Serum united urine metabolic analysis comprehensively
reveals the
disease status for kidney diseases in particular. Thus, the precise
and convenient acquisition of metabolic molecular information from
united biofluids is vitally important for clinical disease diagnosis
and biomarker discovery. Laser desorption/ionization mass spectrometry
(LDI-MS) presents various advantages in metabolic analysis; however,
there remain challenges in ionization efficiency and MS signal reproducibility.
Herein, we constructed a self-assembled hyperbranched black gold nanoarray
(HyBrAuNA) assisted LDI-MS platform to profile serum united urine
metabolic fingerprints (S-UMFs) for diagnosis of early stage renal
cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong
electromagnetic field enhancement and high photothermal conversion
efficacy, enabling effective ionization of low abundant metabolites
for S-UMF collection. With a uniform nanoarray, the platform presented
excellent reproducibility to ensure the accuracy of S-UMFs obtained
in seconds. When it was combined with automated machine learning analysis
of S-UMFs, early stage RCC patients were discriminated from the healthy
controls with an area under the curve (AUC) > 0.99. Furthermore,
we
screened out a panel of 9 metabolites (4 from serum and 5 from urine)
and related pathways toward early stage kidney tumor. In view of its
high-throughput, fast analytical speed, and low sample consumption,
our platform possesses potential in metabolic profiling of united
biofluids for disease diagnosis and pathogenic mechanism exploration.