Guo Zhifeng, Hui Yan, Kong Fanlong, Lin Xiaoxi
Department of Oncology, Chifeng Municipal Hospital, Chifeng, China.
Front Genet. 2022 Jul 22;13:933009. doi: 10.3389/fgene.2022.933009. eCollection 2022.
Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study 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 and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small-cell lung cancer, and LUAD, respectively.
肺癌是癌症相关死亡的主要原因之一。因此，寻找其生物标志物很重要。此外，越来越多的研究报告称，长链非编码RNA（lncRNA）与多种人类复杂疾病存在密切联系。推断新的lncRNA-疾病关联有助于识别肺癌的潜在生物标志物，并进一步了解其发病机制，设计新药，为肺癌患者制定个性化治疗方案。本研究通过整合拉普拉斯正则化最小二乘法和不平衡双随机游走，开发了一种计算方法（LDA-RLSURW）来发现可能的肺癌lncRNA生物标志物。首先，计算lncRNA和疾病的相似性。其次，分别将不平衡双随机游走应用于lncRNA和疾病网络，对疾病和lncRNA之间的关联进行评分。第三，基于计算出的随机游走分数，进一步使用拉普拉斯正则化最小二乘法计算每个lncRNA-疾病对之间的关联概率。使用10种经典的LDA预测方法对LDA-RLSURW进行比较，在lncRNA疾病数据库上获得了最佳AUC值0.9027。我们发现了与肺癌相关的前30个lncRNA，并推断lncRNA TUG1、PTENP1和UCA1可能分别是肺肿瘤、非小细胞肺癌和肺腺癌的生物标志物。
