Single-cell transcriptome-based analysis reveals hub genes and key pathways in acute B-lymphoblastic leukemia

急性B 淋巴细胞白血病(Acute B Lymphoblastic Leukemia B-ALL)发病机制复杂,与基因突变和信号通路的异常密切相关。本研究旨在对B-ALL患者的基因表达和信号通路进行系统分析,我们期望揭示疾病发生和发展的分子机理,为临床治疗提供新的靶点。在GSE130116数据集中,通过使用特定标记基因鉴定B细胞亚型。应用高维加权基因共表达网络分析(high-definition Weighted Gene Co-expression Network Analysis hdWGCNA)对单细胞RNA测序(single-cell RNA sequencing scRNA-seq)数据进行细胞亚群聚类和基因模块关联,结合伪时间轨迹分析,以确定与B-ALL相关的关键细胞状态和基因模块。基因组富集分析识别与基因模块相关的生物过程。使用用于活性推断的通路响应基因(Pathway RespOnsive GENes for activity inference PROGENy)软件包用于基于单细胞转录组数据推断通路活性。分析伪时序变化中的关键基因,并用FindMarkers函数进行差异表达分析,识别不同伪时间状态中显著变化的基因。Acute B Lymphoblastic Leukemia (B-ALL) has a complex pathogenesis that is closely associated with gene mutations and abnormal signaling pathways. This study aims to conduct a systematic analysis of gene expression and signaling pathways in B-ALL patients, with the expectation of revealing the molecular mechanisms underlying disease occurrence and development, and providing novel targets for clinical treatment. Within the GSE130116 dataset, B cell subtypes were identified using specific marker genes. High-definition Weighted Gene Co-expression Network Analysis (hdWGCNA) was applied to perform cell subpopulation clustering and gene module association analysis on single-cell RNA sequencing (scRNA-seq) data, combined with pseudotime trajectory analysis, to identify key cell states and gene modules related to B-ALL. Genome enrichment analysis was used to identify biological processes associated with the gene modules. The Pathway RespOnsive GENes for activity inference (PROGENy) software package was employed to infer pathway activities based on single-cell transcriptomic data. Key genes involved in pseudotemporal changes were analyzed, and differential expression analysis was conducted using the FindMarkers function to identify genes with significant alterations across different pseudotime states.