Metabolomics for searching validated biomarkers in cancer studies: a decade in review.

INTRODUCTION: In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways. AREAS COVERED: This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types. EXPERT OPINION: Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Exploration of oxidized phosphocholine profile in non-small-cell lung cancer.

Introduction: Lung cancer is one of the most frequently studied types of cancer and represents the most common and lethal neoplasm. Our previous research on non-small cell lung cancer (NSCLC) has revealed deep lipid profile reprogramming and redox status disruption in cancer patients. Lung cell membranes are rich in phospholipids that are susceptible to oxidation, leading to the formation of bioactive oxidized phosphatidylcholines (oxPCs). Persistent and elevated levels of oxPCs have been shown to induce chronic inflammation, leading to detrimental effects. However, recent reports suggest that certain oxPCs possess anti-inflammatory, pro-survival, and endothelial barrier-protective properties. Thus, we aimed to measure the levels of oxPCs in NSCLC patients and investigate their potential role in lung cancer. Methods: To explore the oxPCs profiles in lung cancer, we performed in-depth, multi-level metabolomic analyses of nearly 350 plasma and lung tissue samples from 200 patients with NSCLC, including adenocarcinoma (ADC) and squamous cell carcinoma (SCC), the two most prevalent NSCLC subtypes and COPD patients as a control group. First, we performed oxPC profiling of plasma samples. Second, we analyzed tumor and non-cancerous lung tissues collected during the surgical removal of NSCLC tumors. Because of tumor tissue heterogeneity, subsequent analyses covered the surrounding healthy tissue and peripheral and central tumors. To assess whether the observed phenotypic changes in the patients were associated with measured oxPC levels, metabolomics data were augmented with data from medical records. Results: We observed a predominance of long-chain oxPCs in plasma samples and of short-chain oxPCs in tissue samples from patients with NSCLC. The highest concentration of oxPCs was observed in the central tumor region. ADC patients showed higher levels of oxPCs compared to the control group, than patients with SCC. Conclusion: The detrimental effects associated with the accumulation of short-chain oxPCs suggest that these molecules may have greater therapeutic utility than diagnostic value, especially given that elevated oxPC levels are a hallmark of multiple types of cancer.

Emotional Universe of Nurse Case Managers Regarding Care for Elderly at Risk in Spain: A Hermeneutical Study.

The role of nurse case managers (NCM) involves a rarely visible emotional labor, even more when their role focuses on the care of elders at risk (EAR). Motivated by the lack of qualitative research on the emotional universe of NCM, this study explores the emotional universe (EU) of NCM regarding the care they provide to EAR in primary health care as well as the reasons that generate these emotions. An interpretative-phenomenological approach was implemented in southern Spain, with a purposive sampling that included nurses playing the NCM role for at least three years. Data collection was conducted in two periods (between September 2019 and July 2022). The primary collection tool was the semi-structured individual interview, with starting categories based on Bisquerra's EU taxonomy. The analysis followed Ricoeur's considerations, using the Nvivo software. In the NCM's EU, the recognition of the social phenomena stands out, with an open feeling of empathy regarding the desire of the EAR to continue living at home. However, there was also helplessness, resignation, disappointment, and frustration when EAR rejected their proposals. Furthermore, the system's limitations aroused compassion in the NCM and made them go beyond the limits of their role. This EU requires that their role be valued more, and higher responsiveness must be enforced to improve EAR care.

Co-Fermentation of Glucose-Xylose Mixtures from Agroindustrial Residues by Ethanologenic Escherichia coli: A Study on the Lack of Carbon Catabolite Repression in Strain MS04.

The production of biofuels, such as bioethanol from lignocellulosic biomass, is an important task within the sustainable energy concept. Understanding the metabolism of ethanologenic microorganisms for the consumption of sugar mixtures contained in lignocellulosic hydrolysates could allow the improvement of the fermentation process. In this study, the ethanologenic strain Escherichia coli MS04 was used to ferment hydrolysates from five different lignocellulosic agroindustrial wastes, which contained different glucose and xylose concentrations. The volumetric rates of glucose and xylose consumption and ethanol production depend on the initial concentration of glucose and xylose, concentrations of inhibitors, and the positive effect of acetate in the fermentation to ethanol. Ethanol yields above 80% and productivities up to 1.85 gEtOH/Lh were obtained. Furthermore, in all evaluations, a simultaneous co-consumption of glucose and xylose was observed. The effect of deleting the xyIR regulator was studied, concluding that it plays an important role in the metabolism of monosaccharides and in xylose consumption. Moreover, the importance of acetate was confirmed for the ethanologenic strain, showing the positive effect of acetate on the co-consumption rates of glucose and xylose in cultivation media and hydrolysates containing sugar mixtures.

