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Peer-reviewedCC BYHematology, Transfusion and Cell Therapy

PLATELET CYTOSKELETAL AND SIGNALING-RELATED PATHWAYS ARE TRANSCRIPTIONALLY SUPPRESSED IN PHILADELPHIA-NEGATIVE MYELOPROLIFERATIVE NEOPLASMS

  • Lorena Lorena GARCIA AZEVEDO1
  • Vitor Leonardo BASSAN1
  • Luciana AMBRÓSIO CHAIM1
  • Rafaela DE FREITAS MARTINS FELÍCIO1
  • Fabíola ATTIÉ DE CASTRO1
  1. 1Departamento de Análises Clínicas, Toxicológicas e Bromatológicas, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo (FCFRP-USP), Ribeirão Preto, SP, Brazil
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Abstract

Backgroun and aim: Philadelphia-negative myeloproliferative neoplasms (MPN), including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (MF), are clonal hematological malignancies characterized by chronic inflammation and an increased risk of thrombosis. Although platelet hyperreactivity is a frequent finding in MPN, the molecular mechanisms underlying platelet dysfunction remain incompletely understood. Here, we investigated whether platelet transcriptomic alterations influence different biological pathways associated with thromboinflammation across MPN subtypes. Methods: Public transcriptomic datasets (access number GSE26049) from patients with PV (n = 41), ET (n = 19), and MF (n = 9) were normalized and analyzed using Gene Set Enrichment Analysis (GSEA) to extract platelet-associated gene expression. The Blood Transcription Modules (BTM) package was applied. Normalized enrichment score (NES), false discovery rate (FDR), leading-edge genes and rank metric scores were evaluated. Leading-edge genes were functionally classified according to Gene Ontology, Reactome and KEGG annotations, and protein interaction networks were explored to identify conserved molecular programs associated with platelet dysfunction. Results: GSEA demonstrated significant negative enrichment of platelet-related pathways in all MPN subtypes (PV: NES = −1.696, FDR = 0.0209; ET: NES = −1.890, FDR = 0.0059; MF: NES = −2.112, FDR = 0.0006), indicating a strong transcriptional repression from PV to MF. Leading-edge analysis identified six genes shared across all subtypes (FLNA, PFN1, ACTN4, ZYX, HGS, and GNAI2), supporting a convergent platelet molecular dysfunction between PV, ET, and MF. Additional subtype-associated genes included TLN1, MAP7D1, PNPLA6, AKT1, EHMT2, AP2M1, GNB2, SIPA1, CORO1A, H1FX, NUCB1, APEH and PKN1. The strongest repression was observed in MF, including MAP7D1 (−1.224), GNAI2 (−1.043), GNB2 (−0.923) and EHMT2 (−0.809), whereas PV and ET showed similar but less pronounced changes. Positive enrichment was limited to MAP2K2 (PV = 0.742; MF = 0.979) and TGFB1 (PV = 0.304; MF = 0.243), while ET showed no positively enriched leading-edge genes. Functional annotation grouped the downregulated genes into six interconnected modules: (i) actin cytoskeleton organization and focal adhesion; (ii) GPCR-mediated signaling; (iii) vesicle trafficking and endosomal transport; (iv) membrane lipid remodeling; (v) small GTPase signaling; and (vi) epigenetic regulation. Protein interaction network analysis demonstrated extensive connectivity among these modules, identifying cytoskeletal remodeling as the central hub linking receptor signaling, intracellular trafficking and membrane dynamics. Conclusions: Platelets from MPN patients present an altered transcriptomic program characterized by repression of genes regulating cytoskeletal remodeling, receptor trafficking and intracellular signaling. The progressively stronger repression observed from PV to MF supports disease-associated platelet functional reprogramming rather than isolated platelet activation. These findings provide mechanistic insight into platelet dysfunction in MPN, identifying molecular networks potentially contributing to thromboinflammation and thrombosis, and highlight candidate biomarkers and therapeutic targets for future translational studies.

