Dual-Matrix Platform for Highly Specific Multi-Omics Profiling of Renal Cell Carcinoma

Abstract

Multiomics interrogation provides complementary information beyond single-omics approaches for improved disease characterization. To enable such multilayer profiling, we expanded the rapid functionalized mesoporous nanoparticle-coupled laser desorption/ionization mass spectrometry (fMNPLDI-MS) platform by designing two structurally homologous but functionally tailored fMNPs. This design enables efficient acquisition of both serum metabolic and peptide fingerprints from a total of only 2.05 μL of serum, with an LDI MS analysis time of approximately 90 s per sample, while addressing the limitation of single-matrix systems in simultaneously optimizing analytical performance for different biomolecular species. Through statistical analysis and machine learning-based feature selection, an integrated multiomics biomarker panel was established, comprising 5 peptides and 4 metabolites. Notably, this integrated panel outperformed both single-omics panels across all evaluation metrics in the validation set, improving the area under curve from 0.985 to 1.000 and increasing the classification accuracy from 0.947 (metabolites) and 0.930 (peptides) to 0.965, while showing consistent improvements in F1-score, precision, and recall. Collectively, these results demonstrate the robust performance of the dual-matrix design and multiomics integration for renal cell carcinoma classification, with potential relevance for broader applications in complex disease profiling.