhanced CT-based radiomics model effectively predicts lactate-based stratification, demonstrating potential prognostic value in HNSCC and providing novel biomarkers as well as a non-invasive predictive tool for prognostic assessment.

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Cite This Study

Yuan et al. (Sat,) studied this question.

synapsesocial.com/papers/69a75a26c6e9836116a1fb4c https://doi.org/https://doi.org/10.1016/j.ejrad.2026.112666

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