L
Lianbo Yu
Researcher at Ohio State University
Publications - 136
Citations - 7609
Lianbo Yu is an academic researcher from Ohio State University. The author has contributed to research in topics: microRNA & Medicine. The author has an hindex of 33, co-authored 113 publications receiving 5975 citations. Previous affiliations of Lianbo Yu include The Ohio State University Wexner Medical Center.
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Journal ArticleDOI
Tumor microenvironment regulates metastasis and metastasis genes of mouse MMTV-PymT mammary cancer cells in vivo.
Jillian L. Werbeck,Nanda K. Thudi,Chelsea K. Martin,Chris Premanandan,Lianbo Yu,M. C. Ostrowksi,Thomas J. Rosol +6 more
TL;DR: This investigation demonstrates that breast cancer progression and metastasis are regulated by not only the tumor cells but also the experimental model and unique molecular signals from the tumor microenvironment.
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Increased breast cancer risk in women with neurofibromatosis type 1: a meta-analysis and systematic review of the literature.
Lorena P. Suarez-Kelly,Lianbo Yu,David Kline,Eric B. Schneider,Doreen M. Agnese,William E. Carson,William E. Carson +6 more
TL;DR: This systematic literature review and meta-analysis suggests that women with NF1’s have a five-fold increased risk of BC, present with more advanced disease, and may have an increased BC related mortality.
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Stromal ETS2 Regulates Chemokine Production and Immune Cell Recruitment during Acinar-to-Ductal Metaplasia.
Jason R. Pitarresi,Xin Liu,Sudarshana M. Sharma,Maria C. Cuitiño,Raleigh D. Kladney,Thomas A. Mace,Sydney Donohue,Sunayana G. Nayak,Chunjing Qu,James L. Lee,Sarah Woelke,Stefan Trela,Kyle M. LaPak,Lianbo Yu,Joseph P. McElroy,Thomas J. Rosol,Reena Shakya,Thomas Ludwig,Gregory B. Lesinski,Soledad Fernandez,Stephen F. Konieczny,Gustavo Leone,Jinghai Wu,Michael C. Ostrowski +23 more
TL;DR: An unappreciated role for ETS2 in fibroblasts in establishing an immune-suppressive microenvironment in response to oncogenic KrasG12D signaling during the initial stages of tumor development is suggested.
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Fully moderated T-statistic for small sample size gene expression arrays.
Lianbo Yu,Parul Gulati,Soledad Fernandez,Michael L. Pennell,Lawrence S. Kirschner,David Jarjoura +5 more
TL;DR: This work provides evidence against the assumption that moderated t methods assumed a constant coefficient of variation for the gene variances, and extends the method by allowing the CV to vary with gene expression.
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Power analysis for RNA-Seq differential expression studies
TL;DR: A simulation based procedure for power estimation using the negative binomial distribution and assuming a generalized linear model that considers the dependence between gene expression level and its variance (dispersion) and also allows equal or unequal dispersion across conditions is proposed.