Diagnostic and prognostic implications of ribosomal protein transcript expression patterns in human cancers

Abstract

BackgroundRibosomes, the organelles responsible for the translation of mRNA, are comprised of four rRNAs and ~ 80 ribosomal proteins (RPs). Although canonically assumed to be maintained in equivalent proportions, some RPs have been shown to possess differential expression across tissue types. Dysregulation of RP expression occurs in a variety of human diseases, notably in many cancers, and altered expression of some RPs correlates with different tumor phenotypes and patient survival. Little work has been done, however, to characterize overall patterns of RP transcript (RPT) expression in human cancers.MethodsTo investigate the impact of global RPT expression patterns on tumor phenotypes, we analyzed RPT expression of ~ 10,000 human tumors and over 700 normal tissues from The Cancer Genome Atlas (TCGA) using t-distributed stochastic neighbor embedding (t-SNE). Clusters of tumors identified by t-SNE were then analyzed with chi-squared and t-tests to compare phenotypic data, ANOVA to compare individual RPT expression, and Kaplan-Meier curves to assess survival differences.ResultsNormal tissues and cancers possess distinct and readily discernible RPT expression patterns that are independent of their absolute levels of expression. In tumors, RPT patterning is distinct from that of normal tissues, identifies heretofore unrecognized tumor subtypes, and in many cases correlates with molecular, pathological, and clinical features, including survival.ConclusionsRPT expression patterns are both tissue-specific and tumor-specific. These could be used as a powerful and novel method of tumor classification, offering a potential clinical tool for prognosis and therapeutic stratification.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4178-z) contains supplementary material, which is available to authorized users.

Identification of key regulators in Prostate cancer from gene expression datasets of patients

“…It could be due to the crucial role of RPs in cell growth and proliferation propagated through protein synthesis. In cancers, ribosomal biosynthesis increases to meet the requirement of rapidly growing/proliferating cells (48). Some RPs take part in extra-ribosomal functions involved in tumorigenesis, immune cell signalling, and development and regulating diseases through translocation across the nuclear pore complex (49).…”

The roles of moonlight ribosomal proteins in the development of human cancers

“…For many years, scientists believed that unassembled RPs undergo rapid degradation. However, recent findings support the idea that extracellular or intracellular stimuli induce ribosomal stress, which disturbs ribosomal biogenesis followed by the accumulation of free RPs (Dolezal, Dash, & Prochownik, 2017). RPs participate in multiple extraribosomal functions, including DNA damage repair, apoptosis, cell proliferation and differentiation, drug resistance, and cell migration and invasion (Xu, Xiong, & Sun, 2016).…”

Sequential analysis of transcript expression patterns improves survival prediction in multiple cancers

“…RNAseq data were obtained from the 10,227 newly diagnosed and previously untreated cancers of all stages, comprising 34 distinct types, currently maintained in TCGA along with pertinent clinical and demographic data. FPKM-UQ were obtained from the TCGA GDC PANCAN dataset and through the University of California Santa Cruz UCSC Xenabrowser as previously described [9,12,13]. Expression values were initially stored as the log 2 of the incremented-by-one FPKM-UQ value.…”

“…In murine models of hepatoblastoma and hepatocellular carcinoma (HCC), we have previously observed that most transcripts encoding the 80 proteins comprising the 40S and 60S ribosomal subunits were significantly increased relative to those in normal livers [9,10]. Because these increases were not uniform, the overall transcript expression patterns were altered as well.…”

“…This approach identified three distinct RPT "t-SNE clusters" in HCCs that were associated with significantly different long-term survival differences. RPT t-SNE pattern differences were also associated with survival differences in six other cancer types [9]. We subsequently used this same approach to classify the expression patterns of 25 transcripts encoding cholesterol biosynthesis enzymes and 37 mitochondrial fatty acid β-oxidation (FAO) enzyme transcripts into small numbers of t-SNE clusters [12].…”