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Article Abstract
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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12224922 | PMC |
| http://dx.doi.org/10.1002/advs.202501336 | DOI Listing |
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