Hepatocellular carcinoma (HCC) is a major health concern with a high morbidity and mortality rate worldwide. However, the mechanism underlying hepatocarcinogenesis remains unclear. Forkhead box P2 (FOXP2) has been implicated in various human cancer types. However, the role of FOXP2 in HCC remains unknown. Western blot and immunohistochemistry were used to measure the expression of FOXP2 protein in HCC and adjacent normal tissues in 50 patients. Wound healing and transwell assays were used to determine the cell invasion ability. We showed that the level of FOXP2 was significantly reduced in HCC compared with the adjacent non-tumorous tissue. There was statistical significance between the expression of FOXP2 and vein invasion (P = 0.017), number of tumor nodes (P = 0.028), and AFP (P = 0.033). Low expression of FOXP2 correlated with poor survival. Moreover, wound healing and transwell assays showed that FOXP2 could decrease cell invasion and affect the expression of vimentin and E-cadherin. Our results suggested that FOXP2 expression was downregulated in HCC tumor tissues, and reduced FOXP2 expression was associated with poor overall survival. In addition, downregulation of FOXP2 significantly enhanced cell invasiveness. These findings uncover that FOXP2 might be a new prognostic factor and be closely correlated with HCC cell invasion.

This paper proposes an attention-based LSTM (AT-LSTM) model for financial time series prediction. We divide the prediction process into two stages. For the first stage, we apply an attention model to assign different weights to the input features of the financial time series at each time step. In the second stage, the attention feature is utilized to effectively select the relevant feature sequences as input to the LSTM neural network for the prediction in the next time frame. Our proposed framework not only solves the long-term dependence problem of time series prediction effectively, but also improves the interpretability of the time series prediction methods based on the neural network. In the end of this paper, we conducted experiments on financial time series prediction task with three real-world data sets. The experimental results show that our framework for time series pre-diction is state-of-the-art against the baselines.

PFTK1, also known as PFTAIRE1, CDK14, is a novel member of Cdc2-related serine/threonine protein kinases. Recent studies show that PFTK1 is highly expressed in several malignant tumors such as hepatocellular carcinoma, esophageal cancer, breast cancer, and involved in regulation of cell cycle, tumors proliferation, migration, and invasion that further influence the prognosis of tumors. However, the expression and physiological significance of PFTK1 in gastric cancer remain unclear. In this study, we analyzed the expression and clinical significance of PFTK1 by Western blot in 8 paired fresh gastric cancer tissues, nontumorous gastric mucosal tissues and immunohistochemistry on 161 paraffinembedded slices. High PFTK1 expression was correlated with the tumor grade, lymph node invasion as well as Ki-67. Through Cell Counting Kit (CCK)-8 assay, flow cytometry, colony formation, wound healing and transwell assays, the vitro studies demonstrated that PFTK1 overexpression promoted proliferation, migration and invasion of gastric cancer cells, while PFTK1 knockdown led to the opposite results. Our findings for the first time supported that PFTK1 might play an important role in the regulation of gastric cancer proliferation, migration and would provide a novel promising therapeutic strategy against human gastric cancer.

BackgroundIncreasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play an important role in the competitive endogenous RNA (ceRNA) networks in that they regulate protein-coding gene expression by sponging microRNAs (miRNAs). However, the understanding of the ceRNA network in tongue squamous cell carcinoma (TSCC) remains limited. MethodsExpression profile data regarding mRNAs, miRNAs and lncRNAs as well as clinical information on 122 TSCC tissues and 15 normal controls from The Cancer Genome Atlas (TCGA) database were collected. We used the edgR package to identify differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs) and miRNAs (DEmiRNAs) between TSCC samples and normal samples. In order to explore the functions of DEmRNAs, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Subsequently, a ceRNA network was established based on the identified DElncRNAs–DEmiRNAs and DEmiRNAs–DEmRNAs interactions. The RNAs within the ceRNA network were analyzed for their correlation with overall disease survival. Finally, lncRNAs were specifically analyzed for their correlation with clinical features in the included TSCC patient samples. ResultsA total of 1867 mRNAs, 828 lncRNAs and 81 miRNAs were identified as differentially expressed in TSCC tissues (—log 2fold change— ≥ 2; adjusted P value <0.01). The resulting ceRNA network included 16 mRNAs, 56 lncRNAs and 6 miRNAs. Ten out of the 56 lncRNAs were found to be associated with the overall survival in TSCC patients (P < 0.05); 10 lncRNAs were correlated with TSCC progression (P < 0.05). ConclusionOur study deepens the understanding of ceRNA network regulatory mechanisms in TSCC. Furthermore, we identified ten lncRNAs (PART1, LINC00261, AL163952.1, C2orf48, FAM87A, LINC00052, LINC00472, STEAP3-AS1, TSPEAR-AS1 and ERVH48-1) as novel, potential prognostic biomarkers and therapeutic targets for TSCC.