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Related Concept Videos

Non-LTR Retrotransposons03:18

Non-LTR Retrotransposons

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As the name suggests, non-LTR retrotransposons lack the long terminal repeats characteristic of the LTR retrotransposons. Additionally, both LTR and non-LTR retrotransposons use distinct mechanisms of mobilization. Non-LTR retrotransposons are further divided into two classes - Long interspersed nuclear elements (LINEs) and short interspersed nuclear elements (SINEs), both of which occur abundantly in most mammals, including humans. Some of the active non-LTR retrotransposons in humans are L1...
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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
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Molecular feature-based classification of retroperitoneal liposarcoma: a prospective cohort study.

Mengmeng Xiao1,2, Xiangji Li2,3, Fanqin Bu3

  • 1Department of Retroperitoneal Tumor Surgery, Peking University People's Hospital, Beijing, China.

Elife
|May 23, 2025
PubMed
Summary

This study identifies two distinct subtypes of retroperitoneal liposarcoma (RPLS) based on molecular signatures, improving patient stratification. A novel biomarker classification offers a cost-effective approach for guiding treatment decisions in RPLS.

Keywords:
LEPPTTG1cancer biologyhumanmedicinemolecular classificationretroperitoneal liposarcoma

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Area of Science:

  • Oncology
  • Molecular Biology
  • Genomics

Background:

  • Retroperitoneal liposarcoma (RPLS) is a rare and aggressive malignancy with poorly understood molecular heterogeneity.
  • Limited biomarkers exist for monitoring RPLS progression and clinical outcomes.

Purpose of the Study:

  • To elucidate the molecular landscape of RPLS and identify distinct patient subtypes.
  • To develop a clinically relevant and cost-effective classification system for RPLS.

Main Methods:

  • RNA sequencing was performed on 88 RPLS patients to identify dysregulated genes and pathways.
  • Unsupervised clustering and nonnegative matrix factorization were used to define RPLS subtypes.
  • A classification model based on LEP and PTTG1 biomarkers was validated using immunohistochemistry in 241 patients.

Main Results:

  • Two distinct RPLS subtypes were identified, characterized by unique molecular signatures, tumor microenvironments, and clinical outcomes (overall survival [OS] and disease-free survival [DFS]).
  • A simplified classification using LEP and PTTG1 biomarkers achieved high accuracy (AUC > 0.99).
  • Patients classified as LEP+ and PTTG1- exhibited less aggressive features and improved OS (HR=0.41) and DFS (HR=0.60).

Conclusions:

  • This study presents the largest gene expression landscape of RPLS to date.
  • An immunohistochemistry-based molecular classification for RPLS was established, demonstrating clinical relevance and cost-effectiveness.
  • The developed classification can aid in guiding treatment decisions for RPLS patients.