Recently, the understanding of metabolic reprogramming in cancer is expanding continuously.Besides glycolysis, researchers are now paying more attention to amino acid metabolism, especially glutamine metabolism.Glutamine is one of the most abundant non-essential amino acids in human body, but it is a “conditionally essential” amino acid in various types of cancer cells.Thus, targeting glutamine metabolism is becoming a promising strategy for the development of novel cancer therapeutic agents.In addition, another gradually clarified metabolic pathway is serine and glycine metabolism.In this review, we will focus on the alterations, the underlying mechanisms, and significance of glutamine as well as serine and glycine metabolism.We will also discuss cancer therapy opportunities through targeting amino acid metabolism.

Objective:To investigate the expression profile variation of long non-coding RNA( lncRNA) in the peripheral blood mononuclear cells of rheumatoid arthritis ( RA ) and healthy controls, and explore the role of lncRNA in the pathogenesis of RA.Methods:A total of 12 RA patients and 11 age-matched healthy controls from University Hospital of Hubei University for Nationalities were recruited.Using lncRNA microarray technology to detect differently expressed lncRNAs in 3 cases of RA PBMCs and 3 cases of healthy controls.GO and Pathway analysis was performed.The coding-non-coding gene co-expression networks of lncRNA and mRNA was constructed based on the correlation analysis,and then searched lncRNA in pathogenesis of RA through the cis-analysis and trans-analysis.Results:A total of 1 615 deregulated lncRNAs and 878 deregulated mRNAs were detected in RA patients.GO analysis of different expressed mRNA may involve in metal ion binding,protein kinase binding,nucleotide binding,regulation of transcription,et al.Pathway analysis of different expressed mRNA may involve in TNF signaling pathway,B cell receptor signaling pathway,pancreatic cancer,system lupus erythematosus endometrial cancer,et al.REL,SMAD3 and ETS1 may play an important role in the pathogenesis and development of RA through cis-analysis and trans-analysis.As the NONHSAG027875,FR378506 and NONHSAT031501 also had the similar function,and they may be related to the pathogenesis and development of RA.Conclusion:Differentially expressed lncRNAs may exert a partial role in RA,and may provide potential targets for future treatment of RA.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.