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.
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.
Rheumatoid arthritis (RA), afflicting over 1% of the population, is an inflammatory joint disease leading to cartilage damage and ultimately impaired joint function. Disease-modifying anti-rheumatic drugs are considered as the first-line treatment to inhibit the progression of RA, and the treatment depends on the disease status assessment. The disease activity score 28 as clinical gold standard is extensively used for RA assessment, but it has the limitations of delayed assessment and the need for specialized expertise. It is necessary to discover biomarkers that can precisely monitor disease activity, and provide optimized treatment for RA patients. A total of 1,244 participants from two independent centers were divided into five cohorts. Cohorts 1–4 constituted sera samples of moderate to high active RA, low active RA, RA in remission and healthy subjects. Cohort 5 consisted of sera of RA, osteoarthritis (OA), ankylosing spondylitis (AS), systemic lupus erythematosus (SLE), primary Sjogren's syndrome (pSS) and healthy subjects. Biomarkers were found from cohorts 1–2 (screening sets), cohort 3 (discovery and external validation sets), cohort 4 (drug intervention set) and cohort 5 (biomarker-specific evaluation set). We found 68 upregulated and 74 downregulated proteins by TMT-labeled proteomics in cohort 1, and fibrinogen-like protein 1 (FGL1) had the highest area under the receiver operating characteristic curve (AUC) values in cohort 2. In cohort 3, in cross-comparison among moderate/high active RA, low active RA, RA in remission and healthy subjects, FGL1 had AUC values of approximately 0.9000 and predictive values of 90%. Additionally, FGL1 had a predictive value of 91.46% for moderate/high active RA vs. remission/low active RA and 80.77% for RA in remission vs. low active RA in cohort 4. Importantly, FGL1 levels had no significant difference in OA and AS compared with healthy persons. The concentrations in SLE and pSS were improved, but approximately 3-fold lower than that in active RA in cohort 5. In summary, FGL1 is a novel and specific biomarker that could be clinically useful for predicting progression of RA.
Multiple myeloma (MM) is an incurable plasma cell malignancy in the bone marrow characterized by chromosome instability (CIN), which contributes to the acquisition of heterogeneity, along with MM progression, drug resistance, and relapse. In this study, we elucidated that the expression of BUB1B increased strikingly in MM patients and was closely correlated with poor outcomes. Overexpression of BUB1B facilitated cellular proliferation and induced drug resistance in vitro and in vivo, while genetic targeting BUB1B abrogated this effect. Mechanistic studies unveiled that enforced expression of BUB1B evoked CIN resulting in MM poor outcomes mainly through phosphorylating CEP170. Interestingly, we discovered the existence of circBUB1B_544aa containing the kinase catalytic center of BUB1B, which was translated by a circular RNA of BUB1B. The circBUB1B_544aa elevated in MM peripheral blood samples was closely associated with MM poor outcomes and played a synergistic effect with BUB1B on evoking CIN. In addition, MM cells could secrete circBUB1B_544aa and interfere the MM microenvironmental cells in the same manner as BUB1B full-length protein. Intriguingly, BUB1B siRNA, targeting the kinase catalytic center of both BUB1B and circBUB1B_544aa, significantly inhibited MM malignancy in vitro and in vivo. Collectively, BUB1B and circBUB1B_544aa are promising prognostic and therapeutic targets of MM.
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.
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