Correction: Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia.

[This corrects the article DOI: 10.1371/journal.pone.0247093.].

Optimization of capillary electrophoresis coupled to negative mode electrospray ionization-mass spectrometry using polyvinyl alcohol coated capillaries. Application to a study on non-small cell lung cancer.

Despite recent developments in separation techniques, the analysis of relatively small highly polar negatively charged analytes (e.g. small organic acids, phosphorylated sugars, and underivatized amino acids) remains challenging. Capillary electrophoresis coupled to mass spectrometry (CE-MS) has been included in the untargeted metabolomics toolbox, although mostly in positive polarity. The aim of this study was to assess the use of CE-MS to analyze highly polar and negatively charged metabolites at physiological levels without the need for derivatization. After a preliminary selection, conditions regarding CE (buffers, applied potential, injection time and applied pressure), electrospray parameters (sheath liquid flow, temperature and drying gas flow, nebulizer, and capillary voltage), and fragmentor voltage were optimized using a capillary coated with polyvinyl alcohol (PVA) for the metabolic profiling of anionic compounds compared to fused silica as the reference capillary. In addition, a database of 240 metabolites with two relative migration times (RMT) obtained against methionine sulfone and 2-morpholinoethanesulfonic acid (MES) as internal standards (IS) has been compiled. Finally, the optimized method has been used to characterize the metabolic profile of blood plasma in patients with non-small cell lung cancer (NSCLC). The identified compounds are mostly amino acids and their derivatives, carboxylic acids and organic compounds from the TCA cycle, and sugars and their phosphoderivates. In addition, we performed a comparative study to find significant differences between non-small cell lung cancer (NSCLC) vs non-cancer individuals, and squamous cell carcinoma (SCC) and adenocarcinoma (ADC) vs non-cancer individuals, respectively, searching for differences between the various types of NSCLC.

New molecular mechanisms in cholangiocarcinoma: signals triggering interleukin-6 production in tumor cells and KRAS co-opted epigenetic mediators driving metabolic reprogramming.

BACKGROUND: Cholangiocarcinoma (CCA) is still a deadly tumour. Histological and molecular aspects of thioacetamide (TAA)-induced intrahepatic CCA (iCCA) in rats mimic those of human iCCA. Carcinogenic changes and therapeutic vulnerabilities in CCA may be captured by molecular investigations in bile, where we performed bile proteomic and metabolomic analyses that help discovery yet unknown pathways relevant to human iCCA. METHODS: Cholangiocarcinogenesis was induced in rats (TAA) and mice (JnkΔhepa + CCl4 + DEN model). We performed proteomic and metabolomic analyses in bile from control and CCA-bearing rats. Differential expression was validated in rat and human CCAs. Mechanisms were addressed in human CCA cells, including Huh28-KRASG12D cells. Cell signaling, growth, gene regulation and [U-13C]-D-glucose-serine fluxomics analyses were performed. In vivo studies were performed in the clinically-relevant iCCA mouse model. RESULTS: Pathways related to inflammation, oxidative stress and glucose metabolism were identified by proteomic analysis. Oxidative stress and high amounts of the oncogenesis-supporting amino acids serine and glycine were discovered by metabolomic studies. Most relevant hits were confirmed in rat and human CCAs (TCGA). Activation of interleukin-6 (IL6) and epidermal growth factor receptor (EGFR) pathways, and key genes in cancer-related glucose metabolic reprogramming, were validated in TAA-CCAs. In TAA-CCAs, G9a, an epigenetic pro-tumorigenic writer, was also increased. We show that EGFR signaling and mutant KRASG12D can both activate IL6 production in CCA cells. Furthermore, phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme in serine-glycine pathway, was upregulated in human iCCA correlating with G9a expression. In a G9a activity-dependent manner, KRASG12D promoted PHGDH expression, glucose flow towards serine synthesis, and increased CCA cell viability. KRASG12D CAA cells were more sensitive to PHGDH and G9a inhibition than controls. In mouse iCCA, G9a pharmacological targeting reduced PHGDH expression. CONCLUSIONS: In CCA, we identified new pro-tumorigenic mechanisms: Activation of EGFR signaling or KRAS mutation drives IL6 expression in tumour cells; Glucose metabolism reprogramming in iCCA includes activation of the serine-glycine pathway; Mutant KRAS drives PHGDH expression in a G9a-dependent manner; PHGDH and G9a emerge as therapeutic targets in iCCA.

