Journal Article Epublish 3 citations
PubMed: 40263498 DOI: 10.1038/s41598-025-96683-3
Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system.
Sci Rep 15(1):13901
Authors: Junxiao Shen, Luyuan Lu, Zujie Chen, Wei Guo, Shuwen Wang, Ziqiao Liu, Xuke Gong, Yiming Qi, Ruyi Jin, Cheng Zhang
Affiliations
- Department of Urology, The Fourth Affiliated Hospital of the School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, 322000, China.
- Department of General Surgery, The Fourth Affiliated Hospital of the School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, 322000, China.
- Department of Dermatology, NHC Key Laboratory of Immunodermatology, The First Hospital of China Medical University, China Medical University, Shenyang, 110001, People's Republic of China.
- Department of Urology, The Fourth Affiliated Hospital of the School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, 322000, China.
Abstract
Neuroendocrine prostate cancer (NEPC), a subtype of prostate cancer (PCa) with poor prognosis and high heterogeneity, currently lacks accurate markers. This study aims to identify a robust NEPC classifier and provide new perspectives for resolving intra- tumoral heterogeneity. Multi-omics analysis included 19 bulk transcriptomics, 14 single-cell transcriptomics, 1 spatial transcriptomics, 16 published NE signatures and 10 cellular experiments combined with multiple machine learning algorithms to construct a novel NEPC classifier and classification. A comprehensive single-cell atlas of prostate cancer was created from 70 samples, comprising 196,309 cells, among which 9% were identified as NE cells. Within this framework and in combination with bulk transcriptomics, a total of 100 high-quality NE-specific feature genes were identified and differentiated into NEPup sig and NEPdown sig. The random forest (RF) algorithm proved to be the most effective classifier for NEPC, leading to the establishment of the NEP100 model, which demonstrated robust validation across various datasets. In clinical settings, the use of the NEP100 model can greatly improve the diagnostic and prognostic prediction of NEPC. Hierarchical clustering based on NEP100 revealed four distinct NEPC subtypes, designated VR_O, Prol_N, Prol_P, and EMT_Y, each of which presented unique biological characteristics. This allows us to select different targeted therapeutic strategies for different subtypes of phenotypic pathways. Notably, NEP100 expression correlated positively with neuroendocrine differentiation and disease progression, while the VR-NE phenotype dominated by VR_O cells indicated a propensity for treatment resistance. Furthermore, AMIGO2, a component of the NEP100 signature, was associated with chemotherapy resistance and a poor prognosis, indicating that it is a pivotal target for future therapeutic strategies. This study used multi-omics analysis combined with machine learning to construct a novel NEPC classifier and classification system. NEP100 provides a clinically actionable framework for NEPC diagnosis and subtyping.
MeSH
Cells, Neoplasms, Multiple Chronic Conditions, Classification, Prostate, Algorithms, Machine Learning, Prostatic Neoplasms, Therapeutics, Learning, Prognosis