- Project 3 Obesity has become a global pandemic. In the Unites States alone, according the CDC data 2008, 33.8% adults were obese and 68% adults were obese or overweight. The State of Hawaii has multiple ethnic groups, in which Native Hawaiian and Pacific Islanders (NHPI) have a daunting obesity rate of 49.3%, highest of any racial/ethnic group. The effects of maternal obesity are trans-generational, in that maternal obesity increases the likelihood of neonatal obesity, and the neonatal obesity is likely to persist in the adulthood. Additionally, obesity is correlated with various diseases including cancers in adults. Epidemiological studies have provided the link that maternal obesity can lead to increased cancer risks in progenies in later adult life. However, due to ethical and practical reasons in human studies, direct evidence is lacking. Here we propose a case control study with multi-ethnic groups (Asian, Native Hawaiian and Pacific Islanders and Caucasians) to investigate the molecular links between maternal obesity and offspring cancer risks, through the integration of transcriptome and methylome of the cord blood stem cells. The de-identified cord blood tissue will be collected through the Hawaii Biorepository (HiBR). Specifically, we will select the hematopoietic stem cells, and perform RNA-Seq transcriptome and methylome experiments, respectively. These data will be compared to the transcriptome data, methylome data, as well as the integrated transcriptome and methylome data, from The Cancer Genome Atlas. Through such comparison, we hope to identify the cancer-like modules that signify the difference between cord blood stem cells associated with obese mothers vs. those of mothers with normal BMI. This study will hopefully help provide effective strategies to prevent obesity and cancers to occur even before the child is born. It will also help to provide remedial strategies to solve the outstanding health disparity issue in the State of Hawaii.
- Project 3 The goal of this proposal is to investigate the molecular links between maternal obesity and offspring cancer risks, through the integration of transcriptome and methylome of the cord blood stem cells obtained from a multi-ethnic case control study, in comparison with the data of the Cancer Genome Atlas (TCGA).
|Chaudhary, Kumardeep; Poirion, Olivier B; Lu, Liangqun et al. (2018) Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer. Clin Cancer Res 24:1248-1259|
|Poirion, Olivier B; Chaudhary, Kumardeep; Garmire, Lana X (2018) Deep Learning data integration for better risk stratification models of bladder cancer. AMIA Jt Summits Transl Sci Proc 2017:197-206|
|Alarcon, Vernadeth B; Marikawa, Yusuke (2018) ROCK and RHO Playlist for Preimplantation Development: Streaming to HIPPO Pathway and Apicobasal Polarity in the First Cell Differentiation. Adv Anat Embryol Cell Biol 229:47-68|
|Ching, Travers; Garmire, Lana X (2018) Pan-cancer analysis of expressed somatic nucleotide variants in long intergenic non-coding RNA. Pac Symp Biocomput 23:512-523|
|Lee, Ryan W Y; Corley, Michael J; Pang, Alina et al. (2018) A modified ketogenic gluten-free diet with MCT improves behavior in children with autism spectrum disorder. Physiol Behav 188:205-211|
|Kim, Iris Q; Marikawa, Yusuke (2018) Embryoid body test with morphological and molecular endpoints implicates potential developmental toxicity of trans-resveratrol. Toxicol Appl Pharmacol 355:211-225|
|Polgar, Noemi; Fogelgren, Ben (2018) Regulation of Cell Polarity by Exocyst-Mediated Trafficking. Cold Spring Harb Perspect Biol 10:|
|O'Brien, Lori L; Guo, Qiuyu; Bahrami-Samani, Emad et al. (2018) Transcriptional regulatory control of mammalian nephron progenitors revealed by multi-factor cistromic analysis and genetic studies. PLoS Genet 14:e1007181|
|Poirion, Olivier; Zhu, Xun; Ching, Travers et al. (2018) Using single nucleotide variations in single-cell RNA-seq to identify subpopulations and genotype-phenotype linkage. Nat Commun 9:4892|
|Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X (2018) Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data. J Proteome Res 17:337-347|
Showing the most recent 10 out of 135 publications