Machine Learning Integration Framework Constructs a Lactylation-Associated Gene Signature to Improve Prognosis in Bladder Cancer.

BACKGROUND: Bladder cancer remains a significant challenge in oncology owing to its high recurrence rates and limited treatment options, particularly in cases of resistance to standard therapies. AIMS: Our study aimed to pinpoint a lactylation-associated gene signature capable of predicting prognosis and providing important theoretical support for drug development and precision therapy in bladder cancer patients. MATERIALS AND METHODS: Leveraging RNA sequencing data from the TCGA and GEO databases, we scrutinized the expression profiles of lactylation-associated genes and pinpointed a signature comprising eight genes strongly linked to prognosis based on a machine learning integrative framework. Our prognostic model, incorporating the expression levels of these lactylation-associated genes, demonstrated high accuracy in predicting patient outcomes, including survival rates and response to immunotherapy. Furthermore, functional analyses revealed the potential mechanisms through which lactylation-associated genes contribute to bladder cancer progression and treatment resistance. Further validation of the close association of these eight genes with bladder cancer was also confirmed through in vitro RT-PCR experiments and Human Protein Atlas (HPA). The drug enrichment analysis and molecular docking provide us with potential drugs and their binding modes with target proteins. To further investigate the relationship between the model gene and bladder cancer, we conducted a series of in vitro experiments. RESULTS: We found that knockdown of AHNAK reduced the proliferation, migration, and invasion abilities of bladder cancer cells and also promoted cell apoptosis. DISCUSSION: Overall, our study highlights the importance of lactylation-associated genes as prognostic markers and potential therapeutic targets in bladder cancer. CONCLUSION: Our identification of this gene signature lays the groundwork for personalized treatment strategies and enhanced patient management in clinical practice.