Integrating single-cell and bulk transcriptomic analyses to explore key lactylation-related genes in benign prostatic hyperplasia

  • Sci Rep. 2026 May 25. doi: 10.1038/s41598-026-54516-x.
Xiaotong Pang  #  1  2  3 Deyong Zheng  #  4 Rongbin Zhou  #  2 Kun Dong  3 Fubo Wang  5  6  7
Affiliations
  • 1. Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Guangxi, 530021, China.
  • 2. Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • 3. Department of Organ Transplantation, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • 4. Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, China.
  • 5. Department of Urology, The First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Guangxi, 530021, China. [email protected].
  • 6. Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, Nanning, 530021, Guangxi, China. [email protected].
  • 7. School of Life Sciences, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning, 530021, Guangxi, China. [email protected].
  • # Contributed equally.
Abstract

Benign prostatic hyperplasia (BPH) is a prevalent age-related disorder characterized by chronic inflammation, metabolic dysregulation, and abnormal cellular proliferation. Protein lactylation, an emerging post-translational modification closely associated with cellular metabolism, has been implicated in the pathogenesis of various diseases. However, its role and associated molecular features in BPH remain uncharacterized. We integrated publicly available single-cell RNA Sequencing (scRNA-seq) and bulk RNA-seq datasets derived from BPH and normal prostate tissues. Utilizing machine learning algorithms (LASSO, Random Forest, and Boruta) strictly as feature selection tools, we identified key lactylation-related candidate genes. Subsequently, employing the BPH-1 cell line in vitro, we preliminarily investigated the potential biological functions of the prioritized epithelial target, AnxA2, through siRNA-mediated knockdown, Western blotting, ELISA, and cell proliferation assays. We computationally identified AnxA2 and IFI27 as key lactylation-related candidate genes, both of which were significantly downregulated in BPH tissues. scRNA-seq data revealed their cell-type specificity, demonstrating that AnxA2 is predominantly expressed in epithelial cells, whereas IFI27 is primarily expressed in endothelial cells. Furthermore, we uncovered a potential link between metabolism and inflammation involving AnxA2. Specifically, AnxA2 knockdown in BPH-1 cells was associated with alterations in glycolytic gene expression and a concomitant reduction in lactate production. Our data suggest that this decreased lactate level is associated with elevated secretion of the proinflammatory cytokine IL-6 and enhanced cellular proliferation. Additionally, cellular trajectory and communication analyses indicated that ANXA2⁺ epithelial cells and IFI27⁺ endothelial cells represent distinct subpopulations characterized by enhanced intercellular signaling capacities, suggesting their potential roles as critical molecular hubs within the BPH microenvironment. This study presents the first single-cell resolution landscape of lactylation-related gene activity in BPH. We computationally identified AnxA2 and IFI27 as key lactylation-related candidate genes. Importantly, our study suggests a potential link whereby AnxA2 deficiency is associated with BPH cell proliferation and potential metabolic and inflammatory alterations. These findings provide valuable new insights into the underlying pathophysiological mechanisms of BPH and lay the groundwork for the development of targeted therapeutic strategies.

Keywords
BPH; Lactate; Machine learning; Single-cell.
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