Multifactorial diseases such as cancer, cardiovascular conditions and neurological, immunological and metabolic disorders are a group of diseases caused by the combination of genetic and environmental factors. High-throughput RNA sequencing (RNA-seq) technologies have revealed that less than 2% of the genome corresponds to protein-coding genes, although most of the human genome is transcribed. The other transcripts include a large variety of non-coding RNAs (ncRNAs), and the continuous generation of RNA-seq data shows that ncRNAs are strongly deregulated and may be important players in pathological processes. A specific class of ncRNAs, the long non-coding RNAs (lncRNAs), has been intensively studied in human diseases. For clinical purposes, lncRNAs may have advantages mainly because of their specificity and differential expression patterns, as well as their ideal qualities for diagnosis and therapeutics. Multifactorial diseases are the major cause of death worldwide and many aspects of their development are not fully understood. Recent data about lncRNAs has improved our knowledge and helped risk assessment and prognosis of these pathologies. This review summarizes the involvement of some lncRNAs in the most common multifactorial diseases, with a focus on those with published functional data.

Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

Taurine upregulated 1 gene (TUG1) is a long non-coding RNA associated with several types of cancer. Recently, differential expression of TUG1 was found in cancerous breast tissues and associated with breast cancer malignancy features. Although this is evidence of a potential role in breast cancer, TUG1 expression could not be associated with different subtypes, possibly due to the small number of samples analyzed. Breast cancer is a heterogeneous disease and, based on molecular signatures, may be classified into different subtypes with prognostic implications. In the present study, we include analysis of TUG1 expression in 796 invasive breast carcinoma and 105 normal samples of RNA sequencing (RNA-seq) datasets from The Cancer Genome Atlas (TCGA) and describe that TUG1 expression is increased in HER2-enriched and basal-like subtypes compared to luminal A. Additionally, TUG1 expression is associated with survival in HER2-enriched patients. These results reinforce the importance of TUG1 in breast cancer and outline its potential impact on specific subtypes.