modules showed relatively high expression in blood and brain
tissue. Zhou et al. [69] used computational method to analysis of
lncRNA expression profiles across 473 breast cancer patients who
did or did not develop recurrence by repurposing existing micro-
array datasets from the Gene Expression Omnibus database. The
Pearson correlation coefficients were calculated to identify associa-
tions between lncRNAs and breast cancer. They identified 12 differ-
entially expressed lncRNAs that associated with tumor recurrence
of breast cancer patients. Furthermore, they used DAVID tool to
perform functional enrichment analysis. The results showed that
the predicted relapse-related lncRNAs may be involved in known
breast cancer-related biological processes and pathways.
Network-based rank methods are also used to disease-related
lncRNA prediction. Wang et al. [70] proposed a computational
framework to lncRNA-disease interactions identification by using
competing endogenous RNA (ceRNA) information. They calcu-
lated function similarity score between two genes based on the
shortest path of two genes in biological molecular networks.
Then, the lncRNA similarity of two lncRNAs was computed
based on function score, and the lncRNA list was ranked according
to lncRNA similarity. The experimental result demonstrated that
DisLncPri had the highest rank enrichment score and AUC value in
comparison to several other methods for case studies of Alzheimer’s
disease, ovarian cancer, pancreatic cancer, and gastric cancer.4 Conclusion and Future Research
lncRNAs are arbitrarily considered to be more than 200 nucleotides
without the ability of coding protein. It can be translated from
intergenic, intronic, or overlapping regions and have 3^0 poly-
adenylation, 5^0 cap characteristic. Comparing with message RNAs
(mRNAs) and lncRNAs, the average size of lncRNA is smaller than
mRNAs. In addition, lncRNAs also have fewer exons number than
mRNAs [71–74]. Furthermore, lncRNAs show less conservation
than mRNA cross the species. It is interesting that many lncRNAs,
just like mRNA, contain “K4-K36” domain that consist of histone
3 Lys 4 trimethylation at the promoter followed by histone 3 Lys
36 trimethylation along the transcribed region [75, 76]. In addi-
tion, most known lncRNAs are restricted to specific tissue types
[77]. Increasing evidences have demonstrated that long noncoding
RNAs (lncRNAs) play important roles in many human diseases.
Therefore, predicting novel lncRNA-disease association would
assist geneticists and molecular biologists to elucidate the genetic
basis of human diseases. Further, it also contributes to the diagno-
sis, prevention, and treatment of human diseases. Some computa-
tional methods have been developed to infer lncRNA-disease
association. In this article, we have reviewed the recentIdentifying Interactions Between Long Noncoding RNAs and Diseases... 217