ABSTRACT
Objective To investigate the clinical value of serum metabolomic profile of prostate cancer using nuclear magnetic resonance-based metabolomics.Methods The retrospective case control study was adopted.The clinical data of 31 patients with prostate cancer,28 patients of prostatic hyperplasia and 31 healthy volunteers were enrolled in this study from May 2016 to May 2017 at the first affiliated hospital of Xinjiang medical university.In PCa group,the mean age was 66.3 years old,ranging 53-80 years old.In BPH group,the mean age was 59.3 years old,ranging 46-75 years old.In volunteer group,the mean age was 47.8 years old,ranging 35-62 years old..The serum of the 3 groups was measured by 1H-NMR spectroscopy.Multivariate statistical analysis was used to analyze the serum differential metabolism of the 3 groups,including principal components analysis (PCA),partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA).Results The multivariate statistical analysis of PCA that the rate of the first principal component 1 (PC1) was 53.24％,the second principal component 2 (PC2) was 25.31％ and the cumulative contribution rate was 78.55 ％.Results of PLS -DA showed that partial data overlap of the three groups,but the separation trend was appeared.The variance of X(R2X) and Y(R2Y) matrixes and predictive value Q2 were 0.67,0.60,and 0.42.The results of OPLS-DA showed that the difference among the PCa group and BPH group,healthy group were obvious.The separation trend were appeared and the differential metabolites could be screened effectively.The R2X、R2Y and Q2 was 0.24,0.57,0.21 and 0.30,0.65,0.36.26 different serum metabolites were detected in the 3 groups,including citric acid,arginine,threonine,citrulline,glutamine,lactic acid,alanine,unsaturated fats,glycoprotein etc.Conclusions Compared with BPH group and healthy group,the serum of prostate cancer patients showed significant differences in metabolism.Nuclear magnetic resonance metabolomics analysis can effectively distinguish these serum metabolic differences.