Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG

• Published in: International journal of molecular sciences Vol. 23; no. 17; p. 9899
• Main Authors: Lombardi, Giulia, Latorre, Roberta Valeria, Mosca, Alessandro, Calvanese, Diego, Tomasello, Luisa, Boni, Christian, Ferracin, Manuela, Negrini, Massimo, Dewik, Nader Al, Yassin, Mohamed, Ismail, Mohamed A., Carpentieri, Bruno, Sorio, Claudio, Lecca, Paola
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022; MDPI
• Subjects: bio-ontologies; Biological activity; CD36 antigen; Cell cycle; Cell differentiation; Cell growth; Chromosomes; Chronic Myeloid Leukemia; Data collection; differential expression gene analysis; Differentiation (biology); DNA methylation; enrichment gene analysis; Enzyme inhibitors; Experimental research; Gene expression; Gene set enrichment analysis; Genes; Graphical representations; Imatinib; Kinases; Leukemia; Myeloid leukemia; Phenotypes; Phosphatase; Polymerase chain reaction; Protein-tyrosine kinase; Protein-tyrosine-phosphatase; Proteins; Software; Statistical analysis; Statistical tests; Transcription factors; Transcriptomes; Transcriptomics; Transfection; Tumor suppressor genes; Tumors; Tyrosine
• ISSN: 1422-0067; 1661-6596; 1422-0067
• 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.