METHODS AND RESULTS: Autophagy level in the HCC patient-derived cancer and non-cancer tissues was determined by immunohistochemistry (IHC) targeting SQSTM1, LC3A and LC3B proteins. Significance tests of clinicopathological variables were tested using the Fisher's exact or Chi-square tests. Gene and miRNA expression assays were carried out and analyzed using Nanostring platform and software followed by validation of other online bioinformatics tools, namely String and miRabel. Autophagy expression was significantly higher in cancerous tissues compared to adjacent non-cancer tissues. High LC3B expression was associated with advanced tumor histology grade and tumor location. Nanostring gene expression analysis revealed that SQSTM1, PARP1 and ATG9A genes were upregulated in HCC tissues compared to non-cancer tissues while SIRT1 gene was downregulated. These genes are closely related to an autophagy pathway in HCC. Further, using miRabel tool, three downregulated miRNAs (hsa-miR-16b-5p, hsa-miR-34a-5p, and hsa-miR-660-5p) and one upregulated miRNA (hsa-miR-539-5p) were found to closely interact with the abovementioned autophagy-related genes. We then mapped out the possible pathway involving the genes and miRNAs in HCC tissues.
CONCLUSIONS: We conclude that autophagy events are more active in HCC tissues compared to the adjacent non-cancer tissues. We also reported the possible role of several miRNAs in regulating autophagy-related genes in the autophagy pathway in HCC. This may contribute to the development of potential therapeutic targets for improving HCC therapy. Future investigations are warranted to validate the target genes reported in this study using a larger sample size and more targeted molecular technique.
METHODS: Using a panel of antibodies to CD10, Bcl-6, MUM1 and CD138, consecutive cases of primary UAT DLBCL were stratified into subgroups of germinal centre B-cell-like (GCB) and non-GCB, phenotype profile patterns A, B and C, as proposed by Hans et al. and Chang et al., respectively. EBER in situ hybridisation technique was applied for the detection of EBV in the tumours.
RESULTS: In this series of 32 cases of UAT DLBCL, 34% (11/32) were GCB, and 66% (21/32) were non-GCB types; 59% (19/32) had combined patterns A and B, and 41% (13/32) had pattern C. Statistical analysis revealed no significant difference in the occurrence of these prognostic subgroups in the UAT when compared with series of de novo DLBCL from all sites. There was also no site difference in phenotype protein expressions, with the exception of MUM1. EBER in situ hybridisation stain demonstrated only one EBV infected case.
CONCLUSIONS: Prognostic subgroup distribution of UAT DLBCL is similar to de novo DLBCL from all sites, and EBV association is very infrequent.
MATERIALS AND METHODS: 51 cases of DLBCL paraffin-embedded tissue samples were retrieved from a single private hospital in Kuala Lumpur, Malaysia. EBER-ISH was performed to identify the EBV expression; ten EBV(+)-DLBCL cases subjected to immunohistochemistry for LMP1, pJAK1, pSTAT3 and MYC; FISH assay for c-MYC gene rearrangement.
RESULTS: Among 10 cases of EBV(+)-DLBCL, 90% were non-GCB subtype (p=0.011), 88.9% expressed LMP1. 40% EBV(+)-DLBCL had pJAK1 expression.
CONCLUSION: 66.7% EBV(+)-DLBCL showed the positivity of pSTAT3, which implies the involvement of EBV in constitutive JAK/STAT pathway. 44.5% EBV(+)-DLBCL have co-expression of pSTAT3 and MYC, but all EBV(+)-DLBCL was absence with c-MYC gene rearrangement. The finding of clinical samples might shed lights to the lymphomagenesis of EBV associated with non-GCB subtypes, and the potential therapy for pSTAT3-mediated pathway.
METHODS: This study used data from the Global COVID-19 Index provided by PEMANDU Associates. The sample, representing 161 countries, comprised the number of confirmed cases, deaths, stringency indices, population density and GNI per capita (USD). Correlation matrices were computed to reveal the association between the variables at three time points: day-30, day-60 and day-90. Three separate principal component analyses were computed for similar time points, and several standardized plots were produced.
RESULTS: Confirmed cases and deaths due to COVID-19 showed positive but weak correlation with stringency and GNI per capita. Through principal component analysis, the first two principal components captured close to 70% of the variance of the data. The first component can be viewed as the severity of the COVID-19 surge in countries, whereas the second component largely corresponded to population density, followed by GNI per capita of countries. Multivariate visualization of the two dominating principal components provided a standardized comparison of the situation in the161 countries, performed on day-30, day-60 and day-90 since the first confirmed cases in countries worldwide.
CONCLUSION: Visualization of the global spread of COVID-19 showed the unequal severity of the pandemic across continents and over time. Distinct patterns in clusters of countries, which separated many European countries from those in Africa, suggested a contrast in terms of stringency measures and wealth of a country. The African continent appeared to fare better in terms of the COVID-19 pandemic and the burden of mortality in the first 90 days. A noticeable worsening trend was observed in several countries in the same relative time frame of the disease's first 90 days, especially in the United States of America.