Purpose To determine the association between plasma free fatty acid (FFA) levels and primary angle‐closure glaucoma (PACG). Methods Free fatty acid (FFA) levels in patients with PACG (n = 181) and people without glaucoma (n = 340) were compared. Twenty‐two FFAs and six lipid classes were measured using metabolomics analysis. Odds ratio (OR) of these metabolites and their 95% confidence intervals (95%CI) for PACG were obtained by logistic regression. Stepwise forward selection was performed to identify FFAs that influenced PACG risk. Areas under the curve (AUC) were applied to assess the predictive performance. Spearman’s rank correlation was used to assess the relationship between ocular parameters and FFAs. Results Most FFAs in the PACG group were lower than those in the non‐glaucoma group. Docosahexaenoic acid (DHA; OR for fourth quartile (Q4) vs. first quartile (Q1): 0.32 (0.16–0.66); per standard deviation (SD) increase: 0.64 (0.49–0.83); p for trend: 0.0007) and total saturated fatty acids (SFAs; OR for Q4 versus Q1: 0.27 (0.13–0.56); per SD increase: 0.65 (0.50–0.87); p for trend: 0.0004) were associated with decreased PACG risk. The AUC of the model that included DHA, total SFAs, demographic and ophthalmic factors increased from 0.8230 (0.7811–0.8649) to 0.8512 (0.8133–0.8891) (increased AUC: 0.0282 (0.0112–0.0453); p for increased AUC: 0.0012). Additionally, the cup‐disc ratio had a weak negative correlation with DHA and total SFAs (DHA: r = −0.12085, p = 0.0065; total SFAs: r = −0.13318, p = 0.0024). Conclusions Decrease in FFA levels may be related to lipid peroxidation. Docosahexaenoic acid (DHA) and total SFAs may be screening indices for PACG patients.

Background: Ewing's sarcoma (ES) is the second most common primary malignant bone tumor. Although the disease has been studied on a molecular basis, its prognosis has not improved. Therefore, the goal of this study is to screen effective biomarkers for predicting the prognosis and progression of ES.Methods: In this study, the gene expression profile of Ewing's sarcoma was downloaded from public databases. Candidate genes were screened by two methods of weighted gene co-expression network analysis (WGCNA) and differential genes expression analysis, and the Hub gene were determined by the protein–protein interaction (PPI) network. The univariate cox regression and least absolute shrinkage and the selection operator (LASSO) Cox regression were performed on the Hub gene to construct a prognostic model. The receiver operating characteristic (ROC) curve tests the predictive power of the prognostic model. Use the clinical data in the International Cancer Genome Consortium (ICGC) database for verification. Finally, Gene Set Enrichment Analysis (GSEA) was performed to obtain the biological role of prognostic genes in ES.Results: A total of 542 differentially expressed genes（DEGs） were obtained. Through WGCNA, the obtained turquoise module is significantly related to ES. PPI network analysis of candidate genes identified 10 Hub genes. The univariate cox regression results showed that CACNB1, IDH2, ATP1B4, PRKAG3, STAC3 are risk factors affecting the prognosis of ES. A prognostic model was constructed using IDH2 and PRKAG3, and patients were divided into two risk groups. The survival time of patients in the high-risk group was significantly shorter than that in the low-risk group. The ROC curve confirms that this model has certain accuracy. IGCG cohort verification yielded consistent results. GSEA results showed that IDH2 in the development of ES may promote the progression of the disease through G2M CHECKPOINT, GLYCOLYSIS, MITOTIC SPINDLE, MTORC1 SIGNALING, PEROXISOME, PI3K-AKT-MTOR SIGNALING pathways, while PRKAG3 through MYOGENESIS pathway.Conclusions: This study shows that IDH2 and PRKAG3 may play important roles in the progression of ES, and can be used as therapeutic targets and prognostic evaluation biomarkers.