Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative...

Area of Science:

• Hematology
• Oncology
• Genomics

Background:

• Polycythemia Vera (PV) is a myeloproliferative neoplasm with a risk of transforming into Acute Myeloid Leukemia (AML).
• Studying disease transformation is challenging due to its chronic nature, complicating longitudinal sequencing.
• Early detection and intervention are crucial for managing malignant transformation in chronic proliferative diseases.

Purpose of the Study:

• To develop a diagnostic model for malignant transformation of chronic proliferative diseases, specifically PV to AML.
• To identify shared biomarkers and potential therapeutic targets for PV and AML.
• To validate diagnostic markers and assess drug efficacy in preclinical models.

Main Methods:

• Integrated analysis of public PV and AML datasets to identify differentially expressed genes (DEGs).
• Weighted gene correlation network construction and machine learning for biomarker screening.
• Validation using clinical specimens, drug prediction (cMAP, molecular docking), in vitro experiments, and tumor microenvironment (TME) analysis (CIBERSORT, scRNA-seq).

Main Results:

• Identified 24 shared genes between PV and AML, enriched in immune-related pathways.
• Developed a diagnostic nomogram integrating Lactoferrin (LTF) and G protein-coupled receptor 65 (GPR65) with robust predictive power.
• Vemurafenib showed efficacy in inhibiting proliferation and inducing apoptosis in PV and AML cells; biomarkers linked to macrophages in TME.

Conclusions:

• Shared genes and a novel diagnostic nomogram (LTF, GPR65) offer a new approach for diagnosing and managing AML-related PV.
• The findings provide insights into pathogenic mechanisms and potential therapeutic strategies for PV-AML transformation.
• Biomarker validation in clinical samples and drug efficacy assessment support potential clinical applications.