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<p>Lactylation, a recently identified histone modification derived from lactate metabolism, has emerged as a critical regulator of epigenetic reprogramming, tumor proliferation, and immune evasion. In ovarian cancer, lactate dehydrogenase A (LDHA) and other metabolic enzymes contribute to lactate accumulation, which supports chemotherapy resistance and disease progression. Although lactylation is increasingly linked to therapy failure, its precise molecular connection with ovarian cancer, as well as its therapeutic potential are unclear. Traditional analytical approaches often fail to integrate the complexity of multi-omics, limiting the discovery of actionable lactylation-associated vulnerabilities. This research aims to develop an AI-driven multi-omics framework to identify lactylation-related genes, stratify patient drug responses, and establish prognostic signatures in ovarian cancer. Transcriptomic, epigenomic, pharmacogenomic, mutation, and clinical outcome data were collected from The Cancer Genome Atlas (TCGA), the Genomics of Drug Sensitivity in Cancer <i>(</i>GDSC<i>)</i>, and independent ovarian cancer cohorts. Deep learning models, including variational autoencoders (VAEs), Long Short-Term Memory (LSTM) networks, and Multitask Multilayer Perceptrons (MLPs) (LSTM-MLP), were applied for molecular subtyping, survival analysis, and IC50 prediction. Findings were validated through pathway enrichment, mutation mapping, immune infiltration profiling, and structure-guided drug repurposing, the proposed method achieved precision of (0.955). Key lactylation-related genes, including LDHA and SLC16A3, were associated with immune exhaustion and cisplatin resistance. The Gln-TEx score and lactylation risk signature robustly predicted patient survival and drug response across TCGA and validation cohorts. Perturbation sensitivity and repurposing analyses revealed novel therapeutic vulnerabilities. This study establishes a precision oncology framework that integrates lactylation biology with AI-driven analytics to uncover druggable targets, enhance patient stratification, and inform the design of multi-target therapies in ovarian cancer.</p>

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An AI-driven multi-omics framework identifies lactylation-mediated therapeutic targets to overcome drug resistance in ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月20日

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  • Lijia Zhang,
  • Qi Guo,
  • Xue Lei,
  • Xinyu Yin,
  • Yun Ling,
  • Ye Liu,
  • Songjiang Liu

摘要

Lactylation, a recently identified histone modification derived from lactate metabolism, has emerged as a critical regulator of epigenetic reprogramming, tumor proliferation, and immune evasion. In ovarian cancer, lactate dehydrogenase A (LDHA) and other metabolic enzymes contribute to lactate accumulation, which supports chemotherapy resistance and disease progression. Although lactylation is increasingly linked to therapy failure, its precise molecular connection with ovarian cancer, as well as its therapeutic potential are unclear. Traditional analytical approaches often fail to integrate the complexity of multi-omics, limiting the discovery of actionable lactylation-associated vulnerabilities. This research aims to develop an AI-driven multi-omics framework to identify lactylation-related genes, stratify patient drug responses, and establish prognostic signatures in ovarian cancer. Transcriptomic, epigenomic, pharmacogenomic, mutation, and clinical outcome data were collected from The Cancer Genome Atlas (TCGA), the Genomics of Drug Sensitivity in Cancer (GDSC), and independent ovarian cancer cohorts. Deep learning models, including variational autoencoders (VAEs), Long Short-Term Memory (LSTM) networks, and Multitask Multilayer Perceptrons (MLPs) (LSTM-MLP), were applied for molecular subtyping, survival analysis, and IC50 prediction. Findings were validated through pathway enrichment, mutation mapping, immune infiltration profiling, and structure-guided drug repurposing, the proposed method achieved precision of (0.955). Key lactylation-related genes, including LDHA and SLC16A3, were associated with immune exhaustion and cisplatin resistance. The Gln-TEx score and lactylation risk signature robustly predicted patient survival and drug response across TCGA and validation cohorts. Perturbation sensitivity and repurposing analyses revealed novel therapeutic vulnerabilities. This study establishes a precision oncology framework that integrates lactylation biology with AI-driven analytics to uncover druggable targets, enhance patient stratification, and inform the design of multi-target therapies in ovarian cancer.