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      Identifying potential serum biomarkers of breast cancer through targeted free fatty acid profiles screening based on a GC–MS platform

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          Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis

          Background More accurate diagnostic methods are pressingly needed to diagnose breast cancer, the most common malignant cancer in women worldwide. Blood-based metabolomics is a promising diagnostic method for breast cancer. However, many metabolic biomarkers are difficult to replicate among studies. Methods We propose that higher-order functional representation of metabolomics data, such as pathway-based metabolomic features, can be used as robust biomarkers for breast cancer. Towards this, we have developed a new computational method that uses personalized pathway dysregulation scores for disease diagnosis. We applied this method to predict breast cancer occurrence, in combination with correlation feature selection (CFS) and classification methods. Results The resulting all-stage and early-stage diagnosis models are highly accurate in two sets of testing blood samples, with average AUCs (Area Under the Curve, a receiver operating characteristic curve) of 0.968 and 0.934, sensitivities of 0.946 and 0.954, and specificities of 0.934 and 0.918. These two metabolomics-based pathway models are further validated by RNA-Seq-based TCGA (The Cancer Genome Atlas) breast cancer data, with AUCs of 0.995 and 0.993. Moreover, important metabolic pathways, such as taurine and hypotaurine metabolism and the alanine, aspartate, and glutamate pathway, are revealed as critical biological pathways for early diagnosis of breast cancer. Conclusions We have successfully developed a new type of pathway-based model to study metabolomics data for disease diagnosis. Applying this method to blood-based breast cancer metabolomics data, we have discovered crucial metabolic pathway signatures for breast cancer diagnosis, especially early diagnosis. Further, this modeling approach may be generalized to other omics data types for disease diagnosis. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0289-9) contains supplementary material, which is available to authorized users.
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            Free fatty acids rewire cancer metabolism in obesity-associated breast cancer via estrogen receptor and mTOR signaling

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              Targeted metabolomics in colorectal cancer: a strategic approach using standardized laboratory tests of the blood and urine

              Background Glycolytic markers have been detected in colorectal cancer (CRC) using advanced analytical methods. Methods Using commercially available assays, by-products of anaerobic metabolism were prospectively measured in the blood and urine of 20 patients with metastatic colorectal cancer (mCRC) and 20 patients with local disease. Twenty-four-hour urine citrate, plasma lactate, ketones, venous blood gas, anion gap, and osmolar gap were investigated. Results of patients with metastatic and local CRC were compared using two-sample t-tests or equivalent nonparametric tests. In addition, plasma total CO2 concentrations in our local hospital (5,931 inpatients and 1,783 outpatients) were compared retrospectively with those in our dedicated cancer center (1,825 outpatients) over 1 year. Results The average venous pCO2 was higher in patients with mCRC (50.2 mmHg; standard deviation [SD]=9.36) compared with those with local disease (42.8 mmHg; SD=8.98), p=0.045. Calculated serum osmolarity was higher in mCRC and attributed to concomitant sodium and urea elevations. In our retrospective analysis, plasma total CO2 concentrations (median=27 mmol/L) were higher in cancer patients compared to both hospital inpatients (median=23 mmol/L) and outpatients (median=24 mmol/L), p<0.0001. Conclusion Patients with mCRC had higher venous pCO2 levels than those with local disease. Although causation cannot be established, we hypothesize that pCO2 elevation may stem from a perturbed metabolism in mCRC.
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                Author and article information

                Contributors
                Journal
                Biomedical Chromatography
                Biomedical Chromatography
                Wiley
                0269-3879
                1099-0801
                October 2020
                July 02 2020
                October 2020
                : 34
                : 10
                Affiliations
                [1 ]Guangdong Key Laboratory for Genome Stability &amp; Disease Prevention, Department of Pharmacology and Shenzhen University International Cancer Center Shenzhen University School of Medicine Shenzhen China
                [2 ]Department of Breast Surgery Peking University Shenzhen Hospital Shenzhen China
                [3 ]State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health Macau University of Science and Technology Macao China
                [4 ]Clinical Medical Research Center The Second Clinical Medical College of Jinan University, The First Affiliated Hospital of Southern University, Shenzhen People's Hospital Shenzhen China
                [5 ]Department of Chemistry University of California Berkeley CA USA
                Article
                10.1002/bmc.4922
                32537761
                58af74a0-b042-40cc-b0a4-23fb6a230f25
                © 2020

                http://onlinelibrary.wiley.com/termsAndConditions#vor

                http://doi.wiley.com/10.1002/tdm_license_1.1

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