Department of Molecular Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, No. 88, School of Advanced Technologies in Medicine, Italia st, Keshavarz Blvd, Tehran, 1417755469, Iran.
Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Mol Biotechnol. 2023 Nov;65(11):1913-1922. doi: 10.1007/s12033-023-00707-0. Epub 2023 Mar 6.
Resistance against glucocorticoids which are used to reduce inflammation and treatment of a number of diseases, including leukemia, is known as the first stage of treatment failure in acute lymphoblastic leukemia. Since these drugs are the essential components of chemotherapy regimens for ALL and play an important role in stop of cell growth and induction of apoptosis, it is important to identify genes and the molecular mechanism that may affect glucocorticoid resistance. In this study, we used the GSE66705 dataset and weighted gene co-expression network analysis (WGCNA) to identify modules that correlated more strongly with prednisolone resistance in type B lymphoblastic leukemia patients. The PPI network was built using the DEGs key modules and the STRING database. Finally, we used the overlapping data to identify hub genes. out of a total of 12 identified modules by WGCNA, the blue module was find to have the most statistically significant correlation with prednisolone resistance and Nine genes including SOD1, CD82, FLT3, GART, HPRT1, ITSN1, TIAM1, MRPS6, MYC were recognized as hub genes Whose expression changes can be associated with prednisolone resistance. Enrichment analysis based on the MsigDB repository showed that the altered expressed genes of the blue module were mainly enriched in IL2_STAT5, KRAS, MTORC1, and IL6-JAK-STAT3 pathways, and their expression changes can be related to cell proliferation and survival. The analysis performed by the WGCNA method introduced new genes. The role of some of these genes was previously reported in the resistance to chemotherapy in other diseases. This can be used as clues to detect treatment-resistant (drug-resistant) cases in the early stages of diseases.
对抗糖皮质激素的耐药性，糖皮质激素被用于减轻炎症和治疗包括白血病在内的多种疾病。这种耐药性被认为是急性淋巴细胞白血病治疗失败的第一阶段。由于这些药物是 ALL 化疗方案的重要组成部分，在阻止细胞生长和诱导细胞凋亡方面发挥着重要作用，因此确定可能影响糖皮质激素耐药性的基因和分子机制非常重要。在这项研究中，我们使用 GSE66705 数据集和加权基因共表达网络分析（WGCNA）来识别与 B 型淋巴母细胞白血病患者泼尼松龙耐药性相关性更强的模块。使用 DEGs 关键模块和 STRING 数据库构建 PPI 网络。最后，我们使用重叠数据识别枢纽基因。通过 WGCNA 共鉴定出 12 个模块，其中蓝色模块与泼尼松龙耐药性的相关性最显著，确定 SOD1、CD82、FLT3、GART、HPRT1、ITSN1、TIAM1、MRPS6、MYC 等 9 个基因作为枢纽基因，这些基因的表达变化可能与泼尼松龙耐药性有关。基于 MsigDB 数据库的富集分析表明，蓝色模块的改变表达基因主要富集在 IL2_STAT5、KRAS、MTORC1 和 IL6-JAK-STAT3 通路中，其表达变化可能与细胞增殖和存活有关。WGCNA 方法进行的分析引入了新的基因。其中一些基因在其他疾病的化疗耐药性中以前被报道过。这可以作为在疾病早期检测耐药（药物抵抗）病例的线索。
