Does aberrant membrane transport contribute to poor outcome in adult acute myeloid leukemia?

Abstract

Acute myeloid leukemia in adults is a highly heterogeneous disease. Gene expression profiling performed using unsupervised algorithms can be used to distinguish specific groups of patients within a large patient cohort. The identified gene expression signatures can offer insights into underlying physiological mechanisms of disease pathogenesis. Here, the analysis of several related gene expression clusters associated with poor outcome, worst overall survival and highest rates of resistant disease and obtained from the patients at the time of diagnosis or from previously untreated individuals is presented. Surprisingly, these gene clusters appear to be enriched for genes corresponding to proteins involved in transport across membranes (transporters, carriers and channels). Several ideas describing the possible relationship of membrane transport activity and leukemic cell biology, including the “Warburg effect,” the specific role of chloride ion transport, direct “import” of metabolic energy through uptake of creatine phosphate, and modification of the bone marrow niche microenvironment are discussed.

Towards integrated oncogenic marker recognition through mutual information‐based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles

“…(hsa04151) PI3KAkt signaling pathway (p-value = 1.29E-13) [74-76], and the leukemia disease related GO terms like GO:CCs of extracellular space (GO:0005615) (p-value = 1.49E-33) [83], extracellular region (GO:0005576) (p-value = 3.91E-07) [82], and GO:MF of cytokine activity (GO:0005125) (p-value = 1.51E-10) [84]. Besides that, the PTK2 gene is connected with leukemia as mentioned in [3,68,69].…”

“…We have obtained several literature evidences [87,88] suggesting that there is association between PROS1 and the leukemia. In addition, we obtain several leukemia-related KEGG pathways (such as hsa04610: complement and coagulation cascades [89] having pvalue = 5.68E-05) as well as Gene-Ontology-terms (viz., GO:BPs of negative regulation of wound healing (GO:0061045) [90,91] having p-value = 2.19E-09, regulation of response to wounding (GO:1903034) [90] having p-value = 2.78E-09, negative regulation of blood coagulation (GO:0030195) [92] (p-value = 2.21E-08); GO:CCs of extracellular space (GO:0005615) [83] having Table 7. The disease-associative GO terms of the participating oncogenes of the top twenty integrated markers, the pvalues of the GO terms, and the supportive literatures regarding the association between the GO terms and the disease for the AML gene expression and methylation datasets.…”

Venetoclax and pegcrisantaspase for complex karyotype acute myeloid leukemia

“…These genes were selected to delineate the specificity of each treatment, based on the transcriptome dataset as well as on their proposed impact on leukemia cell survival. Expression of genes namely, ASNS (Asparagine Synthetase) [ 29 ] and SLC6A9 (Solute Carrier Family 6 Member 9, Glycine transporter) [ 30 ] were altered with PegC treatment (and not Ven treatment), while the expression of oncogenes like HK3 (Hexokinase 3) [ 31 ] and DUSP4 (Dual specificity protein phosphatase 4) [ 32 ] were altered after Ven treatment (and not PegC treatment). Using the differentially expressed genes, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) term enrichment analysis to assess the similarities and differences in molecular processes associated with each treatment.…”

Link of sorafenib resistance with the tumor microenvironment in hepatocellular carcinoma: Mechanistic insights

“…In addition, HIF-1α was shown to reduce the expression of miR-199a that directly targets PKM2 and HK2 in liver cancer ( Guo et al, 2015 ). As a HIF target gene, GLUT1 expression is associated with resistance to multiple drugs in various cancers ( Chigaev, 2015 ; Sawayama et al, 2019 ; Guo et al, 2020 ). It has been demonstrated that genistein inhibited HK2 and GLUT1 to suppress aerobic glycolysis and improve sorafenib sensitivity by downregulation of HIF-1α ( Li et al, 2017 ).…”