Metastasis is the main cause of cancer‐related death, yet the underlying mechanisms are still poorly understood. Long noncoding RNAs (lncRNAs) are emerging as crucial regulators of malignancies; however, their functions in tumor metastasis remain largely unexplored. In this study, we identify a lncRNA, termed metabolism‐induced tumor activator 1 (MITA1), which is up‐regulated in hepatocellular carcinoma (HCC) and contributes to metastasis. MITA1, a chromatin‐enriched lncRNA discovered by our nuclear RNA sequencing, is significantly induced by energy stress. This induction of MITA1 is governed by the liver kinase B1–adenosine monophosphate‐activated protein kinase (LKB1‐AMPK) pathway and DNA methylation. Knockdown of MITA1 dramatically inhibits the migration and invasion of liver cancer cells in vitro and HCC metastasis in vivo. Mechanistically, MITA1 promotes the epithelial–mesenchymal transition, an early and central step of metastasis, which may partly attribute to an increase in Slug (snail family zinc finger 2) transcription. MITA1 deficiency reduces the expression of the mesenchymal cell markers, especially Slug, whereas Slug overexpression greatly impairs the effects of MITA1 deficiency on HCC migration and invasion. Correspondingly, there is a positive correlation between the levels of MITA1 and Slug precursors in HCC tissues. Conclusion: Our data reveal MITA1 as a crucial driver of HCC metastasis, and highlight the identified AMPK‐MITA1‐Slug axis as a potential therapeutic strategy for HCC.

No abstract

No abstract

Feature selection method is designed to select the representative feature subsets from the original feature set by different evaluation of feature relevance, which focuses on reducing the dimension of the features while maintaining the predictive accuracy of a classifier. In this study, we propose a feature selection method for text classification based on independent feature space search. Firstly, a relative document-term frequency difference (RDTFD) method is proposed to divide the features in all text documents into two independent feature sets according to the features’ ability to discriminate the positive and negative samples, which has two important functions: one is to improve the high class correlation of the features and reduce the correlation between the features and the other is to reduce the search range of feature space and maintain appropriate feature redundancy. Secondly, the feature search strategy is used to search the optimal feature subset in independent feature space, which can improve the performance of text classification. Finally, we evaluate several experiments conduced on six benchmark corpora, the experimental results show the RDTFD method based on independent feature space search is more robust than the other feature selection methods.