SERSomes are a hyperspectral surface-enhanced Raman spectroscopy technique used to profile metabolic phenotypes in various biofluids, including cell culture medium and human serum, offering a rapid, low-cost, and highly sensitive method for accurate diagnosis and metabolite biomarker discovery. The technique involves the use of a spectral set called SERSome, which captures Raman spectra from biofluidic metabolite extraction in 15 minutes. SERSomes enable the identification of subtle differences and correlations in Raman features between cultured cells during growth and reveal metabolic changes in human serum of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) patients, uncovering potential biomarkers. SERSomes significantly improve the diagnosis of PCa and the prediction of tissue-level aggressiveness compared to conventional biomarkers like prostate-specific antigen (PSA). The method uses deep learning models to enhance diagnostic accuracy, achieving 80.8% accuracy on an internal test set and 73% on an external validation set. SERSomes provide a powerful tool for the rapid and sensitive characterization of metabolites in complex biological fluids, with applications in cancer liquid biopsies and broader implications for basic research and clinical practice. The technique involves the use of silver nanoparticles to enhance Raman signals, allowing for the detection of metabolites at nanomolar levels. SERSomes also enable the identification of rare signal events and enhance the information obtained from label-free Raman spectroscopy. The study demonstrates the potential of SERSomes for accurate and minimally invasive preoperative screening of PCa, which could mitigate overdiagnosis and overtreatment. The method is robust, with high sensitivity and accuracy, and has the potential for further improvement in sensitivity and comprehensive SERS database to facilitate model interpretation on potential biomarkers. The study also highlights the importance of incorporating SERSomes with clinical data for the prediction of tissue-level aggressiveness with minimally invasive methods. The findings underscore the clinical significance of SERSomes in the diagnosis and biomarker discovery for PCa.SERSomes are a hyperspectral surface-enhanced Raman spectroscopy technique used to profile metabolic phenotypes in various biofluids, including cell culture medium and human serum, offering a rapid, low-cost, and highly sensitive method for accurate diagnosis and metabolite biomarker discovery. The technique involves the use of a spectral set called SERSome, which captures Raman spectra from biofluidic metabolite extraction in 15 minutes. SERSomes enable the identification of subtle differences and correlations in Raman features between cultured cells during growth and reveal metabolic changes in human serum of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) patients, uncovering potential biomarkers. SERSomes significantly improve the diagnosis of PCa and the prediction of tissue-level aggressiveness compared to conventional biomarkers like prostate-specific antigen (PSA). The method uses deep learning models to enhance diagnostic accuracy, achieving 80.8% accuracy on an internal test set and 73% on an external validation set. SERSomes provide a powerful tool for the rapid and sensitive characterization of metabolites in complex biological fluids, with applications in cancer liquid biopsies and broader implications for basic research and clinical practice. The technique involves the use of silver nanoparticles to enhance Raman signals, allowing for the detection of metabolites at nanomolar levels. SERSomes also enable the identification of rare signal events and enhance the information obtained from label-free Raman spectroscopy. The study demonstrates the potential of SERSomes for accurate and minimally invasive preoperative screening of PCa, which could mitigate overdiagnosis and overtreatment. The method is robust, with high sensitivity and accuracy, and has the potential for further improvement in sensitivity and comprehensive SERS database to facilitate model interpretation on potential biomarkers. The study also highlights the importance of incorporating SERSomes with clinical data for the prediction of tissue-level aggressiveness with minimally invasive methods. The findings underscore the clinical significance of SERSomes in the diagnosis and biomarker discovery for PCa.