Network modeling identifies molecular functions targeted by miR-204 to suppress head and neck tumor metastasis.
Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1-22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases.
Lee
Younghee
Y
Section of Genetic Medicine and Center for Biomedical Informatics, Department of Medicine, The University of Chicago, Chicago, Illinois, United States of America.
Yang
Xinan
X
Huang
Yong
Y
Fan
Hanli
H
Zhang
Qingbei
Q
Wu
Youngfei
Y
Li
Jianrong
J
Hasina
Rifat
R
Cheng
Chao
C
Lingen
Mark W
MW
Gerstein
Mark B
MB
Weichselbaum
Ralph R
RR
Xing
H Rosie
HR
Lussier
Yves A
YA
D002448:Cell Adhesion / Q000235:genetics; D045744:Cell Line, Tumor; D002465:Cell Movement / Q000235:genetics; D002899:Chromosomes, Human, Pair 9; D019295:Computational Biology / Q000379:methods*; D066246:ErbB Receptors / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D020022:Genetic Predisposition to Disease; D015894:Genome, Human; D006258:Head and Neck Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology*; D006801:Humans; D019656:Loss of Heterozygosity; D035683:MicroRNAs / Q000235:genetics* / Q000378:metabolism; D009362:Neoplasm Metastasis; D025941:Protein Interaction Mapping
C549404:MIRN204 microRNA, human; D035683:MicroRNAs; C512478:EGFR protein, human; D066246:ErbB Receptors