title: Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer.
abstract: Endometrial Cancer (EC) is one of the most common female cancers. Genome-wide association studies (GWAS) have been investigated to identify genetic polymorphisms that are predictive of EC risks. Here we utilized a meta-dimensional integrative approach to seek genetically susceptible pathways that may be associated with tumorigenesis and progression of EC. We analyzed GWAS data obtained from Connecticut Endometrial Cancer Study (CECS) and identified the top 20 EC susceptible pathways. To further verify the significance of top 20 EC susceptible pathways, we conducted pathway-level multi-omics analyses using EC exome-Seq, RNA-Seq and survival data, all based on The Cancer Genome Atlas (TCGA) samples. We measured the overall consistent rankings of these pathways in all four data types. Some well-studied pathways, such as p53 signaling and cell cycle pathways, show consistently high rankings across different analyses. Additionally, other cell signaling pathways (e.g. IGF-1/mTOR, rac-1 and IL-5 pathway), genetic information processing pathway (e.g. homologous recombination) and metabolism pathway (e.g. sphingolipid metabolism) are also highly associated with EC risks, diagnosis and prognosis. In conclusion, the meta-dimensional integration of EC cohorts has suggested some common pathways that may be associated from predisposition, tumorigenesis to progression.
authors.affiliation: Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA.|Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
authors.forename: Runmin
authors.initials: R
authors.lastname: Wei
authors.affiliation: Harvard School of Public Health, Harvard University, Boston, MA, USA.
authors.forename: Immaculata
authors.initials: I
authors.lastname: De Vivo
authors.affiliation: Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA.|Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
authors.forename: Sijia
authors.initials: S
authors.lastname: Huang
authors.affiliation: Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA.|Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
authors.forename: Xun
authors.initials: X
authors.lastname: Zhu
authors.affiliation: Yale School of Public Health, Yale University, New Haven, CT, USA.
authors.forename: Harvey
authors.initials: H
authors.lastname: Risch
authors.affiliation: Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.|Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.affiliation: Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
authors.forename: Herbert
authors.initials: H
authors.lastname: Yu
authors.affiliation: Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA.|Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
authors.forename: Lana X
authors.initials: LX
authors.lastname: Garmire
mesh_terms: D063646:Carcinogenesis; D019091:Critical Pathways; D018450:Disease Progression; D016889:Endometrial Neoplasms / Q000209:etiology* / Q000235:genetics; D059472:Exome; D005260:Female; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D017423:Sequence Analysis, RNA
keywords: GWAS; data integration; data mining; endometrial cancer (EC); pathways