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      Metabolite Characteristics in Tongue Coating from Damp Phlegm Pattern in Patients with Gastric Precancerous Lesion

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          Abstract

          Objective

          In this study, we analyzed the metabolite profile of the tongue coating of patients having gastric precancerous lesion (GPL) with damp phlegm pattern and proposed a mechanism of pathological transition.

          Methods

          The changes in tongue-coating metabolites in patients with GPL damp phlegm pattern were analyzed using GC-TOF-MS and UHPLC-QE-MS metabolomics methods.

          Results

          When compared with 20 patients who did not exhibit a nondamp phlegm pattern, 12 metabolites were highly expressed and 10 metabolites were under expressed in 40 cases of damp phlegm pattern, of which involved 9 metabolic pathways. Compared with 15 healthy people, 134 metabolites were upregulated and 3 metabolites were downregulated in 40 cases exhibiting a damp phlegm pattern, of which involved 17 metabolic pathways. The patients with damp phlegm pattern were compared with nondamp phlegm pattern patients and healthy people, the main differential metabolites were primarily lipids and lipid-like molecules, and the main differential metabolic pathways were related to glycerophospholipid metabolism. In the glycerophospholipid metabolism, the metabolites with changes were phosphatidylethanolamine and lysoPC(18 : 1 (9z)). Among them, phosphatidylethanolamine exists in the synthesis stage of glycerophospholipid metabolism.

          Conclusions

          Abnormal expression of lipids and lipid-like molecules, as the major metabolic change, was involved in the formation of GPL patients with damp phlegm pattern.

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          Most cited references61

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Gastric cancer

            Gastric cancer is the fifth most common cancer and the third most common cause of cancer death globally. Risk factors for the condition include Helicobacter pylori infection, age, high salt intake, and diets low in fruit and vegetables. Gastric cancer is diagnosed histologically after endoscopic biopsy and staged using CT, endoscopic ultrasound, PET, and laparoscopy. It is a molecularly and phenotypically highly heterogeneous disease. The main treatment for early gastric cancer is endoscopic resection. Non-early operable gastric cancer is treated with surgery, which should include D2 lymphadenectomy (including lymph node stations in the perigastric mesentery and along the celiac arterial branches). Perioperative or adjuvant chemotherapy improves survival in patients with stage 1B or higher cancers. Advanced gastric cancer is treated with sequential lines of chemotherapy, starting with a platinum and fluoropyrimidine doublet in the first line; median survival is less than 1 year. Targeted therapies licensed to treat gastric cancer include trastuzumab (HER2-positive patients first line), ramucirumab (anti-angiogenic second line), and nivolumab or pembrolizumab (anti-PD-1 third line).
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              XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification.

              Metabolite profiling in biomarker discovery, enzyme substrate assignment, drug activity/specificity determination, and basic metabolic research requires new data preprocessing approaches to correlate specific metabolites to their biological origin. Here we introduce an LC/MS-based data analysis approach, XCMS, which incorporates novel nonlinear retention time alignment, matched filtration, peak detection, and peak matching. Without using internal standards, the method dynamically identifies hundreds of endogenous metabolites for use as standards, calculating a nonlinear retention time correction profile for each sample. Following retention time correction, the relative metabolite ion intensities are directly compared to identify changes in specific endogenous metabolites, such as potential biomarkers. The software is demonstrated using data sets from a previously reported enzyme knockout study and a large-scale study of plasma samples. XCMS is freely available under an open-source license at http://metlin.scripps.edu/download/.
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                Author and article information

                Contributors
                Journal
                Evid Based Complement Alternat Med
                Evid Based Complement Alternat Med
                ECAM
                Evidence-based Complementary and Alternative Medicine : eCAM
                Hindawi
                1741-427X
                1741-4288
                2021
                2 June 2021
                2 June 2021
                : 2021
                : 5515325
                Affiliations
                1Shanghai Key Laboratory of Health Identification and Assessment/Laboratory of TCM Four Diagnostic Information, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
                2Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
                3Department of Pharmaceutical Sciences, College of Pharmacy, University of South Florida, Tampa, FL 33612, USA
                Author notes

                Academic Editor: Yu CAI

                Author information
                https://orcid.org/0000-0002-0280-7495
                https://orcid.org/0000-0001-9374-2369
                Article
                10.1155/2021/5515325
                8189775
                edc4fd03-9ad1-4f24-9718-69658760c04c
                Copyright © 2021 Yifeng Xu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 February 2021
                : 12 April 2021
                : 21 May 2021
                Funding
                Funded by: National Natural Science Foundation of China
                Award ID: 81703982
                Funded by: Shanghai Biotree Biotech Co. Ltd.
                Categories
                Research Article

                Complementary & Alternative medicine
                Complementary & Alternative medicine

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