ABSTRACT
BACKGROUND: Phenotypic changes during cancer progression are associated with alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival. METHODS: Using information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new signatures for stage and prognosis. This study was also performed in breast tumors according to estrogen receptor (ER) status and melanoma tumors with proliferative and invasive phenotypes. RESULTS: Transcript isoform signatures accurately separate early from late-stage groups and metastatic from non-metastatic tumors, and are predictive of the survival of patients with undetermined lymph node invasion or metastatic status. These signatures show similar, and sometimes better, accuracies compared with known gene expression signatures in retrospective data and are largely independent of gene expression changes. Furthermore, we show frequent transcript isoform changes in breast tumors according to ER status, and in melanoma tumors according to the invasive or proliferative phenotype, and derive accurate predictive models of stage and survival within each patient subgroup. CONCLUSIONS: Our analyses reveal new signatures based on transcript isoform abundances that characterize tumor phenotypes and their progression independently of gene expression. Transcript isoform signatures appear especially relevant to determine lymph node invasion and metastasis and may potentially contribute towards current strategies of precision cancer medicine.
Subject(s)
Alternative Splicing , Biomarkers, Tumor/genetics , Breast Neoplasms/diagnosis , Melanoma/diagnosis , RNA, Messenger/genetics , Receptors, Estrogen/genetics , Skin Neoplasms/diagnosis , Biomarkers, Tumor/metabolism , Breast Neoplasms/genetics , Breast Neoplasms/mortality , Breast Neoplasms/pathology , Female , Gene Expression Profiling , Humans , Information Theory , Lymphatic Metastasis , Machine Learning , Male , Melanoma/genetics , Melanoma/mortality , Melanoma/pathology , Neoplasm Staging , Prognosis , RNA, Messenger/metabolism , Receptors, Estrogen/metabolism , Skin Neoplasms/genetics , Skin Neoplasms/mortality , Skin Neoplasms/pathology , Survival Analysis , TranscriptomeABSTRACT
Epitope databases and the protein sequences of published plant genomes are suitable to identify some of the proteins causing food allergies and sensitivities. Brachypodium distachyon, a diploid wild grass with a sequenced genome and low prolamin content, is the closest relative of the allergen cereals, such as wheat or barley. Using the Brachypodium genome sequence, a workflow has been developed to identify potentially harmful proteins which may cause either celiac disease or wheat allergy-related symptoms. Seed tissue-specific expression of the potential allergens has been determined, and intact epitopes following an in silico digestion with several endopeptidases have been identified. Molecular function of allergen proteins has been evaluated using Gene Ontology terms. Biologically overrepresented proteins and potentially allergen protein families have been identified.