Ethanol production by Escherichia coli from detoxified lignocellulosic teak wood hydrolysates with high concentration of phenolic compounds.

Teak wood residues were subjected to thermochemical pretreatment, enzymatic saccharification, and detoxification to obtain syrups with a high concentration of fermentable sugars for ethanol production with the ethanologenic Escherichia coli strain MS04. Teak is a hardwood, and thus a robust deconstructive pretreatment was applied followed by enzymatic saccharification. The resulting syrup contained 60 g l-1 glucose, 18 g l-1 xylose, 6 g l-1 acetate, less than 0.1 g l-1 of total furans, and 12 g l-1 of soluble phenolic compounds (SPCs). This concentration of SPC is toxic to E. coli, and thus two detoxification strategies were assayed: (1) treatment with Coriolopsis gallica laccase followed by addition of activated carbon and (2) overliming with Ca(OH)2. These reduced the phenolic compounds by 40% and 76%, respectively. The detoxified syrups were centrifuged and fermented with E. coli MS04. Cultivation with the overlimed hydrolysate showed a 60% higher volumetric productivity (0.45 gETOH l-1 hr-1). The bioethanol/sugar yield was over 90% in both strategies.

Estimation of occupational radiation doses in neuroendovascular procedures.

To estimate the mean effective dose per procedure with multiple dosimetry, to calculate the annual effective dose to personnel working in neuroendovascular procedures and compared with methods reported in the literature and with national and international limits. The radiation dose to personnel was monitored in 20 procedures classified as diagnostic or therapeutic. During each procedure, the equivalent dose to eyes, thyroid, under and over the lead apron at chest level, hands, gonads and knees was measured with lithium fluoride thermoluminescent dosimeter chips (TLD-100). Estimations of the annual effective dose from different methods found in literature that use one or two dosimeters and from this work were compared. Also, a comparison was made with the safety limits recommended in national and international regulations. Radiation exposure to eyes, thyroid, gonads and knees is relevant to the effective dose, and therefore to the annual effective dose estimations. Personnel position is important, as the performing physician, who is closer to the patient, received the highest dose measured. In particular, this was observed in the equivalent dose received over the apron. However, the equivalent dose to the right eye was higher for neuroanaesthesiologists than for performing physicians due to their position relative to the patient. In general, effective doses estimated using one- and two-dosimeter methods found in the literature were, respectively, lower and higher than those obtained with the ten-dosimeter method in this work. The annual effective doses to personnel estimated with the multiple dosimetry algorithm ranged from 1.3 to 1.5 mSv y-1and are within the national and international limits.

Cost-Effectiveness of an Exercise Programme That Provided Group or Individual Training to Reduce the Fall Risk in Healthy Community-Dwelling People Aged 65-80: A Secondary Data Analysis.

Research has demonstrated that some exercise programs are effective for reducing fall rates in community-dwelling older people; however, the literature is limited in providing clear recommendations of individual or group training as a result of economic evaluation. The objective of this study was to assess the cost-effectiveness of the Otago Exercise Program (OEP) for reducing the fall risk in healthy, non-institutionalized older people. An economic evaluation of a multicenter, blinded, randomized, non-inferiority clinical trial was performed on 498 patients aged over 65 in primary care. Participants were randomly allocated to the treatment or control arms, and group or individual training. The program was delivered in primary healthcare settings and comprised five initial sessions, ongoing encouragement and support to exercise at home, and a reinforcement session after six months. Our hypothesis was that the patients who received the intervention would achieve better health outcomes and therefore need lower healthcare resources during the follow-up, thus, lower healthcare costs. The primary outcome was the incremental cost-effectiveness ratio, which used the timed up and go test results as an effective measure for preventing falls. The secondary outcomes included differently validated tools that assessed the fall risk. The cost per patient was USD 51.28 lower for the group than the individual sessions in the control group, and the fall risk was 10% lower when exercises had a group delivery. The OEP program delivered in a group manner was superior to the individual method. We observed slight differences in the incremental cost estimations when using different tools to assess the risk of fall, but all of them indicated the dominance of the intervention group. The OEP group sessions were more cost-effective than the individual sessions, and the fall risk was 10% lower.

