Identification Of Potential Serum Metabolic Biomarkers Of Systemic Sclerosis

Objective: Systemic sclerosis(SSc)is a multisystem autoimmune disease,characterized by functional and structural abnormalities of small blood vessels,fibrosis of skin and internal organs,and production of autoantibodies.Early identification of SSc is potentially important for the effective prevention and control of disease.However,the available biomarkers are lacking in the early diagnosis of SSc.This study is aimed at identifying novel sensitive and specific biomarkers to diagnose SSc effectively based on the untargeted metabolomics approach from patients with systemic sclerosis.Methods:(1)This case-control study involved 30 SSc patients and 30 controls.Among them,30 SSc patients were divided as dc SSc and lc SSc subsets,and SSc with lung involvement and SSc without lung involvement subsets.The Ultra-high-pressure liquid chromatography and quadrupole-time-of-flight mass spectrometry(UHPLC Q-TOF MS)was applied for the assessment and analysis of the serum metabolic profiles.(2)Original metabolomic data were handled with peak alignment,retention time adjustment,and extraction of the peak area for XCMS analysis.The remaining data were normalized through Pareto scaling,and then the valid data were handled using multivariate data analysis,such as principal component analysis(PCA)and supervised partial least square-discriminate analysis(PLS-DA),using Metabo Analysis software.(3)The valid data are handled with unidimensional statistical analysis,such as the t-test,variation ratio analysis,and volcano plot analysis through R software.The diagnostic potential of the candidates is assessed using the receiver operating characteristic(ROC)curve.(4)Metabolites that showed differences between groups were identified and analyzed according to the multidimensional statistical analysis selection criteria using the Kyoto Encyclopedia of Genes and Genomes(KEGG;http://www.genome.jp/kegg/)database.Results:(1)A total of 38 metabolites are identified by non-targeted metabolomics,which are differed significantly between SSc and controls.These metabolites mainly included fatty acids,amino acids and Glycerophospholipid.(2)Metabolism pathway mainly altered in SSc patients are related to the metabolism of fatty acids,amino acids,choline and Glycerophospholipid.(3)Based on the AUC value of the significant metabolites,16 of them show the AUC value greater than 0.9.Conclusion:(1)These findings suggested that metabolic profiles and pathways differed between SSc patients and healthy people and may provide new targets for SSc-directed therapeutics and diagnostics.(2)It has been found that at least 16 serum metabolites show good predictive power in the diagnosis of SSc and provide serum biomarkers with high sensitivity and specificity in non-invasive diagnosis of the disease.The mechanism of the development of SSc is revealed by the level of serum metabolomics.Further studies are needed to elucidate the mechanism of the effects of the serum metabolomics with consideration of their impacts on the SSc development.