lncRNA DLEU2 Accelerates Oral Cancer Progression via miR-30a-5p/RAP1B Axis to Regulate p38 MAPK Signaling Pathway

Zhang, Wenbo; Wang, Yanchun; Xu, Pu; Du, Yongxiu; Guan, Weiwei

doi:10.1155/2022/9310048

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…In Table , only one out of ten lncRNAs predicted has not been found to have any evidence described in the literature of PubMed database. Although there is no direct description of the association between “DLEU2” and breast cancer in the literature so far, the literature − have demonstrated that DLEU2 binds to miR-30a-5p through the same binding site, facilitating the expression of ETS2. ETS2 is overexpressed in breast carcinoma.…”

Section: Resultsmentioning

confidence: 99%

GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA–Disease Associations

Zhang,

Cai,

et al. 2023

ACS Omega

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Using computational models to predict potential lncRNA-disease associations (LDAs) has emerged as an effective supplement to bioexperiments for exploring the pathogenesis of diseases. However, current computational models still face limitations in their ability to learn the complex features of bionetworks. In this study, HGCNLDA, a model which combines graph convolutional network (GCN)-based aggregation, heterogeneous information fusion, and a bilinear-decoder to infer LDAs was proposed. Recognizing the need to extract essential features during data processing, our HGCNLDA explored four key steps for uncovering interaction patterns within the bionetwork: (1) a novel type of tripartite heterogeneous network, known as the lncRNA-disease-miRNA network (LDMN), was constructed using computed similarities and known associations. (2) Homogeneous and heterogeneous features of nodes were extracted from domains within the LDMN by a GCN-based encoder. (3) Feature fusions, including bipolymerization operations and attention mechanism, were employed to capture a more accurate and comprehensive representation of nodes. (4) Bilinear-decoder was used to rebuild the edge type (or rating type) for a specific node pair, resulting in the predicted association score. Through a 5-fold cross-validation on two data sets, namely, data set1 and data set2, our HGCNLDA consistently demonstrated superior performance compared to five related models. It almost achieved the highest AUROC and AUPR values on both data sets, especially on data set2 where the results obtained were more challenging and objective. Case studies involving three real cancer scenarios further validated the practicality of HGCNLDA in identifying potential LDAs in real-world contexts. The source code and data for this study are available at .

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Section: Resultsmentioning

confidence: 99%