Research Papers:

Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer

Runmin Wei, Immaculata De Vivo, Sijia Huang, Xun Zhu, Harvey Risch, Jason H. Moore, Herbert Yu and Lana X. Garmire _

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Oncotarget. 2016; 7:55249-55263. https://doi.org/10.18632/oncotarget.10509

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Runmin Wei1,2, Immaculata De Vivo3, Sijia Huang1,2, Xun Zhu1,2, Harvey Risch4, Jason H. Moore5,6, Herbert Yu2, Lana X. Garmire1,2

1Molecular Biosciences and Bioengineering Graduate Program, University of Hawaii at Manoa, Honolulu, HI, USA

2Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA

3Harvard School of Public Health, Harvard University, Boston, MA, USA

4Yale School of Public Health, Yale University, New Haven, CT, USA

5Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

6Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

Correspondence to:

Lana X. Garmire, email: lgarmire@cc.hawaii.edu

Keywords: endometrial cancer (EC), GWAS, data integration, pathways, data mining

Received: July 01, 2015     Accepted: May 02, 2016     Published: July 9, 2016


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.

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