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Tumor stemness is associated with the recurrence and incurability of colorectal cancer (CRC), which lacks effective therapeutic targets and drugs. Glycinamide ribonucleotide transformylase (GART) fulfills an important role in numerous types of malignancies. The present study aims to identify the underlying mechanism through which GART may promote CRC stemness, as to developing novel therapeutic methods. An elevated level of GART is associated with poor outcomes in CRC patients and promotes the proliferation and migration of CRC cells. CD133+ cells with increased GART expression possess higher tumorigenic and proliferative capabilities both in vitro and in vivo. GART is identified to have a novel methyltransferase function, whose enzymatic activity center is located at the E948 site. GART also enhances the stability of RuvB‐like AAA ATPase 1 (RUVBL1) through methylating its K7 site, which consequently aberrantly activates the Wnt/β‐catenin signaling pathway to induce tumor stemness. Pemetrexed (PEM), a compound targeting GART, combined with other chemotherapy drugs greatly suppresses tumor growth both in a PDX model and in CRC patients. The present study demonstrates a novel methyltransferase function of GART and the role of the GART/RUVBL1/β‐catenin signaling axis in promoting CRC stemness. PEM may be a promising therapeutic agent for the treatment of CRC. [ABSTRACT FROM AUTHOR]

Background: Glucose-6-phosphate dehydrogenase (G6PD) as the rate-limiting enzyme in the pentose phosphate pathway (PPP) is well-established as an aberrantly expressed protein in numerous clinical diseases; however, its role in cancer, specifically in multiple myeloma (MM) remains elusive. Methods: In this study, serum metabolites in 70 normal people and 70 newly diagnosed MM patients were analyzed using untargeted metabolomics and the results were verified using ELISA. The survival analysis of multiple clinical datasets was performed to identify a potential target gene in MM. The oncogenic role of G6PD was investigated using lentivirus-based overexpression or knockdown of G6PD using RNAi or an inhibitor in vitro, and in a xenograft mouse model in vivo. The mechanisms of induced Dexamethasone (Dexa)-resistance of G6PD were further explored using the above established MM cell lines in vitro. Results: Based on the screening of potential genes, PPP was shown to be involved in the occurrence of MM, which was evidenced by the differential expression of serum metabolites of G6P and Dehydroepiandrosterone sulfate (DHEAS, the more stable sulfate ester form of an endogenously uncompetitive G6PD inhibitor known as DHEA). Elevated G6PD promoted MM cell proliferation. Mechanistically, high G6PD expression enhanced enzymatic generation of the antioxidant NADPH via the PPP and decreased the production of reactive oxygen species (ROS), thus inducing the proliferation and Dexa resistance in MM cells. Furthermore, canonical Wnt/β-catenin signaling also participated in regulating G6PD-induced drug resistance and cellular redox levels of ROS. Intriguingly, DHEA treatment could enhance the sensitivity of MM cells to Dexa primarily through augmenting cellular oxidative stress. Conclusions: Our data demonstrate that G6PD enhances the generation of the enzymatic anti-oxidant NADPH and decreases ROS generation, thereby promoting resistance to Dexa-induced apoptosis via the enzymatic PPP and non-enzymatic Wnt/β-catenin signaling pathway in MM. Targeting G6PD to harness cellular redox may serve as a promising novel strategy for the management of MM. [ABSTRACT FROM AUTHOR]

The chimera antigen receptor (CAR) T cell therapy is a novel and potential targeted therapy and has achieved satisfactory efficacy in patients with relapsed or refractory multiple myeloma (MM) in recent years. However, cytokine release syndrome (CRS) and clinical efficacy have become the major obstacles which limit the application of CAR-T in clinics. To explore the potential biomarkers in plasma for evaluating CRS and clinical efficacy, we performed metabolomic and lipidomic profiling of plasma samples from 17 relapsed or refractory MM patients received CAR-T therapy. Our study showed that glycerophosphocholine (GPC), an intermediate of platelet-activating factor (PAF)-like molecule, was significantly decreased when the participants underwent CRS, and the remarkable elevation of lysophosphatidylcholines (lysoPCs), which were catalyzed by lysoPC acyltransferase (LPCAT) was a distinct metabolism signature of relapsed or refractory MM patients with prognostic value post-CAR-T therapy. Both GPC and lysoPC are involved in platelet-activating factor (PAF) remodeling pathway. Besides, these findings were validated by LPCAT1 expression, a key factor in the PAF pathway, associated with poor outcome in three MM GEP datasets of MM. In conclusion, CAR-T therapy alters PAF synthesis in MM patients, and targeting PAF remodeling may be a promising strategy to enhance MM CAR-T therapy. [ABSTRACT FROM AUTHOR]

Objectives This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and to find potential underlying pathological mechanisms therefrom. Methods Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as centre 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as centre 2 (external validation set), including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by ultra high performance liquid chromatography plus Q-Exactive mass spectrometry (UHPLC-Q Exactive MS). Differential metabolites were identifed by both variable importance in the projection ≥1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted P ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis. Results In the discovery set, a total of 245 and 150 metabolites, respectively, were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included diacylglycerol, triacylglycerol (TAG), phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under the receiver operating characteristic ROC curve was 0.8288, the sensitivity was 82% and the specificity was 78%, at a 95% CI 0.747, 0.9106. In the internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while it was 84.03% in external validation set. Conclusion We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients. [ABSTRACT FROM AUTHOR]

