Comprehensive functional profiling of long non-coding RNAs through a novel pan-cancer integration approach and modular analysis of their protein-coding gene association networks

Autor: Radmir Sarsenov, Wen Siong Too, Ian C. Paterson, Daniel W. Lambert, Stephen Brown, James R. Bradford, Roseanna K. Hare, Kevin Walters
Jazyk: angličtina
Rok vydání: 2018
Předmět:
0106 biological sciences
lcsh:QH426-470
lcsh:Biotechnology
Functional profiling
Disease
Computational biology
Biology
Proteomics
01 natural sciences
Extracellular matrix
03 medical and health sciences
lncRNA
lcsh:TP248.13-248.65
Neoplasms
Gene expression
Genetics
Transcriptional regulation
medicine
Tumor Microenvironment
Humans
Gene Regulatory Networks
Epithelial–mesenchymal transition
RNA
Messenger

Gene
Genetic Association Studies
030304 developmental biology
Cancer
0303 health sciences
Tumour microenvironment
Gene Expression Profiling
Computational Biology
medicine.disease
Epithelial-mesenchymal transition
Phenotype
Gene Expression Regulation
Neoplastic

lcsh:Genetics
RNA
Long Noncoding

DNA microarray
Genes networks
Function (biology)
010606 plant biology & botany
Biotechnology
Research Article
Zdroj: BMC Genomics
BMC Genomics, Vol 20, Iss 1, Pp 1-15 (2019)
DOI: 10.1101/254722
Popis: Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes in diseases such as cancer, although the functions of most remain poorly understood. To address this, here we apply a novel strategy to integrate gene expression profiles across 32 cancer types, and cluster human lncRNAs based on their pan-cancer protein-coding gene associations. By doing so, we derive 16 lncRNA modules whose unique properties allow simultaneous inference of function, disease specificity and regulation for over 800 lncRNAs. Remarkably, modules could be grouped into just four functional themes: transcription regulation, immunological, extracellular, and neurological, with module generation frequently driven by lncRNA tissue specificity. Notably, three modules associated with the extracellular matrix represented potential networks of lncRNAs regulating key events in tumour progression. These included a tumour-specific signature of 33 lncRNAs that may play a role in inducing epithelialmesenchymal transition through modulation of TGFβ signalling, and two stromal-specific modules comprising 26 lncRNAs linked to a tumour suppressive microenvironment, and 12 lncRNAs related to cancer-associated fibroblasts. At least one member of the 12-lncRNA signature was experimentally supported by siRNA knockdown, which resulted in attenuated differentiation of quiescent fibroblasts to a cancer-associated phenotype. Overall, the study provides a unique pan-cancer perspective on the lncRNA functional landscape, acting as a global source of novel hypotheses on lncRNA contribution to tumour progression.Author SummaryThe established view of protein production is that genomic DNA is transcribed into RNA, which is then translated into protein. Proteins play a critical role in shaping the function of each individual cell in the human body yet they represent less than 2% of human genomic sequence whilst up to 90% of the genome is transcribed. To explain this disparity, the existence of thousands of long non-coding RNAs (lncRNAs) has emerged that do not encode proteins but perform function as an RNA molecule. Most lncRNAs have yet to be assigned a specific biological role, so to address this we apply a novel computational approach to characterise the function of >800 lncRNAs through consistent association with protein coding genes across multiple cancer types. By doing so, we discover 16 “modules” of closely related lncRNAs that share broad functional themes, the most compelling of which consists of 12 lncRNAs that could regulate activation of specific cells neighbouring the tumour, leading to accelerated tumour progression and invasion. Overall, the study provides the most robust view of the lncRNA-protein coding gene landscape to date, adding to growing evidence that lncRNAs are key regulators of cancer, and have therapeutic potential comparable to proteins.
Databáze: OpenAIRE