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Background: Malignant pleural mesothelioma (MPM) is a rare and aggressive carcinoma located in pleural cavity. Due to lack of effective diagnostic biomarkers and therapeutic targets in MPM, the prognosis is extremely poor. Because of difficulties in sample extraction, and the high rate of misdiagnosis, MPM is rarely studied. Therefore, novel modeling methodology is crucially needed to facilitate MPM research., Methods: A novel patient-derived xenograft (PDX) modeling strategy was designed, which included preliminary screening of patients with pleural thickening using computerized tomography (CT) scan, further reviewing history of disease and imaging by a senior sonographer as well as histopathological analysis by a senior pathologist, and PDX model construction using ultrasound-guided pleural biopsy from MPM patients. Gas chromatography-mass spectrometry-based metabolomics was further utilized for investigating circulating metabolic features of the PDX models. Univariate and multivariate analysis, and pathway analysis were performed to explore the differential metabolites, enriched metabolism pathways and potential metabolic targets., Results: After screening using our strategy, 5 out of 116 patients were confirmed to be MPM, and their specimens were used for modeling. Two PDX models were established successfully. Metabolomics analysis revealed significant metabolic shifts in PDX models, such as dysregulations in amino acid metabolism, TCA cycle and glycolysis, and nucleotide metabolism., Conclusions: To sum up, we suggested a novel modeling strategy that may facilitate specimen availability for MM research, and by applying metabolomics in this model, several metabolic features were identified, whereas future studies with large sample size are needed., (© 2021. The Author(s).)

Targeted metabolomics has significant advantages for quantification but suffers from reduced metabolite coverage. In this study, we developed a large-scale targeted metabolomics method and expanded its applicability to various human samples. This approach initially involved unbiased identification of metabolites in human cells, tissues and body fluids using ultra high-performance liquid chromatography (UHPLC) coupled to high-resolution Orbitrap mass spectrometry (MS). Targeted metabolomics method was established with utility of UHPLC-triple quadrupole MS, which enables targeted profiling of over 400 biologically important metabolites (e.g., amino acids, sugars, nucleotides, dipeptides, coenzymes, and fatty acids), covering 92 metabolic pathways (e.g., Krebs cycle, glycolysis, amino acids metabolism, ammonia recycling, and one-carbon metabolism). The present method displayed better sensitivity, repeatability and linearity than the Orbitrap MS-based untargeted metabolomics approach and demonstrated excellent performance in lung cancer biomarker discovery, in which 107 differential metabolites were able to discriminate between carcinoma and adjacent normal tissues, implicating the Warburg effect, alteration of redox state, and nucleotide metabolism of lung cancer. This new method is flexible and expandable and offers many advantages for metabolomics analysis, such as wide metabolite coverage, good repeatability and linearity and excellent capability in biomarker discovery, making it useful for both basic and clinical metabolic research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The chemical composition of black tea during tea processing is in a state of flux. However, the dynamic changes of this sophisticated metabolic process are far from clear. GC-MS-based metabolomic analyses were performed to examine changes in volatile and non-volatile compounds throughout the five stages of tea processing. The results showed that the most striking differences were observed at the withering and rolling stages, during which 62 non-volatile and 47 volatile compounds were significantly changed. The levels of most monosaccharides decreased at the withering stage and increased in subsequent stages while di-saccharides decreased as the process progressed. Free amino acids increased sharply at the withering stage, and most kept increasing or remained stable afterwards. However, levels of catechin, epicatechin, epigallocatechin, and epigallocatechin gallate decreased after withering and remained at low levels afterwards. Among the 47 volatile compounds with altered levels, phenylpropanoids/benzenoids and carotenoid-derived volatiles, which contribute to the honey-like and rose-like fragrances and quality of Danxia2 tea, kept increasing during the processing, among them eight were newly produced. Furthermore, 19 volatiles with a grassy odor decreased during processing. This study provides a comprehensive profile of metabolic changes during black tea processing, which is potentially important for both quality control and improvement of the flavor of black teas., (Copyright © 2019. Published by Elsevier Ltd.)