Background: Diagnosing seronegative rheumatoid arthritis (RA) can be challenging due to complex diagnostic criteria. We sought to discover diagnostic biomarkers for seronegative RA cases by studying metabolomic and lipidomic changes in RA patient serum.Methods: We performed comprehensive metabolomic and lipidomic profiling in serum of 225 RA patients and 100 normal controls. These samples were divided into a discovery set (n = 243) and a validation set (n = 82). A machine-learning-based multivariate classification model was constructed using distinctive metabolites and lipids signals.Results: Twenty-six metabolites and lipids were identified from the discovery cohort to construct a RA diagnosis model. The model was subsequently tested on a validation set and achieved accuracy of 90.2%, with sensitivity of 89.7% and specificity of 90.6%. Both seropositive and seronegative patients were identified using this model. A co-occurrence network using serum omics profiles was built and parsed into six modules, showing significant association between the inflammation and immune activity markers and aberrant metabolism of energy metabolism, lipids metabolism and amino acid metabolism. Acyl carnitines (20:3), aspartyl-phenylalanine, pipecolic acid, phosphatidylethanolamine PE (18:1) and lysophosphatidylethanolamine LPE (20:3) were positively correlated with the RA disease activity, while histidine and phosphatidic acid PA (28:0) were negatively correlated with the RA disease activity.Conclusions: A panel of 26 serum markers were selected from omics profiles to build a machine-learning-based prediction model that could aid in diagnosing seronegative RA patients. Potential markers were also identified in stratifying RA cases based on disease activity. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A) modification is the most prevalent modification in eukaryotic RNAs while accumulating studies suggest that m6A aberrant expression plays an important role in cancer. HNRNPA2B1 is a m6A reader which binds to nascent RNA and thus affects a perplexing array of RNA metabolism exquisitely. Despite unveiled facets that HNRNPA2B1 is deregulated in several tumors and facilitates tumor growth, a clear role of HNRNPA2B1 in multiple myeloma (MM) remains elusive. Herein, we analyzed the function and the regulatory mechanism of HNRNPA2B1 in MM. We found that HNRNPA2B1 was elevated in MM patients and negatively correlated with favorable prognosis. The depletion of HNRNPA2B1 in MM cells inhibited cell proliferation and induced apoptosis. On the contrary, the overexpression of HNRNPA2B1 promoted cell proliferation in vitro and in vivo. Mechanistic studies revealed that HNRNPA2B1 recognized the m6A sites of ILF3 and enhanced the stability of ILF3 mRNA transcripts, while AKT3 downregulation by siRNA abrogated the cellular proliferation induced by HNRNPA2B1 overexpression. Additionally, the expression of HNRNPA2B1, ILF3 and AKT3 was positively associated with each other in MM tissues tested by immunohistochemistry. In summary, our study highlights that HNRNPA2B1 potentially acts as a therapeutic target of MM through regulating AKT3 expression mediated by ILF3-dependent pattern. [ABSTRACT FROM AUTHOR]

Objective The relationship between serum lipid variations in SS and healthy controls was investigated to identify potential predictive lipid biomarkers. Methods Serum samples from 230 SS patients and 240 healthy controls were collected. The samples were analysed by ultrahigh-performance liquid chromatography coupled with Q Exactive™ spectrometry. Potential lipid biomarkers were screened through orthogonal projection to latent structures discriminant analysis and further evaluated by receiver operating characteristic analysis. Results A panel of three metabolites [phosphatidylcholine (18:0/22:5), triglyceride (16:0/18:0/18:1) and acylcarnitine (12:0)] was identified as a specific biomarker of SS. The receiver operating characteristic analysis showed that the panel had a sensitivity of 84.3% with a specificity of 74.8% in discriminating patients with SS from healthy controls. Conclusion Our approach successfully identified serum biomarkers associated with SS patients. The potential lipid biomarkers indicated that SS metabolic disturbance might be associated with oxidized lipids, fatty acid oxidation and energy metabolism. [ABSTRACT FROM AUTHOR]

[Display omitted] • The biomarkers of rheumatoid arthritis (RA) were screened by large cohort of samples. • 36 lipid metabolites related to RA progression were identified by clinical lipidomics. • PE 16:0-18:2, TG 18:0-18:1-18:2 and PE 18:2-18:2 were identified as biomarkers of RA. • 3 biomarkers were target for lipid aldehydes produced from the lipid peroxidation. Rheumatoid arthritis (RA) is a common chronic inflammatory disorder characterized by inflammation of the synovium which causes joint damage, chronic pain, and disability. Changes in lipid profile are seen before overt RA disease manifestations suggesting that lipids contribute to the inflammation-driven metabolic changes in tumor necrosis factor. Currently, the pathogenesis of RA is unclear. Moreover, it is important to search for lipids biomarkers for early diagnosis of the disease. Herein, we compared the lipid profile of healthy individuals and RA patients to provide evidence for the diagnosis and management of persons with RA. Serum samples were collected from 511 RA patients and 396 health controls (HC). The samples were analyzed by ultra-performance liquid chromatography Q-Exactive mass spectrometry. Potential lipid biomarkers were screened and validated using the partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), random forest, binary logistic regression (BLR), receiver operating characteristic (ROC) and counter propagation artificial neural network (CP-ANN) analysis. A total of 36 differential lipid metabolites were identified, including phosphatidylethanolamine (PE), triglyceride (TG), acylcarnitine (ACar) and phosphatidylcholine (PC). Among them, PE 16:0-18:2, TG 18:0-18:1-18:2 and PE 18:2-18:2 were identified as specific biomarkers for differential diagnosis of RA. ROC analysis showed that the biomarkers had a sensitivity of 68% with a specificity of 63% in discriminating RA from HC individuals. The resulting CP-ANN can be used to identify unknown RA samples with predictive accuracy of 97% as determined through cross validation. The external validation accuracy of CP-ANN was 100% in all evaluated cases. The three lipid serum biomarkers were associated with the disease activity in RA. These lipid biomarkers showed that lipid peroxidation is disrupted in RA. [ABSTRACT FROM AUTHOR]