Reactive Oxygen Species and Long Non-Coding RNAs, an Unexpected Crossroad in Cancer Cells.

Long non-coding RNAs (lncRNA) have recently been identified as key regulators of oxidative stress in several malignancies. The level of reactive oxygen species (ROS) must be constantly regulated to maintain cancer cell proliferation and chemoresistance and to prevent apoptosis. This review will discuss how lncRNAs alter the ROS level in cancer cells. We will first describe the role of lncRNAs in the nuclear factor like 2 (Nrf-2) coordinated antioxidant response of cancer cells. Secondly, we show how lncRNAs can promote the Warburg effect in cancer cells, thus shifting the cancer cell's "building blocks" towards molecules important in oxidative stress regulation. Lastly, we explain the role that lncRNAs play in ROS-induced cancer cell apoptosis and proliferation.

The use of metabolomics and machine learning algorithms to predict post-transplant diabetes mellitus in renal transplant patients on Tacrolimus therapy.

Background and aim: Tacrolimus (TAC) has significantly improved kidney graft survival following transplantation, though it is associated with adverse side effects. The most prevalent complication resulting from excessive TAC exposure is the onset of de novo diabetes mellitus (DM), a condition that can negatively impact both renal graft function and patient outcomes. De novo DM is linked to an increased risk of chronic transplant dysfunction, as well as cardiovascular morbidity and mortality. Although the underlying mechanisms remain unclear, emerging research in the field of omics shows promise. The aim of this study was to investigate the metabolomic profile of kidney transplant patients who developed de novo DM, in comparison to those who did not, following TAC exposure, using untargeted metabolomic analysis through ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and machine learning algorithms. Methods: A cohort of 34 kidney transplant patients on a Tacrolimus regimen for at least 6 months was enrolled in the study, with serum samples collected from each patient. Comprehensive profiling of serum metabolites was performed, enabling the classification of patients into de novo diabetes mellitus and non diabetes groups. The metabolomic analysis of serum was conducted using UHPLC-MS. Results: Of the 34 patients, 16 were diagnosed with TAC-induced diabetes. A total of 334 metabolites were identified in the serum samples, of which 10 demonstrated a significant correlation with the de novo diabetes mellitus group. Most of these metabolites were linked to alterations in lipid metabolism. Conclusion: The application of metabolomics in kidney transplant patients undergoing a Tacrolimus regimen is both feasible and effective in identifying metabolites associated with de novo diabetes mellitus. This approach may provide valuable insights into the metabolic alterations underlying TAC-induced diabetes.

The Use of Machine Learning Algorithms and the Mass Spectrometry Lipidomic Profile of Serum for the Evaluation of Tacrolimus Exposure and Toxicity in Kidney Transplant Recipients.

Tacrolimus has a narrow therapeutic window; a whole-blood trough target concentration of between 5 and 8 ng/mL is considered a safe level for stable kidney transplant recipients. Tacrolimus serum levels must be closely monitored to obtain a balance between maximizing efficacy and minimizing dose-related toxic effects. Currently, there is no specific tacrolimus toxicity biomarker except a graft biopsy. Our study aimed to identify specific serum metabolites correlated with tacrolinemia levels using serum high-precision liquid chromatography-mass spectrometry and standard laboratory evaluation. Three machine learning algorithms were used (Naïve Bayes, logistic regression, and Random Forest) in 19 patients with high tacrolinemia (8 ng/mL) and 23 patients with low tacrolinemia (5 ng/mL). Using a selected panel of five lipid metabolites (phosphatidylserine, phosphatidylglycerol, phosphatidylethanolamine, arachidyl palmitoleate, and ceramide), Mg2+, and uric acid, all three machine learning algorithms yielded excellent classification accuracies between the two groups. The highest classification accuracy was obtained by Naïve Bayes, with an area under the curve of 0.799 and a classification accuracy of 0.756. Our results show that using our identified five lipid metabolites combined with Mg2+ and uric acid serum levels may provide a novel tool for diagnosing tacrolimus toxicity in kidney transplant recipients. Further validation with targeted MS and biopsy-proven TAC toxicity is needed.

COX-2 as a potential biomarker and therapeutic target in melanoma.

With a constantly increasing incidence, cutaneous melanoma has raised the need for a better understanding of its complex microenvironment that may further guide therapeutic options. Melanoma is a model tumor in immuno-oncology. Inflammation represents an important hallmark of cancer capable of inducing and sustaining tumor development. The inflammatory process also orchestrates the adaptative immunosuppression of tumor cells that helps them to evade immune destruction. Besides its role in proliferation, angiogenesis, and apoptosis, cyclooxygenase-2 (COX-2) is a well-known promoter of immune suppression in melanoma. COX-2 inhibitors are closely involved in this condition. This review attempts to answer two controversial questions: is COX-2 a valuable prognostic factor? Among all COX-2 inhibitors, is celecoxib a suitable adjuvant in melanoma therapy?

Adiponectin is related to markers of endothelial dysfunction and neoangiogenesis in diabetic patients.

PURPOSE: Adiponectin an adipokine, produced by mature adipocyte, has an important effect on several aspects of endothelial function, including leukocyte adhesion (mediated by adhesion molecules like intercellular adhesion molecule 1 (ICAM1) endothelial cell selective adhesion molecule ESAM). Recently, it has been linked to vascular endothelial growth factor (VEGF)-modulated angiogenesis. ESAM might also be involved in modulating VEGF-dependent actions. We studied relationship of adiponectin to ESAM, ICAM1, and VEGF in type 2 diabetic patients (T2DP) with or without microvascular complications. METHODS: Incident T2DP referred for nephrologic evaluation were included (patients with no nephropathy or stage 1-4 nephropathy). T2DP with stage 5 chronic kidney disease (CKD) were selected from a dialysis center. Clinical, standard laboratory assessment and adiponectin, ESAM, ICAM1, and VEGF (ELISA) were recorded. RESULTS: Eighty-seven patients were included, 15 had no CKD, 30 with stage 1 or 2 CKD, 20 with stage 3 or 4 CKD and 22 patients on dialysis. ESAM was higher in patients with CKD than in those without CKD (p = 0.02), adiponectin, ICAM1, and VEGF were similar. Adiponectin correlated in univariate analysis to ESAM (r = 0.32, p = 0.002), ICAM1 (r = 0.23, p = 0.038), and CRP (r = 0.31, p = 0.012), and inversely to serum albumin (r = - 0.57, < 0.0001). In predialysis patients, adiponectin also correlated to albuminuria (r = 0.54, p < 0.0001) and glomerular filtration rate (r = - 0.46, p = 0.0001). In multivariate regression ESAM (p = 0.04), VEGF (p = 0.03), and albumin (p < 0.0001) are significant predictors of adiponectin. None of these cytokines were different when comparing patients with and without retinopathy. CONCLUSION: Adiponectin is directly linked to adhesion molecules and VEGF in T2DP.