Dataset Information

Molecular signatures of epithelial ovarian cancer: analysis of associations with tumor characteristics and epidemiologic risk factors.

ABSTRACT: BACKGROUND:Six gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas (TCGA) project subsequently replicated these subtypes and identified a signature predictive of survival in high-grade serous (HGS) cancers. We previously validated these signatures for use in formalin-fixed paraffin-embedded tissues. The aim of the present study was to determine whether these signatures are associated with specific ovarian cancer risk factors, which would add to the evidence that they reflect the heterogeneous etiology of this disease. METHODS:We modeled signature-specific tumor characteristics and epidemiologic risk factor relationships using multiple regression and multivariate response multiple regression models in 193 patients from a case-control study of epithelial ovarian cancer. RESULTS:We observed associations between the Tothill gene expression subtype signatures and both age at diagnosis (P = 0.0008) and race (P = 0.008). Although most established epidemiologic risk factors were not associated with molecular signatures, there was an association between breast feeding (P = 0.024) and first-degree family history of breast or ovarian cancer (P = 0.034) among the 106 HGS cases. Some of the above associations were validated using gene expression microarray data from the TCGA project. Weak associations were seen with age at menarche and duration of oral contraceptive use and the TCGA survival signature. CONCLUSIONS:These data support the potential for genomic characterization to elucidate the etiologic heterogeneity of epithelial ovarian cancer. IMPACT:This study suggests that molecular signatures may augment the ability to define etiologic subtypes of epithelial ovarian cancer.

SUBMITTER: Schildkraut JM

PROVIDER: S-EPMC3799825 | biostudies-literature | 2013 Oct

REPOSITORIES: biostudies-literature

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Molecular signatures of epithelial ovarian cancer: analysis of associations with tumor characteristics and epidemiologic risk factors.

Schildkraut Joellen M JM Iversen Edwin S ES Akushevich Lucy L Whitaker Regina R Bentley Rex C RC Berchuck Andrew A Marks Jeffrey R JR

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20130805 10

<h4>Background</h4>Six gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas (TCGA) project subsequently replicated these subtypes and identified a signature predictive of survival in high-grade serous (HGS) cancers. We previously validated these signatures for use in formalin-fixed paraffin-embedded tissues. The aim of the present study was to determine whether these signatures are associated wit ...[more]

PMID: 23917454

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Project description:OBJECTIVE:Previous studies suggest that breastfeeding reduces epithelial ovarian cancer (EOC) risk. However, the effects of age, timing and episode details on the EOC-breastfeeding relationship have not been examined. The objective of this study was to examine the association between breastfeeding factors and epithelial ovarian cancer. METHODS:We examined breastfeeding factors among parous women in a population-based, case-control study conducted in Pennsylvania, Ohio, and New York from 2003 to 2008. We compared 689 incident EOC cases to 1572 community controls. Multivariable unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) associated with breastfeeding patterns adjusting for potential confounders. RESULTS:Compared to never breastfeeding, breastfeeding any offspring was associated with a 30% reduction in EOC risk (OR = 0.70; 95%CI = 0.58-0.85). That association lasted more than 30 years (OR = 0.69, 95%CI = 0.53-0.88). An average breastfeeding episode of 3 months was also associated with reduced risk (OR = 0.73, 95%CI = 0.58-0.80). A greater number of breastfeeding episodes was associated with greater risk reduction (OR = 0.78, 95%CI = 0.64-0.96 and OR = 0.49, 95%CI = 0.36-0.68 1-2 and 3+ episodes, respectively, compared to never breastfed, trend p = 0.01). Longer breastfeeding duration was also associated with reduced risk (OR = 0.75 and 0.62 for less than and greater than 1-year total duration, respectively, compared to never breastfed). An earlier age at first breastfeeding was further associated with increased protection (OR = 0.50-0.80, for first episode at age <25, 25-29, and 30+, respectively, trend p = 0.001). CONCLUSIONS:Breastfeeding for as few as 3 months is associated with reduced EOC risk. Although this association decreases over time, it persists for more than 30 years. Longer cumulative duration, increasing number of breastfeeding episodes, and earlier age at first breastfeeding episode are each associated with increased benefit.

Project description:BackgroundThe disease burden of tuberculosis (TB) was heavy in Hainan Province, China, and the information on transmission patterns was limited with few studies. This atudy aims to further explore the epidemiological characteristics and influencing factors of TB in Hainan Province, and thereby contribute valuable scientific evidences for TB elimination in Hainan Province.MethodsThe TB notification data in Hainan Province from 2013 to 2022 were collected from the Chinese National Disease Control Information System Tuberculosis Surveillance System, along with socio-economic data. The spatial-temporal and population distributions were analyzed, and spatial autocorrelation analysis was conducted to explore TB notification rate clustering. In addition, the epidemiological characteristics of the cases among in-country migrants were described, and the delay pattern in seeking medical care was investigated. Finally, a geographically and temporally weighted regression (GTWR) model was adopted to analyze the relationship between TB notification rate and socio-economic indicators. The tailored control suggestions in different regions for TB elimination was provided by understanding epidemiological characteristics and risk factors obtained by GTWR.ResultsFrom 2013 to 2022, 64,042 cases of TB were notified in Hainan Province. The estimated annual percentage change of TB notification rate in Hainan Province from 2013 to 2020 was - 6.88% [95% confidence interval (CI): - 5.30%, - 3.69%], with higher rates in central and southern regions. The majority of patients were males (76.33%) and farmers (67.80%). Cases among in-country migrants primarily originated from Sichuan (369 cases), Heilongjiang (267 cases), Hunan (236 cases), Guangdong (174 cases), and Guangxi (139 cases), accounting for 53%. The majority (98.83%) of TB cases were notified through passive case finding approaches, with delay in seeking care. The GTWR analysis showed that gross domestic product per capita, the number of medical institutions and health personnel per 10,000 people were main factors affecting the high TB notification rates in some regions in Hainan Province. Different regional tailored measures such as more TB specialized hospitals were proposed based on the characteristics of each region.ConclusionsThe notification rate of TB in Hainan Province has been declining overall but still remained high in central and southern regions. Particular attention should be paid to the prevalence of TB among males, farmers, and out-of-province migrant populations. The notification rate was also influenced by economic development and medical conditions, indicating the need of more TB specialized hospitals, active surveillance and other tailored prevention and control measures to promote the progress of TB elimination in Hainan Province.

Dataset Information

Molecular signatures of epithelial ovarian cancer: analysis of associations with tumor characteristics and epidemiologic risk factors.

Publications

Molecular signatures of epithelial ovarian cancer: analysis of associations with tumor characteristics and epidemiologic risk factors.

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