Simultaneous Time-Dependent Surface-Enhanced Raman Spectroscopy, Metabolomics, and Proteomics Reveal Cancer Cell Death Mechanisms Associated with Gold Nanorod Photothermal Therapy

In cancer plasmonic photothermal therapy (PPTT), plasmonic nanoparticles are used to convert light into localized heat, leading to cancer cell death. Among plasmonic nanoparticles, gold nanorods (AuNRs) with specific dimensions enabling them to absorb near-infrared laser light have been widely used. The detailed mechanism of PPTT therapy, however, still remains poorly understood. Typically, surface-enhanced Raman spectroscopy (SERS) has been used to detect time-dependent changes in the intensity of the vibration frequencies of molecules that appear or disappear during different cellular processes. A complete proven assignment of the molecular identity of these vibrations and their biological importance has not yet been accomplished. Mass spectrometry (MS) is a powerful technique that is able to accurately identify molecules in chemical mixtures by observing their <i>m</i>/<i>z</i> values and fragmentation patterns. Here, we complemented the study of changes in SERS spectra with MS-based metabol­omics and proteomics to identify the chemical species responsible for the observed changes in SERS band intensities during PPTT. We observed an increase in intensity of the bands at around 1000, 1207, and 1580 cm^–1, which were assigned in the literature to phenyl­alanine, albeit with dispute. Our metabol­omics results showed increased levels of phenyl­alanine, its derivatives, and phenyl­alanine-containing peptides, providing evidence for more confidence in the SERS peak assignments. To better understand the mechanism of phenyl­alanine increase upon PPTT, we combined metabol­omics and proteomics results through network analysis, which proved that phenyl­alanine metabolism was perturbed. Furthermore, several apoptosis pathways were activated via key proteins (e.g., HADHA and ACAT1), consistent with the proposed role of altered phenyl­alanine metabolism in inducing apoptosis. Our study shows that the integration of the SERS with MS-based metabol­omics and proteomics can assist the assignment of signals in SERS spectra and further characterize the related molecular mechanisms of the cellular processes involved in PPTT

Feasibility of Detecting Prostate Cancer by Ultra­performance Liquid Chromatography–Mass Spectrometry Serum Metabolomics

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, over­diagnosis, and over­treatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultra­performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multi­variate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromato­graphically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lyso­phospho­lipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings