Regularization and grouping -omics data by GCA method: A transcriptomic case.

The paper presents the application of Grade Correspondence Analysis (GCA) and Grade Correspondence Cluster Analysis (GCCA) for ordering and grouping -omics datasets, using transcriptomic data as an example. Based on gene expression data describing 256 patients with Multiple Myeloma it was shown that the GCA method could be used to find regularities in the analyzed collections and to create characteristic gene expression profiles for individual groups of patients. GCA iteratively permutes rows and columns to maximize the tau-Kendall or rho-Spearman coefficients, which makes it possible to arrange rows and columns in such a way that the most similar ones remain in each other's neighbourhood. In this way, the GCA algorithm highlights regularities in the data matrix. The ranked data can then be grouped using the GCCA method, and after that aggregated in clusters, providing a representation that is easier to analyze-especially in the case of large sets of gene expression profiles. Regularization of transcriptomic data, which is presented in this manuscript, has enabled division of the data set into column clusters (representing genes) and row clusters (representing patients). Subsequently, rows were aggregated (based on medians) to visualise the gene expression profiles for patients with Multiple Myeloma in each collection. The presented analysis became the starting point for characterisation of differentiated genes and biochemical processes in which they are involved. GCA analysis may provide an alternative analytical method to support differentiation and analysis of gene expression profiles characterising individual groups of patients

Caffeic Acid Targets AMPK Signaling and Regulates Tricarboxylic Acid Cycle Anaplerosis while Metformin Downregulates HIF-1α-Induced Glycolytic Enzymes in Human Cervical Squamous Cell Carcinoma Lines

The small molecules, natural antioxidant Caffeic Acid (trans-3,4-Dihydroxycinnamic acid CA) and anti-diabetic drug Metformin (Met), activate 5′-adenosine monophosphate-activated protein kinase (AMPK) and interfere with metabolic reprogramming in human cervical squamous carcinoma cells. Here, to gain more insight into the ability of CA, Met and the combination of both compounds to impair aerobic glycolysis (the “Warburg effect”) and disrupt bioenergetics of cancer cells, we employed the cervical tumor cell lines C-4I and HTB-35/SiHa. In epithelial C-4I cells derived from solid tumors, CA alleviated glutamine anaplerosis by downregulation of Glutaminase (GLS) and Malic Enzyme 1 (ME1), which resulted in the reduction of NADPH levels. CA treatment of the cells altered tricarboxylic acid (TCA) cycle supplementation with pyruvate via Pyruvate Dehydrogenase Complex (PDH), increased ROS formation and enhanced cell death. Additionally, CA and CA/Met evoked intracellular energetic stress, which was followed by activation of AMPK and the impairment of unsaturated FA de novo synthesis. In invasive HTB-35 cells, Met inhibited Hypoxia-inducible Factor 1 (HIF-1α) and suppressed the expression of the proteins involved in the “Warburg effect”, such as glucose transporters (GLUT1, GLUT3) and regulatory enzymes of glycolytic pathway Hexokinase 2 (HK2), 6-Phosphofructo-2-Kinase/Fructose-2,6-Biphosphatase 4 (PFKFB4), Pyruvate Kinase (PKM) and Lactate Dehydrogenase A (LDH). Met suppressed the expression of c-Myc, BAX and cyclin-D1 (CCND1) and evoked apoptosis in HTB-35 cells. In conclusion, both small molecules CA and Met are capable of disrupting energy homeostasis, regulating oxidative metabolism/glycolysis in cervical tumor cells in regard to specific metabolic phenotype of the cells. CA and Met may provide a promising approach in the prevention of cervical cancer progression