Clinical big-data-based design of GLUT2-targeted carbon nanodots for accurate diagnosis of hepatocellular carcinoma.

Despite advances in diagnostic and therapeutic methods, the prognosis of patients with hepatocellular carcinoma (HCC) remains poor due to the delay in diagnosis. Herein, we aimed to discover a highly sensitive and specific biomarker for HCC based on genomic big data analysis and create an HCC-targeted imaging probe using carbon nanodots (CNDs) as contrast agents. In genomic analysis, we selected glucose transporter 2 (GLUT2) as a potential imaging target for HCC. We confirmed the target suitability by immunohisto-chemistry tests of 339 patient samples, where 81.1% of the patients exhibited underexpression of GLUT2, i.e., higher GLUT2 intensity in non-tumor tissues than in tumor tissues. To visualize GLUT2, we conjugated CNDs with glucosamine (GLN) as a targeting ligand to yield glucosamine-labeled CNDs (GLN-CNDs). A series of in vitro and in vivo experiments were conducted on GLUT2-modified HepG2 cells to confirm the specificity of the GLN-CNDs. Since the GLUT2 expression is higher in hepatocytes than in HCC cells, the GLUT2-targeted contrast agent is highly attached to normal cells. However, it is possible to produce images in the same form as the images obtained with a cancer cell-targeted contrast agent by inverting color scaling. Our results indicate that GLUT2 is a promising target for HCC and that GLN-CNDs may potentially be used as targeted imaging probes for diagnosing HCC.

Early-stage invasive ductal carcinoma: Association of tumor apparent diffusion coefficient values with axillary lymph node metastasis.

PURPOSE: To evaluate any association between tumor apparent diffusion coefficient (ADC) values and axillary lymph node metastasis (ALNM) in early-stage invasive ductal carcinoma. MATERIALS AND METHODS: Records of 270 invasive ductal carcinoma patients with stages T1 and T2 disease who underwent breast magnetic resonance imaging, including diffusion-weighted imaging with b values of 0 and 1000s/mm(2) were reviewed retrospectively. The tumor ADC values were analyzed for their utility in predicting ALNM using multivariate regression analysis and receiver operating characteristic (ROC) curve analysis. RESULTS: Of the 270 patients, 58 (21.5%) experienced ALNM. The mean tumor ADC values were significantly lower in patients with ALNM than in those without metastasis (0.880 × 10(-3) vs. 0.999 × 10(-3)mm(2)/s, P<0.001). A ROC curve demonstrated a tumor ADC value of 0.991 × 10(-3)mm(2)/s to be the optimal cut-off for predicting ALNM. In a multivariate analysis, lower tumor ADC (≤ 0.991 × 10(-3)mm(2)/s; adjusted odds ratio (OR)=5.861, P<0.001), large tumor size (>2 cm; adjusted OR=3.156, P=0.002) and the presence of lymphovascular invasion (adjusted OR=4.125, P<0.001) were independent variables associated with ALNM. When tumor ADC value was added to known risk factors (i.e., tumor size and lymphovascular invasion), a significant improvement in the accuracy of risk prediction for axillary node metastasis was shown (c-statistic=0.758 vs. 0.816, P=0.026). CONCLUSION: In early-stage invasive ductal carcinoma, lower tumor ADC values are associated with the presence of ALNM.