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

Abstract Abstract: Objective：Essential thrombocythemia (ET) is a chronic myeloproliferative neoplasm that can progress to post-ET myelofibrosis (post-ET MF), a more advanced disease stage characterized by bone marrow fibrosis, splenomegaly, and worsening cytopenia. This study aimed to identify potential biomarkers associated with fibrotic transformation through proteomic analysis of bone marrow biopsies from patients with ET and post-ET MF, in order to offer a promising tool for individualized monitoring in ET fibrosis progression. Methods: ET and post-ET MF patients from the NICHE cohort (NCT04645199) diagnosed between December 1, 2020, and December 1, 2024, were randomly assigned to two cohorts. Cohort 1 (370 ET, 65 post-ET MF) was used to identify diagnostic clinical variables via logistic regression. Cohort 2 (64 ET, 24 post-ET MF) underwent DIA-based proteomic differential profiling of bone marrow FFPE samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the biological functions of differentially expressed proteins (DEPs). A two-step machine learning strategy—Random Forest followed by LASSO regression—was used to select the most informative protein features. Receiver operating characteristic (ROC) curves were generated to assess the predictive performance of both protein-based and clinical models. Results: Initial screening of clinical variables using independent samples t-tests followed by backward stepwise logistic regression identified hemoglobin (Hb; AUC = 0.728, 95% CI: 0.654–0.802), lactate dehydrogenase (LDH; AUC = 0.794, 95% CI: 0.719–0.869), high-density lipoprotein cholesterol (HDL-C; AUC = 0.688, 95% CI: 0.603–0.773), and CALR mutation status (AUC = 0.581, 95% CI: 0.502–0.660) as independent predictors. The clinical multivariable model demonstrated strong discriminatory capacity, with an AUC of 0.902 (95% CI: 0.858–0.946; p < 0.0001) .A total of Nine DEPs were identified by machine learning, including five upregulated proteins—von Willebrand factor A domain-containing protein 1 (VWA1), interferon-induced transmembrane protein 3 (IFITM3), cadherin-11 (CDH11), caldesmon (CALD1), and serpin H1 (SERPINH1)—mainly associated with extracellular matrix remodeling, immune response, and cell adhesion. Four downregulated proteins—C-type lectin domain family 4 member G (CLEC4G), stabilin-2 (STAB2), alpha-hemoglobin-stabilizing protein (AHSP), and DNA replication ATP-dependent helicase (DNA2)—were related to immune signaling, erythropoiesis, and DNA repair. We performed a comprehensive proteomics-based enrichment analysis using GO and KEGG databases. In post-ET MF, upregulated proteins were significantly enriched in GO and KEGG pathways linked to extracellular matrix (ECM) organization, cell adhesion , angiogenesis, and inflammatory response—hallmarks of stromal remodeling and pro-inflammatory marrow niche. Enriched processes also included antiviral defense, suggesting innate immune activation. Conversely, downregulated GO terms included T cell signaling, DNA repair and apoptotic pathways, indicating immune dysfunction and genomic instability associated with disease progression. And the Downregulated functions included GTPase and ATPase activity pointing to impaired hematopoietic regulation in fibrotic marrow. Activated KEGG pathways included ECM-receptor interaction, PI3K-Akt, focal adhesion, and MAPK signaling ——known drivers of fibrosis, survival, and transformation. Downregulated pathways involved immune signaling (e.g., Th17 differentiation, phagosome), cell cycle, and DNA replication, reflecting immune evasion and suppressed proliferation in hematopoietic progenitors. Conclusion: Proteomics-based modelling combined with clinical molecular models demonstrates higher diagnostic accuracy in distinguishing ET from post-ET MF compared to their independent models. GO and KEGG enrichment analyses and the potential biomarkers highlight that chronic inflammatory signaling, stromal remodeling, and impaired immune and genomic maintenance are central features of fibrotic progression from ET to post-ET MF. These findings provide a promising tool for individualized monitoring in ET fibrosis progression. Keywords: Essential thrombocythemia; Post-essential thrombocythemia myelofibrosis; Proteomic; Biomarkers