肝细胞肝癌（下称肝癌）是一种高发生率和高致死率的恶性肿瘤，其严重威胁人民的健康。由于其癌变机制仍尚未明确，研究新基因的功能与肝癌发生、发展的关系具有重要的研究意义。近期文献报道，长非编码RNA（lncRNA）编码小肽在肿瘤进展中扮演重要的角色，但是小肽的识别困难限制其在肿瘤中的功能研究。我们的前期研究基于核糖体保护测序数据，定量分析蛋白编码基因的核糖体结合特性并结合4种机器学习算法，构建准确性达99.7%且适用于小肽预测的ORF编码能力预测分类器。应用分类器及联合7种肿瘤lncRNA表达谱，预测出22个肿瘤相关且编码小肽的lncRNAs。我们根据表达谱挑选四个lncRNAs，并证明其中三个能够编码小肽。更重要的是，其中在肝癌组织中上调表达的LINC270能够促进肝癌细胞迁移。因此，本课题拟联合全转录组和核糖体保护测序，从转录和翻译层面阐明LINC270调控肝癌细胞迁移的分子机制。

Hepatocellular carcinoma (HCC) is a malignant tumor with high incidence and high mortality, which threatens people’s health gravely. As the mechanism of tumorigenesis is still obscure, it is of great significance to investigate its relationship with novel genes. Recently, the micro-peptide encoded by long non-coding RNA (lncRNA) has been reported to play essential roles in tumor progression. However, the difficult recognition of micro-peptides limited its functional investigation in cancers. Based on ribosome-protected fragment sequencing (RPF-Seq), we quantified the ribosome binding features of protein coding genes and then combined with four machine learning algorithms to construct the coding ORF prediction classifier, which accuracy was up to 99.7%. Besides, the classifier was suitable to predict micro-peptide encoded by lncRNAs. Based on our classifier and the lncRNA expression profiles originated from 7 cancer types, 22 cancer-related lncRNA-encoded micro-peptides were identified. According to their expression profiles, we selected four of them to validate their coding potentials. Moreover, three lncRNAs were proved to encode micro-peptides. Furthermore, the up-regulated LINC270 in HCC tissues could promote cancer cell migration. Using RNA-Seq and RPF-Seq, we aim to elucidate the molecular mechanisms of LINC270 on regulation of cell migration at transcriptional and translational levels.