Epidemiologic paradigms for progress in ovarian cancer research

Tworoger, Shelley S.; Doherty, Jennifer A.

doi:10.1007/s10552-017-0877-z

Cited by 4 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, further work to understand the possible mechanisms through which factors that appear to influence ovarian cancer in these analyses promote oncogenesis (e.g., genetic liability to endometriosis, low CRP levels) could help to increase the scope for prevention opportunities across the life course. Lastly, for the vast majority of women who develop ovarian cancer with no previous history of smoking and who do not have endometriosis [9,55,73], there is a need to identify novel modifiable risk factors for this condition, as has been advocated elsewhere [74,75].…”

Section: Resultsmentioning

confidence: 99%

Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: A Mendelian randomization analysis

et al. 2019

View full text Add to dashboard Cite

Background Various risk factors have been associated with epithelial ovarian cancer risk in observational epidemiological studies. However, the causal nature of the risk factors reported, and thus their suitability as effective intervention targets, is unclear given the susceptibility of conventional observational designs to residual confounding and reverse causation. Mendelian randomization (MR) uses genetic variants as proxies for risk factors to strengthen causal inference in observational studies. We used MR to evaluate the association of 12 previously reported risk factors (reproductive, anthropometric, clinical, lifestyle, and molecular factors) with risk of invasive epithelial ovarian cancer, invasive epithelial ovarian cancer histotypes, and low malignant potential tumours. Methods and findings Genetic instruments to proxy 12 risk factors were constructed by identifying single nucleotide polymorphisms (SNPs) that were robustly ( P < 5 × 10 −8 ) and independently associated with each respective risk factor in previously reported genome-wide association studies. These risk factors included genetic liability to 3 factors (endometriosis, polycystic ovary syndrome, type 2 diabetes) scaled to reflect a 50% higher odds liability to disease. We obtained summary statistics for the association of these SNPs with risk of overall and histotype-specific invasive epithelial ovarian cancer (22,406 cases; 40,941 controls) and low malignant potential tumours (3,103 cases; 40,941 controls) from the Ovarian Cancer Association Consortium (OCAC). The OCAC dataset comprises 63 genotyping project/case–control sets with participants of European ancestry recruited from 14 countries (US, Australia, Belarus, Germany, Belgium, Denmark, Finland, Norway, Canada, Poland, UK, Spain, Netherlands, and Sweden). SNPs were combined into multi-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 95% confidence intervals (CIs). Three complementary sensitivity analyses were performed to examine violations of MR assumptions: MR–Egger regression and weighted median and mode estimators. A Bonferroni-corrected P value threshold was used to establish strong evidence ( P < 0.0042) and suggestive evidence (0.0042 < P < 0.05) for associations. In MR analyses, there was strong or suggestive evidence that 2 of the 12 risk factors were associated with invasive epithelial ovarian cancer and 8 of the 12 were associated with 1 or more invasive epithelial ovarian cancer histotypes. There was strong evidence that genetic liability to endometriosis was associated with an increased risk of invasive epithelial ovarian cancer (odds ratio [OR] per 50% higher odds liability: 1.10, 95% CI 1.06–1.15; P = 6.94 × 10 −7 ) and suggestive evidence that lifetime smoking exposure was associated wi...

show abstract

Section: Resultsmentioning

confidence: 99%

Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: A Mendelian randomization analysis

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Additionally, further work understanding possible mechanisms through which factors that appear to causally influence ovarian cancer in these analyses promote oncogenesis (e.g., genetic liability to endometriosis, C-reactive protein levels) could help to increase scope for prevention opportunities across the life-course. Lastly, for the vast majority of women who develop ovarian cancer with no previous history of smoking and who do not have endometriosis 9,53,70 , there is a need to identify novel modifiable risk factors for this condition, as has been advocated elsewhere 71,72 .…”

Section: Discussionmentioning

confidence: 99%

Evaluating causal associations between previously reported risk factors and epithelial ovarian cancer: a Mendelian randomization analysis

