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September 10, 2025Frontiers in PharmacologyOpen Access

Integrative analysis of lactylation related genes in prostate cancer: unveiling heterogeneity through single-cell RNA-seq, bulk RNA-seq and machine learning

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Authors

CZChenhao ZhouLDLifeng DingHWHuailan Wang

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Overview

Single-cell RNA sequencing unveiled distinct lactylation signatures and developed a machine learning prognostic model for prostate cancer, highlighting potential biomarkers.

Key Points

  • Single-cell RNA sequencing revealed distinct lactylation signatures, indicating heterogeneity in prostate cancer cell types.
  • Bulk RNA-seq identified 56 prognostic lactylation-related genes, classifying patients into two prognostic clusters.
  • A machine learning-based prognostic signature demonstrated robust predictive accuracy for treatment responses in prostate cancer.
  • Potential biomarkers identified through lactylation analysis may inform personalized treatment strategies in prostate cancer.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68c24009b210217d647998c5https://doi.org/10.3389/fphar.2025.1634985
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