Recent studies have implicated the critical roles of microRNAs (miRNAs) in the pathogenesis of cancer, suggesting that miRNAs can be clinically useful as biomarkers for cancer prognosis, diagnosis and treatment. To date, the miRNA information in cancer studies has varied greatly due to data heterogeneity and disease complexity. In this application, in Aim 1, we will develop novel statistical methods to systematically perform meta- analysis of miRNA expression in the first four cancers (glioblastoma, ovarian cancer, colorectal cancer, and lung cancer) reported by The Cancer Genome Atlas (TCGA) project. For each of these cancers, more than 300 dysregulated miRNAs have been reported, which makes this aim not only feasible but immediately needed.
In Aim 2, we will develop innovative strategies to explore miRNAs' functions in cancer through miRNA and transcription factor (TF) co-regulatory network analysis. For each cancer, we will build cancer-specific regulatory networks using miRNA/mRNA co-expression profiling and TF/gene regulation derived from the corresponding TCGA dataset. We will then identify network modules that reflect miRNA and TF co-regulation in cancer. We will investigate both common regulatory modules among four types of cancer and unique modules for each specific cancer.
In Aim 3, we will experimentally validate selected miRNAs and their targets in common regulatory modules from Aim 2 using already available tissue and matched normal samples as well as established cell lines. This application will be the first systematic investigation of all available miRNA studes in the first four TCGA cancers. The successful completion of Aim 1 will provide us with a list of evidence-based miRNAs in glioblastoma, ovarian cancer, colorectal cancer, and lung cancer; the successful completion of Aim 2 will provide us with a comprehensive exploration of miRNA and TF co-regulation at the regulatory network level in these cancers; the successful completion of Aim 3 will validate our meta- and network- approaches, help us understand the miRNA regulatory mechanisms, and provide us with potential therapeutic targets in these cancers. Although quite exploratory, we expect this project is highly feasible and timely due to the large amount of data available in literature and from TCGA. This pioneering effort to detect functionally important miRNAs in complex diseases will greatly enhance our understanding of the regulatory systems in cancer, which will likely lead to the development of effective prevention, diagnosis, and treatment strategies.
Numerous studies have implicated the critical roles of microRNAs in the pathogenesis of cancer, suggesting that microRNAs might be clinically useful as biomarkers for cancer prognosis, diagnosis, and treatment. To date, the microRNA information in cancer studies has varied greatly due to data heterogeneity and disease complexity. In this application, we will develop novel statistical methods to systematically perform meta-analysis of microRNA expression, then explore microRNA regulatory networks in four major types of cancer that have been reported by The Cancer Genome Atlas (TCGA) project, and, finally, experimentally validate the top selected critical microRNAs in these four types of cancer.
| Kim, Pora; Jia, Peilin; Zhao, Zhongming (2018) Kinase impact assessment in the landscape of fusion genes that retain kinase domains: a pan-cancer study. Brief Bioinform 19:450-460 |
| Wang, Hao; Luo, Jiamao; Liu, Chun et al. (2017) Investigating MicroRNA and transcription factor co-regulatory networks in colorectal cancer. BMC Bioinformatics 18:388 |
| Kim, Pora; Zhao, Junfei; Lu, Pinyi et al. (2017) mutLBSgeneDB: mutated ligand binding site gene DataBase. Nucleic Acids Res 45:D256-D263 |
| Liu, Shuang; Mitra, Ramkrishna; Zhao, Ming-Ming et al. (2016) The Potential Roles of Long Noncoding RNAs (lncRNA) in Glioblastoma Development. Mol Cancer Ther 15:2977-2986 |
| Wang, Yuanyuan; Guo, Xingyi; Bray, Michael J et al. (2016) An integrative genomics approach for identifying novel functional consequences of PBRM1 truncated mutations in clear cell renal cell carcinoma (ccRCC). BMC Genomics 17 Suppl 7:515 |