The aim of this study was to develop a diagnostic strategy for esophageal squamous cell carcinoma (ESCC) that combines plasma metabolomics with machine learning algorithms. Plasma-based untargeted metabolomics analysis was performed with samples derived from 88 ESCC patients and 52 healthy controls. The dataset was split into a training set and a test set. After identification of differential metabolites in training set, single-metabolite-based receiver operating characteristic (ROC) curves and multiple-metabolite-based machine learning models were used to distinguish between ESCC patients and healthy controls. Kaplan-Meier survival analysis and Cox proportional hazards regression analysis were performed to investigate the prognostic significance of the plasma metabolites. Finally, twelve differential plasma metabolites (six up-regulated and six down-regulated) were annotated. The predictive performance of the six most prevalent diagnostic metabolites through the diagnostic models in the test set were as follows: arachidonic acid (accuracy: 0.887), sebacic acid (accuracy: 0.867), indoxyl sulfate (accuracy: 0.850), phosphatidylcholine (PC) (14:0/0:0) (accuracy: 0.825), deoxycholic acid (accuracy: 0.773), and trimethylamine N-oxide (accuracy: 0.653). The prediction accuracies of the machine learning models in the test set were partial least-square (accuracy: 0.947), random forest (accuracy: 0.947), gradient boosting machine (accuracy: 0.960), and support vector machine (accuracy: 0.980). Additionally, survival analysis demonstrated that acetoacetic acid was an unfavorable prognostic factor (hazard ratio (HR): 1.752), while PC (14:0/0:0) (HR: 0.577) was a favorable prognostic factor for ESCC. This study devised an innovative strategy for ESCC diagnosis by combining plasma metabolomics with machine learning algorithms and revealed its potential to become a novel screening test for ESCC. • Six most prevalent diagnostic plasma metabolites were identified in ESCC. • Plasma-metabolite-based machine learning models (PLS, RF, GBM, and SVM) for ESCC diagnosis. • Acetoacetic acid was an unfavorable prognostic factor, while PC (14:0/0:0) was a favorable prognostic factor for ESCC. [ABSTRACT FROM AUTHOR]

Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified. Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC. Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue. Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC. [ABSTRACT FROM AUTHOR]

Background: Malignant mesothelioma (MM) is an aggressive and incurable carcinoma that is primarily caused by asbestos exposure. However, the current diagnostic tool for MM is still under-developed. Therefore, the aim of this study is to explore the diagnostic significance of a strategy that combined plasma-based metabolomics with machine learning algorithms for MM. Methods: Plasma samples collected from 25 MM patients and 32 healthy controls (HCs) were randomly divided into train set and test set, after which analyzation was performed by liquid chromatography-mass spectrometry-based metabolomics. Differential metabolites were screened out from the samples of the train set. Subsequently, metabolite-based diagnostic models, including receiver operating characteristic (ROC) curves and Random Forest model (RF), were established, and their prediction accuracies were calculated for the test set samples. Results: Twenty differential plasma metabolites were annotated in the train set; 10 of these metabolites were validated in the test set. The seven metabolites with most significant diagnostic values were taurocholic acid (accuracy = 0.6429), uracil (accuracy = 0.7143), biliverdin (accuracy = 0.7143), tauroursodeoxycholic acid (accuracy = 0.5000), histidine (accuracy = 0.8571), pyrroline hydroxycarboxylic acid (accuracy = 0.8571), and phenylalanine (accuracy = 0.7857). Furthermore, RF based on 20 annotated metabolites showed a prediction accuracy of 0.9286, and its optimized version achieved 1.0000 in the test set. Moreover, the comparison between the samples of peritoneal MM (n = 8) and pleural MM (n = 17) illustrated a significant increase in levels of taurocholic acid and tauroursodeoxycholic acid, as well as an evident decrease in biliverdin. Conclusions: Our results revealed the potential diagnostic value of plasma-based metabolomics combined with machine learning for MM. Further research with large sample size is worthy conducting. Moreover, our data demonstrated dysregulated metabolism pathways in MM, which aids in better understanding of molecular mechanisms related to the initiation and development of MM. [ABSTRACT FROM AUTHOR]

