title: Knowledge about the presence or absence of miRNA isoforms (isomiRs) can successfully discriminate amongst 32 TCGA cancer types.
abstract: Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on 'binarized' isomiR profiles: each isomiR is simply labeled as 'present' or 'absent'. The resulting classifier successfully labeled tumor datasets with an average sensitivity of 90% and a false discovery rate (FDR) of 3%, surpassing the performance of expression-based classification. The classifier maintained its power even after a 15× reduction in the number of isomiRs that were used for training. Notably, the classifier could correctly predict the cancer type in non-TCGA datasets from diverse platforms. Our analysis revealed that the most discriminatory isomiRs happen to also be differentially expressed between normal tissue and cancer. Even so, we find that these highly discriminating isomiRs have not been attracting the most research attention in the literature. Given their ability to successfully classify datasets from 32 cancers, isomiRs and our resulting 'Pan-cancer Atlas' of isomiR expression could serve as a suitable framework to explore novel cancer biomarkers.
authors.affiliation: Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, Thomas Jefferson University, PA 19107, USA.
authors.forename: Aristeidis G
authors.initials: AG
authors.lastname: Telonis
authors.affiliation: Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, Thomas Jefferson University, PA 19107, USA.
authors.forename: Rogan
authors.initials: R
authors.lastname: Magee
authors.affiliation: Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, Thomas Jefferson University, PA 19107, USA.
authors.forename: Phillipe
authors.initials: P
authors.lastname: Loher
authors.affiliation: Division of Biostatistics, Thomas Jefferson University, Philadelphia, PA 19107, USA.
authors.forename: Inna
authors.initials: I
authors.lastname: Chervoneva
authors.affiliation: Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, Thomas Jefferson University, PA 19107, USA.
authors.forename: Eric
authors.initials: E
authors.lastname: Londin
authors.affiliation: Computational Medicine Center, Sidney Kimmel Medical College, Thomas Jefferson University, Thomas Jefferson University, PA 19107, USA.
authors.forename: Isidore
authors.initials: I
authors.lastname: Rigoutsos
mesh_terms: D016000:Cluster Analysis; D066264:Datasets as Topic; D006801:Humans; D035683:MicroRNAs / Q000378:metabolism*; D009369:Neoplasms / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D059368:RNA Isoforms / Q000378:metabolism
chemical_list: D035683:MicroRNAs; D059368:RNA Isoforms