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Biology subjects

Misura, K.

Publications and source records attributed to Misura, K..

2 recordsLinked to original sources

HDAC1 acts as tumor suppressor in ALK-positive anaplastic large-cell lymphoma: Implications for HDAC inhibitor therapy

Histone deacetylases (HDACs) play essential roles in T cell development, and several HDAC inhibitors (HDACi) have gained approval for treating peripheral T cell lymphomas. In this study, we investigated the effects of genetic or pharmacological HDAC inhibition on NPM-ALK positive anaplastic large cell lymphoma (ALCL) development to elucidate potential contraindications or indications for the use of HDACi for the treatment of this rare T-cell lymphoma. Short-term systemic pharmacological inhibition of HDACs using the class I-specific HDACi Entinostat in a premalignant ALCL mouse model postponed or even abolished lymphoma development, despite high expression of the NPM-ALK fusion oncogene. To further disentangle the effects of systemic HDAC inhibition from thymocyte intrinsic effects, conditional genetic deletions of highly homologous class I HDAC1 and HDAC2 enzymes were employed. In sharp contrast to the systemic inhibition, T cell-specific deletion of Hdac1 or Hdac2 in the ALCL mouse model significantly accelerated NPM-ALK-driven lymphomagenesis, with Hdac1 loss having a more pronounced effect. Integration of gene expression and chromatin accessibility data revealed that Hdac1 deletion selectively perturbed cell type specific transcriptional programs, crucial for T cell differentiation and signaling. Moreover, multiple oncogenic signaling pathways, including PDGFRB signaling, were highly upregulated. The accelerated lymphomagenesis primarily depended on the catalytic activity of HDAC1, as the expression of a catalytically inactive HDAC1 protein showed similar effects to the complete knockout. Our findings underscore the tumor-suppressive function of class I HDAC1 and HDAC2 in T cells during ALCL development, however systemic pharmacological inhibition of HDACs is still a valid treatment strategy, which could potentially improve current therapeutic outcomes.

cancer biology↗

Integrating weighted correlation network analysis and machine learning identifies common trajectories of prostate cancer

BackgroundProstate cancer diagnosis and prognosis is currently limited by the availability of sensitive and specific biomarkers. There is an urgent need to develop molecular biomarkers that allow for the distinction of indolent from aggressive disease, the sensitive detection of heterogeneous tumors, or the evaluation of micro-metastases. The availability of multi-omics datasets in publicly accessible databases provides a valuable foundation to develop computational workflows for the identification of suitable biomarkers for clinical management of cancer patients. ResultsWe combined transcriptomic data of primary localized and advanced prostate cancer from two cancer databases. Transcriptomic analysis of metastatic tumors unveiled a distinct overexpression pattern of genes encoding cell surface proteins intricately associated with cell-matrix components and chemokine signaling pathways. Utilizing an integrated approach combining machine learning and weighted gene correlation network modules, we identified the EZH2-TROAP axis as the main trajectory from initial tumor development to lethal metastatic disease. In addition, we identified and independently validated 58 promising biomarkers that were specifically upregulated in primary localized or metastatic disease. Among those biomarkers, 22 were highly significant for predicting biochemical recurrence. Notably, we confirmed TPX2 upregulation at the protein level in an independent cohort of primary prostate cancer and matched lymph node metastases. ConclusionsThis study demonstrates the effectiveness of using advanced bioinformatics approaches to identify the biological factors that drive prostate cancer progression. Furthermore, the targets identified show promise as prognostic biomarkers in clinical settings. Thus, integrative bioinformatics methods provide both deeper understanding of disease dynamics and open the doors for future personalized interventions.

bioinformatics↗