• Combination of metabolomics and machine learning algorithms. • ESCC displays a disturbed amino acid metabolism. • Prognostic significance of tissue metabolites. • Amino acid transporters (SLC7A5, SLC1A5, and SLC16A10) are up-regulated in ESCC. Prognosis for esophageal squamous cell carcinoma (ESCC) is poor, so it is essential to develop a more complete understanding of the disease. The purpose of this study was to explore metabolic biomarkers and potential therapeutic targets for ESCC. An ultra-high-performance liquid chromatography coupled with high resolution mass (UPLC/MS)-based metabolomic analysis was performed in 141 ESCC cancerous tissue samples and 70 non-cancerous counterparts. The results showed that 41 differential metabolites were annotated in the training set, and 37 were validated in the test set. Single-metabolite-based receiver operating characteristic (ROC) curves as well as metabolite-based machine learning models, including Partial Least Squares (PLS), Support Vector Machine (SVM), and Random Forest (RF), were investigated for cancerous and non-cancerous tissue classification. Six most prevalent diagnostic metabolites—adenylsuccinic acid, UDP-GalNAc, maleylacetoacetic acid, hydroxyphenylacetylglycine, galactose, and kynurenine—showed testing predictive accuracies of 0.89, 0.95, 0.97, 0.89, 0.84, and 0.84, respectively. Moreover, the metabolite-based models (PLS, SVM, and RF) had testing predictive accuracies of 0.95, 0.95, and 1.00, respectively. Kaplan–Meier survival analysis and Cox proportional hazards regression analysis demonstrated that 2-hydroxymyristoylcarnitine (HR: 0.55, 95 % CI: 0.32 to 0.92), 3-hydroxyhexadecanoylcarnitine (HR: 0.49, 95 % CI: 0.29 to 0.83), and 2,3-Dinor-TXB1 (HR: 0.56, 95 % CI: 0.33 to 0.95) to be significantly associated with OS. Based on the observation of accumulation in amino acids, immunohistochemistry (IHC) staining revealed that the amino acid transporters SLC7A5/LAT1, SLC1A5/ASCT2, and SLC16A10/MCT10 were up-regulated in ESCC cancerous tissues when compared to non-cancerous equivalents. Consistently, the same panel of amino acids were downregulated in cells with SLC1A5 knockdown. Herein, it is concluded that this study not only identified several metabolites with diagnostic and/or prognostic value, but also provided accurate metabolite-based prediction models for ESCC tissue classification. Furthermore, the three up-regulated amino acid transporters were identified as potential therapeutic targets for ESCC, especially SLC1A5. [ABSTRACT FROM AUTHOR]

Colorectal cancer is a common malignancy occurring in the digestive system, which is the third common cause of cancer mortality in developed countries. Shikonin, a naphthoquinone compound extracted from the root of Lithospermum erythrorhizon , is extensively reported to exert antitumor activity against various types of cancer. However, the systematic effect of shikonin in colon cancer remains poorly understood. In the present study, we evaluated the antitumor activity of shikonin in human colon cancer cells and the therapeutic effect on a xenograft mouse model. Transcriptomics and metabolomics were further integrated to provide a systematic perspective of the shikonin-induced antitumor mechanism. The results demonstrated that shikonin had a remarkable antitumor potency both in vitro and in vivo. Moreover, metabolic pathways, including the purine metabolism, amino acid metabolism, and glycerophospholipid metabolism, were perturbed and subsequently led to cell cycle arrest in the G2/M phase. In particular, the disturbance of purine metabolism may account for the major mechanism resulting from shikonin antitumor activity. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is a common malignancy occurring in the digestive system. Despite progress in surgery and therapy options, CRC is still a considerable cause of cancer mortality worldwide. In this study, a colon cancer patient-derived xenograft model was established to evaluate the antitumor activity of Shikonin. The protective effect underlying Shikonin was determined through assessing serum levels of liver enzymes (ALT, AST) and kidney functions (BuN, Scr) in PDX mice. Proteomics and metabolomics profiles were integrated to provide a systematic perspective in dynamic changes of proteins and global endogenous metabolites as well as their perturbed pathways. A total of 456 differently expressed proteins (DEPs), 32 differently expressed metabolites (DEMs) in tumor tissue, and 20 DEMs in mice serum were identified. The perturbation of arginine biosynthesis, purine metabolism, and biosynthesis of amino acids may mainly account for therapeutic mechanism of Shikonin. Furthermore, the expression of mRNAs participating in arginine biosynthesis (CPS1, OTC, Arg1) and do novo purine synthesis (GART, PAICS, ATIC) were validated through RT-qPCR. Our study provides new insights into the drug therapeutic strategies and a better understanding of antitumor mechanisms that might be valuable for further studies on Shikonin in the clinical treatment of colorectal cancer. [ABSTRACT FROM AUTHOR]

