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This study aimed to assess effects of the sets of EBV and HPV expressed proteins simultaneously on the sets of cellular/inflammatory factors in breast and thyroid cancers using structural equation modeling. In this multi-center case-control study, according to the inclusion and exclusion criteria, 83 breast and 57 thyroid specimens were collected from the eligible patients. In addition, 31 and 18 histopathological evaluated normal breast and thyroid samples were also examined as age-matched healthy controls. In addition, ELISA and Real-time PCR were used to measure the expression level of viral and cellular/inflammatory genes and proteins. Structural equation modeling was used to test the causal associations between the sets of EBV and HPV expressed proteins with inflammatory factors in breast and thyroid cancers development. Breast cancer patients had a higher incidence of HPV-positively and EBV-positively than healthy controls (OR=1.66, 95%CI=0.79-3.47, P-value=0.177), (OR=3.18, 95%CI=1.52-6.63, P-value=0.002), respectively. In addition, thyroid cancer patients had a significantly higher incidence of EBV-positivity than healthy controls (OR=3.72, 95% CI=1.65-8.36, P-value=0.001). After fitting the SEM model, HPV proteins factor has significant direct and total effects on the cellular/inflammatory factors in breast cancer (direct effect: β=0.426, P-value=0.01; total effect: β=0.549, P-value<0.001). However, EBV proteins factor has most significant total effect on the cellular/inflammatory factors in breast cancer (total effect: β=0.804, P-value<0.001) than the cellular/inflammatory factors in thyroid cancer (total effect: β=0.789, P-value<0.001). For the first time, a significant association between EBV and HPV -genes, anoikis resistance and the development of breast and thyroid cancers demonstrated by using SEM, Simultaneously., .

Background: Among the proposals for joint disease mapping, the shared component model has become more popular. Another advance to strengthen inference of disease data is the extension of purely spatial models to include time aspect. We aim to combine the idea of multivariate shared components with spatio-temporal modelling in a joint disease mapping model and apply it for incidence rates of seven prevalent cancers in Iran which together account for approximately 50% of all cancers. Methods: In the proposed model, each component is shared by different subsets of diseases, spatial and temporal trends are considered for each component, and the relative weight of these trends for each component for each relevant disease can be estimated. Results: For esophagus and stomach cancers the Northern provinces was the area of high risk. For colorectal cancer Gilan, Semnan, Fars, Isfahan, Yazd and East-Azerbaijan were the highest risk provinces. For bladder and lung cancer, the northwest were the highest risk area. For prostate and breast cancers, Isfahan, Yazd, Fars, Tehran, Semnan, Mazandaran and Khorasane-Razavi were the highest risk part. The smoking component, shared by esophagus, stomach, bladder and lung, had more effect in Gilan, Mazandaran, Chaharmahal and Bakhtiari, Kohgilouyeh and Boyerahmad, Ardebil and Tehran provinces, in turn. For overweight and obesity component, shared by esophagus, colorectal, prostate and breast cancers the largest effect was found for Tehran, Khorasane-Razavi, Semnan, Yazd, Isfahan, Fars, Mazandaran and Gilan, in turn. For low physical activity component, shared by colorectal and breast cancers North-Khorasan, Ardebil, Golestan, Ilam, Khorasane-Razavi and South-Khorasan had the largest effects, in turn. The smoking component is significantly more important for stomach than for esophagus, bladder and lung. The overweight and obesity had significantly more effect for colorectal than of esophagus cancer. Conclusions: The presented model is a valuable model to model geographical and temporal variation among diseases and has some interesting potential features and benefits over other joint models., (Creative Commons Attribution License)

Background: The aim of this study was to model the geographical variation in incidence and risk factors of seven prevalent cancers in Iran., Methods: The data for cancers of esophagus, stomach, bladder, colorectal, lung, prostate, and female breast along with their risk factors in all 30 provinces of Iran for the year 2007 were included into study. Smoking, overweight, inadequate consumption of fruits and vegetables, socioeconomic status and low physical activity were studied as risk factors. Standardized incidence ratios were estimated using full bayesian model. In addition, the shared component model was used to explore the spatial pattern of the cancers and to estimate the relative importance of their corresponding risk factors., Results: Fars and the Northwestern provinces were observed as high risk areas and Hormozgan (a Persian Gulf coastal province), Sistan and Baluchestan, South Khorasan, and Kerman provinces located in Southeast were areas of low risk for most cancers. For all five risk factors, larger effects on incidence of the relevant cancers were found in the Northern provinces compared to other areas. Smoking, overweight, inadequate consumption of fruit and vegetable, socioeconomic status, and low physical activity were found to have more effects on incidence of stomach, breast, esophagus, and breast cancers, respectively., Conclusions: Most of the high risk areas for seven cancers were in accordance with the results for spatial patterns of related risk factors and their relative weights on relevant cancers. The multivariate shared component model of the seven cancers achieves a considerable improvement in terms of Deviance Information Criterion over the individual modeling of diseases.