Expression Specificity of Disease-Associated lncRNAs: Toward Personalized Medicine

Abstract

Long noncoding RNAs (lncRNAs) perform diverse regulatory functions in transcription, translation' chromatin modification, and cellular organization. Misregulation of lncRNAs is found linked to various human diseases. Compared to protein-coding RNAs' lncRNAs are more specific to organs, tissues, cell types, developmental stages, and disease conditions' making them promising candidates as diagnostic and prognostic biomarkers and as gene therapy targets. The functional annotation of mammalian genome (FANTOM) consortium utilizes cap analysis of gene expression (CAGE) method to quantify genome-wide activities of promoters and enhancers of coding and noncoding RNAs across a large collection of human and mouse tissues' cell types' diseases, and time-courses. The project discovered widespread transcription of major lncRNA classes, including lncRNAs derived from enhancers' bidirectional promoters' antisense lncRNAs' and repetitive elements. Results from FANTOM project enable assessment of lncRNA expression specificity across tissue and disease conditions' based on differential promoter and enhancer usage. More than 85 % of disease-related SNPs are within noncoding regions and are strikingly overrepresented in enhancer and promoter regions, suggestive of the importance of lncRNA loci at these SNP harboring regions to human diseases. In this chapter' we discuss lncRNA expression specificity' review diverse functions of disease-associated lncRNAs' and present perspectives on their potential therapeutic applications for personalized medicine. The future development of lncRNA applications relies on technologies to identify and validate their functions' structures' and mechanisms. Comprehensive understanding of genome-wide interaction networks of lncRNAs with proteins, chromatins, and other RNAs in regulating cellular processes will allow personalized medicine to use lncRNAs as highly specific biomarkers in diagnosis' prognosis, and therapeutic targets.

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

“…Because the roles of lncRNAs (Hon et al, 2017; Nguyen and Carninci, 2016) in UM largely remain to be clarified, we compared correlations of lncRNA abundance with PARADIGM pathway activities and MARINa regulator activities in the M3/ BAP1 -abberant lncRNA transcriptional clusters 3 and 4 (Figure 6 and Table S4). In cluster 3, LINC00403, RMRP, and SNHG11, and uncharacterized lncRNAs such as RP11-14N7.2 and CTB-193M12.5, were correlated with activated transcriptional regulators of proliferation (e.g., FOXM1, FOXA1, E2F1), low MYC/MAX complex pathway activation, diminished HIF1A/ARNT complex activity, and low DDR pathway activity.…”

Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer

“…Because lncRNAs can be more specific to biological state than coding RNAs (Nguyen and Carninci, 2016), we calculated transcript abundances for 8167 (Ensembl v82) lncRNAs and processed transcripts. Four unsupervised consensus clusters were associated with purity (p = 2.3 × 10 −27 ), EMT score (p = 9.9 × 10 −34 ), expression of CIS gene sets (p < 1 × 10 −39 ) (Dyrskjot et al, 2004), and 5-year survival (p = 0.015) (Figure 4).…”

Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma

“…Integrated platform analyses that also considered cellularity revealed non-coding RNA associations with tumor-specific subtypes. While biogenesis similarities for coding mRNAs and many lncRNAs (Quinn and Chang, 2016) suggest that subtypes identified from the two data types should be largely concordant, lncRNA expression can be specific for cell type and disease state (Mele et al, 2017; Nguyen and Carninci, 2016), and functionally-characterized lncRNAs can be specifically dysregulated in cancers (Huarte, 2015; Quinn and Chang, 2016). Differential expression of the EVADR, DEANR1 , and GATA6 - AS1 lncRNAs was associated with the classical (or pancreatic progenitor) molecular subtype of pancreatic cancer.…”