• Metabolomic and lipidomic profiling of malignant and benign pleural effusions. • Several ether lipids showed excellent diagnostic potential, with highest AUC 0.953. • Implied high oxidation stress and peroxisome disorder in lung cancer patients. Malignant pleural effusion (MPE) is an important hallmark for late-stage lung cancer with metastasis. Current clinical diagnosis methods require tedious work to distinguish MPE from benign pleural effusion (BPE). The objective of this study was to characterize the metabolic signatures in MPE of lung cancer, and identify potential metabolite biomarkers for diagnosis of MPE. MPE from lung cancer (n = 46) and BPE from tuberculosis patients (n = 32) were investigated by liquid chromatography–tandem mass spectrometry (LC–MS/MS)-based global metabolomic and lipidomic profiling. Multivariate partial least-square discriminative analysis models exhibited distinct metabolic profiles between MPE and BPE. A total of 25 ether lipids, including phosphatidylcholines (PC), lysophosphatidylcholines (LPC) and phosphatidylethanolamines (PE), were observed to be significantly downregulated in MPE with excellent diagnostic potential. Plasmalogen PC(40:3p) showed highest AUC value of 0.953 in receiver operating characteristic (ROC) model. Oxidized polyunsaturated fatty acids (PUFA) were upregulated in MPE. The obtained results implied a high oxidative stress and peroxisome disorder in lung cancer patients. Combined metabolomic and lipidomic profiling have discovered potential biomarkers in MPE with excellent clinical diagnostic capability. Dysregulated ether lipids and oxidized PUFAs have implied an aberrant redox metabolism, which provides novel insights into the pathology of MPE in lung cancer. [ABSTRACT FROM AUTHOR]

• The metabolic profiles of pleural effusion in patients with EGFR-TKI resistance were altered. • Amino acid metabolism was mainly disturbed in acquired EGFR-TKI resistant group. • 3-hydroxyphenylacetic acid and N-acetyl- d -phenylalanine showed great AUC values in ROC mode. • EGFR-TKIs combined with metabolic interventions might be a new therapeutic strategy. Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) acquired resistance remains a major barrier in the clinical treatment of lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutations. Despite extensive efforts, mechanism of acquired resistance has not yet been elucidated clearly. The subject of this study was to characterize the metabolic signatures relevant to acquired EGFR-TKI resistance in pleural effusion (PE), and identify potential biomarkers in PE of patients with acquired EGFR-TKI resistance. PE from EGFR-TKI untreated group (n = 30) and EGFR-TKI resistant group (n = 18) was analyzed using liquid chromatography-mass spectrometry (LC MS) based metabolomic. Multivariate statistical analysis revealed distinctive diff ;erences between the groups. A total of 34 significantly differential metabolites in PE were identified, among which, the acquired EGFR-TKI resistant group had higher levels of l -lysine, taurine, ornithine and citrulline, and lower levels of l -tryptophan, kynurenine, l -phenylalanine, l -leucine, N-formyl- l -methionine, 3-hydroxyphenylacetic acid and N-acetyl- d -phenylalanine in PE than that of the EGFR-TKI untreated group. These metabolites are mainly involved in six amino acid metabolic pathways. In addition, 3-hydroxyphenylacetic acid and N-acetyl- d -phenylalanine showed the highest AUC values of 0.934 and 0.929 in receiver operating characteristic analysis. Through LC MS metabolomics, our study identified potential biomarkers in PE, differentiating EGFR-TKI resistant patients from untreated patients, as well as the mechanisms underlying acquired EGFR-TKI resistance; thus, providing novel insights into acquired EGFR-TKI resistance. [ABSTRACT FROM AUTHOR]