The ion channels and transporters gene expression profile indicates a shift in excitability and metabolisms during malignant progression of Follicular Lymphoma.
Alberto MagiMarika MasselliCesare SalaAngela GuerrieroPasquale LaiseBenedetta PucciniLuigi RigacciCarla BreschiOlivia CrocianiSerena PillozziAnnarosa ArcangeliPublished in: Scientific reports (2019)
The definition of the gene expression profile of genes encoding Ion Channels and Transporters (ICT-GEP) represents a novel and attracting aspect in cancer. We determined the ICT-GEP of Follicular Lymphoma (FL), and compared it with that of the more aggressive Diffuse Large B Cell Lymphoma (DLBCL). cDNA microarray data were collected both from patients enrolled for this study, and from public datasets. In FL the ICT-GEP indicated the overexpression of both the K+ channel encoding gene KCNN4, and SLC2A1, which encodes the Glut1 glucose transporter. SLC2A1 turned out to represent the hub of a functional network, connecting channels and transporters in FL. Relapsed FL patients were characterised by 38 differentially expressed ICT genes, among which ATP9A, SLC2A1 and KCNN4 were under-expressed, indicating a down-regulation of both excitability and glycolysis. A completely different profile of K+ channel encoding genes emerged in DLBCL accompanied by the over-expression of the fatty acid transporter-encoding gene SLC27A1 as well as of the metabolism regulator NCoR1. This indicates a change in excitability and a shift towards an oxidative metabolism in DLBCL. Overall, the ICT-GEP may contribute to identifying novel lymphoma biomarkers related to excitability and metabolic pathways, with particular relevance for drug resistant, relapsed FL.
Keyphrases
- diffuse large b cell lymphoma
- drug resistant
- genome wide
- genome wide identification
- end stage renal disease
- epstein barr virus
- gene expression
- ejection fraction
- chronic kidney disease
- dna methylation
- newly diagnosed
- multidrug resistant
- transcranial direct current stimulation
- peritoneal dialysis
- copy number
- transcription factor
- genome wide analysis
- healthcare
- bioinformatics analysis
- acute lymphoblastic leukemia
- acute myeloid leukemia
- poor prognosis
- type diabetes
- acinetobacter baumannii
- multiple myeloma
- pseudomonas aeruginosa
- metabolic syndrome
- cell proliferation
- hodgkin lymphoma
- mental health
- electronic health record
- skeletal muscle
- binding protein
- adverse drug
- big data
- drug induced
- long non coding rna