Table_2_Identifying Discriminative Biological Function Features and Rules for Cancer-Related Long Non-coding RNAs.XLSX

Cancer has been a major public health problem worldwide for many centuries. Cancer is a complex disease associated with accumulative genetic mutations, epigenetic aberrations, chromosomal instability, and expression alteration. Increasing lines of evidence suggest that many non-coding transcripts, which are termed as non-coding RNAs, have important regulatory roles in cancer. In particular, long non-coding RNAs (lncRNAs) play crucial roles in tumorigenesis. Cancer-related lncRNAs serve as oncogenic factors or tumor suppressors. Although many lncRNAs are identified as potential regulators in tumorigenesis by using traditional experimental methods, they are time consuming and expensive considering the tremendous amount of lncRNAs needed. Thus, effective and fast approaches to recognize tumor-related lncRNAs should be developed. The proposed approach should help us understand not only the mechanisms of lncRNAs that participate in tumorigenesis but also their satisfactory performance in distinguishing cancer-related lncRNAs. In this study, we utilized a decision tree (DT), a type of rule learning algorithm, to investigate cancer-related lncRNAs with functional annotation contents [gene ontology (GO) terms and KEGG pathways] of their co-expressed genes. Cancer-related and other lncRNAs encoded by the key enrichment features of GO and KEGG filtered by feature selection methods were used to build an informative DT, which further induced several decision rules. The rules provided not only a new tool for identifying cancer-related lncRNAs but also connected the lncRNAs and cancers with the combinations of GO terms. Results provided new directions for understanding cancer-related lncRNAs.

癌症作为全球范围内历经数百年的重大公共卫生问题，其本质是一种复杂的疾病，与累积的遗传突变、表观遗传学异常、染色体不稳定性和表达改变密切相关。越来越多的证据表明，众多非编码转录本，即所谓的非编码RNA，在癌症的发生发展中扮演着重要的调控角色。特别是，长非编码RNA（lncRNA）在肿瘤发生过程中发挥着至关重要的作用。与癌症相关的lncRNA可作为癌基因或肿瘤抑制因子。尽管通过传统实验方法已识别出许多lncRNA作为肿瘤发生过程中的潜在调控因子，但鉴于需要大量lncRNA，这些方法在时间和成本上均较为高昂。因此，开发有效且快速识别肿瘤相关lncRNA的方法势在必行。所提出的方法不仅有助于我们理解参与肿瘤发生的lncRNA的机制，还能确保其在区分癌症相关lncRNA方面的出色表现。在本研究中，我们运用决策树（DT）这一类规则学习算法，结合共同表达基因的功能注释内容（基因本体学（GO）术语和KEGG通路）来探究与癌症相关的lncRNA。利用特征选择方法筛选出的GO和KEGG关键富集特征编码的与癌症相关及其他lncRNA构建了信息丰富的决策树，进而诱导出数条决策规则。这些规则不仅为识别癌症相关lncRNA提供了一种新的工具，而且将lncRNA与癌症通过GO术语的组合联系起来。研究结果为理解癌症相关lncRNA提供了新的方向。