An integrated pathological research for precise diagnosis of schizophrenia combining LC-MS/H NMR metabolomics and transcriptomics.

BACKGROUND

Lack of clinically specific biomarkers has impeded the precise diagnosis of schizophrenia, meanwhile, limited comprehending of pathogenesis for schizophrenia has restricted the effective treatment.

METHOD

An integrated multi-omic approach, combining metabolomic platform (LC-MS and H NMR) and transcriptomic platform, was established to differentiate healthy subjects from schizophrenia patients. Based on filtered metabolites and genes, characteristic spectrums were further built. Then, representative metabolites and genes were screened out through Boruta algorithm. Moreover, characteristic diagnostic formulas were established via LASSO regression analysis.

RESULT

As a result, 86 differential metabolites (in line with amino acid metabolism, etc.) and 189 differential expression genes (involving in amino acid metabolic process, etc.) were obtained as potential biomarkers for schizophrenia. The latent interaction between metabolites with genes, such as HMGCLL1 with energy metabolism, etc., was further studied through the analysis of pathway-based integration. Moreover, fine predictive ability was attributed to characteristic metabolomic/transcriptomic diagnostic spectrums/formulas.

CONCLUSION

The functional relationships of filtered metabolites and genes were studied, which could elaborate the pathological process of schizophrenia more systemically, supplying more precise information on mechanism description and diagnostic evidence of schizophrenia.