Dall'Olio Daniele, Magnani Federico, Casadei Francesco, Matteuzzi Tommaso, Curti Nico, Merlotti Alessandra, Simonetti Giorgia, Della Porta Matteo Giovanni, Remondini Daniel, Tarozzi Martina, Castellani Gastone
Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy.
IRCCS Istituto delle Scienze Neurologiche di Bologna, 40139 Bologna, Italy.
Int J Mol Sci. 2024 Dec 19;25(24):13588. doi: 10.3390/ijms252413588.
Hematological malignancies are a diverse group of cancers developing in the peripheral blood, the bone marrow or the lymphatic system. Due to their heterogeneity, the identification of novel and advanced molecular signatures is essential for enhancing their characterization and facilitate its translation to new pharmaceutical solutions and eventually to clinical applications. In this study, we collected publicly available microarray data for more than five thousand subjects, across thirteen hematological malignancies. Using PANDA to estimate gene regulatory networks (GRNs), we performed hierarchical clustering and network analysis to explore transcription factor (TF) interactions and their implications on biological pathways. Our findings reveal distinct clustering patterns among leukemias and lymphomas, with notable differences in gene and TF expression profiles. Gene Set Enrichment Analysis (GSEA) identified 57 significantly enriched KEGG pathways, highlighting both common and unique biological processes across HMs. We also identified potential drug targets within these pathways, emphasizing the role of TFs such as and in disease pathophysiology. Our comprehensive analysis enhances the understanding of the molecular landscape of HMs and suggests new avenues for targeted therapeutic strategies. These findings also motivate the adoption of regulatory networks, combined with modern biotechnological possibilities, for insightful pan-cancer exploratory studies.
血液系统恶性肿瘤是一类发生在外周血、骨髓或淋巴系统的多种癌症。由于其异质性,识别新的和先进的分子特征对于加强其特征描述并促进其转化为新的药物解决方案以及最终转化为临床应用至关重要。在本研究中,我们收集了超过五千名受试者的公开可用微阵列数据,涵盖十三种血液系统恶性肿瘤。使用PANDA估计基因调控网络(GRN),我们进行了层次聚类和网络分析,以探索转录因子(TF)相互作用及其对生物途径的影响。我们的研究结果揭示了白血病和淋巴瘤之间不同的聚类模式,基因和TF表达谱存在显著差异。基因集富集分析(GSEA)确定了57条显著富集的KEGG途径,突出了血液系统恶性肿瘤中常见和独特的生物学过程。我们还在这些途径中确定了潜在的药物靶点,强调了诸如[具体转录因子1]和[具体转录因子2]等TF在疾病病理生理学中的作用。我们的综合分析增强了对血液系统恶性肿瘤分子格局的理解,并为靶向治疗策略提出了新途径。这些发现还促使采用调控网络,并结合现代生物技术可能性,进行有洞察力的泛癌探索性研究。