Comprehensive Plasma Metabolomic Profile of Patients with Advanced Neuroendocrine Tumors (NETs). Diagnostic and Biological Relevance.

PURPOSE: High-throughput "-omic" technologies have enabled the detailed analysis of metabolic networks in several cancers, but NETs have not been explored to date. We aim to assess the metabolomic profile of NET patients to understand metabolic deregulation in these tumors and identify novel biomarkers with clinical potential. METHODS: Plasma samples from 77 NETs and 68 controls were profiled by GC-MS, CE-MS and LC-MS untargeted metabolomics. OPLS-DA was performed to evaluate metabolomic differences. Related pathways were explored using Metaboanalyst 4.0. Finally, ROC and OPLS-DA analyses were performed to select metabolites with biomarker potential. RESULTS: We identified 155 differential compounds between NETs and controls. We have detected an increase of bile acids, sugars, oxidized lipids and oxidized products from arachidonic acid and a decrease of carnitine levels in NETs. MPA/MSEA identified 32 enriched metabolic pathways in NETs related with the TCA cycle and amino acid metabolism. Finally, OPLS-DA and ROC analysis revealed 48 metabolites with diagnostic potential. CONCLUSIONS: This study provides, for the first time, a comprehensive metabolic profile of NET patients and identifies a distinctive metabolic signature in plasma of potential clinical use. A reduced set of metabolites of high diagnostic accuracy has been identified. Additionally, new enriched metabolic pathways annotated may open innovative avenues of clinical research.

In vitro generation of oxidized standards for lipidomics. Application to major membrane lipid components.

Membrane lipids (sphingolipids, glycerophospholipids, cardiolipins, and cholesteryl esters) are critical in cellular functions. Alterations in the levels of oxidized counterparts of some of these lipids have been linked to the onset and development of many pathologies. Unfortunately, the scarce commercial availability of chemically defined oxidized lipids is a limitation for accurate quantitative analysis, characterization of oxidized composition, or testing their biological effects in lipidomic studies. To address this dearth of standards, several approaches rely on in-house prepared mixtures of oxidized species generated under in vitro conditions from different sources - non-oxidized commercial standards, liposomes, micelles, cells, yeasts, and human preparations - and using different oxidant systems - UVA radiation, air exposure, enzymatic or chemical oxidant systems, among others. Moreover, high-throughput analytical techniques such as liquid chromatography coupled to mass spectrometry (LC-MS) have provided evidence of their capabilities to study oxidized lipids both in in vitro models and complex biological samples. In this review, we describe the commercial resources currently available, the in vitro strategies carried out for obtaining oxidized lipids as standards for LC-MS analysis, and their applications in lipidomics studies, specifically for lipids found in cell and mitochondria membranes.

Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data.

Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools.

Detection of Rare Germline Variants in the Genomes of Patients with B-Cell Neoplasms.

There is growing evidence indicating the implication of germline variation in cancer predisposition and prognostication. Here, we describe an analysis of likely disruptive rare variants across the genomes of 726 patients with B-cell lymphoid neoplasms. We discovered a significant enrichment for two genes in rare dysfunctional variants, both of which participate in the regulation of oxidative stress pathways (CHMP6 and GSTA4). Additionally, we detected 1675 likely disrupting variants in genes associated with cancer, of which 44.75% were novel events and 7.88% were protein-truncating variants. Among these, the most frequently affected genes were ATM, BIRC6, CLTCL1A, and TSC2. Homozygous or germline double-hit variants were detected in 28 cases, and coexisting somatic events were observed in 17 patients, some of which affected key lymphoma drivers such as ATM, KMT2D, and MYC. Finally, we observed that variants in six different genes were independently associated with shorter survival in CLL. Our study results support an important role for rare germline variation in the pathogenesis and prognosis of B-cell lymphoid neoplasms.

Gene expression profiling identifies FLT3 mutation-like cases in wild-type FLT3 acute myeloid leukemia.

