School of Mathematics, Statistics and Data Science, Sungshin Women's University, Seoul 02844, Republic of Korea.
M&D Data Science Center, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan.
Int J Mol Sci. 2024 Mar 14;25(6):3302. doi: 10.3390/ijms25063302.
Azacitidine, a DNA methylation inhibitor, is employed for the treatment of acute myeloid leukemia (AML). However, drug resistance remains a major challenge for effective azacitidine chemotherapy, though several studies have attempted to uncover the mechanisms of azacitidine resistance. With the aim to identify the mechanisms underlying acquired azacitidine resistance in cancer cell lines, we developed a computational strategy that can identify differentially regulated gene networks between drug-sensitive and -resistant cell lines by extending the existing method, differentially coexpressed gene sets (DiffCoEx). The technique specifically focuses on cell line-specific gene network analysis. We applied our method to gene networks specific to azacitidine sensitivity and identified differentially regulated gene networks between azacitidine-sensitive and -resistant cell lines. The molecular interplay between the metallothionein gene family, C19orf33, ELF3, GRB7, IL18, NRN1, and RBM47 were identified as differentially regulated gene network in drug resistant cell lines. The biological mechanisms associated with azacitidine and AML for the markers in the identified networks were verified through the literature. Our results suggest that controlling the identified genes (e.g., the metallothionein gene family) and "cellular response"-related pathways ("cellular response to zinc ion", "cellular response to copper ion", and "cellular response to cadmium ion", where the enriched functional-related genes are MT2A, MT1F, MT1G, and MT1E) may provide crucial clues to address azacitidine resistance in patients with AML. We expect that our strategy will be a useful tool to uncover patient-specific molecular interplay that provides crucial clues for precision medicine in not only gastric cancer but also complex diseases.
阿扎胞苷是一种 DNA 甲基化抑制剂，用于治疗急性髓系白血病 (AML)。然而，药物耐药性仍是有效阿扎胞苷化疗的主要挑战，尽管有几项研究试图揭示阿扎胞苷耐药的机制。为了确定癌细胞系获得性阿扎胞苷耐药的机制，我们开发了一种计算策略，该策略通过扩展现有的方法（差异共表达基因集（DiffCoEx）），可以识别药物敏感和耐药细胞系之间差异调节的基因网络。该技术特别侧重于细胞系特异性基因网络分析。我们将我们的方法应用于阿扎胞苷敏感性特异性基因网络，并鉴定了阿扎胞苷敏感和耐药细胞系之间差异调节的基因网络。金属硫蛋白基因家族、C19orf33、ELF3、GRB7、IL18、NRN1 和 RBM47 之间的分子相互作用被鉴定为耐药细胞系中差异调节的基因网络。通过文献验证了鉴定网络中标记物与阿扎胞苷和 AML 相关的生物学机制。我们的结果表明，控制鉴定基因（例如金属硫蛋白基因家族）和“细胞反应”相关途径（“细胞对锌离子的反应”、“细胞对铜离子的反应”和“细胞对镉离子的反应”，其中富集的功能相关基因是 MT2A、MT1F、MT1G 和 MT1E）可能为解决 AML 患者的阿扎胞苷耐药提供重要线索。我们期望我们的策略将成为一种有用的工具，用于揭示患者特异性的分子相互作用，为不仅在胃癌而且在复杂疾病中的精准医学提供重要线索。
