Lactylation-based machine algorithm combined with multi-omics analysis to predict prognosis in cervical cancer

Area of Science:

• Oncology
• Biochemistry
• Genomics

Background:

• Lactylation's role in cervical cancer is largely unknown.
• Understanding lactylation mechanisms is crucial for developing novel therapeutic strategies.

Purpose of the Study:

• To identify lactylation-associated genes (LAGs) in cervical cancer.
• To develop and validate a prognostic model based on LAGs.
• To explore the therapeutic potential of identified LAGs.

Main Methods:

• Integrated multi-omics data (RNA-seq, single-cell) from TCGA and GEO.
• Employed differential expression analysis, WGCNA, and machine learning for model construction.
• Validated the model using Cox regression, ROC analysis, immune profiling, and functional enrichment.

Main Results:

• Developed a 14-LAG-based prognostic model with robust predictive performance.
• Identified significant immune infiltration differences and enrichment in metabolic pathways.
• HMGN1 was causally linked to cervical cancer risk and its inhibition suppressed cancer cell proliferation.

Conclusions:

• A reliable LAG-based prognostic model for cervical cancer was established.
• Key lactylation-related mechanisms, including HMGN1's role, were uncovered.
• Findings provide insights for biomarker discovery and personalized therapeutic strategies in cervical cancer.