A Proteomics Approach Identifies RREB1 as a Crucial Molecular Target of Imidazo-Pyrazole Treatment in SKMEL-28 Melanoma Cells.

Cutaneous melanoma is the most dangerous and deadly form of human skin malignancy. Despite its rarity, it accounts for a staggering 80% of deaths attributed to cutaneous cancers overall. Moreover, its final stages often exhibit resistance to drug treatments, resulting in unfavorable outcomes. Hence, ensuring access to novel and improved chemotherapeutic agents is imperative for patients grappling with this severe ailment. Pyrazole and its fused systems derived thereof are heteroaromatic moieties widely employed in medicinal chemistry to develop effective drugs for various therapeutic areas, including inflammation, pain, oxidation, pathogens, depression, and fever. In a previous study, we described the biochemical properties of a newly synthesized group of imidazo-pyrazole compounds. In this paper, to improve our knowledge of the pharmacological properties of these molecules, we conduct a differential proteomic analysis on a human melanoma cell line treated with one of these imidazo-pyrazole derivatives. Our results detail the changes to the SKMEL-28 cell line proteome induced by 24, 48, and 72 h of 3e imidazo-pyrazole treatment. Notably, we highlight the down-regulation of the Ras-responsive element binding protein 1 (RREB1), a member of the zinc finger transcription factors family involved in the tumorigenesis of melanoma. RREB1 is a downstream element of the MAPK pathway, and its activation is mediated by ERK1/2 through phosphorylation.

Fraisinib: a calixpyrrole derivative reducing A549 cell-derived NSCLC tumor in vivo acts as a ligand of the glycine-tRNA synthase, a new molecular target in oncology.

Background and purpose: Lung cancer is the leading cause of death in both men and women, constituting a major public health problem worldwide. Non-small-cell lung cancer accounts for 85%-90% of all lung cancers. We propose a compound that successfully fights tumor growth in vivo by targeting the enzyme GARS1. Experimental approach: We present an in-depth investigation of the mechanism through which Fraisinib [meso-(p-acetamidophenyl)-calix(4)pyrrole] affects the human lung adenocarcinoma A549 cell line. In a xenografted model of non-small-cell lung cancer, Fraisinib was found to reduce tumor mass volume without affecting the vital parameters or body weight of mice. Through a computational approach, we uncovered that glycyl-tRNA synthetase is its molecular target. Differential proteomics analysis further confirmed that pathways regulated by Fraisinib are consistent with glycyl-tRNA synthetase inhibition. Key results: Fraisinib displays a strong anti-tumoral potential coupled with limited toxicity in mice. Glycyl-tRNA synthetase has been identified and validated as a protein target of this compound. By inhibiting GARS1, Fraisinib modulates different key biological processes involved in tumoral growth, aggressiveness, and invasiveness. Conclusion and implications: The overall results indicate that Fraisinib is a powerful inhibitor of non-small-cell lung cancer growth by exerting its action on the enzyme GARS1 while displaying marginal toxicity in animal models. Together with the proven ability of this compound to cross the blood-brain barrier, we can assess that Fraisinib can kill two birds with one stone: targeting the primary tumor and its metastases "in one shot." Taken together, we suggest that inhibiting GARS1 expression and/or GARS1 enzymatic activity may be innovative molecular targets for cancer treatment.

Dual Classification Approach for the Rapid Discrimination of Metabolic Syndrome by FTIR.

Metabolic syndrome is a complex of interrelated risk factors for cardiovascular disease and diabetes. Thus, new point-of-care diagnostic tools are essential for unambiguously distinguishing MetS patients, providing results in rapid time. Herein, we evaluated the potential of Fourier transform infrared spectroscopy combined with chemometric tools to detect spectra markers indicative of metabolic syndrome. Around 105 plasma samples were collected and divided into two groups according to the presence of at least three of the five clinical parameters used for MetS diagnosis. A dual classification approach was studied based on selecting the most important spectral variable and classification methods, linear discriminant analysis (LDA) and SIMCA class modelling, respectively. The same classification methods were applied to measured clinical parameters at our disposal. Thus, the classification's performance on reduced spectra fingerprints and measured clinical parameters were compared. Both approaches achieved excellent discrimination results among groups, providing almost 100% accuracy. Nevertheless, SIMCA class modelling showed higher classification performance between MetS and no MetS for IR-reduced variables compared to clinical variables. We finally discuss the potential of this method to be used as a supportive diagnostic or screening tool in clinical routines.