A Novel lncRNA-Disease Association Prediction Model Using Laplacian Regularized Least Squares and Space Projection-Federated Method

Min Chen,Zejun Li,Yinwei Deng,Yan Peng,Ang Li

doi:10.1109/access.2020.3002588

Abstract

Recent studies indicated that numerous long noncoding RNAs (lncRNAs) are closely related to human diseases and can serve as potential biomarkers and drug targets for complex diseases. Therefore, identifying lncRNAs associated with diseases through computational methods is conducive to the exploration of disease pathogenesis. Most previous studies had shortcomings, such as low prediction accuracy, the need for negative samples, and weak generalization. Such studies established shallow prediction models and failed to fully capture the complex relationships among lncRNA-disease associations, lncRNA similarity, and disease similarity. LRLSSP, a new computational method based on Laplacian regularized least squares (LRLS) and space projection was used to predict candidate disease lncRNAs in this study. LRLSSP deeply integrates information on lncRNA similarity, disease similarity, and known lncRNA-disease associations. The estimated score of lncRNA-disease association was obtained through LRLS, and network projection was utilized to reliably predict disease-related lncRNAs. Leave-one-out cross validation(LOOCV) was implemented to evaluate the prediction performance of LRLSSP. Results showed that LRLSSP performed was better than other state-of-the-art methods in predicting lncRNA-disease associations. In addition, case studies conducted on melanoma,cervical cancer, ovarian cancer and breast cancer indicated that LRLSSP can discover potential and novel lncRNA-disease associations. Overall, the results demonstrated that LRLSSP may serve as a reliable and effective computational tool for disease-related lncRNAs prediction.

Highlights

LncRNAs are RNA molecules that are not translated into proteins and exceed 200 nt in length. long noncoding RNAs (lncRNAs) have long been considered as transcriptional noise
PERFORMANCE EVALUATION OF LRLS and refinement via matrix space projection (LRLSSP) LOOCV was implemented on the two datasets to evaluate the predictive performance of LRLSSP
The findings address the problem with many lncRNA–disease association prediction models that cannot be applied to the prediction of isolated disease-related lncRNAs

Summary

Introduction

LncRNAs are RNA molecules that are not translated into proteins and exceed 200 nt in length. lncRNAs have long been considered as transcriptional noise. Considerable evidence suggests that lncRNAs play fundamental and key regulatory roles in important biological processes, including chromatin remodeling, epigenetic regulation, genomic splicing, immune response, and cell cycle control [1]–[4]. The associate editor coordinating the review of this manuscript and approving it for publication was Yizhang Jiang. HOTAIR promotes serous ovarian cancer cell proliferation by regulating cell cycle arrest and apoptosis [6]. TDRG1 can enhance the tumorigenicity of endometrial cancer by binding to and targeting VEGF-A proteins [7]. MEG3 is involved in the epigenetic regulation of epithelial–mesenchymal transformation in lung cancer cell lines [8]. GAS5 promotes bladder cancer cell apoptosis by inhibiting the transcription of EZH2 [9].

Methods

Results

Discussion

Conclusion