Li Yan, Dong Yongcheng, Huang Ziyan, Kuang Qifan, Wu Yiming, Li Yizhou, Li Menglong
College of Chemistry, Sichuan University, Chengdu, China.
College of Life Science, Sichuan University, Chengdu, China.
PLoS One. 2017 Mar 27;12(3):e0174436. doi: 10.1371/journal.pone.0174436. eCollection 2017.
Hepatocellular carcinoma (HCC) is currently still a major factor leading to death, lacking of reliable biomarkers. Therefore, deep understanding the pathogenesis for HCC is of great importance. The emergence of circular RNA (circRNA) provides a new way to study the pathogenesis of human disease. Here, we employed the prediction tool to identify circRNAs based on RNA-seq data. Then, to investigate the biological function of the circRNA, the candidate circRNAs were associated with the protein-coding genes (PCGs) by GREAT. We found significant candidate circRNAs expression alterations between normal and tumor samples. Additionally, the PCGs associated with these candidate circRNAs were also found have discriminative expression patterns between normal and tumor samples. The enrichment analysis illustrated that these PCGs were predominantly enriched for liver/cardiovascular-related diseases such as atherosclerosis, myocardial ischemia and coronary heart disease, and participated in various metabolic processes. Together, a further network analysis indicated that these PCGs play important roles in the regulatory and the PPI network. Finally, we built a classification model to distinguish normal and tumor samples by using candidate circRNAs and their associated genes, respectively. Both of them obtained satisfactory results (~ 0.99 of AUC for circRNA and PCG). Our findings suggested that the circRNA could be a critical factor in HCC, providing a useful resource to explore the pathogenesis of HCC.
肝细胞癌（HCC）目前仍是导致死亡的主要因素，缺乏可靠的生物标志物。因此，深入了解HCC的发病机制至关重要。环状RNA（circRNA）的出现为研究人类疾病的发病机制提供了新途径。在此，我们利用预测工具基于RNA测序数据鉴定circRNA。然后，为了研究circRNA的生物学功能，通过基因关系到功能的综合注释工具（GREAT）将候选circRNA与蛋白质编码基因（PCG）相关联。我们发现正常样本和肿瘤样本之间候选circRNA表达存在显著差异。此外，还发现与这些候选circRNA相关的PCG在正常样本和肿瘤样本之间也有不同的表达模式。富集分析表明，这些PCG主要富集于动脉粥样硬化、心肌缺血和冠心病等肝脏/心血管相关疾病，并参与各种代谢过程。综合来看，进一步的网络分析表明这些PCG在调控和蛋白质-蛋白质相互作用（PPI）网络中发挥重要作用。最后，我们分别使用候选circRNA及其相关基因构建了区分正常样本和肿瘤样本的分类模型。两者均取得了满意的结果（circRNA和PCG的曲线下面积约为0.99）。我们的研究结果表明，circRNA可能是HCC中的关键因素，为探索HCC的发病机制提供了有用的资源。
