Background Abdominal aortic aneurysm (AAA) is a complicated aortic dilatation disease. Metabolomics is an emerging system biology method. This aim of this study was to identify abnormal metabolites and metabolic pathways associated with AAA and to discover potential biomarkers that could affect the size of AAAs. Material/Methods An untargeted metabolomic method was used to analyze the plasma metabolic profiles of 39 patients with AAAs and 30 controls. Multivariate analysis methods were used to perform differential metabolite screening and metabolic pathway analysis. Cluster analysis and univariate analysis were performed to identify potential metabolites that could affect the size of an AAA. Results Forty-five different metabolites were identified with an orthogonal projection to latent squares-discriminant analysis model and the differences between them in the patients with AAAs and the control group were compared. A variable importance in the projection score >1 and P<0.05 were considered statistically significant. In patients with AAAs, the pathways involving metabolism of alanine, aspartate, glutamate, D-glutamine, D-glutamic acid, arginine, and proline; tricarboxylic acid cycling; and biosynthesis of arginine are abnormal. The progression of an AAA may be related to 13 metabolites: citric acid, 2-oxoglutarate, succinic acid, coenzyme Q1, pyruvic acid, sphingosine-1-phosphate, platelet-activating factor, LysoPC (16: 00), lysophosphatidylcholine (18: 2(9Z,12Z)/0: 0), arginine, D-aspartic acid, and L- and D-glutamine. Conclusions An untargeted metabolomic analysis using ultraperformance liquid chromatography-tandem mass spectrometry identified metabolites that indicate disordered metabolism of energy, lipids, and amino acids in AAAs.

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Background: Due to high invasiveness and heterogeneity, the morbidity and mortality of intrahepatic cholangiocarcinoma (ICC) remain unsatisfied. Recently, the exploration of genomic variants has decoded the underlying mechanisms of initiation and progression for multiple tumors, while has not been fully investigated in ICC.Methods: We comprehensively analyzed 899 clinical and somatic mutation data of ICC patients from three large-scale cohorts. Based on the mutation landscape, we identified the common high-frequency mutation genes (FMGs). Subsequently, the clinical features, prognosis, tumor mutation burden (TMB), and pharmacological landscape from patients with different mutation carriers were further analyzed.Results: We found TP53 and KRAS were the common FMGs in the three cohorts. Kaplan–Meier survival curves and univariate and multivariate analysis displayed that TP53 and KRAS mutations were associated with poor prognosis. Considering the co-mutation phenomenon of TP53 and KRAS, we stratified patients into “Double-WT,” “Single-Hit,” and “Double-Hit” phenotypes by mutation status. Patients with the three phenotypes showed significant differences in the mutation landscape. Additionally, compared with “Double-WT” and “Single-Hit” phenotypes, patients with “Double-Hit” presented a dismal prognosis and significantly high TMB. Through chemotherapy sensitivity analysis, we identified a total of 30 sensitive drugs for ICC patients, of which 22 were drugs sensitive to “Double-WT,” 7 were drugs sensitive to “Double-Hit,” and only one was a drug sensitive to “Single-Hit.”Conclusion: Our study defined a novel mutation classification based on the common FMGs, which may contribute to the individualized treatment and management of ICC patients.

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