Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA–disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify biomarkers for disease diagnosis, treatment, and prevention. Previous research methods have mostly integrated the similarity and association information of lncRNAs and diseases, without considering the topological structure information among these nodes, which is important for predicting lncRNA–disease associations. We propose a method based on information flow propagation and convolutional neural networks, called LDAPred, to predict disease-related lncRNAs. LDAPred not only integrates the similarities, associations, and interactions among lncRNAs, diseases, and miRNAs, but also exploits the topological structures formed by them. In this study, we construct a dual convolutional neural network-based framework that comprises the left and right sides. The embedding layer on the left side is established by utilizing lncRNA, miRNA, and disease-related biological premises. On the right side of the frame, multiple types of similarity, association, and interaction relationships among lncRNAs, diseases, and miRNAs are calculated based on information flow propagation on the bi-layer networks, such as the lncRNA–disease network. They contain the network topological structure and they are learned by the right side of the framework. The experimental results based on five-fold cross-validation indicate that LDAPred performs better than several state-of-the-art methods. Case studies on breast cancer, colon cancer, and osteosarcoma further demonstrate LDAPred’s ability to discover potential lncRNA–disease associations.
It is well known that the unusual expression of long non-coding RNAs (lncRNAs) is closely related to the physiological and pathological processes of diseases. Therefore, inferring the potential lncRNA–disease associations are helpful for understanding the molecular pathogenesis of diseases. Most previous methods have concentrated on the construction of shallow learning models in order to predict lncRNA-disease associations, while they have failed to deeply integrate heterogeneous multi-source data and to learn the low-dimensional feature representations from these data. We propose a method based on the convolutional neural network with the attention mechanism and convolutional autoencoder for predicting candidate disease-related lncRNAs, and refer to it as CNNDLP. CNNDLP integrates multiple kinds of data from heterogeneous sources, including the associations, interactions, and similarities related to the lncRNAs, diseases, and miRNAs. Two different embedding layers are established by combining the diverse biological premises about the cases that the lncRNAs are likely to associate with the diseases. We construct a novel prediction model based on the convolutional neural network with attention mechanism and convolutional autoencoder to learn the attention and the low-dimensional network representations of the lncRNA–disease pairs from the embedding layers. The different adjacent edges among the lncRNA, miRNA, and disease nodes have different contributions for association prediction. Hence, an attention mechanism at the adjacent edge level is established, and the left side of the model learns the attention representation of a pair of lncRNA and disease. A new type of lncRNA similarity and a new type of disease similarity are calculated by incorporating the topological structures of multiple bipartite networks. The low-dimensional network representation of the lncRNA-disease pairs is further learned by the autoencoder based convolutional neutral network on the right side of the model. The cross-validation experimental results confirm that CNNDLP has superior prediction performance compared to the state-of-the-art methods. Case studies on stomach cancer, breast cancer, and prostate cancer further show the ability of CNNDLP for discovering the potential disease lncRNAs.
Rationale:
Pancreatic cancer is one of the most difficult cancers to manage and its poor prognosis stems from the lack of a reliable early disease biomarker coupled with its highly metastatic potential. Liver metastasis accounts for the high mortality rate in pancreatic cancer. Therefore, a better understanding of the mechanism(s) underlying the acquisition of the metastatic potential in pancreatic cancer is highly desirable.
Methods:
Microarray analysis in wild-type and highly liver metastatic human pancreatic cancer cell lines was performed to identify gene expression signatures that underlie the metastatic process. We validated our findings in patient samples, nude mice, cell lines and database analysis.
Results:
We identified a metastasis-related gene, laminin subunit alpha 4 (
LAMA4
), that was upregulated in highly liver metastatic human pancreatic cancer cell lines. Downregulation of LAMA4 reduced the liver metastatic ability of pancreatic cancer cells
in vivo
. Furthermore, LAMA4 expression was positively correlated with tumor severity and
in silico
analyses revealed that LAMA4 was associated with altered tumor microenvironment. In particular, our
in vitro
and i
n vivo
results showed that LAMA4 expression was highly correlated with cancer-associated fibroblasts (CAFs) level which may contribute to pancreatic cancer metastasis. We further found that LAMA4 had a positive effect on the recruitment and activity of CAFs.
Conclusions:
These data provide evidence for LAMA4 as a possible biomarker of disease progression and poor prognosis in pancreatic cancer. Our findings indicate that LAMA4 may contribute to pancreatic cancer metastasis via recruitment or activation of CAFs.
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