Hepatocellular carcinoma: molecular interactions between hepatitis C virus and p53 in hepatocarcinogenesis

2003 ◽  
Vol 5 (28) ◽  
pp. 1-16 ◽  
Author(s):  
Mónica Anzola ◽  
Juan José Burgos
Keyword(s):  
Hepatocellular Carcinoma ◽  
Suppressor Gene ◽  
Tumour Suppressor Gene ◽  
Cancer Type ◽  
Crucial Importance ◽  
Hepatitis Viruses ◽  
Common Cancer ◽  
Correct Function ◽  
P53 Tumour Suppressor Gene ◽  
Common Cancer Type

Hepatocellular carcinoma (HCC) is the most important primary hepatic cancer and is a common cancer type worldwide. Many aetiological factors have been related to HCC development, such as liver cirrhosis, hepatitis viruses and alcohol consumption. Inactivation of the p53 tumour suppressor gene is one of the most common abnormalities in many tumours, including HCC. p53 is of crucial importance for the regulation of the cell cycle and the maintenance of genomic integrity. In HCC, hepatitis B and C virus (HBV and HCV) effect carcinogenic pathways, independently leading to anomalies in p53 function. Several authors have reported that some HCV proteins, such as the core, NS5A and NS3 proteins, interact with p53 and prevent its correct function. The mechanisms of action of these HCV proteins in relation to p53 are not completely clear, but they might cause its cytoplasmic retention or accumulation in the perinuclear region where the protein is not functional. The identification of the interactions between p53 and HCV proteins is of great importance for therapeutic strategies aimed at reducing the chronicity and/or carcinogenicity of the virus.

scholarly journals Role of Immunohistochemistry Markers (p53 and CEA), in Study of Colorectal Tumours

2021 ◽  
pp. 94-106
Author(s):  
Sapam Chingkhei Lakpa ◽  
R. Vinoth Kumar ◽  
Mary Lilly
Keyword(s):  
Suppressor Gene ◽  
Tumour Suppressor Gene ◽  
High Rate ◽  
Common Cancer ◽  
Formalin Fixed Paraffin ◽  
Formalin Fixed Paraffin Embedded ◽  
P53 Tumour Suppressor Gene ◽  
Formalin Fixed ◽  
Colorectal Tumours

Colorectal cancer is the third most common cancer in men and the second in women globally. There is a marked variation in the incidence of colorectal carcinoma worldwide, where western countries having high rate compared to others. p53 tumour suppressor gene is one of the most intensively studied tumour markers in the colorectal tumours. Two markers were used, p53 (oncoprotein p53) and CEA (carcinoembryonic antigen) in the study. The 102 cases of paraffin-embedded samples were processed for the immunohistochemistry examination. After the analysis of the selected patients regarding the antibodies distribution, statistical analysis was performed. The current study showed that there was a statistically significant correlation existing between p53 and CEA in each tumour type irrespective of its histological grades. The immunohistochemistry (IHC) was performed on 4-µm thick sections from 10% formalin- fixed paraffin-embedded tissue blocks.

scholarly journals Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

2022 ◽  
Author(s):  
Malvika Sudhakar ◽  
Raghunathan Rengaswamy ◽  
Karthik Raman
Keyword(s):  
Tumour Progression ◽  
Suppressor Gene ◽  
Tumour Suppressor Gene ◽  
Driver Mutations ◽  
Cancer Type ◽  
Expression Data ◽  
Multiple Cancer ◽  
Driver Genes ◽  
Cancer Types ◽  
Omic Data

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

scholarly journals Vloga medijev pri ozaveščanju bolnikov z rakom

2015 ◽  
Vol 84 (5) ◽  
Author(s):  
Mirjana Rajer ◽  
Luka Čavka ◽  
Amela Duratović
Keyword(s):  
Cancer Patients ◽  
Information Needs ◽  
Medical Decision ◽  
Cancer Type ◽  
Huge Amount ◽  
Everyday Clinical Practice ◽  
Lung Cancer Patients ◽  
Common Cancer ◽  
Search For Information ◽  
Common Cancer Type

ABSTRACTBACKGROUNDNowadays cancer patients tend to be more involved in the medical decision process. Active participation improves health outcomes and patient satisfaction. To participate effectively patients require a huge amount of information, but time limits make it impossible to satisfy all information needs at clinics. We tried to find out which kind of media cancer patients use when searching for information and how often. Lastly, we try to find out how popular the Internet is in this regard.METODSIn this research we invited cancer patients, who had regular clinic examinations at the Oncology Institute between 21st and 25th May in 2012. We carried out a prospective research by anonymous questionnaires. We were investigating which media were used and how often. We analysed results with descriptive statistics, ANOVA, the χ²-Test and the t-test.RESULTS478 of 919 questionnaires distributed among cancer patients were returned. Mean age was 59.9 years. 61 % of responders were female, and the most common level of education was high school (33 %). Most common cancer type was breast cancer (33 %), followed by gastrointestinal and lung cancer. Patients search for information most often on television (81.4% responders), followed by specialized brochures (78%), internet (70.8%) and newspapers (67.6%). Patients who do not use media for information searching are older than average (62.5 years vs. 59.9 years; p<0,000).CONCLUSIONSAccording to our results patients search for information most often on television, followed by brochures, internet and newspapers. Older patients less often search for information. This data might help doctors in everyday clinical practice.

scholarly journals Analysis of the Most Viewed First 50 Videos on YouTube about Breast Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Isil Yurdaisik
Keyword(s):  
Breast Cancer ◽  
Healthcare Professionals ◽  
The Internet ◽  
Accurate Information ◽  
Cancer Type ◽  
Common Cancer ◽  
Health Related ◽  
The Mean ◽  
Common Cancer Type

Objective. Breast cancer is the most common cancer type among women worldwide. Today, health consumers search the Internet to gain health information about many diseases including breast cancer. YouTube™ is the second most commonly used website on the Internet. However, the quality and accuracy of health-related YouTube™ videos are controversial. The objective of this study was to investigate the quality and accuracy of breast cancer-related videos on YouTube™. Material and Methods. “Breast cancer” keyword was entered into YouTube™ search bar, and after excluding advertisement, duplicate, and non-English videos, the first most viewed 50 videos were analyzed. Videos’ length, the number of views, comments, likes, and dislikes were recorded. DISCERN and JAMA scores and Video Power Index (VPI) values of the videos were calculated. All videos were evaluated by two independent radiologists experienced on breast cancer. The correlation between the two observers was also analyzed. Results. Of all videos, 14% were uploaded by physicians, 26% by health channels, 20% by patients, 10% by news channels, 2% by herbalists, 2% by blog channels, and 2% by nonprofit activism channels. The mean DISCERN score was calculated as 26.70±10.99 and the mean JAMA score as 2.23±0.97. The mean VPI value, which was calculated to determine the popularity of the videos, was found as 94.10±4.48. A strong statistically significant correlation was found between the two observers in terms of both DISCERN and JAMA scores. There was an excellent agreement between the two observers. Conclusion. The overall quality of the viewed videos was found as poor. Healthcare professionals should be encouraged to upload breast cancer-related videos with accurate information to promote patients for screening and direct them appropriately.

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