Viscosalactone B, a natural LSD1 inhibitor, inhibits proliferation in vitro and in vivo against prostate cancer cells.

Lysine-specific demethylase 1 (LSD1) has been a promising target to treat prostate cancer, and discovery of novel LSD1 inhibitors would have great clinical significance. In this work, viscosalactone B was first identified as a novel LSD1 inhibitor. Viscosalactone B isolated from Withania Somnifera displayed antiproliferative activity against PC3, DU145, C42B, PC3/MDVR, DU145/MDVR, and C42B/MDVR cells with IC50 values of 1.17, 0.72, 3.86, 2.06, 0.96 and 1.15 µM, respectively. In comparison, it was a selective LSD1 inhibitor with an IC50 value of 970.27 nM and could induce a significant accumulation of LSD1 substrates H3K9me1, H3K9me2, and H3K4me1 in a concentration-dependent manner in DU145 cells. According to docking studies, it formed hydrogen bonds with the Thr11, Lys14, and Arg8 residues of LSD1. Importantly, while it displayed potent antitumor efficacy in vivo, it did not show obvious cytotoxicity on the major organs of nude mice. Therefore, viscosalactone B, as a novel LSD1 inhibitor, is a potential candidate that can be used for the treatment of prostate cancer in clinics.

Identification of serum glycobiomarkers for Hepatocellular Carcinoma using lectin microarrays.

Objective: Hepatocellular carcinoma (HCC) is the sixth most commonly occurring cancer and ranks third in mortality among all malignant tumors; as a result, HCC represents a major human health issue. Although aberrant glycosylation is clearly implicated in HCC, changes in serum immunoglobulin (Ig)G and IgM glycosylation have not been comprehensively characterized. In this study, we used lectin microarrays to evaluate differences in serum IgG and IgM glycosylation among patients with HCC, hepatitis B cirrhosis (HBC), or chronic hepatitis B (CHB), and healthy normal controls (NC) and aimed to establish a model to improve the diagnostic accuracy of HCC. Methods: In total, 207 serum samples collected in 2019-2020 were used for lectin microarray analyses, including 97 cases of HCC, 50 cases of HBC, 30 cases of CHB, and 30 cases of NC. Samples were randomly divided into training and validation groups at a 2:1 ratio. Training group data were used to investigate the diagnostic value of the relative signal intensity for the lectin probe combined with alpha-fetoprotein (AFP). The efficacy of models for HCC diagnosis were analyzed by receiver operating characteristic (ROC) curves. Results: In terms of IgG, a model combining three lectins and AFP had good diagnostic accuracy for HCC. The area under the ROC curve was 0.96 (P < 0.05), the sensitivity was 82.54%, and the specificity was 100%. In terms of IgM, a model including one lectin combined with AFP had an area under the curve of 0.90 (P < 0.05), sensitivity of 75.41%, and specificity of 100%. Conclusion: Estimation of serum IgG and IgM glycosylation could act as complementary techniques to improve diagnosis and shed light on the occurrence and development of the HCC.

MicroRNAs as potential diagnostic biomarkers in renal cell carcinoma.

Emerging evidence has suggested that microRNAs (miRNAs) may be promising novel biomarkers for the diagnosis of renal cell carcinoma (RCC). However, the results of current studies are still conflicting. Hence, we undertake the current meta-analysis to comprehensively assess the diagnostic potential of miRNAs in RCC. The bivariate meta-analysis model was employed to summarize the sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). Summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were used to evaluate the diagnostic accuracy. Subgroup analyses and meta-regression were used to explore the between-study heterogeneity. Deeks' funnel plot asymmetry test was used to test the potential of publication bias. All analyses were performed using STATA software (version 12.0). The pooled sensitivity and specificity of miRNAs for the diagnosis of RCC were 0.85 (95% confidence interval (CI), 0.77-0.90) and 0.84 (95% CI, 0.70-0.92). The value of AUC was 0.91 (95% CI, 0.88-0.93), suggesting that the diagnostic accuracy of miRNAs achieved a relatively high level. Furthermore, subgroup analyses showed that tissue-based miRNA assay is recommended to improve the diagnostic accuracy. In conclusion, the high degree of diagnostic accuracy suggests that miRNA in RCC patients may serve as next-generation biomarkers for detection of the disease. However, large-scale investigations and additional improvements are urgently needed to confirm our results and verify the feasibility of routine clinical utilization.