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.

Background: The expression of CYP4Z1 and the pseudogene CYP4Z2P has been shown to be specifically increased in breast cancer by our group and others. Additionally, we previously revealed the roles of the competitive endogenous RNA (ceRNA) network mediated by these genes (ceRNET_CC) in breast cancer angiogenesis, apoptosis, and tamoxifen resistance. However, the roles of ceRNET_CC in regulating the stemness of breast cancer cells and the mechanisms through which ceRNET_CC is regulated remain unclear.Methods: Transcriptional factor six2, CYP4Z1-3′UTR, and CYP4Z2P-3′UTR were stably overexpressed or knocked down in breast cancer cells via lentivirus infection. ChIP-sequencing and RNA-sequencing analysis were performed to reveal the mechanism through which ceRNET_CC is regulated and the transcriptome change mediated by ceRNET_CC. Clinical samples were used to validate the correlation between six2 and ceRNET_CC. Finally, the effects of the six2/ceRNET_CC axis on the stemness of breast cancer cells and chemotherapy sensitivity were evaluated by in vitro and in vivo experiments.Results: We revealed that ceRNET_CC promoted the stemness of breast cancer cells. Mechanistically, six2 activated ceRNET_CC by directly binding to their promoters, thus activating the downstream PI3K/Akt and ERK1/2 pathways. Finally, we demonstrated that the six2/ceRNET_CC axis was involved in chemoresistance.Conclusions: Our results uncover the mechanism through which ceRNET_CC is regulated, identify novel roles for the six2/ceRNET_CC axis in regulating the stemness of breast cancer cells, and propose the possibility of targeting the six2/ceRNET_CC axis to inhibit breast cancer stem cell (CSC) traits.