Yarmolinsky

Relton

Lophatananon

et al. 2018

Preprint

View full text Add to dashboard Cite

Background: Previously reported observational associations between risk factors and epithelial ovarian cancer (EOC) could reflect residual confounding, reverse causation, or measurement error. Mendelian randomization (MR) uses genetic variants as proxies for modifiable risk factors to strengthen causal inference in observational studies. Methods:We used MR to evaluate the causal role of 13 previously reported risk factors in overall and histotype-specific EOC in up to 25,509 case subjects and 40,941 controls in the Ovarian Cancer Association Consortium. Inverse-variance weighted models were employed to generate effect estimates and MR-Egger, weighted median, and weighted mode were performed to examine evidence of horizontal pleiotropy. A Bonferroni-corrected P-value threshold was used to establish "strong evidence" (P<0.0038) and "suggestive evidence" (0.0038

show abstract

“…OC is difficult to detect and metastasizes early in disease progression; therefore, patients with OC frequently have a poor prognosis ( 5 ). Globally, OC affects 1.2 million women and led to 161,100 mortalities in 2015 ( 6 , 7 ). Thus, obtaining an improved understanding of OC progression and recurrence is of great importance for improving its prognosis.…”

Section: Introductionmentioning

confidence: 99%

A random forest classifier predicts recurrence risk in patients with ovarian cancer

Cheng

Wang

et al. 2018

Mol Med Report

View full text Add to dashboard Cite

Ovarian cancer (OC) is associated with a poor prognosis due to difficulties in early detection. The aims of the present study were to construct a recurrence risk prediction model and to reveal important OC genes or pathways. RNA sequencing data was obtained for 307 OC samples, and the corresponding clinical data were downloaded from The Cancer Genome Atlas database. Additionally, two validation datasets, GSE44104 (20 recurrent and 40 non-recurrent OC samples) and GSE49997 (204 OC samples), were obtained from the Gene Expression Omnibus database. Differentially expressed genes were screened using the differential expression via distance synthesis algorithm, followed by gene ontology enrichment analysis and weighted gene coexpression network analysis (WGCNA). Furthermore, subnetwork analysis was conducted for the protein-protein interaction (PPI) network using the BioNet package. Finally, a random forest classifier was constructed based on the subnetwork nodes, and its reliability was validated using the GSE44104 and GSE49997 validation datasets. A total of 44 upregulated and 117 downregulated genes were identified in the recurrent samples. Enrichment analysis indicated that cytochrome P450 family 17 subfamily A member 1 (CYP17A1) was associated with ‘positive regulation of steroid hormone biosynthetic processes’. WGCNA identified turquoise and grey modules that were significantly correlated with status and prognosis. A significant PPI subnetwork containing 16 nodes was also identified, including: Transcription factor GATA-4; fibroblast growth factor 9; aromatase; 3β-hydroxysteroid dehydrogenase/δ5-4-isomerase type 2; corticosteroid 11β-dehydrogenase isozyme 1; CYP17A1; pituitary homeobox 2; left-right determination factor 1; homeobox protein ARX; estrogen receptor β; steroidogenic factor 1; forkhead box protein L2; myocardin; steroidogenic acute regulatory protein mitochondrial; vesicular inhibitory amino acid transporter; and twist-related protein 1. A random forest classifier was constructed using the subnetwork nodes as feature genes, which exhibited a 92% true positive rate when classifying recurrent and non-recurrent OC samples. The classifying efficiency of the random forest classifier was validated using the two other independent datasets. Overall, 44 upregulated and 117 downregulated genes associated with OC recurrence were identified. Furthermore, the 16 subnetwork node genes that were identified may be important molecules in OC recurrence.

show abstract

Epidemiologic paradigms for progress in ovarian cancer research

Cited by 4 publications

References 19 publications

Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: A Mendelian randomization analysis

Appraising the role of previously reported risk factors in epithelial ovarian cancer risk: A Mendelian randomization analysis

Evaluating causal associations between previously reported risk factors and epithelial ovarian cancer: a Mendelian randomization analysis

A random forest classifier predicts recurrence risk in patients with ovarian cancer

Contact Info

Product

Resources

About