Integrated analysis of miRNAs expression in breast cancer patients to detect genes deregulation involved in malignant transformation.

Boiles, Alejandro Recio; Prasanna, Devika; Shaker, Ahmad; Oswald, Michaela; Mason, Cathleen; Keogh, Mary; Lee, Annette; Shapira, Iuliana

doi:10.1200/jco.2015.33.15_suppl.e12560

Cited by 2 publications

(2 citation statements)

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“…TGFBR3 inhibits breast cancer progression through TGF-beta signaling (Lee et al, 2010). In addition, other molecules in module 1 such as ADAMTS5 (Fontanil et al, 2017), ARHGAP20 (Asaduzzaman et al, 2017), C2orf88 (Lo et al, 2015), EZH1 (Liu et al, 2012), FAM13A (Goto-Yamaguchi et al, 2018), FGF1 (Slattery et al, 2013), GNAL (Yi et al, 2009), GRAMD3 (Boiles et al, 2015), PELI2 (Zang et al, 2017), PLSCR4 (Sahay et al, 2015), PTPN14 (Belle et al, 2015), RBMS3 (Zhu et al, 2019a), SH3BGRL2 (Alexe et al, 2007;Wen et al, 2018), let-7c (Fu et al, 2017), mir-100 (Jiang et al, 2016b), mir-10b (Wang et al, 2016b), mir-125b (Wang et al, 2019a), mir-139 (Dai et al, 2017), and mir-21 (Yan et al, 2008;Yanwirasti and Arisanty, 2017;Zhu et al, 2019b) have been reported to play important roles in breast cancer. Module 2 had nine nodes, including two miRNAs, five lncRNAs, and two protein-coding genes.…”

Section: Modules In the Dysregulated Network Relate To The Survival Omentioning

confidence: 99%

Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma

et al. 2020

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Breast cancer is the most common cancer in women, but few biomarkers are effective in clinic. Previous studies have shown the important roles of non-coding RNAs in diagnosis, prognosis, and therapy selection for breast cancer and have suggested the significance of integrating molecules at different levels to interpret the mechanism of breast cancer. Here, we collected transcriptome data including long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA for~1,200 samples, including 1079 invasive breast carcinoma samples and 104 normal samples, from The Cancer Genome Atlas (TCGA) project. We identified differentially expressed lncRNAs, miRNAs, and mRNAs that distinguished invasive carcinoma samples from normal samples. We further constructed an integrated dysregulated network consisting of differentially expressed lncRNAs, miRNAs, and mRNAs and found housekeeping and cancer-related functions. Moreover, 58 RNA binding proteins (RBPs) involved in biological processes that are essential to maintain cell survival were found in the dysregulated network, and 10 were correlated with overall survival. In addition, we identified two modules that stratify patients into high-and low-risk subgroups. The expression patterns of these two modules were significantly different in invasive carcinoma versus normal samples, and some molecules were high-confidence biomarkers of breast cancer. Together, these data demonstrated an important clinical application for improving outcome prediction for invasive breast cancers.

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Section: Modules In the Dysregulated Network Relate To The Survival Omentioning

confidence: 99%

Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma

et al. 2020

View full text Add to dashboard Cite

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“…From Fig 6A, it can be seen that the target gene set related to hsa-mir-371a was significantly enriched in several terms closely related to breast cancer, including Transcriptional activity of SMAD2/SMAD3:SMAD4 heterotrimer, pathways in cancer, mitotic cell cycle process, and Signaling by Rho GTPases. For example, Transcriptional activity of SMAD2/ SMAD3:SMAD4 heterotrimer involved in the degradation of SKI/SKIL, thus causing malignant transformation in breast cancer [57]. The deregulation of cell cycle is a hallmark of cancer including breast cancer, which allows for limitless cell division [58,59].…”

Section: Case Studiesmentioning

confidence: 99%

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations

Ouyang,

Liang,

Wang

et al. 2024

PLoS Comput Biol

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Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

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Integrated analysis of miRNAs expression in breast cancer patients to detect genes deregulation involved in malignant transformation.

Cited by 2 publications

References 0 publications

Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma

Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations

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