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Related Experiment Video

Updated: Jul 26, 2026

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients

Published on: November 19, 2019

Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system.

Junxiao Shen¹, Luyuan Lu², Zujie Chen¹

¹Department of Urology, The Fourth Affiliated Hospital of the School of Medicine, International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, 322000, China.

Scientific Reports

|April 22, 2025

Summary

Researchers developed NEP100, a novel classifier for neuroendocrine prostate cancer (NEPC), improving diagnosis and revealing four subtypes for targeted therapies. This advances understanding of NEPC heterogeneity and treatment resistance.

Keywords:

Computational biology and bioinformatics Multi-omics Neuroendocrine prostate cancer (NEPC)Tumor biomarkers Tumor heterogeneity

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Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Neuroendocrine prostate cancer (NEPC) presents poor prognosis and high heterogeneity, lacking accurate diagnostic markers.
Current understanding of NEPC intra-tumoral heterogeneity is limited, hindering effective treatment strategies.

Purpose of the Study:

To identify a robust classifier for NEPC.
To develop a classification system for NEPC subtypes.
To provide insights into NEPC intra-tumoral heterogeneity and potential therapeutic targets.

Main Methods:

Multi-omics analysis integrating bulk, single-cell, and spatial transcriptomics data.
Application of machine learning algorithms, including random forest, to construct a classifier.
Development of a comprehensive single-cell atlas of prostate cancer (70 samples, 196,309 cells).

Main Results:

Identification of 100 high-quality NE-specific feature genes (NEPup sig and NEPdown sig).
Establishment of the NEP100 model using random forest, demonstrating robust validation.
Discovery of four distinct NEPC subtypes (VR_O, Prol_N, Prol_P, EMT_Y) with unique biological characteristics.
Correlation of NEP100 expression with neuroendocrine differentiation, disease progression, and treatment resistance (e.g., AMIGO2).

Conclusions:

The NEP100 model offers a clinically actionable framework for NEPC diagnosis and subtyping.
NEPC subtyping enables selection of targeted therapeutic strategies.
AMIGO2 is identified as a potential therapeutic target for chemotherapy resistance in NEPC.