Serum Lipidomic Fingerprints Encode Early Diagnosis and Staging of Lung Cancer on a Novel PbS/Au-Layered Substrate.

Lung cancer (LC) is a major cause of mortality among malignant tumors. Early diagnosis through lipidomic profiling can improve prognostic outcomes. In this study, a uniform PbS/Au-layered substrate that enhances the laser desorption/ionization process, an interfacial process triggered on the substrate surface upon laser excitation, was designed to efficiently characterize the lipidomic profiles of LC patient serum. By controlling the stacking arrangement and particle sizes of PbS QDs and AuNPs, the optimized substrate promotes the generation of excited electrons and creates an enhanced electric field that polarizes analyte molecules, facilitating ion adduction formation ([M + Na]+ and [M + K]+) and enhancing detection sensitivity down to the femtomole level. Combining multivariate statistics and machine learning, a distinct lipidomic biomarker panel is successfully identified for the early diagnosis and staging of LC, with an accurate prediction validated by an area under the curve of 0.9479 and 0.9034, respectively. We also found that 18 biomarkers were significantly correlated with six metabolic pathways associated with LC. These results demonstrate the potential of this innovative PbS/Au-layered substrate as a sensitive platform for accurate diagnosis of LC and facilitate the development of lipidomic-based diagnostic tools for other cancers.

Covalent Bonding and Coulomb Repulsion-Guided AuNP Array: A Tunable and Reusable Substrate for Metabolomic Characterization of Lung Cancer Patient Sera.

Surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has gained increased attention in the metabolic characterization of human biofluids. However, the stability and reproducibility of nanoparticle-based substrates remain two of the biggest challenges in high-salt environments. Here, by controlling the extent of Coulomb repulsion of 26 nm positively charged AuNPs, a homogeneous layer of covalently bonded AuNPs on a coverslip with tunable interparticle distances down to 16 nm has been successfully fabricated to analyze small biomolecules in human serum. Compared with the self-assembled AuNP array, the covalently bonded AuNP array showed superior performances on stability, reproducibility, and sensitivity in high-salt environments. The stable attachment of AuNPs maintained a detection reproducibility with a RSD less than 12% and enabled the reusability of the array for 10 experiments without significant signal deterioration (<15%) and carryover effects. Moreover, the closely positioned AuNPs allowed the coupling of photoinduced plasmons to generate an enhanced electric field, which promotes the generation of excited electrons to facilitate the desorption/ionization processes instead of the heat dissipation, thus enhancing the detection sensitivity with detection limits down to the femtomole level. Combined with machine learning methods, the AuNP array has been successfully applied to discover seven biomarkers for differentiating early-stage lung cancer patients from healthy controls. It is anticipated that this simple approach of developing robust AuNP arrays can also be extended to other types of NP arrays for wider applications of SALDI-MS technology.

Combining MALDI-MS with machine learning for metabolomic characterization of lung cancer patient sera.

An increasing amount of evidence has proven that serum metabolites can instantly reflect disease states. Therefore, sensitive and reproducible detection of serum metabolites in a high-throughput manner is urgently needed for clinical diagnosis. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a high-throughput platform for metabolite detection, but it is hindered by significant signal fluctuations because of the "sweet spot" effect of organic matrices. Here, by screening two transformation methods and four normalization techniques to reduce the significant signal fluctuations of the DHB matrix, an integrated MALDI-MS data processing approach combined with machine learning methods was established to reveal metabolic biomarkers of lung cancer. In our study, 13 distinctive features with statistically significant differences (p < 0.001) between 34 lung cancer patients and 26 healthy controls were selected as significant potential biomarkers of lung cancer. 6 out of the 13 distinctive features were identified as intact metabolites. Our results demonstrate the potential for clinical application of MALDI-MS in serum metabolomics for biomarker screening in lung cancer.