Engineering a Bifunctional Smart Nanoplatform Integrating Nanozyme Activity and Self-Assembly for Kidney Cancer Diagnosis and Classification.

Accurate diagnosis and classification of kidney cancer are crucial for high-quality healthcare services. However, the current diagnostic platforms remain challenges in the rapid and accurate analysis of large-scale clinical biosamples. Herein, we fabricated a bifunctional smart nanoplatform based on tannic acid-modified gold nanoflowers (TA@AuNFs), integrating nanozyme catalysis for colorimetric sensing and self-assembled nanoarray-assisted LDI-MS analysis. The TA@AuNFs presented peroxidase (POD)- and glucose oxidase-like activity owing to the abundant galloyl residues on the surface of AuNFs. Combined with the colorimetric assay, the TA@AuNF-based sensing nanoplatform was used to directly detect glucose in serum for kidney tumor diagnosis. On the other hand, TA@AuNFs could self-assemble into closely packed and homogeneous two-dimensional (2D) nanoarrays at liquid-liquid interfaces by using Fe3+ as a mediator. The self-assembled TA@AuNFs (SA-TA@AuNFs) arrays were applied to assist the LDI-MS analysis of metabolites, exhibiting high ionization efficiency and excellent MS signal reproducibility. Based on the SA-TA@AuNF array-assisted LDI-MS platform, we successfully extracted metabolic fingerprints from urine samples, achieving early-stage diagnosis of kidney tumor, subtype classification, and discrimination of benign from malignant tumors. Taken together, our developed TA@AuNF-based bifunctional smart nanoplatform showed distinguished potential in clinical disease diagnosis, point-of-care testing, and biomarker discovery.

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis.

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.

Serum and Urine Metabolic Fingerprints Characterize Renal Cell Carcinoma for Classification, Early Diagnosis, and Prognosis.

Renal cell carcinoma (RCC) is a substantial pathology of the urinary system with a growing prevalence rate. However, current clinical methods have limitations for managing RCC due to the heterogeneity manifestations of the disease. Metabolic analyses are regarded as a preferred noninvasive approach in clinics, which can substantially benefit the characterization of RCC. This study constructs a nanoparticle-enhanced laser desorption ionization mass spectrometry (NELDI MS) to analyze metabolic fingerprints of renal tumors (n = 456) and healthy controls (n = 200). The classification models yielded the areas under curves (AUC) of 0.938 (95% confidence interval (CI), 0.884-0.967) for distinguishing renal tumors from healthy controls, 0.850 for differentiating malignant from benign tumors (95% CI, 0.821-0.915), and 0.925-0.932 for classifying subtypes of RCC (95% CI, 0.821-0.915). For the early stage of RCC subtypes, the averaged diagnostic sensitivity of 90.5% and specificity of 91.3% in the test set is achieved. Metabolic biomarkers are identified as the potential indicator for subtype diagnosis (p < 0.05). To validate the prognostic performance, a predictive model for RCC participants and achieve the prediction of disease (p = 0.003) is constructed. The study provides a promising prospect for applying metabolic analytical tools for RCC characterization.