BACKGROUND: FLT3 mutation is present in 25-30% of all acute myeloid leukemias (AML), and it is associated with adverse outcome. FLT3 inhibitors have shown improved survival results in AML both as upfront treatment and in relapsed/refractory disease. Curiously, a variable proportion of wild-type FLT3 patients also responded to these drugs. METHODS: We analyzed 6 different transcriptomic datasets of AML cases. Differential expression between mutated and wild-type FLT3 AMLs was performed with the Wilcoxon-rank sum test. Hierarchical clustering was used to identify FLT3-mutation like AMLs. Finally, enrichment in recurrent mutations was performed with the Fisher's test. RESULTS: A FLT3 mutation-like gene expression pattern was identified among wild-type FLT3 AMLs. This pattern was highly enriched in NPM1 and DNMT3A mutants, and particularly in combined NPM1/DNMT3A mutants. CONCLUSIONS: We identified a FLT3 mutation-like gene expression pattern in AML which was highly enriched in NPM1 and DNMT3A mutations. Future analysis about the predictive role of this biomarker among wild-type FLT3 patients treated with FLT3 inhibitors is envisaged.

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling.

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

Oxidized lipids in the metabolic profiling of neuroendocrine tumors - Analytical challenges and biological implications.

Untargeted metabolomics can be a great tool for exploring new scientific areas; however, wrong metabolite annotation questions the credibility and puts the success of the entire research at risk. Therefore, an effort should be made to improve the quality and robustness of the annotation despite of the challenges, especially when final identification with standards is not possible. Through non-targeted analysis of human plasma samples, from a large cancer cohort study using RP-LC-ESI-QTOF-MS/MS, we have resolved MS/MS annotation through spectral matching, directed to hydroxyeicosatetraenoic acids (HETEs) and, MS/MS structural elucidation for newly annotated oxidized lyso-phosphatidylcholines (oxLPCs). The annotation of unknowns is supported with structural information from fragmentation spectra as well as the fragmentation mechanisms involved, necessarily including data from both polarity modes and different collision energies. In this work, we present evidences that various oxidation products show significant differences between cancer patients and control individuals and we establish a workflow to help identify such modifications. We report here the upregulation of HETEs and oxLPCs in patients with neuroendocrine tumors (NETs). To our knowledge, this is the first attempt to determine HETEs in NETs and one of very few studies where oxLPCs are annotated. The obtained results provide an important insight regarding lipid oxidation in NETs, although their physiological functions still have to be established and require further research.

Post-operative pain management among surgical trauma patients in an acute ward: a best practice implementation project.

INTRODUCTION: Effective pain management should be one of the main goals of healthcare professionals. The trauma unit of a hospital in an urban area of Spain carried out an implementation project, guided by the evidence-based criteria from JBI, to put in place the processes and initiatives needed to reduce post-operative pain. OBJECTIVES: The aim of this implementation project was to promote evidence-based practice in managing post-operative pain. METHODS: A pre-post implementation audit was implemented using the JBI Practical Application of Clinical Evidence System (PACES) and Getting Research into Practice (GRiP) tool. Each audit included 30 post-operative patients from a randomized sample who were evaluated before the project started and six months after key strategies had been implemented. The criteria were audited according to evidence-based process criteria. RESULTS: At the baseline audit, the compliance percentages for the evidence-based criteria ranged from 10% to 43%. Seven obstacles were identified in relation to post-operative pain management. Following the GRiP table, the team established a series of strategies and resources to implement the improvement actions. When the implementation period ended, all the criteria had improved. CONCLUSIONS: The quality improvement cycle allowed us to implement the clinical best practice recommendations, with subsequent outcome improvements for patients. Future audits should be performed to drive new cycles of improvement in evidence-based practice.

Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns.

Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in western countries. CLL evolution is frequently indolent, and treatment is mostly reserved for those patients with signs or symptoms of disease progression. In this work, we used RNA sequencing data from the International Cancer Genome Consortium CLL cohort to determine new gene expression patterns that correlate with clinical evolution.We determined that a 290-gene expression signature, in addition to immunoglobulin heavy chain variable region (IGHV) mutation status, stratifies patients into four groups with notably different time to first treatment. This finding was confirmed in an independent cohort. Similarly, we present a machine learning algorithm that predicts the need for treatment within the first 5 years following diagnosis using expression data from 2,198 genes. This predictor achieved 90% precision and 89% accuracy when classifying independent CLL cases. Our findings indicate that CLL progression risk largely correlates with particular transcriptomic patterns and paves the way for the identification of high-risk patients who might benefit from prompt therapy following diagnosis.

A review of validated biomarkers obtained through metabolomics.

INTRODUCTION: Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.