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Global mRNA 3'UTR lengthening in small-cell neuroendocrine carcinoma
Yi Zhang1, Xiaofan Zhao1, Huan Wang2
1Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA.
Abstract:
Small-cell neuroendocrine carcinoma (SCNC) is a rare but highly malignant tumor subtype that primarily arises in the lung, also rarely in other organs, and as a consequence of treatment induced lineage transdifferentiation of prostate adenocarcinomas. The molecular convergence of SCNC across diverse tissues enables its identification through conserved SCNC-specific molecular markers, facilitating tumor subtype classification. As a critical post-transcriptional regulatory mechanism, alternative polyadenylation (APA) modulates 3'UTR length and significantly impacts tumor progression. However, its role in SCNC remains largely unclear. Here, we report a global 3'UTR lengthening pattern driven by APA in SCNC. We identified a set of conserved 3'UTR lengthening events across SCNCs of different tissue origins, which are strongly associated with neural development and related signaling pathways. Furthermore, we developed a neural network-based prediction model to classify SCNC by leveraging these specific APA signatures. Our study provides new insights into the post-transcriptional landscape of SCNCs and highlights APA signatures as promising biomarkers for SCNC identification.
Insights
Small-cell neuroendocrine carcinoma (SCNC) exhibits a global 3'UTR lengthening pattern due to alternative polyadenylation (APA). These APA signatures can serve as biomarkers for identifying SCNC across various tissues.
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Small-cell neuroendocrine carcinoma (SCNC) is a rare, highly malignant tumor.
- Alternative polyadenylation (APA) is a key post-transcriptional regulator impacting tumor progression.
- The role of APA in SCNC development and classification is largely unknown.
Purpose of the Study:
- To investigate the role of APA in SCNC.
- To identify conserved APA signatures across different SCNC tissue origins.
- To develop a predictive model for SCNC classification using APA signatures.
Main Methods:
- Analysis of 3'UTR length patterns in SCNC.
- Identification of conserved APA events.
- Development of a neural network-based prediction model.
Main Results:
- A global 3'UTR lengthening pattern driven by APA was observed in SCNC.
- Conserved APA events associated with neural development pathways were identified.
- A neural network model effectively classified SCNC using APA signatures.
Conclusions:
- APA plays a significant role in the post-transcriptional landscape of SCNC.
- Conserved APA signatures are linked to SCNC development.
- APA signatures show promise as biomarkers for SCNC identification and classification.
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