Molecular diagnosing, typing, and staging have been considered
to be the ideal alternatives of imaging-based detection methods in
clinics. Designer matrix-based analytical tools, with high speed,
throughout, efficiency and low/noninvasiveness, have attracted much
attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on
highly porous metal oxides derived from available metal–organic
frameworks (MOFs), which elaborately inherit the morphology and porosity
of MOFs and newly incorporate laser adsorption capacity of metal oxides.
Through optimized conditions, direct high-quality fingerprinting spectra
in 0.5 μL of urine are acquired. Using these fingerprinting
spectra, we can discriminate the renal cell carcinoma (RCC) from healthy
controls with higher than 0.99 of area under the curve (AUC) values
(R
2
Y(cum) = 0.744, Q
2 (cum) = 0.880), as well, from patients with
other tumors (R
2
Y(cum)
= 0.748, Q
2(cum) = 0.871). We also realize
the typing of three RCC subtypes, including clear cell RCC, chromophobe
RCC (R
2
Y(cum) = 0.620, Q
2(cum) = 0.656), and the staging of RCC (R
2
Y(cum) = 0.755, Q
2(cum) = 0.857). Moreover, the tumor sizes (threshold
value is 3 cm) can be remarkably recognized by this advanced metabolic
analysis tool (R
2
Y(cum)
= 0.710, Q
2(cum) = 0.787). Our work brings
a bright prospect for designer matrix-based analytical tools in disease
diagnosis, typing and staging.