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Objective To investigate the effects of lncRNA-MEG3 knockdown on the proliferation, apoptosis, clonal formation, cell cycle, and to analyze the targeted relationship between lncRNA-MEG3 and miR-93 in human uterine leiomyoma (UL) cell line. Methods Human UL cell line SK-UT-1 was cultured in vitro, and the cells were divided into lncRNA-MEG3 knockdown group and negative control group; the lncRNA-MEG3 knockdown plasmid and negative control plasmid were transfected into cells of the two groups, respectively. The expression of lncRNA-MEG3 and miR-93 in the cells was detected by qRT-PCR, the proliferation activity was detected by CCK8 assay, the clonal formation ability was detected by plate colony formation assay, the cell cycle and apoptosis rate were detected by flow cytometry, and the expression levels of proliferation-related proteins (Ki67, PCNA), apoptosis-related proteins (Bax, Bcl-2), and Wnt/β-catenin pathway-related proteins (Wnt3a, β-catenin) were detected by Western blotting. The targeted relationship between lncRNA-MEG3 and miR-93 was verified by dual luciferase reporter gene assay. Results Compared with the negative control group, the expression of lncRNA-MEG3 decreased, the expression of miR-93 increased, the OD values decreased at 48, 72, and 96 h, the number of clonal formation was reduced, the proportion of cells in the G0/G1 phase increased, the proportion of cells in the G2/M phase decreased, the apoptosis rate increased, the expression of Wnt3a, β-catenin, Ki67, PCNA, Bcl-2 decreased, and the expression of Bax increased in the lncRNA-MEG3 knockdown group (all P<0. 05). LncRNA-MEG3 could target miR-93 to reduce the relative luciferase activity (P<0. 05). Conclusions Knocking down lncRNA-MEG3 can inhibit the proliferation, colony formation, and Wnt/β-catenin pathway of UL cells, block the cell cycle in G0/G1 phase, and induce apoptosis, thereby inhibiting the growth of UL cells. There is a targeted relationship between lncRNA-MEG3 and miR-93. [ABSTRACT FROM AUTHOR]

Currently, there are two main limitations in single-label text classification tasks based on hierarchical graph attention networks. First, it cannot effectively extract text features. Second, there are few studies that highlight text features through the connection between text and labels. To address these two issues, a hierarchical graph attention network text classification model that integrates label information is proposed. The model constructs an adjacency matrix based on the relevance between sentence keywords and topics, and then uses word-level graph attention network to obtain vector representations of sentences. This method is based on randomly initialized target vectors and utilizes maximum pooling to extract specific target vectors for sentences, making the obtained sentence vectors have more obvious category features. After the word-level graph attention layer, a sentence-level graph attention network is used to obtain new text representations with word weight information, and pooling layers are used to obtain feature information for the text. On the other hand, GloVe pre-trained word vectors are used to initialize vector representations for all text labels, which are then interacted and fused with the feature information of the text to reduce the loss of original features, obtaining feature representations that are distinct from different texts. Experimental results on five public datasets (R52, R8, 20NG, Ohsumed, and MR) show that the classification accuracy of the model significantly exceeds other mainstream baseline models. [ABSTRACT FROM AUTHOR]

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