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Genetics and Genomics

Lactylation-related prognostic signature characterized in pancreatic ductal adenocarcinoma through public scRNA-seq dataset and machine learning algorithms: the TOP2A-H3K18la-NQO1 axis orchestrates malignant progression

British Journal of Cancer (2026) Cite this article

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Abstract

Background

Lactate promotes histone lactylation, which affects protein transcription and translation, thereby influencing tumour cell progression. However, the role of lactylation in pancreatic ductal adenocarcinoma (PDAC) remains underexplored and warrants further investigation.

Methods

Single-cell RNA sequencing (scRNA-seq) data (GSE154778) underwent quality control, dimensionality reduction, and clustering. Lactylation scores were computed using the “AUCell” R package, and differential expression between high and low lactylation groups was analysed. A risk score model based on lactylation was developed using TCGA-PAAD, GSE57495, and GSE79668 datasets. The relationships between risk scores, clinical features, immune profiles, mutation burden, and biological functions were assessed. CUT&Tag analysis was employed to identify the target of TOP2A mediated by H3K18la. In vitro experiments, including CCK-8 assay, colony formation assay, wound healing assay, transwell migration assay, lactate quantification, Western blotting, and qRT‒PCR, in combination with subcutaneous xenograft models, were conducted to further validate the findings.

Results

We successfully established a lactylation-based prognostic risk score model for PDAC, which effectively distinguishes patient survival and biological characteristics. Additionally, we demonstrated that the lactate-TOP2A-H3K18la-NQO1 signalling axis forms a positive feedback loop that accelerates the malignant progression of PDAC.

Conclusions

This study presents a lactylation-related risk score model with significant potential for improving the management of PDAC patients. The identification of the lactate-TOP2A-H3K18la-NQO1 axis enhances the understanding of lactylation mechanisms in PDAC, thereby providing a foundation for targeted therapeutic approaches.

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Fig. 1: Identification of lactylation-related genes and pathways in PDAC based on single-cell transcriptomics.
The alternative text for this image may have been generated using AI.
Fig. 2: Development of a prognostic risk model for PDAC based on lactylation-related features using machine learning algorithms.
The alternative text for this image may have been generated using AI.
Fig. 3: Prognostic significance and biological mechanisms of lactylation-associated risk score.
The alternative text for this image may have been generated using AI.
Fig. 4: Predicting the efficacy of chemotherapy and immunotherapy.
The alternative text for this image may have been generated using AI.
Fig. 5: TOP2A-Induced Lactylation Enhances the Progression of PDAC.
The alternative text for this image may have been generated using AI.
Fig. 6: TOP2A-H3K18la-NQO1 axis orchestrates malignant progression in pancreatic cancer.
The alternative text for this image may have been generated using AI.
Fig. 7: In vivo validation of TOP2A-NQO1 axis promoting PDAC progression through EMT.
The alternative text for this image may have been generated using AI.
Fig. 8
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Data availability

The analysed data sets generated during the study are available from the corresponding authors on reasonable request.

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Acknowledgements

We would like to express our gratitude to the staff at the Public Scientific Research Platform of Zhongda Hospital Affiliated to Southeast University for their technical support. We also thank BioRender (https://app.biorender.com/) for their assistance in creating the flowcharts and graphical abstracts used in this study.

Funding

This project was supported by the Advanced Programme of the Affiliated Hospital of Xuzhou Medical University (Grant No. PYJH2025302), and the Xuzhou Full-Time Introduced Medical Distinguished Talent Programme (Grant No. 2025209001).

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Author notes

  1. These authors contributed equally: Haodong Tang, Tonglei Xu.

Authors and Affiliations

  1. Department of General Surgery, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China

    Haodong Tang, Li Liu, Yusheng Du, Aozhong Hu, Xiangxu Shi, Hongqin Ma, Ji Wang & Wenxing Zhao

  2. Department of Surgery, School of Medicine, Southeast University, Nanjing, Jiangsu, China

    Tonglei Xu, Siyuan Tan & Peiyuan Shen

  3. Department of Traditional Chinese Medicine, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China

    Dan Liu

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Contributions

HT, HM, and WZ conceived and designed the study. LL, DL, ST, PS, AH, XS, and YD were responsible for materials. HT and TX drafted the article. HM, JW, and WZ revised the article critically. All authors had final approval of the submitted version.

Corresponding authors

Correspondence to Haodong Tang, Hongqin Ma, Ji Wang or Wenxing Zhao.

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Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All patients involved in this study provided informed consent. The research was approved by the Ethics Committee of Zhongda Hospital, Southeast University (Approval Number: 2024ZDSYLL378-P01) and was conducted in accordance with the ethical standards stipulated in the 1964 Declaration of Helsinki. Animal experiments were conducted in accordance with the guidelines of the Institutional Animal Care and Use Committee of Southeast University, which also approved all animal procedures (Approval Number: 20230306002).

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Tang, H., Xu, T., Liu, L. et al. Lactylation-related prognostic signature characterized in pancreatic ductal adenocarcinoma through public scRNA-seq dataset and machine learning algorithms: the TOP2A-H3K18la-NQO1 axis orchestrates malignant progression. Br J Cancer (2026). https://doi.org/10.1038/s41416-026-03477-z

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