ENCODE and the parts of the human genome

MLSNet: a deep learning model for predicting transcription factor binding sites

Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be enhanced. In this study, we propose MLSNet, a novel deep learning architecture designed specifically to predict TFBSs. MLSNet innovatively integrates multisize convolutional fusion with long short-term memory (LSTM) networks to effectively capture DNA-sparse higher-order sequence features. Further, MLSNet incorporates super token attention and Bi-LSTM to systematically extract and integrate higher-order DNA shape features. Experimental results on 165 ChIP-seq (chromatin immunoprecipitation followed by sequencing) datasets indicate that MLSNet consistently outperforms several state-of-the-art algorithms in the prediction of TFBSs. Specifically, MLSNet reports average metrics: 0.8306 for ACC, 0.8992 for AUROC, and 0.9035 for AUPRC, surpassing the second-best methods by 1.82%, 1.68%, and 1.54%, respectively. This research delineates the effectiveness of combining multi-size convolutional layers with LSTM and DNA shape-based features in enhancing predictive accuracy. Moreover, this study comprehensively assesses the variability in model performance across different cell lines and transcription factors. The source code of MLSNet is available at https://github.com/minghaidea/MLSNet.

Construction and analysis of an aberrant lncRNA-miRNA-mRNA network associated with papillary thyroid cancer

Accumulating evidence has indicated that long noncoding RNAs (lncRNAs) are the main constituents of competing endogenous RNA (ceRNA) networks. Nonetheless, in the lncRNA-related ceRNA network of papillary thyroid cancer (PTC), the function of cancer-specific lncRNAs, as well as their use for the potential prediction of PTC prognosis, remains unclear. In this study, 384 RNA sequencing (RNA-seq) profiles of PTC patients were attained from The Cancer Genome Atlas (TCGA), an open-source database that offers vast amounts of RNA-seq data, and 75 miRNAs, 495 lncRNAs, and 1099 mRNAs (P < .05 and |logFC| >2) were detected when compared with normal tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed using the Cytoscape plug-in BinGo. An aberrant lncRNA-mRNA-miRNA ceRNA network consisting of 31 differentially expressed (DE)-lncRNAs, 13 DE-miRNAs, and 134 DE-mRNAs was built in TCGA. On the basis of overall survival (OS) analysis, 6 lncRNAs (CCAT1, SYNPR, SFTA1P, HOTAIR, HCG22, and CLDN10) were identified as prognostic biomarkers for patients in TCGA (P < .05). Through qRT-PCR, we designated 6 cancer-specific lncRNAs as having great significance for survival by verifying their expression in the 60 PTC patients who were diagnosed. The qRT-PCR and TCGA results were completely consistent. Our research provides data for further understanding the lncRNA-miRNA-mRNA ceRNA network and elucidating the molecular mechanisms of PTC.

Long noncoding RNA H19 is a critical oncogenic driver and contributes to epithelial-mesenchymal transition in papillary thyroid carcinoma

BACKGROUND

Growing evidence has indicated that the long noncoding RNA H19 (lncRNA H19), frequently deregulated in almost all tumor types tested, acted as a pivotal contributor to both cancer initiation and progression. However, the role of lncRNA H19 in human papillary thyroid carcinoma (PTC) remains controversial. The aim of the study was to investigate the expression and potential function of lncRNA H19 in human PTC.

PATIENTS AND METHODS

The lncRNA H19 level was determined by quantitative real-time (RT)-PCR analyses in 58 PTC tissue samples and their paired paracancerous tissue samples. RNA interference, RT-PCR analysis, and Western blot assay were used to determine the impact of lncRNA H19 on epithelial-mesenchymal transition (EMT) markers in human PTC cells. The migratory and invasive capacities of PTC cells were determined by wound-healing and transwell migration and invasion assays.

RESULTS

lncRNA H19 expression was 2.417-fold higher in PTC tissues than their paired paracancerous tissue (95% CI: 1.898-2.935, P<0.0001). Higher level of lncRNA H19 was correlated to elevated expression of Vimentin, ZEB2, Twist, and Snail2. Inhibition of lncRNA H19 resulted in upregulation of E-cadherin and downregulation of Vimentin both at mRNA and protein levels. Conversely, enforced expression of the exogenous lncRNA H19 led to E-cadherin mRNA and protein downregulation and relative upregulation of Vimentin. Moreover, wound-healing and transwell migration and invasion assays showed that lncRNA H19 could promote the migratory and invasive abilities of PTC cells.

CONCLUSION

The level of lncRNA H19 was significantly higher in PTC tissues than paired paracancerous tissue or normal tissues. Overexpression of lncRNA H19 was correlated with higher tumor burden of PTC. It also contributes to EMT process, as well as promotes migration and invasion of PTC cells.