ZIA CP010181 10611 (ZIA) | |||
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Title | Identify susceptibility loci in p53 binding regions based on genome-wide associat | ||
Institution | NCI, Bethesda, MD | ||
Principal Investigator | Shi, Jianxin | NCI Program Director | N/A |
Cancer Activity | N/A | Division | DCEG |
Funded Amount | $161,620 | Project Dates | null - null |
Fiscal Year | 2018 | Project Type | Intramural |
Research Topics w/ Percent Relevance | Cancer Types w/ Percent Relevance | ||
Biochemical Epidemiology (45.0%) Cancer (100.0%) |
Breast (50.0%) Lung (50.0%) |
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Research Type | |||
Exogenous Factors in the Origin and Cause of Cancer Endogenous Factors in the Origin and Cause of Cancer |
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Abstract | |||
The tumor suppressor p53 plays critical roles in tumor suppression. Rare germline mutations of the TP53 gene cause Li-Fraumeni syndrome. However, no common single nucleotide polymorphism (SNP) in TP53 gene has been linked to cancer risk with consistently replicable evidence in large-scale GWAS. This is expected; because of the negative selection pressure, the deleterious SNPs in TP53 gene are too rare to be detected by GWAS. On the other hand, no single p53 downstream target genes fully explains the phenotype of TP53 gene loss, suggesting that each p53 downstream target gene only mediates partial role of p53. Hence, SNPs associated with cancer risk are more likely to be carried in multiple p53 downstream target genes, each of which makes a small contribution to cancer risks. It is well-known that p53 exerts its protective role mostly as a transcription factor by binding to its target genes or non-coding RNAs. Our central hypothesis is that the some SNPs within p53 binding sites are collectively associated with cancer risk or interact with SNPs in TP53 gene to contribute to cancer risks. Here, we propose an integrative genome-wide approach to test this hypothesis. High-throughput ChIP-Seq dataset of p53 binding sites will be compared to the lung and breast cancer GWAS dataset to identify the cancer-associated-SNPs in p53 binding regions. Successful identification of these cancer-associated-SNPs will provide us genome-wide insights into the tumor suppressive function of p53. |