LAUSR

The presence of a micropapillary (MPP) component is a crucial determinant of surgical strategies for lung adenocarcinoma (LUAD), yet reliable blood biomarkers for predicting MPP⁺ LUAD remain elusive. Here, we integrate 4D label‐free quantitative proteomics, a nanomixing‐enhanced microfluidic surface‐enhanced Raman spectroscopy (SERS) platform, and machine learning to sensitively identify and validate blood protein biomarkers associated with MPP⁺ LUAD. Comparative proteomics reveal 44 differentially expressed proteins (DEPs) between MPP⁺ and MPP⁻ LUADs, with bioinformatics uncovering their roles in MPP⁺ LUAD formation. To enable sensitive, multiplex detection of 4 upregulated DEPs, the nanomixing effect is leveraged to enhance target protein‐SERS barcode interactions while minimizing nonspecific binding to antibody‐functionalized gold electrodes. The SERS barcode cocktail allows simultaneous detection of the 4 selected DEPs. Machine learning models based on SERS detection effectively distinguish MPP⁺ from MPP⁻ LUAD patients, as well as LUAD patients from healthy donors. This approach demonstrates strong diagnostic potential for early, non‐invasive MPP detection in LUAD, advancing nanotechnology‐driven disease diagnosis and monitoring.