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Published on: February 21, 2018
Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG
Giulia Lombardi1, Roberta Valeria Latorre2, Alessandro Mosca1
1Faculty of Computer Science, Free University of Bozen-Bolzano, Piazza Domenicani 3, 39100 Bolzano, Italy.
Abstract:
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.
Insights
This study reveals how the tumor suppressor Protein Tyrosine Phosphatase Receptor Type G (PTPRG) influences Chronic Myeloid Leukemia (CML) cells. It identified RARG and CD36 as key genes, suggesting a new tumor suppression mechanism.
Area of Science:
- Molecular Biology
- Cancer Research
- Bioinformatics
Background:
- Chronic Myeloid Leukemia (CML) is a cancer of white blood cells.
- Understanding gene expression changes in CML is crucial for developing new therapies.
- The tumor suppressor gene Protein Tyrosine Phosphatase Receptor Type G (PTPRG) role in CML is not fully understood.
Purpose of the Study:
- To analyze the transcriptome modulation in a CML cell model (K562) after PTPRG transfection and Imatinib treatment.
- To identify genes differentially expressed due to PTPRG modulation and Imatinib concentration.
- To compare two distinct analytical methods for transcriptomic data analysis in CML.
Main Methods:
- Differential expression analysis (DEA) and gene set enrichment analysis (GSEA).
- Two methods were compared: GSEA followed by transcription factor identification (Method 1) and vice versa (Method 2).
- Ontological term analysis, knowledge mining, and word cloud analysis were employed.
Main Results:
- RARG and CD36 were identified as potential PTPRG up-regulated genes.
- These findings suggest PTPRG may induce cell differentiation towards an erythromyeloid phenotype.
- Confirmation at both mRNA and protein levels validated the identified molecular mechanism.
Conclusions:
- PTPRG plays a role in tumor suppression in CML by potentially inducing cell differentiation.
- The study identified RARG and CD36 as novel targets associated with PTPRG function in CML.
- The findings provide a basis for future experimental research into the CML transcriptomic landscape.

