Glioblastoma multiforme (GBM) is the most common and lethal brain tumor in humans. It is highly resistant to radiation and chemotherapy. Understanding its molecular mechanisms is critical in order to develop more effective treatments. Recent studies revealed that microRNAs (miRNAs) play critical roles in the pathogenesis of GBM. To date, more than 100 dysregulated miRNAs have been found in GBM from various miRNA expression studies, which provide us with astonishing insights into the patterns of miRNA expression in GBM. However, the results reported so far have been inconsistent, presenting a great challenge in deciphering the underlying miRNA regulatory mechanisms in GBM. Therefore, a systematic examination of previous miRNA data is immediately needed and executable. In this project, we will develop innovative strategies to identify functionally important miRNAs significantly associated with GBM in the context of miRNA regulatory networks. The project will start by prioritizing miRNAs through integrating results from multiple studies using a mixed effects model, then build GBM-specific regulatory networks comprised of weighted molecules, i.e. GBM miRNAs, GBM genes and human transcription factors (TFs), and finally perform dense module search (DMS) of the regulatory networks to detect functionally critical miRNAs in GBM regulatory networks. We propose three specific aims. (1) To develop a novel, statistical integrative framework for the meta-analysis of miRNA expression data from multiple studies using a mixed effects model. Compared to traditional pooled analysis, we will integrate all possible effect sizes of each miRNA into a mixed effects model and calculate a P-value as its overall effect size to GBM. (2) To develop a novel computational pipeline to construct GBM-specific miRNA- mediated regulatory networks consisting of GBM miRNAs, GBM genes, and TFs. (3) To develop a novel dense module search (DMS) algorithm for identifying functionally important miRNAs in GBM. In this DMS algorithm, a module is defined as a set of FFLs, each of which includes miRNA, gene(s), and TF, and their regulatory relationships. This project constitutes a pioneering effort to establish an integrative and comprehensive modeling framework, as well as practical computational methods for detecting functionally important miRNAs in complex diseases and demonstrates it in GBM. Successful completion of this project will greatly enhance our understanding of the regulatory systems in GBM, which will likely lead to the development of effective prevention, diagnosis, and treatment strategies.
Many recent studies have implicated the critical roles of microRNAs in the pathogenesis of glioblastoma, the most common and most lethal brain tumor in humans, suggesting that microRNAs might be clinically useful as biomarkers for brain tumors and other cancers. However, to date, the microRNA information in glioblastoma studies has varied greatly due to data heterogeneity and disease complexity. In this application, we will develop novel statistical methods to integrate microRNA data in glioblastoma and apply unique systems biology approaches to identify microRNA-mediated regulatory networks underlying glioblastoma and functionally important microRNAs, leading to more effective prevention and treatment strategies.
|Edmonds, Mick D; Boyd, Kelli L; Moyo, Tamara et al. (2016) MicroRNA-31 initiates lung tumorigenesis and promotes mutant KRAS-driven lung cancer. J Clin Invest 126:349-64|
|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|
|Mitra, Ramkrishna; Lin, Chen-Ching; Eischen, Christine M et al. (2015) Concordant dysregulation of miR-5p and miR-3p arms of the same precursor microRNA may be a mechanism in inducing cell proliferation and tumorigenesis: a lung cancer study. RNA 21:1055-65|
|Lin, Chen-Ching; Mitra, Ramkrishna; Cheng, Feixiong et al. (2015) A cross-cancer differential co-expression network reveals microRNA-regulated oncogenic functional modules. Mol Biosyst 11:3244-52|
|Jiang, Wei; Jia, Peilin; Hutchinson, Katherine E et al. (2015) Clinically relevant genes and regulatory pathways associated with NRASQ61 mutations in melanoma through an integrative genomics approach. Oncotarget 6:2496-508|
|Xu, Yaomin; Guo, Xingyi; Sun, Jiayang et al. (2015) Snowball: resampling combined with distance-based regression to discover transcriptional consequences of a driver mutation. Bioinformatics 31:84-93|
|Mitra, Ramkrishna; Sun, Jingchun; Zhao, Zhongming (2015) microRNA regulation in cancer: One arm or two arms? Int J Cancer 137:1516-8|
|Bandyopadhyay, Sanghamitra; Ghosh, Dip; Mitra, Ramkrishna et al. (2015) MBSTAR: multiple instance learning for predicting specific functional binding sites in microRNA targets. Sci Rep 5:8004|
|Lin, Chen-Ching; Jiang, Wei; Mitra, Ramkrishna et al. (2015) Regulation rewiring analysis reveals mutual regulation between STAT1 and miR-155-5p in tumor immunosurveillance in seven major cancers. Sci Rep 5:12063|
|Lin, Chen-Ching; Mitra, Ramkrishna; Zhao, Zhongming (2014) A tri-component conservation strategy reveals highly confident microRNA-mRNA interactions and evolution of microRNA regulatory networks. PLoS One 9:e103142|
Showing the most recent 10 out of 21 publications