Panahiazar, Maryam

Early detection plays a key role to enhance the outcome for Coronary Artery Disease. We utilized a big data analytics platform on ∼32,000 patients to trace patients from the first encounter to CAD treatment. There are significant gender-based differences in patients younger than 60 from the time of the first encounter to Coronary Artery Bypass Grafting with a p-value=0.03. This recognition makes significant changes in outcome by avoiding delay in treatment.

The study of precision medicine that measures the effects of social, cultural, and environmental influences on health is essential to improve health outcomes. Race is a social concept used historically to divide, track, control populations, and reinforce social hierarchies. Beyond genetics, race is also a surrogate for other socioeconomic factors affecting patient outcomes. Our data analytics study aims to analyze the Electronic Medical Record (EMR) to study patients of different races in diagnosing and treating Coronary Artery Disease (CAD). We found no race discrepancies at the University of California San Francisco Medical Centers. This study opens several new hypotheses for further research in this crucial field.

The development of an ontology facilitates the organization of the variety of concepts used to describe different terms in different resources. The proposed ontology will facilitate the study of cardiothoracic surgical education and data analytics in electronic medical records (EMR) with the standard vocabulary.

333 Background: Hepatitis B is a cause of hepatocellular carcinoma (HCC) globally, irrespective of viral loads. Interestingly, this process is not necessarily mediated through cirrhosis and may in fact involve oncogenic processes. Prior studies have suggested specific oncogenic gene expression pathways affected by viral regulatory proteins. Thus, identifying these genes and associated pathways could highlight predictive factors for HCC transformation, and has implications in early diagnosis and treatment. Methods: We employed our Search, Tag, Analyze, Resource (STARGEO) platform to conduct a meta-analysis of public data from NCBI's Gene Expression Omnibus. We performed meta-analysis consisting of 155 tumor samples compared against 185 adjacent non-tumor samples. We present the results from the meta-analysis in Ingenuity Pathway Analysis. Results:Our analysis revealed LXR/RXR activation, LPS-IL-1 mediation inhibition of RXR, and FXR/RXR activation as top canonical pathways amongst others. Top upstream regulators identified included the Ras family gene RABL6. The role of RABL6 in oncogenesis is beginning to unfold but its specific role in HBV-related HCC remains undefined. Our causal analysis suggests RABL6 mediates pathogenesis of HBV-related HCC through promotion of genes related to cell division, epigenetic regulation, and Akt signaling. Additionally, HOXA10 was a top upstream regulator and was strongly upregulated in our analysis. While described in other cancer, HOXA10 has not been well documented in HBV-related HCC and our causal analysis suggests it mediates pathogenesis through downregulation of tumor suppressors and its protective effect from oxidative stress. Conclusions: This meta-analysis describes possible role of viral regulatory proteins such as RABL6 and HOXA10 in the pathogenesis of HBV -related HCC. Further prospective studies are needed to confirm our findings.