Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
School of Mathematics and Computer Science, University of Tehran, Tehran, Iran.
Cell Oncol (Dordr). 2017 Feb;40(1):33-45. doi: 10.1007/s13402-016-0303-7. Epub 2016 Oct 31.
Despite vast improvements that have been made in the treatment of children with acute lymphoblastic leukemia (ALL), the majority of infant ALL patients (~80 %, < 1 year of age) that carry a chromosomal translocation involving the mixed lineage leukemia (MLL) gene shows a poor response to chemotherapeutic drugs, especially glucocorticoids (GCs), which are essential components of all current treatment regimens. Although addressed in several studies, the mechanism(s) underlying this phenomenon have remained largely unknown. A major drawback of most previous studies is their primary focus on individual genes, thereby neglecting the putative significance of inter-gene correlations. Here, we aimed at studying GC resistance in MLL-rearranged infant ALL patients by inferring an associated module of genes using co-expression network analysis. The implications of newly identified candidate genes with associations to other well-known relevant genes from the same module, or with associations to known transcription factor or microRNA interactions, were substantiated using literature data.
A weighted gene co-expression network was constructed to identify gene modules associated with GC resistance in MLL-rearranged infant ALL patients. Significant gene ontology (GO) terms and signaling pathways enriched in relevant modules were used to provide guidance towards which module(s) consisted of promising candidates suitable for further analysis.
Through gene co-expression network analysis a novel set of genes (module) related to GC-resistance was identified. The presence in this module of the S100 and ANXA genes, both well-known biomarkers for GC resistance in MLL-rearranged infant ALL, supports its validity. Subsequent gene set net correlation analyses of the novel module provided further support for its validity by showing that the S100 and ANXA genes act as 'hub' genes with potentially major regulatory roles in GC sensitivity, but having lost this role in the GC resistant phenotype. The detected module implicates new genes as being candidates for further analysis through associations with known GC resistance-related genes.
From our data we conclude that available systems biology approaches can be employed to detect new candidate genes that may provide further insights into drug resistance of MLL-rearranged infant ALL cases. Such approaches complement conventional gene-wise approaches by taking putative functional interactions between genes into account.
尽管在治疗儿童急性淋巴细胞白血病（ALL）方面已经取得了巨大的进展，但大多数携带涉及混合谱系白血病（MLL）基因的染色体易位的婴儿 ALL 患者（~80%，<1 岁）对化疗药物，尤其是糖皮质激素（GCs）反应不佳，GCs 是所有当前治疗方案的重要组成部分。尽管在几项研究中已经提到，但这种现象的机制在很大程度上仍然未知。大多数先前研究的一个主要缺点是它们主要关注单个基因，从而忽略了基因间相关性的潜在意义。在这里，我们通过共表达网络分析推断与 MLL 重排婴儿 ALL 患者 GC 耐药相关的相关基因模块，旨在研究 MLL 重排婴儿 ALL 患者的 GC 耐药性。使用文献数据证实了与同一模块中其他已知相关基因或与已知转录因子或 microRNA 相互作用相关的新鉴定候选基因的关联的意义。
构建加权基因共表达网络以鉴定与 MLL 重排婴儿 ALL 患者 GC 耐药相关的基因模块。富集在相关模块中的显著基因本体（GO）术语和信号通路用于指导哪些模块包含适合进一步分析的有前途的候选基因。
通过基因共表达网络分析，确定了一组与 GC 耐药相关的新基因（模块）。该模块中存在 S100 和 ANXA 基因，这两种基因都是 MLL 重排婴儿 ALL 中 GC 耐药的众所周知的生物标志物，这支持了其有效性。新型模块的基因集净相关分析随后提供了进一步的支持，表明 S100 和 ANXA 基因作为“枢纽”基因，在 GC 敏感性中具有潜在的主要调节作用，但在 GC 耐药表型中失去了这种作用。检测到的模块暗示新基因作为候选基因通过与已知的 GC 耐药相关基因的关联进行进一步分析。
从我们的数据中，我们得出结论，可用的系统生物学方法可用于检测可能为 MLL 重排婴儿 ALL 病例的药物耐药性提供进一步见解的新候选基因。这种方法通过考虑基因之间的潜在功能相互作用来补充传统的基因方法。
