Lung cancer (LC) is responsible for most cancer deaths. One of the main factors contributing to the lethality of this disease is the fact that a large proportion of patients are diagnosed at advanced stages when a clinical intervention is unlikely to succeed. In this study, we evaluated the potential of metabolomics by 1H-NMR to facilitate the identification of accurate and reliable biomarkers to support the early diagnosis and prognosis of non-small cell lung cancer (NSCLC).We found that the metabolic profile of NSCLC patients, compared with healthy individuals, is characterized by statistically significant changes in the concentration of 18 metabolites representing different amino acids, organic acids and alcohols, as well as different lipids and molecules involved in lipid metabolism. Furthermore, the analysis of the differences between the metabolic profiles of NSCLC patients at different stages of the disease revealed the existence of 17 metabolites involved in metabolic changes associated with disease progression.Our results underscore the potential of metabolomics profiling to uncover pathophysiological mechanisms that could be useful to objectively discriminate NSCLC patients from healthy individuals, as well as between different stages of the disease.

Purpose: Multiple myeloma remains an incurable disease. New approaches to develop better tools for improving patient prognostication and monitoring treatment efficacy are very much needed. In this study, we aimed to evaluate the potential of metabolomics by 1 H-NMR to provide information on metabolic profiles that could be useful in the management of multiple myeloma. Experimental Design: Serum samples were collected from multiple myeloma patients at the time of diagnosis and after achieving complete remission. A matched control set of samples was also included in the study. The 1 H-NMR measurements used to obtain the metabolic profile for each patient were followed by the application of univariate and multivariate statistical analyses to determine significant differences.Results: Metabolic profiles of multiple myeloma patients at diagnosis exhibited higher levels of isoleucine, arginine, acetate, phenylalanine, and tyrosine, and decreased levels of 3-hydroxybutyrate, lysine, glutamine, and some lipids compared with the control set. A similar analysis conducted in multiple myeloma patients after achieving complete remission indicated that some of the metabolic changes (i.e., glutamine, cholesterol, lysine) observed at diagnosis displayed a variation in the opposite direction upon responding to treatment, thus contributing to multiple myeloma patients having a closer metabolic profile to those of healthy individuals after the disappearance of major disease manifestations.Conclusions: The results highlight the potential of metabolic profiles obtained by 1 H-NMR in identifying multiple myeloma biomarkers that may be useful to objectively discriminate individuals with and without multiple myeloma, and monitor response to treatment.

Objective. Although oral methotrexate (MTX) remains the anchor drug for rheumatoid arthritis (RA), up to 50% of patients do not achieve a clinically adequate outcome. In addition, there is a lack of prognostic tools for treatment response prior to drug initiation. This study was undertaken to investigate whether interindividual differences in the human gut microbiome can aid in the prediction of MTX efficacy in new-onset RA.Methods. We performed 16S ribosomal RNA gene and shotgun metagenomic sequencing on the baseline gut microbiomes of drug-naive patients with new-onset RA (n = 26). Results were validated in an additional independent cohort (n = 21). To gain insight into potential microbial mechanisms, we conducted ex vivo experiments coupled with metabolomics analysis to evaluate the association between microbiome-driven MTX depletion and clinical response.Results. Our analysis revealed significant associations of the abundance of gut bacterial taxa and their genes with future clinical response (q < 0.05), including orthologs related to purine and MTX metabolism. Machine learning techniques were applied to the metagenomic data, resulting in a microbiome-based model that predicted lack of response to MTX in an independent group of patients. Finally, MTX levels remaining after ex vivo incubation with distal gut samples from pretreatment RA patients significantly correlated with the magnitude of future clinical response, suggesting a possible direct effect of the gut microbiome on MTX metabolism and treatment outcomes.Conclusion. Taken together, these findings are the first step toward predicting lack of response to oral MTX in patients with new-onset RA and support the value of the gut microbiome as a possible prognostic tool and as a potential target in RA therapeutics.