» Articles » PMID: 27345524

Long Non-coding RNAs and Complex Diseases: from Experimental Results to Computational Models

Overview
Journal Brief Bioinform
Specialty Biology
Date 2016 Jun 28
PMID 27345524
Citations 306
Authors
Affiliations
Soon will be listed here.
Abstract

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA-disease associations and predicting potential human lncRNA-disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

Citing Articles

Identification and Validation of a m6A-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer.

Jiang P, Chu M, Liang Y J Cell Mol Med. 2025; 29(2):e70376.

PMID: 39868645 PMC: 11770481. DOI: 10.1111/jcmm.70376.


SE-lncRNAs in Cancer: Classification, Subcellular Localisation, Function and Corresponding TFs.

Bao Y, Teng S, Zhai H, Zhang Y, Xu Y, Li C J Cell Mol Med. 2024; 28(24):e70296.

PMID: 39690143 PMC: 11652108. DOI: 10.1111/jcmm.70296.


Global trends in machine learning applied to clinical research in liver cancer: Bibliometric and visualization analysis (2001-2024).

Zhuo E, Yang W, Wang Y, Tang Y, Wang W, Zhou L Medicine (Baltimore). 2024; 103(49):e40790.

PMID: 39654222 PMC: 11631000. DOI: 10.1097/MD.0000000000040790.


Long Non-Coding RNA LINC01116 Promotes the Proliferation of Lung Adenocarcinoma by Targeting miR-9-5p/CCNE1 Axis.

Zhang H, Cai W, Miao Y, Gu Y, Zhou X, Kaneda H J Cell Mol Med. 2024; 28(23):e70270.

PMID: 39648148 PMC: 11625508. DOI: 10.1111/jcmm.70270.


The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs.

Wei Y, Zhang Q, Liu L Brief Bioinform. 2024; 26(1).

PMID: 39592154 PMC: 11596098. DOI: 10.1093/bib/bbae627.


References
1.
Kaneko S, Li G, Son J, Xu C, Margueron R, Neubert T . Phosphorylation of the PRC2 component Ezh2 is cell cycle-regulated and up-regulates its binding to ncRNA. Genes Dev. 2010; 24(23):2615-20. PMC: 2994035. DOI: 10.1101/gad.1983810. View

2.
Bavarva J, Tae H, Settlage R, Garner H . Characterizing the Genetic Basis for Nicotine Induced Cancer Development: A Transcriptome Sequencing Study. PLoS One. 2013; 8(6):e67252. PMC: 3688980. DOI: 10.1371/journal.pone.0067252. View

3.
Espinoza C, Goodrich J, Kugel J . Characterization of the structure, function, and mechanism of B2 RNA, an ncRNA repressor of RNA polymerase II transcription. RNA. 2007; 13(4):583-96. PMC: 1831867. DOI: 10.1261/rna.310307. View

4.
Chen X, Liu M, Cui Q, Yan G . Prediction of disease-related interactions between microRNAs and environmental factors based on a semi-supervised classifier. PLoS One. 2012; 7(8):e43425. PMC: 3427386. DOI: 10.1371/journal.pone.0043425. View

5.
Jalali S, Kapoor S, Sivadas A, Bhartiya D, Scaria V . Computational approaches towards understanding human long non-coding RNA biology. Bioinformatics. 2015; 31(14):2241-51. DOI: 10.1093/bioinformatics/btv148. View