Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk - Guo, Zhifeng; Hui, Yan; Kong, Fanlong; Lin, Xiaoxi (Frontiers in Genetics, 2022)
Lung cancer is one of the leading causes of cancer-related deaths. It is important to find biomarkers for lung cancer. Increasing researches have reported that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations (LDAs) helps to identify potential biomarkers for lung cancer and further understand the pathogenesis of lung cancer, design new drugs, and formulate individualized therapeutic options for lung cancer patients. In this study, we developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA similarity and the disease similarity are computed. Second, unbalanced bi-random walk is respectively applied to the lncRNA network and the disease network to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares are further used to compute association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW is compared with ten classical LDA prediction methods and obtains the best AUC value of 0.9027 on the lncRNADisease database. We find the top 30 lncRNAs associated with lung cancers and infer that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, NSCLC, and LUAD, respectively.