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Aim of databaseThe Danish Gynecological Cancer Database (DGCD) is a nationwide clinical cancer database and its aim is to monitor the treatment quality of Danish gynecological cancer patients, and to generate data for scientific purposes. DGCD also records detailed data on the diagnostic measures for gynecological cancer.Study populationDGCD was initiated January 1, 2005, and includes all patients treated at Danish hospitals for cancer of the ovaries, peritoneum, fallopian tubes, cervix, vulva, vagina, and uterus, including rare histological types.Main variablesDGCD data are organized within separate data forms as follows: clinical data, surgery, pathology, pre- and postoperative care, complications, follow-up visits, and final quality check. DGCD is linked with additional data from the Danish “Pathology Registry”, the “National Patient Registry”, and the “Cause of Death Registry” using the unique Danish personal identification number (CPR number).Descriptive dataData from DGCD and registers are available online in the Statistical Analysis Software portal. The DGCD forms cover almost all possible clinical variables used to describe gynecological cancer courses. The only limitation is the registration of oncological treatment data, which is incomplete for a large number of patients.ConclusionThe very complete collection of available data from more registries form one of the unique strengths of DGCD compared to many other clinical databases, and provides unique possibilities for validation and completeness of data. The success of the DGCD is illustrated through annual reports, high coverage, and several peer-reviewed DGCD-based publications.

Background:In an attempt to decrease social disparities in cancer survival, it is important to consider the mechanisms by which socioeconomic position influences cancer prognosis. We aimed to investigate whether any associations between socioeconomic factors and survival after cervical cancer could be explained by socioeconomic differences in cancer stage, comorbidity, lifestyle factors or treatment.Methods:We identified 1961 cases of cervical cancer diagnosed between 2005 and 2010 in the Danish Gynaecological Cancer database, with information on prognostic factors, treatment and lifestyle. Age, vital status, comorbidity and socioeconomic data were obtained from nationwide administrative registers. Associations between socioeconomic indicators (education, income and cohabitation status) and mortality by all causes were analysed in Cox regression models with inclusion of possible mediators. Median follow-up time was 3.0 years (0.01–7.0).Results:All cause mortality was higher in women with shorter rather than longer education (hazard ratio (HR), 1.46; 1.20–1.77), among those with lower rather than higher income (HR, 1.32; 1.07–1.63) and among women aged<60 years without a partner rather than those who cohabited (HR, 1.60; 1.29–1.98). Socioeconomic differences in survival were partly explained by cancer stage and less by comorbidity or smoking (stage- and comorbidty- adjusted HRs being 1.07; 0.96–1.19 for education and 1.15; 0.86–1.52 for income).Conclusion:Socioeconomic disparities in survival after cervical cancer were partly explained by socioeconomic differences in cancer stage. The results point to the importance of further investigations into reducing diagnosis delay among disadvantaged groups.

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Large-scale genome-wide association studies (GWAS) have identified approximately 35 loci associated with epithelial ovarian cancer (EOC) risk. The majority of GWAS-identified disease susceptibility variants are located in noncoding regions, and causal genes underlying these associations remain largely unknown. Here, we performed a transcriptome-wide association study to search for novel genetic loci and plausible causal genes at known GWAS loci. We used RNA sequencing data (68 normal ovarian tissue samples from 68 individuals and 6,124 cross-tissue samples from 369 individuals) and high-density genotyping data from European descendants of the Genotype-Tissue Expression (GTEx V6) project to build ovarian and cross-tissue models of genetically regulated expression using elastic net methods. We evaluated 17,121 genes for their -predicted gene expression in relation to EOC risk using summary statistics data from GWAS of 97,898 women, including 29,396 EOC cases. With a Bonferroni-corrected significance level of < 2.2 × 10, we identified 35 genes, including at 11q14.2 (Z = 5.08, = 3.83 × 10, the cross-tissue model; 1 Mb away from any GWAS-identified EOC risk variant), a potential novel locus for EOC risk. All other 34 significantly associated genes were located within 1 Mb of known GWAS-identified loci, including 23 genes at 6 loci not previously linked to EOC risk. Upon conditioning on nearby known EOC GWAS-identified variants, the associations for 31 genes disappeared and three genes remained ( < 1.47 × 10). These data identify one novel locus ) and 34 genes at 13 known EOC risk loci associated with EOC risk, providing new insights into EOC carcinogenesis. Transcriptomic analysis of a large cohort confirms earlier GWAS loci and reveals FZD4 as a novel locus associated with EOC risk. .