Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100 Bolzano, Italy.
Department of Medicine, Division of General Pathology, University of Verona, Strada Le Grazie 8, 37134 Verona, Italy.
Int J Mol Sci. 2022 Aug 31;23(17):9899. doi: 10.3390/ijms23179899.
This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.
本研究关注的是在转染编码蛋白酪氨酸磷酸酶受体 G 型（PTPRG）的肿瘤抑制基因并使用酪氨酸激酶抑制剂（TKI）伊马替尼治疗后，慢性髓系白血病（CML）细胞模型 K562 转录组的调制分析。具体来说，我们旨在确定 PTPRG 调节和伊马替尼浓度改变表达水平的基因。统计测试如差异表达分析（DEA），辅以基因集富集分析（GSEA）和当前转录组 CML 领域的实验研究的一些结果，以及一些当前感兴趣的结果，均得到了呈现。特别是，我们提出了两种在分析步骤顺序上有所不同的方法。在基于倍数变化值阈值的基因选择之后，我们应用统计测试来选择差异表达基因。因此，我们在差异表达基因集上应用了两种不同的方法。使用第一种方法（方法 1），我们实施了 GSEA，随后鉴定了转录因子。使用第二种方法（方法 2），我们首先从差异表达基因集中选择转录因子，并在此集上实施 GSEA。方法 1 是这种类型的分析中常用的标准方法，而方法 2 是非常规的，其动机是更具体地识别与 CML 条件相关的生物学过程中更具体涉及的转录因子。两种方法都配备了本体论知识挖掘和词云分析，作为我们分析过程中的新颖元素。数据分析确定 RARG 和 CD36 为潜在的 PTPRG 上调基因，表明细胞分化为红白血病表型的可能性增加。在 mRNA 和蛋白质水平上的预测得到了验证，进一步验证了该方法，并在 CML 背景下确定了由 PTPRG 控制的新的肿瘤抑制分子机制。
