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December 8, 2025BloodOpen Access

Proteomic analysis identifying biomarkers in the progression from essential thrombocythemia to post-essential thrombocythemia myelofibrosis: A retrospective cohort study

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TSTing Sun

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Overview

Retrospective cohort study identifies biomarkers and machine learning methods for monitoring fibrosis progression in myeloproliferative neoplasm.

Key Points

  • Proteomic analysis revealed biomarkers associated with fibrotic progression in post-essential thrombocythemia myelofibrosis.
  • Machine learning techniques identified hemoglobin and lactate dehydrogenase as key clinical predictors with AUC values of 0.728 and 0.794.
  • Differentially expressed proteins were associated with immune response and extracellular matrix remodeling in bone marrow biopsies.
  • Findings underscore the potential of proteomic modeling to enhance diagnostic accuracy in myelofibrosis detection.

Cite This Study

Ting Sun (2025) studied this question.

synapsesocial.com/papers/69362f604fa91c937236dd5dhttps://doi.org/10.1182/blood-2025-1986
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