BackgroundAcute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis.MethodsThis study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed into two sub-networks, specific to each phenotype. Finally, both sub-networks were clustered into modules using weighted gene co-expression network analysis (WGCNA) and further analyzed with functional enrichment analysis.ResultsThrough the integration of DC analysis and PPI network, four protein modules were found active under the GC-resistance phenotype but not under the GC-sensitive. Functional enrichment analysis revealed that these modules are related to proteasome, electron transport chain, tRNA-aminoacyl biosynthesis, and peroxisome signaling pathways. These findings are in accordance with previous findings related to GC-resistance in other hematological malignancies such as pediatric ALL.ConclusionsDifferential co-expression analysis is a promising approach to incorporate the dynamic context of gene expression profiles into the well-documented protein interaction networks. The approach allows the detection of relevant protein modules that are highly enriched with DC gene pairs. Functional enrichment analysis of detected protein modules generates new biological hypotheses and may help in explaining the GC-resistance in MLL-rearranged infant ALL patients.

中文翻译：

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差异网络分析和蛋白质-蛋白质相互作用研究揭示了婴儿急性淋巴细胞白血病糖皮质激素抵抗中的活性蛋白质模块

背景急性淋巴细胞白血病 (ALL) 是儿童中诊断出的最常见的癌症类型，糖皮质激素 (GC) 是大多数治疗方案中标准化疗的重要组成部分。携带涉及混合谱系白血病 (MLL) 基因 (基因 KMT2A) 易位的婴儿 ALL 患者类别的特点是对 GC 有抗性和临床结果不佳。尽管一些研究检查了婴儿 ALL 患者的 GC 耐药性，但对这种现象的理解仍然有限，阻碍了改善预后的努力。 方法本研究整合了差异共表达 (DC) 和蛋白质-蛋白质相互作用 (PPI) 网络以寻找活性蛋白质MLL 重排婴儿 ALL 患者中与 GC 抗性相关的模块。通过将 GC 抗性和 GC 敏感样本之间的差异共表达基因对连接起来构建网络，然后通过保持 PPI 网络中也存在的链接与 PPI 网络集成。由此产生的网络被分解为两个子网络，特定于每个表型。最后，使用加权基因共表达网络分析（WGCNA）将两个子网络聚类成模块，并通过功能富集分析进一步分析。 结果通过 DC 分析和 PPI 网络的整合，发现四个蛋白质模块在 GC 抗性下是活跃的表型但不在 GC 敏感下。功能富集分析表明，这些模块与蛋白酶体、电子传递链、tRNA-氨酰基生物合成和过氧化物酶体信号通路有关。这些发现与之前在其他血液系统恶性肿瘤（如儿科 ALL）中与 GC 抗性相关的发现一致。结论差异共表达分析是一种有前景的方法，可将基因表达谱的动态背景整合到有据可查的蛋白质相互作用网络中。该方法允许检测高度富含 DC 基因对的相关蛋白质模块。检测到的蛋白质模块的功能富集分析产生了新的生物学假设，可能有助于解释 MLL 重排婴儿 ALL 患者的 GC 抗性。结论差异共表达分析是将基因表达谱的动态背景整合到有据可查的蛋白质相互作用网络中的一种很有前景的方法。该方法允许检测高度富含 DC 基因对的相关蛋白质模块。检测到的蛋白质模块的功能富集分析产生了新的生物学假设，可能有助于解释 MLL 重排婴儿 ALL 患者的 GC 抗性。结论差异共表达分析是将基因表达谱的动态背景整合到有据可查的蛋白质相互作用网络中的一种很有前景的方法。该方法允许检测高度富含 DC 基因对的相关蛋白质模块。检测到的蛋白质模块的功能富集分析产生了新的生物学假设，可能有助于解释 MLL 重排婴儿 ALL 患者的 GC 抗性。