ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1647384
This article is part of the Research TopicThe Role of Metabolic Reprogramming in Tumor TherapyView all 20 articles
Integrative Single-Cell and Machine Learning Analysis Predicts Lactylation-Driven Therapy Resistance in Prostate Cancer: A Molecular Docking and Experiments-Validated Framework for Treatment Optimization
Provisionally accepted
- 1The Affiliated Hospital of Southwest Medical University, Luzhou, China
- 2Southwest Medical University, Luzhou, China
- 3Santai Hospital Affiliated to North Sichuan Medical College, Mianyang, China
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Background: Prostate cancer (PCa) is a common malignancy in males. Predicting its prognosis and addressing drug resistance remain challenging. This study develops a novel prognostic model focusing on lactylation and resistance, which plays a crucial role in tumor biology. Methods: Single-cell analysis was employed to identify subpopulations expressing lactylation-related genes. Transcriptomic sequencing was used to identify drug resistance-associated genes. Univariate Cox proportional hazards models and machine learning techniques were used to identify prognostic genes, assisting in the development of a risk assessment framework. Additionally, we investigated how features related to lactylation and drug resistance correlate with clinical characteristics, the tumor microenvironment, and treatment responses, revealing potential interconnections. Results: In this study, a model composed of 29 biomarkers was developed by integrating single-cell data and machine learning algorithms. The model predictive efficacy was validated through Kaplan-Meier (KM) analysis, univariate Cox (HR=3.59, 95%CI: 2.78-4.63) and multivariate Cox (HR=2.81, 95%CI: 1.96-4.03) regression. Comprehensive analysis revealed significant differences in tumor immune dysfunction and exclusion (TIDE) scores, immunophenoscore (IPS) scores, and chemotherapy drug sensitivity between high-risk and low-risk groups, suggesting that specific biomarkers may be closely associated with prognosis. Furthermore, molecular docking analysis and experiments were conducted to explore the relationship between drug resistance and risk gene-encoded proteins. Conclusions: The prognostic model effectively predicts the progression-free interval (PFI) and drug response, with accurate risk stratification for PCa patients. Our findings highlight the potential of risk genes in the development of personalized treatment strategies and enhancing PCa prognostic assessment.
Keywords: Prostate adenocarcinoma, Drug Resistance, lactylation, single-cell sequencing, machine learning, biomarkers
Received: 15 Jun 2025; Accepted: 15 Aug 2025.
Copyright: © 2025 Liu, Wang, Li, Zeng, Wu, Zhu, Zhou and Deng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Tao Zhou, Santai Hospital Affiliated to North Sichuan Medical College, Mianyang, China
Qingfu Deng, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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