While predicting the secondary structure of RNA is vital for researching its function, determining RNA secondary structure is challenging, especially for that with pseudoknots. Typically, several excellent computational methods can be utilized to predict the secondary structure (with or without pseudoknots), but they have their own merits and demerits. These methods can be classified into two categories: the multi-sequence method and the single-sequence method. The main advantage of the multi-sequence method lies in its use of the auxiliary sequences to assist in predicting the secondary structure, but it can only successfully predict in the presence of multiple highly homologous sequences. The single-sequence method is associated with the major merit of easy operation (only need the target sequence to predict secondary structure), but its folding parameters are the common features of diversity RNA, which cannot describe the unique characteristics of RNA, thus potentially resulting in the low prediction accuracy in some RNA. In this paper, “DMfold,” a method based on the Deep Learning and Improved Base Pair Maximization Principle, is proposed to predict the secondary structure with pseudoknots, which fully absorbs the advantages and avoids some disadvantages of those two methods. Notably, DMfold could predict the secondary structure of RNA by learning similar RNA in the known structures, which uses the similar RNA sequences instead of the highly homogeneous sequences in the multi-sequence method, thereby reducing the requirement for auxiliary sequences. In DMfold, it only needs to input the target sequence to predict the secondary structure. Its folding parameters are fully extracted automatically by deep learning, which could avoid the lack of folding parameters in the single-sequence method. Experiments show that our method is not only simple to operate, but also improves the prediction accuracy compared to multiple excellent prediction methods. A repository containing our code can be found at https://github.com/linyuwangPHD/RNA-Secondary-Structure-Database .

Gastric cancer (GC) is a considerable global health burden. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are aberrantly expressed in many cancers and play important roles in GC. However, only a few lncRNAs have been functionally characterized. In this study, we identified that long intergenic non-protein coding RNA 941 (LINC00941) is a potential biomarker for diagnosis and prognosis from the cancer genome atlas (TCGA), and we found that the expression of LINC00941 is associated with tumor depth and distant metastasis in GC. Furthermore, functional enrichment analysis of LINC00941 co-expression network demonstrated that LINC00941 might be an essential regulator of tumor metastasis and cancer cell proliferation. To validate our findings, we utilized the loss-of-function analysis to reveal the biological function of LINC00941 in GC cells. Loss-of-function analysis revealed that silence of LINC00941 inhibits GC cells proliferation, migration, and invasion in vitro and modulates tumor growth in vivo. Our findings confirmed that LINC00941 plays an important oncogenic function in GC and may serve as a potential biomarker for diagnosis and prognosis of GC.

Idiopathic pulmonary arterial hypertension (IPAH) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for IPAH have not been identified. The aim of this study was to explore the potential diagnostic biomarkers and possible regulatory targets of IPAH. We performed a weighted gene coexpression network analysis and calculated module-trait correlations based on a public microarray data set (GSE703) and six modules were found to be related to IPAH. Two modules which have the strongest correlation with IPAH were further analyzed and the top 10 hub genes in the two modules were identified. Furthermore, we validated the data by quantitative real-time polymerase chain reaction (qRT-PCR) in an independent sample set originated from our study center. Overall, the qRT-PCR results were consistent with most of the results of the microarray analysis.Intriguingly, the highest change was found for YWHAB, a gene encodes a protein belonging to the 14-3-3 family of proteins, members of which mediate signal transduction by binding to phosphoserine-containing proteins. Thus, YWHAB was subsequently selected for validation. In congruent with the gene expression analysis, plasma 14-3-3β concentrations were significantly increased in patients with IPAH compared with healthy controls, and 14-3-3β expression was also positively correlated with mean pulmonary artery pressure (R 2 = 0.8783; p < 0.001). Taken together, using weighted gene coexpression analysis, YWHAB was identified and validated in association with IPAH progression, which might serve as a biomarker and/or therapeutic target for IPAH. K E Y W O R D S biomarker, idiopathic pulmonary arterial hypertension, weighted gene coexpression network analysis (WGCNA), YWHAB J Cell Physiol. 2019;234:6449-6462.wileyonlinelibrary.com/journal/jcp