Comprehensive Analysis Strategy of Nervous–Endocrine–Immune-Related Metabolites to Evaluate Arachidonic Acid as a Novel Diagnostic Biomarker in Depression

Depression is one of the most complex
multifactorial diseases affected
by genetic and environmental factors. The molecular mechanism underlying
depression remains largely unclear. To address this issue, a novel
nervous–endocrine–immune (NEI) network module was used
to find the metabolites and evaluate the diagnostic ability of patients
with depression. During this process, metabolites were acquired from
a professional depression metabolism database. Over-representation
analysis was performed using IMPaLA. Then, the metabolite–metabolite
interaction (MMI) network of the NEI system was used to select key
metabolites. Finally, the receiver operating characteristic curve
analysis was evaluated for the diagnostic ability of arachidonic acid.
The results show that the numbers of the nervous system, endocrine
system, and immune system pathways are 10, 19, and 12 and the numbers
of metabolites are 38, 52, and 13, respectively. The selected shared
metabolite-enriched pathways can be 97.56% of the NEI-related pathways.
Arachidonic acid was extracted from the NEI system network by using
an optimization formula and validated by in vivo experiments.
It was indicated that the proposed model was good at screening arachidonic
acid for the diagnosis of depression. This method provides reliable
evidences and references for the diagnosis and mechanism research
of other related diseases.