Global Prioritizing Disease Candidate lncRNAs via a Multi-level Composite Network

Abstract

LncRNAs play pivotal roles in many important biological processes, but research on the functions of lncRNAs in human disease is still in its infancy. Therefore, it is urgent to prioritize lncRNAs that are potentially associated with diseases. In this work, we developed a novel algorithm, LncPriCNet, that uses a multi-level composite network to prioritize candidate lncRNAs associated with diseases. By integrating genes, lncRNAs, phenotypes and their associations, LncPriCNet achieves an overall performance superior to that of previous methods, with high AUC values of up to 0.93. Notably, LncPriCNet still performs well when information on known disease lncRNAs is lacking. When applied to breast cancer, LncPriCNet identified known breast cancer-related lncRNAs, revealed novel lncRNA candidates and inferred their functions via pathway analysis. We further constructed the human disease-lncRNA landscape, revealed the modularity of the disease-lncRNA network and identified several lncRNA hotspots. In summary, LncPriCNet is a useful tool for prioritizing disease-related lncRNAs and may facilitate understanding of the molecular mechanisms of human disease at the lncRNA level.

Systematic identification of lincRNA‐based prognostic biomarkers by integrating lincRNA expression and copy number variation in lung adenocarcinoma

“…This included lincRNAs such as MEG3 and H19 , which are associated with lung cancer (Fig. b ) . We further determined the tissue specificity of these differentially expressed lincRNAs by analyzing 16 normal tissues (including adrenal, adipose, brain, breast, colon, heart, kidney, liver, lung, lymph, ovary, prostate, skeletal muscle, testes, thyroid, and white blood cells) from the Human Body Map 2.0 (GSE30611).…”

“…Many lincRNAs such as MEG3 , H19 and GAS5 are dysregulated in LUAD patients . Several LUAD‐specific lincRNAs were differentially expressed in different LUAD subtypes.…”

“…2b). 42 We further determined the tissue specificity of these differentially expressed lincRNAs by analyzing 16 normal tissues (including adrenal, adipose, brain, breast, colon, heart, kidney, liver, lung, lymph, ovary, prostate, skeletal muscle, testes, thyroid, and white blood cells) from the Human Body Map 2.0 (GSE30611). The nonsubtype and subtype-specific lincRNAs, which are differentially expressed in the LUAD samples, showed lung-specific expression patterns (GSEA FDR < 0.05; Fig.…”

ProphTools: general prioritization tools for heterogeneous biological networks

“…These results show that predictions made by ProphTools with the proposed heterogeneous network configuration are consistent with current knowledge about lncRNAs and diseases and therefore likely to provide new predictions of interest. These AUC values are competitive with state-of-the art ad hoc approaches, such as IRWRLDA (0.7242 and 0.7872 AUC values) [29], LRLSLDA (0.7760 AUC value) [37] , and RWRlncD (0.822 AUC value) [30], and the recent LncPriCNet (0.93 AUC value) [31]. Furthermore, single prioritization queries on the dataset ran on average between 8.14 (±0.04) seconds for lncRNA-disease prioritization and 11.86 (±0.52) seconds for disease-lncRNA on our server (Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz (×48), 256GiB RAM).…”

“…More recently, Yao et al. proposed LncPriCNet [31], a method that built a multi-level network in order to perform lncRNA-disease prioritization.…”