High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS 2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS 2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS 2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.
The prostate-specific antigen (PSA) has been widely used for the early diagnosis of prostate cancer during routine check-ups. However, the low sensitivity of regular PSA tests in the PSA gray zone often means that patients are required to undergo further invasive needle biopsy for the diagnosis of prostate cancer, which may lead to potential overdiagnosis and overtreatment. In this study, a circulating tumor cell (CTC)-chip based on an evaporation-induced reduced graphene oxide (rGO) coating is presented, which enables a highly specific and non-invasive diagnosis of prostate cancer in the PSA gray zone. During the evaporation process of the rGO dispersion, the Marangoni effect induces the self-assembly of a hierarchical micro/nanowrinkled rGO coating, which can capture CTCs after subsequent surface modification of capture agents. Compared to the low diagnostic sensitivity (58.3%) of regular PSA tests, a combination of CTC detection and PSA-based hematological tests via machine-learning analysis can greatly upgrade the diagnostic sensitivity of this disease to 91.7% in clinical trial. Therefore, this study provides a non-invasive alternative with high sensitivity for the diagnosis of prostate cancer in the PSA gray zone.
危险品化学品经营许可证（带存储）