Whole urine-based multiple cancer diagnosis and metabolite profiling using 3D evolutionary gold nanoarchitecture combined with machine learning-assisted SERS

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To develop onsite applicable cancer diagnosis technologies, a noninvasive human biofluid detection method with high sensitivity and specificity is required, available for classifying cancer from the normal group. Herein, a three-dimensional evolutionary gold nanoarchitecture (3D-EGN) is developed by forming Au nanosponge (AuS) on a 96-well plate, followed by a decoration of Au nanoparticles (AuNPs) evolved with Au nanolamination (AuNL) for high-throughput urine sensing in liquid phase. The 3D-EGN exhibits not only strong electromagnetic field generated from numerous hotspot regions between AuNPs and further enhanced light scattering from multigrain boundaries after lamination process, but also highly volumetric field due to nanoporous structure of AuS, which is advantageous for sensitive liquid-phase SERS detection. SERS activity of the 3D-EGN platform is characterized using malachite green, showing a limit detection of 1.23 × 10−9 M in liquid phase, and excellent uniformities both within single well and well-to-well with relative standard deviation (RSD) values of about 10%. The 3D-EGN platform has been demonstrated for the detection of whole clinical human urine samples, proving effective molecular sensing in the presence of Brownian motion from liquid medium. Subsequently, cancer metabolite candidates are investigated to verify the metabolic alternations of multicancer, including pancreatic, prostate, lung, and colorectal cancers, simultaneously classifying them into five different groups, including normal with an accuracy of 95.6%, using machine-learning methods. The integration of nanomaterials with the conventional clinical platform provides rapid and high-throughput multicancer diagnostic system and opens a new era for noninvasive diseases diagnosis using clinical human biofluids.