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Cite

BibTeXRIS
BibTeX
@article{azevedo2026platelet,
  title = {{PLATELET CYTOSKELETAL AND SIGNALING-RELATED PATHWAYS ARE TRANSCRIPTIONALLY SUPPRESSED IN PHILADELPHIA-NEGATIVE MYELOPROLIFERATIVE NEOPLASMS}},
  author = {Lorena Lorena GARCIA AZEVEDO and Vitor Leonardo BASSAN and Luciana AMBRÓSIO CHAIM and Rafaela DE FREITAS MARTINS FELÍCIO and Fabíola ATTIÉ DE CASTRO},
  journal = {Hematology, Transfusion and Cell Therapy},
  year = {2026},
  month = oct,
  doi = {10.1016/j.htct.2026.107529},
  url = {https://doi.org/10.1016/j.htct.2026.107529},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - PLATELET CYTOSKELETAL AND SIGNALING-RELATED PATHWAYS ARE TRANSCRIPTIONALLY SUPPRESSED IN PHILADELPHIA-NEGATIVE MYELOPROLIFERATIVE NEOPLASMS
AU  - Lorena Lorena GARCIA AZEVEDO
AU  - Vitor Leonardo BASSAN
AU  - Luciana AMBRÓSIO CHAIM
AU  - Rafaela DE FREITAS MARTINS FELÍCIO
AU  - Fabíola ATTIÉ DE CASTRO
PY  - 2026
DA  - 2026/10/23
JO  - Hematology, Transfusion and Cell Therapy
DO  - 10.1016/j.htct.2026.107529
UR  - https://doi.org/10.1016/j.htct.2026.107529
AB  - Backgroun and aim: Philadelphia-negative myeloproliferative neoplasms (MPN), including polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (MF), are clonal hematological malignancies characterized by chronic inflammation and an increased risk of thrombosis. Although platelet hyperreactivity is a frequent finding in MPN, the molecular mechanisms underlying platelet dysfunction remain incompletely understood. Here, we investigated whether platelet transcriptomic alterations influence different biological pathways associated with thromboinflammation across MPN subtypes. Methods: Public transcriptomic datasets (access number GSE26049) from patients with PV (n = 41), ET (n = 19), and MF (n = 9) were normalized and analyzed using Gene Set Enrichment Analysis (GSEA) to extract platelet-associated gene expression. The Blood Transcription Modules (BTM) package was applied. Normalized enrichment score (NES), false discovery rate (FDR), leading-edge genes and rank metric scores were evaluated. Leading-edge genes were functionally classified according to Gene Ontology, Reactome and KEGG annotations, and protein interaction networks were explored to identify conserved molecular programs associated with platelet dysfunction. Results: GSEA demonstrated significant negative enrichment of platelet-related pathways in all MPN subtypes (PV: NES = −1.696, FDR = 0.0209; ET: NES = −1.890, FDR = 0.0059; MF: NES = −2.112, FDR = 0.0006), indicating a strong transcriptional repression from PV to MF. Leading-edge analysis identified six genes shared across all subtypes (FLNA, PFN1, ACTN4, ZYX, HGS, and GNAI2), supporting a convergent platelet molecular dysfunction between PV, ET, and MF. Additional subtype-associated genes included TLN1, MAP7D1, PNPLA6, AKT1, EHMT2, AP2M1, GNB2, SIPA1, CORO1A, H1FX, NUCB1, APEH and PKN1. The strongest repression was observed in MF, including MAP7D1 (−1.224), GNAI2 (−1.043), GNB2 (−0.923) and EHMT2 (−0.809), whereas PV and ET showed similar but less pronounced changes. Positive enrichment was limited to MAP2K2 (PV = 0.742; MF = 0.979) and TGFB1 (PV = 0.304; MF = 0.243), while ET showed no positively enriched leading-edge genes. Functional annotation grouped the downregulated genes into six interconnected modules: (i) actin cytoskeleton organization and focal adhesion; (ii) GPCR-mediated signaling; (iii) vesicle trafficking and endosomal transport; (iv) membrane lipid remodeling; (v) small GTPase signaling; and (vi) epigenetic regulation. Protein interaction network analysis demonstrated extensive connectivity among these modules, identifying cytoskeletal remodeling as the central hub linking receptor signaling, intracellular trafficking and membrane dynamics. Conclusions: Platelets from MPN patients present an altered transcriptomic program characterized by repression of genes regulating cytoskeletal remodeling, receptor trafficking and intracellular signaling. The progressively stronger repression observed from PV to MF supports disease-associated platelet functional reprogramming rather than isolated platelet activation. These findings provide mechanistic insight into platelet dysfunction in MPN, identifying molecular networks potentially contributing to thromboinflammation and thrombosis, and highlight candidate biomarkers and therapeutic targets for future translational studies.
SN  - 2531-1379
SN  - 2531-1387
ER  -