Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.

Early diagnosis of prostate cancer (PCa) is always a great challenge in clinical practice, especially in distinguishing benign prostatic hyperplasia (BPH) from early cancer, due to the high similarity in pathology from the prostate‐specific antigen (PSA) test and radiological detection. The conventional diagnostic methods are often less efficient in specificity and accuracy, leading to quite a few unnecessary biopsies. This work establishes a noninvasive diagnostic method for PCa by investigating urine samples using Raman spectroscopy and convolutional neural network (CNN) algorithm. The results of urine Raman spectra show the intensities of characteristic peaks for lipids, nucleic acids, and some amino acids are distinguishable between PCa and BPH, suggesting an abnormal metabolism caused by PCa, which can be detected by Raman spectroscopy. These data are then used to train an intelligent diagnostic model with CNN algorithm. The cross‐validation results show the mean diagnostic accuracy, sensitivity, and specificity for PCa are 74.95%, 77.32%, and 72.46%, respectively. This noninvasive diagnostic method is a promising method for the early diagnosis of PCa, and the idea of using urine Raman spectroscopy with deep learning techniques for diagnosing PCa provides a reference for the application of artificial intelligence in the field of clinical medicine research.

Background: Most patients with prostate cancer inevitably progress to castration-resistant prostate cancer (CRPC), at which stage chemotherapeutics like docetaxel become the first-line treatment. However, chemotherapy resistance typically develops after an initial period of therapeutic efficacy. Increasing evidence indicates that cancer stem cells confer chemotherapy resistance via exosomes. This study demonstrated that AGD1, derived from prostate cancer stem cells (PCSCs), enhanced the stemness of prostate cancer cells and reduced the therapeutic effect of docetaxel in CRPC. Methods: Quantitative real-time PCR (qPCR) was employed to determine the expression levels of AGD1 and METTL13 mRNAs in PCSCs and exosomes. Protein expression levels were examined using western blots and dot blots. The potential functions of AGD1 and METTL13 in CRPC were investigated through cell proliferation assay, Transwell assay, EdU incorporation assays, Annexin V-FITC/PI staining, and sphere formation assays. To uncover the underlying mechanisms of AGD1, RNA pull-down assay, RIP, co-Immunoprecipitation (co-IP), mass spectrometry (MS), Methylated RNA immunoprecipitation (MeRIP) and single-base elongation and ligation-based qPCR amplification method (SELECT) were performed. The effects of AGD1 and METTL13 on CRPC development and metastasis under docetaxel treatment were analyzed using a xenograft mouse model and an organoid model. Additionally, liposomal-chitosan nanocomplex drug delivery systems were designed to explore AGD1’s role in regulating docetaxel treatment resistance in CRPC. Results: AGD1 expression was upregulated in PCSCs and exosomes. Downregulating AGD1 enhanced the sensitivity of CRPC to docetaxel treatment by inhibiting their stemness, with the reverse also being true. RNA pull-down, combined with MS, co-IP and RIP assays, demonstrated that AGD1 binds to METTL13 and USP10, forming a complex that facilitates METTL13 protein accumulation through USP10-induced deubiquitination. MeRIP assay and SELECT assay revealed that METTL13 transcriptionally controls the mRNA decay of CD44 via m6A methylation. Additionally, this process activates the pSTAT3/PI3K-AKT signaling pathway. Organoid models and liposomal-chitosan nanocomplex drug delivery systems showed that reducing AGD1 expression enhanced the therapeutic effect of docetaxel in CRPC. Conclusions: AGD1 mediates the stemness and apoptosis of PCSCs and promotes docetaxel treatment resistance by enhancing tumor growth and metastasis through USP10/METTL13-mediated CD44 mRNA decay in CRPC.