Identifying candidate agents for lung adenocarcinoma by walking the human interactome

Identification of lung-adenocarcinoma-related long non-coding RNAs by random walking on a competing endogenous RNA network

Background

Identification of novel risk long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) is still a significant challenge in cancer research.

Methods

In this study, we first constructed a LUAD-specific competing endogenous RNA (ceRNA) network using both experimental- and computational-supported datasets. Then, a random walking with restart method was performed to predict LUAD-associated risk lncRNAs based on the ceRNA network. The role of lncRNA MAPKAPK5-AS1 was assessed by siRNA transfection, followed by a colony formation assay, the CCK-8 assay, and immunofluorescence on A549 cells.

Results

Our method achieved an area under the curve (AUC) value of over 0.83. Of the several potential novel LUAD-related lncRNAs identified, the highest ranked lncRNA was SNHG12, which, interestingly, was also shown to promote tumorigenesis and metastasis in LUAD in a recent study. Furthermore, we found that the expression of MAPKAPK5-AS1, which was ranked second, was higher in both LUAD tissues and three LUAD cell lines. After the silencing of MAPKAPK5-AS1 by siRNA transfection, a colony formation assay revealed fewer colonies, and a CCK-8 assay revealed significantly suppressed growth of A549 cells. Moreover, immunofluorescence staining of Ki-67, a proliferation marker, revealed that the proliferation capability of A549 was dramatically reduced following MAPKAPK5-AS1 downregulation. AO/EB staining showed an increased proportion of apoptotic cells among A549 cells depleted of MAPKAPK5-AS1.

Conclusions

In brief, the lncRNAs were predicted to serve as potential biomarkers for the diagnosis, treatment, and prognosis of LUAD.

Global network random walk for predicting potential human lncRNA-disease associations

There is more and more evidence that the mutation and dysregulation of long non-coding RNA (lncRNA) are associated with numerous diseases, including cancers. However, experimental methods to identify associations between lncRNAs and diseases are expensive and time-consuming. Effective computational approaches to identify disease-related lncRNAs are in high demand; and would benefit the detection of lncRNA biomarkers for disease diagnosis, treatment, and prevention. In light of some limitations of existing computational methods, we developed a global network random walk model for predicting lncRNA-disease associations (GrwLDA) to reveal the potential associations between lncRNAs and diseases. GrwLDA is a universal network-based method and does not require negative samples. This method can be applied to a disease with no known associated lncRNA (isolated disease) and to lncRNA with no known associated disease (novel lncRNA). The leave-one-out cross validation (LOOCV) method was implemented to evaluate the predicted performance of GrwLDA. As a result, GrwLDA obtained reliable AUCs of 0.9449, 0.8562, and 0.8374 for overall, novel lncRNA and isolated disease prediction, respectively, significantly outperforming previous methods. Case studies of colon, gastric, and kidney cancers were also implemented, and the top 5 disease-lncRNA associations were reported for each disease. Interestingly, 13 (out of the 15) associations were confirmed by literature mining.