TY - JOUR
T1 - Integrated analysis reveals microRNA networks coordinately expressed with key proteins in breast cancer
AU - Oslo Breast Cancer Research Consortium (OSBREAC)
AU - Aure, Miriam Ragle
AU - Jernström, Sandra
AU - Krohn, Marit
AU - Vollan, Hans Kristian Moen
AU - Due, Eldri U.
AU - Rødland, Einar
AU - Kåresen, Rolf
AU - Ram, Prahlad
AU - Lu, Yiling
AU - Mills, Gordon B.
AU - Sahlberg, Kristine Kleivi
AU - Børresen-Dale, Anne Lise
AU - Lingjærde, Ole Christian
AU - Kristensen, Vessela N.
AU - Fritzman, Britt
AU - Naume, Bjørn
AU - Borgen, Elin
AU - Fodstad, Øystein
AU - Nesland, Jahn M.
AU - Skjerven, Helle
AU - Mælandsmo, Gunhild Mari
AU - Bathen, Tone F.
AU - Schlichting, Ellen
AU - Wist, Erik
AU - Bukholm, Ida
AU - Engebråten, Olav
AU - Sauer, Toril
AU - Russnes, Hege
AU - Garred, Øystein
N1 - Funding Information:
We would like to acknowledge Phuong Vu, Anja Valen and Anita Halvei for assisting the performance of array experiments. We would also like to thank Charles Vaske for valuable input and Daniel Nebdal for excellent technical assistance in making of the figures. The research leading to these results has received funding from the KG Jebsen Centre for Breast Cancer Research. MR Aure was a PhD fellow of the Research Council of Norway (grant 193387/V50 to A-L Børresen-Dale and VN Kristensen) and a Postdoc fellow of the South Eastern Norway Health Authority (grant 2014021 to A-L Børresen-Dale). S Jernström was a PhD fellow of the South Eastern Norway Health Authority (grant 2011049 to KK Sahlberg). Expression profiling was performed with funding from the Research Council of Norway (grant 193387/H10 to A-L Børresen-Dale and VN Kristensen), South Eastern Norway Health Authority (grant 39346 to A-L Børresen-Dale) and the Norwegian Cancer Society. The study was partly supported by the Research Council of Norway through its Centers of Excellence funding scheme, project number 179571. We acknowledge the MD Anderson Cancer Center Support Grant 5 P30 CA016672 36 for the RPPA studies on tumors and the Cancer Center Support Grant-funded (CCSG) Functional Proteomics, which is funded by NCI # CA16672 for the miRNA library screen. STR DNA fingerprinting was done by the CCSG Characterized Cell Line core, NCI # CA016672.
Publisher Copyright:
© 2015 Aure et al.; licensee BioMed Central.
PY - 2015/2/2
Y1 - 2015/2/2
N2 - Background: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. Methods: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. Results: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. Conclusions: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.
AB - Background: The role played by microRNAs in the deregulation of protein expression in breast cancer is only partly understood. To gain insight, the combined effect of microRNA and mRNA expression on protein expression was investigated in three independent data sets. Methods: Protein expression was modeled as a multilinear function of powers of mRNA and microRNA expression. The model was first applied to mRNA and protein expression for 105 selected cancer-associated genes and to genome-wide microRNA expression from 283 breast tumors. The model considered both the effect of one microRNA at a time and all microRNAs combined. In the latter case the Lasso penalized regression method was applied to detect the simultaneous effect of multiple microRNAs. Results: An interactome map for breast cancer representing all direct and indirect associations between the expression of microRNAs and proteins was derived. A pattern of extensive coordination between microRNA and protein expression in breast cancer emerges, with multiple clusters of microRNAs being associated with multiple clusters of proteins. Results were subsequently validated in two independent breast cancer data sets. A number of the microRNA-protein associations were functionally validated in a breast cancer cell line. Conclusions: A comprehensive map is derived for the co-expression in breast cancer of microRNAs and 105 proteins with known roles in cancer, after filtering out the in-cis effect of mRNA expression. The analysis suggests that group action by several microRNAs to deregulate the expression of proteins is a common modus operandi in breast cancer.
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U2 - 10.1186/s13073-015-0135-5
DO - 10.1186/s13073-015-0135-5
M3 - Article
AN - SCOPUS:84928335700
SN - 1756-994X
VL - 7
JO - Genome Medicine
JF - Genome Medicine
IS - 1
M1 - 21
ER -