Kusuma School of Biological Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
Department of Reproductive Biology, All India Institute of Medical Sciences, New Delhi, 110029, India.
Interdiscip Sci. 2019 Dec;11(4):668-678. doi: 10.1007/s12539-019-00325-y. Epub 2019 Apr 10.
Acute lymphoblastic leukemia (ALL) is a hematologic tumor caused by cell cycle aberrations due to accumulating genetic disturbances in the expression of transcription factors (TFs), signaling oncogenes and tumor suppressors. Though survival rate in childhood ALL patients is increased up to 80% with recent medical advances, treatment of adults and childhood relapse cases still remains challenging. Here, we have performed bioinformatics analysis of 207 ALL patients' mRNA expression data retrieved from the ICGC data portal with an objective to mark out the decisive genes and pathways responsible for ALL pathogenesis and aggression. For analysis, 3361 most variable genes, including 276 transcription factors (out of 16,807 genes) were sorted based on the coefficient of variance. Silhouette width analysis classified 207 ALL patients into 6 subtypes and heat map analysis suggests a need of large and multicenter dataset for non-overlapping subtype classification. Overall, 265 GO terms and 32 KEGG pathways were enriched. The lists were dominated by cancer-associated entries and highlight crucial genes and pathways that can be targeted for designing more specific ALL therapeutics. Differential gene expression analysis identified upregulation of two important genes, JCHAIN and CRLF2 in dead patients' cohort suggesting their possible involvement in different clinical outcomes in ALL patients undergoing the same treatment.
急性淋巴细胞白血病 (ALL) 是一种血液系统肿瘤，是由于转录因子 (TFs)、信号转导致癌基因和肿瘤抑制基因表达的遗传紊乱导致细胞周期异常而引起的。尽管随着最近医学的进步，儿童 ALL 患者的生存率提高到 80%，但成人和儿童复发病例的治疗仍然具有挑战性。在这里，我们对从 ICGC 数据门户中检索到的 207 名 ALL 患者的 mRNA 表达数据进行了生物信息学分析，目的是确定决定 ALL 发病机制和侵袭性的关键基因和途径。为此，我们根据方差系数对 3361 个最具变异性的基因（包括 16807 个基因中的 276 个转录因子）进行了排序。轮廓宽度分析将 207 名 ALL 患者分为 6 个亚型，热图分析表明需要大型和多中心数据集进行非重叠亚型分类。总的来说，有 265 个 GO 术语和 32 个 KEGG 途径被富集。这些列表主要由与癌症相关的条目组成，突出了可以作为设计更特异的 ALL 治疗方法的关键基因和途径。差异基因表达分析鉴定出在死亡患者队列中两个重要基因 JCHAIN 和 CRLF2 的上调，表明它们可能参与了接受相同治疗的 ALL 患者的不同临床结局。
