Serum
lipid metabolites have been emerging as ideal biomarkers
for disease diagnosis and prediction. In the current stage, nontargeted
or targeted lipidomic research mainly relies on a liquid chromatography–mass
spectrometry (LC–MS) platform, but future clinical applications
need more robust and high-speed platforms. Surface-assisted laser
desorption ionization mass spectrometry (SALDI-MS) has shown excellent
advantages in the high-speed analysis of lipid metabolites. However,
the platform in the positive ion mode is more inclined to target a
certain class of lipids, leading to the low coverage of lipid detection
and limiting its practical translation to clinical applications. Herein,
we proposed a dual-mechanism-driven strategy for high-coverage detection
of serum lipids on a novel SALDI-MS target, which is a composite nanostructure
comprising vertical silicon nanowires (VSiNWs) decorated with AuNPs
and polydopamine (VSiNW-Au-PDA). The performance of laser desorption
and ionization on the target can be enhanced by charge-driven desorption
coupled with thermal-driven desorption. Simultaneous detection of
236 serum lipids (S/N ≥ 5) including neutral and polar lipids
can be achieved in the positive ion mode. Among these, 107 lipid peaks
were successfully identified. When combined with VSiNW-Au-PDA and
VSiNW chips, 479 lipid peaks can be detected in serum samples in positive
and negative ion modes, respectively. Based on the platform, serum
samples from 57 hepatocellular carcinoma (HCC) patients and 76 healthy
controls were analyzed. After data mining, 14 lipids containing different
lipid types (TAG, CE, PC) were selected as potential lipidomic biomarkers.
With the assistance of an artificial neural network, a diagnostic
model with a sensitivity of 92.7% and a specificity of 96% was constructed
for HCC diagnosis.