Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transgenic or knock-in methods. However, there are some potential pitfalls in using this method, such as retroviral insertion mutagenesis, non-physiological level gene expression, non-physiological expansion, and difficulty to construct. The mRNAsi index for each sample of the Cancer Genome Atlas (TCGA) could avoid these potential pitfalls by machine learning. In this work, we aimed to construct a network of LSC genes utilizing the mRNAsi. First, mRNAsi value was analyzed with expressions distributions, survival analysis, age, and gender in acute myeloid leukemia (AML) samples. Then, we used the weighted gene co-expression network analysis (WGCNA) to construct modules of stemness genes. The correlation of the LSC genes transcription and interplay among LSC proteins was analyzed. We performed functional and pathway enrichment analysis to annotate stemness genes. Survival analysis further identified prognostic biomarkers by clinical data of TCGA and the Gene Expression Omnibus (GEO) database. We found that the result of mRNAsi overall survival is not significant, which may be due to the heterogeneity of AML in the stage of myeloid differentiation, French–American–British (FAB) classification systems. Enrichment analysis indicated that the stemness genes were biologically clustered as a group and mainly associated with cell cycle and mitosis. Moreover, 10 key genes (SNRNP40, RFC4, RFC5, CDC6, HSPE1, PA2G4, SNAP23P, DARS2, MIS18A, and HPRT1) were screened by survival analysis with the data from TCGA and GEO. Among them, RFC4 and RFC5 were the distinguished biomarkers for their double-validated prognostic value in both databases. Additionally, the expression of RFC4 and RFC5 had the same trend as mRNAsi score in FAB subtypes. In conclusion, our result demonstrated that mRNAsi based LSC-related genes were found to have strong interactions as a cluster. These genes, especially RFC4 and RFC5, could be the therapeutic targets for inhibiting the stemness characteristics of AML. This work is also a comprehensive pipeline for future cancer stem cell studies.

了解白血病干细胞 (LSC) 生物学特性的传统方法包括通过转基因或基因敲入方法构建 LSC 样细胞和小鼠模型。然而，使用这种方法存在一些潜在的缺陷，例如逆转录病毒插入诱变、非生理水平的基因表达、非生理扩增和构建困难。癌症基因组图谱 (TCGA) 每个样本的 mRNAsi 指数可以通过机器学习避免这些潜在的陷阱。在这项工作中，我们旨在利用 mRNAsi 构建 LSC 基因网络。首先，使用急性髓性白血病 (AML) 样本中的表达分布、存活分析、年龄和性别来分析 mRNAsi 值。然后，我们使用加权基因共表达网络分析（WGCNA）构建干性基因模块。分析了 LSC 基因转录和 LSC 蛋白之间相互作用的相关性。我们进行了功能和通路富集分析以注释干性基因。生存分析通过 TCGA 的临床数据和基因表达综合 (GEO) 数据库进一步确定了预后生物标志物。我们发现mRNAsi总生存率的结果并不显着，这可能是由于AML在髓系分化阶段、法美英（FAB）分类系统中的异质性所致。富集分析表明干性基因在生物学上聚类为一个组，主要与细胞周期和有丝分裂有关。此外，利用TCGA和GEO的数据通过生存分析筛选了10个关键基因（SNRNP40、RFC4、RFC5、CDC6、HSPE1、PA2G4、SNAP23P、DARS2、MIS18A和HPRT1）。他们之中，RFC4 和 RFC5 是两个数据库中双重验证预后价值的杰出生物标志物。此外，RFC4 和 RFC5 的表达与 FAB 亚型中的 mRNAsi 评分具有相同的趋势。总之，我们的结果表明，发现基于 mRNAsi 的 LSC 相关基因作为一个簇具有强相互作用。这些基因，尤其是 RFC4 和 RFC5，可能是抑制 AML 干性特征的治疗靶点。这项工作也是未来癌症干细胞研究的综合管道。我们的结果表明，发现基于 mRNAsi 的 LSC 相关基因作为一个簇具有强相互作用。这些基因，尤其是 RFC4 和 RFC5，可能是抑制 AML 干性特征的治疗靶点。这项工作也是未来癌症干细胞研究的综合管道。我们的结果表明，发现基于 mRNAsi 的 LSC 相关基因作为一个簇具有强相互作用。这些基因，尤其是 RFC4 和 RFC5，可能是抑制 AML 干性特征的治疗靶点。这项工作也是未来癌症干细胞研究的综合管道。