Establishment of a post‐race biomarkers database and application of pathway analysis to identify potential biomarkers in post‐race equine plasma

2021 ◽  
Author(s):  
Kohei Ohnuma ◽  
Taiga Uchida ◽  
Gary Ngai‐Wa Leung ◽  
Toshiki Ueda ◽  
Taku Obara ◽  
...  
Keyword(s):  
Pathway Analysis ◽  
Equine Plasma ◽  
Potential Biomarkers

scholarly journals Identification of potential plasma biomarkers and metabolic dysfunction for unstable angina pectoris and its complication based on global metabolomics

2019 ◽  
Vol 39 (3) ◽  
Author(s):  
Juan Wang ◽  
Wenjuan Xu ◽  
Huihui Zhao ◽  
Jianxin Chen ◽  
Bin Zhu ◽  
...  
Keyword(s):  
Angina Pectoris ◽  
Unstable Angina ◽  
Pathway Analysis ◽  
Clinical Symptoms ◽  
Area Under The Curve ◽  
Unstable Angina Pectoris ◽  
High Morbidity ◽  
Coronary Syndrome ◽  
Global Metabolomics ◽  
Potential Biomarkers

Abstract Unstable angina pectoris (UA) is one of the most dangerous clinical symptoms of acute coronary syndrome due to the risk of myocardial ischemia, which can lead to high morbidity and mortality worldwide. Though there are many advantages in understanding the pathophysiology of UA, the identification of biomarkers for the diagnosis, prognosis, and treatment of UA remains a challenge in the clinic. A global metabolomics research based on ultra-performance liquid chromatography (UPLC) combined with Q-TOF/MS was performed to discover the metabolic profile of health controls, UA patients, and UA patients with diabetes mellitus (DM), and screen for potential biomarkers. Twenty-seven potential biomarkers were determined using pattern recognition. These biomarkers, which include free fatty acids, amino acids, lysoPE and lysoPC species, and organic acids, can benefit the clinical diagnosis of UA. Pathway analysis indicated that arginine and proline metabolism, glycerophospholipid metabolism, and purine metabolism were affected in the UA patients, uniquely. Additionally, alterations in the metabolic signatures between UA and UA-complicated DM were also explored. As a result, six differential metabolites with an area under the curve (AUC) of more than 0.85 were identified as biomarkers for the diagnosis of UA and UA complicated with DM. Pathway analysis implied tryptophan metabolism was a key metabolic pathway in UA patients with DM, which provides new insights into the pathological study and drug discovery of UA.

scholarly journals Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of intestinal-type gastric cancer and precancerous stages

2020 ◽  
Author(s):  
Lijing Du ◽  
Shasha Li ◽  
Xue Xiao ◽  
Jin Li ◽  
Huizi Jin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
Pathway Analysis ◽  
Roc Analysis ◽  
Characteristic Curve ◽  
Disease Process ◽  
Intestinal Type ◽  
Metabolic Pathway Analysis ◽  
Potential Biomarker ◽  
Potential Biomarkers

Abstract Background: Gastric cancer (GC) with majority of intestinal-type adenocaricinoma remains one of the most common cancers all over the world. GC faces a great challenge in the clinical diagnosis, that it often can be detected at advanced stages, and leads to the loss of optimum time for treatment and poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: Totally, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 intestinal-type early gastric cancer (EGC) and 25 intestinal-type advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Principal components analysis, orthogonal partial least squares-discriminant analysis as well as Random forest were utilized to evaluate the variation on endogenous metabolites for intestinal-type GC patients and to screen potential biomarkers. Furthermore, the proposed biomarkers were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathway analysis were carried out on MetaboAnalyst.Results: Totally 50 metabolites were detected with differentially expression among CSG, intestinal-type EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated in AGC patients, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde down-regulated. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disorder, including amino acid and lipid metabolisms, in intestinal-type GC patients.Conclusions: In this study, a total of six metabolites were identified to contribute significantly to the diagnosis of intestinal-type GC and precancerous stages, respectively, and over 93.1% AUC value was achieved in AUC test on biomarker panels, It indicated that the biomarker panels are· sensitive to the early diagnosis of intestinal-type GC disease, which is expected to be developed as a promising diagnostic and prognostic tool for disease stratification studies.

scholarly journals Metabolomic profiling on plasma reveals potential biomarkers for screening and early diagnosis of intestinal-type gastric cancer and precancerous stages

2020 ◽  
Author(s):  
Lijing Du ◽  
Shasha Li ◽  
Xue Xiao ◽  
Jin Li ◽  
Huizi Jin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Diagnosis ◽  
Pathway Analysis ◽  
Roc Analysis ◽  
Characteristic Curve ◽  
Disease Process ◽  
Intestinal Type ◽  
Metabolic Pathway Analysis ◽  
Potential Biomarker ◽  
Potential Biomarkers

Abstract Background: Gastric cancer (GC) with majority of intestinal-type adenocaricinoma remains one of the most common cancers all over the world. GC faces a great challenge in the clinical diagnosis, that it often can be detected at advanced stages, and leads to the loss of optimum time for treatment and poor prognosis. Thus, there is a critical need to develop effective and noninvasive strategies for early diagnosis of the disease process. Methods: Totally, 82 participants were enrolled in the study, including 50 chronic superficial gastritis (CSG) patients, 7 intestinal-type early gastric cancer (EGC) and 25 intestinal-type advanced gastric cancer (AGC) ones. Metabolites profiling on patient plasma was performed using ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF/MS). Principal components analysis, orthogonal partial least squares-discriminant analysis as well as Random Forest were utilized to evaluate the variation on endogenous metabolites for intestinal-type GC patients and to screen potential biomarkers. Furthermore, the proposed biomarkers were used to create logistic regression models, which discrimination efficiency and accuracy was ascertained by receiver operating characteristic curve (ROC) analysis. Metabolic pathway analysis was carried out on MetaboAnalyst.Results: Totally 50 metabolites were detected with differentially expression among CSG, intestinal-type EGC and AGC patients. L-carnitine, L-proline, pyruvaldehyde, phosphatidylcholines (PC) (14:0/18:0), lysophosphatidylcholine (14:0) (LysoPC 14:0), lysinoalanine were defined as the potential biomarker panel for the diagnosis among CSG and EGC patients. Compared with EGC patients, 6 significantly changed metabolites, PC(O-18:0/0:0) and LysoPC(20:4(5Z,8Z,11Z,14Z)) were found to be up-regulated in AGC patients, whereas L-proline, L-valine, adrenic acid and pyruvaldehyde down-regulated. ROC analysis demonstrated a high diagnostic performance for metabolite panels with area under the curve (AUC) of 0.931 to 1. Moreover, the metabolomic pathway analysis revealed several metabolism pathway disorders, including amino acid and lipid metabolisms, in intestinal-type GC patients.Conclusions: In this study, a total of six metabolites were identified to contribute significantly to the diagnosis of intestinal-type GC and precancerous stages, respectively, and over 93.1% AUC value was achieved in AUC test on biomarker panels. It indicated that the biomarker panels are sensitive to the early diagnosis of intestinal-type GC disease, which is expected to be developed as a promising diagnostic and prognostic tool for disease stratification studies.

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