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Recent theoretical and experimental studies indicate that long-chain noncoding RNAs (lncRNAs) are essential for the growth and differentiation of cells and the occurrence and development of tumors in epigenetics, but the regulation of lncRNA on gene expression, transcriptional activation, and transcriptional interference in diseases is still unclear. There is an urgent need for effective methods to discover significant lncRNAs with their functions on gene regulatory mechanisms. For this purpose, a new method of extracting significant lncRNA based on pathway crosstalk and dysfunction caused by the differentially expressed genes in lung adenocarcinoma (LUAD) was proposed. The pathway analysis method based on global influence (PAGI) was first applied to find the feature genes that play an important role in the crosstalks of disease-related pathways. Then to explore the hub lncRNAs, the weighted gene coexpression network analysis (WGCNA) was used to construct coexpression models of the feature genes and lncRNAs. The experiment results showed that 64 out of the 322 hub lncRNAs were closely related to the clinical features of patients with LUAD. Among them, nine lncRNAs (UCA1, LINC00857, PVT1, PCAT6, LINC00460, LINC00319, AP000553.1, AP000439.2, and AP005233.2) were identified to be tightly correlated with non-small-cell lung cancer (NSCLC) pathways. In summary, we offer an effective way to extract significant lncRNA by dysfunctional pathway crosstalk in LUAD which allows the selected lncRNAs with more biologically interpreted and reproducible results. This method can be applied to other diseases and provide useful information for understanding the pathogenesis of human cancer. K E Y W O R D S long-chain noncoding RNAs (lncRNAs), lung adenocarcinoma (LUAD), pathway analysis method based on global influence (PAGI), pathway crosstalk, weighted gene coexpression network analysis (WGCNA)

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The focus of modern biomedical research concentrates on molecular level regulatory mechanisms and how the normal and abnormal phenotypes of tissue functional are affected by regulatory mechanisms. Most of the research on regulatory mechanism starts from the reconstruction of gene regulation network. At present, a large number of reconstruction methods construct the network using a single data set. These methods of inferring and predicting the relationship between the target gene and the transcription factor (TF) can be used to identify individual interactions between genes, while there is not much research on the interaction of many functional-related genes. In this paper, an integrated approach based on multi-data fusion is used to reconstruct the network on Alzheimer's disease (AD) which is the most common form of dementia. It not only considers the interaction between many functional-related genes and the TFs that have important implications for regulatory mechanisms, but also detects new genes associated with specific gene function expression. Protein interaction data, motif data and gene expression data of AD were integrated to gain insight into the underlying biological processes of AD. This method takes into account the TF on the target gene regulation, at the same time also considers co-expression mechanism of the TF and co-regulatory mechanism of the target gene. Eventually, not only a number of genes such as E2F4 and ATF1 related to the pathogenesis of AD have been identified, but also several significant biological processes, such as immunoregulation and neurogenesis, have been found to be associated with AD.

MRNA and lncRNA serve as a type of endogenous RNA in cell, which can competitively bind to the same miRNA through miRNA response elements (MREs), thereby regulating their respective expression levels, playing an important role in post-transcriptional regulation, and regulating the progress of tumors. The proposed competing endogenous RNA (ceRNA) hypothesis provides novel clues for the occurrence and development of tumors, but the integrative analysis methods of diverse RNA data are significantly limited. In order to find out the relationship among miRNA, mRNA and lncRNA, the previous studies only used individual dataset as seeds to search two other related data in the database to construct ceRNA network, but it was difficult to identify the synchronized effects from multiple regulatory levels. Here, we developed the joint matrix factorization method integrating prior knowledge to map the three types of RNA data of lung cancer to the common coordinate system and construct the ceRNA network corresponding to the common module. The results show that more than 90% of the modules are closely related to cancer, including lung cancer. Furthermore, the resulting ceRNA network not only accurately excavates the known correlation of the three types of RNA molecular, but also further discovers the potential biological associations of them. Our work provides support and foundation for future biological validation how competitive relationships of multiple RNAs affects the development of tumors.