Cancer biotherapy & radiopharmaceuticals · 2026
Lactylation Signatures as Predictors of Prognosis and Therapeutic Response in Lung Adenocarcinoma: Implications for Ultrasound-Based Therapeutic Strategies.
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Cai Qingchun, Huang Chenglu, Huang Yu, Bu Shi, Wang Zhiqiang
Аннотация
BACKGROUND: Lactylation, a unique post-translational alteration, has been identified as an important epigenetic regulator of cancer metabolism, immune evasion, and treatment resistance. Recent research reveals that ultrasound-based biophysical stimulation may change tumor microenvironmental variables that affect lactate metabolism and subsequent lactylation processes. The purpose of this study was to better understand the predictive significance of lactylation-associated genes in lung adenocarcinoma (LUAD) and how they could interact with ultrasound-mediated treatment response.
METHODS: Transcriptomic data from 867 LUAD samples were used to identify lactylation-related genes. Consensus clustering was used to separate LUAD cases into various lactylation subtypes. Differentially expressed genes were assessed for functional enrichment, mutation load, and immunological infiltration. A prognostic risk model was created using multivariate Cox regression analysis.
RESULTS: Nineteen major lactylation-related genes divided patients with LUAD into three molecular subgroups with different clinical outcomes. A 10-gene prognostic model accurately predicted overall survival and was associated with stronger tumor stemness, increased mutational frequency, and decreased immunotherapy effectiveness in high-risk patients. Notably, computational predictions suggested that metabolic regulation might slow lactylation-driven tumor development, implying a synergistic treatment window.
CONCLUSION: Lactylation-associated gene profiles may serve as prognostic biomarkers and therapeutic modulators in LUAD. The combination of ultrasound-based therapies targeting lactylation pathways could be a viable technique for improving precision oncology and overcoming resistance mechanisms in lung cancer therapy.
METHODS: Transcriptomic data from 867 LUAD samples were used to identify lactylation-related genes. Consensus clustering was used to separate LUAD cases into various lactylation subtypes. Differentially expressed genes were assessed for functional enrichment, mutation load, and immunological infiltration. A prognostic risk model was created using multivariate Cox regression analysis.
RESULTS: Nineteen major lactylation-related genes divided patients with LUAD into three molecular subgroups with different clinical outcomes. A 10-gene prognostic model accurately predicted overall survival and was associated with stronger tumor stemness, increased mutational frequency, and decreased immunotherapy effectiveness in high-risk patients. Notably, computational predictions suggested that metabolic regulation might slow lactylation-driven tumor development, implying a synergistic treatment window.
CONCLUSION: Lactylation-associated gene profiles may serve as prognostic biomarkers and therapeutic modulators in LUAD. The combination of ultrasound-based therapies targeting lactylation pathways could be a viable technique for improving precision oncology and overcoming resistance mechanisms in lung cancer therapy.