Sialic Acid-Modified O‑GlcNAc Transferase Inhibitor Liposome Presents Antitumor Effect in Hepatocellular Carcinoma

O-linked-N-acetylglucosaminylation (O-GlcNAcylation) plays a key role in hepatocellular carcinoma (HCC) development, and the inhibition of O-GlcNAcylation has therapeutic potential. To decrease the systemic adverse events and increase targeting, we used sialic acid (SA)-decorated liposomes loaded with OSMI-1, an inhibitor of the O-GlcNAcylation, to further improve the anti-HCC effect. Fifty pairs of HCC tissue samples and the cancer genome atlas database were used to analyze the expression of O-GlcNAc transferase (OGT) and its effects on prognosis and immune cell infiltration. OSMI-1 cells were treated with SA and liposomes. Western blotting, immunofluorescence, cell proliferation assay, flow cytometry, enzyme-linked immunosorbent assay, immunohistochemistry, and tumorigenicity assays were used to investigate the antitumor effect of SA-modified OSMI-1 liposomes in vitro and in vivo. OGT was highly expressed in HCC tissues, negatively correlated with the degree of tumor infiltration of CD8+ and CD4+T cells and prognosis, and positively correlated with the degree of Treg cell infiltration. SA-modified OSMI-1 liposome (OSMI-1-SAL) was synthesized with stable hydrodynamic size distribution. Both in vitro and in vivo, OSMI-1-SAL exhibited satisfactory biosafety and rapid uptake by HCC cells. Compared to free OSMI-1, OSMI-1-SAL had a stronger capacity for suppressing the proliferation and promoting the apoptosis of HCC cells. Moreover, OSMI-1-SAL effectively inhibited tumor initiation and development in mice. OSMI-1-SAL also promoted the release of damage-associated molecular patterns, including anticalreticulin, high-mobility-group protein B1, and adenosine triphosphate, from HCC cells and further promoted the activation and proliferation of the CD8+ and CD4+T cells. In conclusion, the OSMI-1-SAL synthesized in this study can target HCC cells, inhibit tumor proliferation, induce tumor immunogenic cell death, enhance tumor immunogenicity, and promote antitumor immune responses, which has the potential for clinical application in the future

DataSheet_1_Deep Learning Radiomics to Predict Regional Lymph Node Staging for Hilar Cholangiocarcinoma.docx

BackgroundOur aim was to establish a deep learning radiomics method to preoperatively evaluate regional lymph node (LN) staging for hilar cholangiocarcinoma (HC) patients. Methods and MaterialsOf the 179 enrolled HC patients, 90 were pathologically diagnosed with lymph node metastasis. Quantitative radiomic features and deep learning features were extracted. An LN metastasis status classifier was developed through integrating support vector machine, high-performance deep learning radiomics signature, and three clinical characteristics. An LN metastasis stratification classifier (N1 vs. N2) was also proposed with subgroup analysis.ResultsThe average areas under the receiver operating characteristic curve (AUCs) of the LN metastasis status classifier reached 0.866 in the training cohort and 0.870 in the external test cohorts. Meanwhile, the LN metastasis stratification classifier performed well in predicting the risk of LN metastasis, with an average AUC of 0.946.ConclusionsTwo classifiers derived from computed tomography images performed well in predicting LN staging in HC and will be reliable evaluation tools to improve decision-making.</p