Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs

Li, Jie; Lei, Kecheng; Wu, Zengrui; Li, Weihua; Liu, Guixia; Liu, Jianwen; Cheng, Feixiong; Tang, Yun

doi:10.18632/oncotarget.10052

Cited by 80 publications

(83 citation statements)

References 57 publications

(88 reference statements)

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“…Then the two obtained networks are compared to highlight cancer-associated microRNAs (44). Variants of the network reconstruction procedure were also explored, based on measures different from correlation (45), or combining miRNA expression with alternative sources of information as drug response and miRNA targets (46). In this last case, the approach led to the identification of 11 oncomiRs (e.g.…”

Section: Methods Based On Mirna Expression Datamentioning

confidence: 99%

“…Some computational methods have then been designed to predict genes and pathway jointly regulated by a set of microRNAs. The networkbased methods that we have described above for the prioritization of differentially expressed microRNAs (41,(44)(45)(46) represent a first example of such kind of methodologies. Given that from the structure of the miRNA-miRNA network, used to prioritize miRNAs, it is also possible to elucidate their combined effect.…”

Section: Methods Considering the Mirna-mirna Synergistic Effectmentioning

confidence: 99%

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A review of computational approaches detecting microRNAs involved in cancer

et al. 2017

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MicroRNAs (miRNAs) are small non-coding RNAs playing an essential role in gene expression regulation. Multiple studies have demonstrated that miRNAs are dysregulated in cancer initiation and progression, pointing out their potential as biomarkers for diagnosis, prognosis and response to treatment. With the introduction of high-throughput technologies several computational approaches have been proposed to identify cancer-associated miRNAs. Here, we present a systematic and comprehensive overview of the current knowledge concerning the computational detection of miRNAs involved in tumor onset and subtyping, with possible theranostic employment. An overview of the state of art in this field is thus proposed with the aim of supporting researchers, especially experimentalists and pathologists, in choosing the optimal approach for their case of study.

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Section: Methods Based On Mirna Expression Datamentioning

confidence: 99%

Section: Methods Considering the Mirna-mirna Synergistic Effectmentioning

confidence: 99%

A review of computational approaches detecting microRNAs involved in cancer

et al. 2017

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“…They identified optimally subnetwork markers in the protein–protein interaction network based on minimizing the distances of samples from the same class while maximizing the distances of samples from different classes. Furthermore, another group proposed a network‐based framework, namely the SMiR‐NBI model, to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs based on the heterogeneous network connecting drugs, miRNAs and genes . Although the continuous optimization of the methods improved the performance of the predictors, the network marker dissection was only based on gene expression and is insufficient to interpret complex mechanisms that underlie chemotherapy resistance at multiple levels.…”

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confidence: 99%

Accurate prediction and elucidation of drug resistance based on the robust and reproducible chemoresponse communities

Dai

Wang

Yang

et al. 2017

Intl Journal of Cancer

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Selecting the available treatment for each cancer patient from genomic context is a core goal of precision medicine, but innovative approaches with mechanism interpretation and improved performance are still highly needed. Through utilizing in vitro chemotherapy response data coupled with gene and miRNA expression profiles, we applied a network-based approach that identified markers not as individual molecules but as functional groups extracted from the integrated transcription factor and miRNA regulatory network. Based on the identified chemoresponse communities, the predictors of drug resistance achieved high accuracy in cross-validation and were more robust and reproducible than conventional single-molecule markers. Meanwhile, as candidate communities not only enriched abundant cellular process but also covered a variety of drug enzymes, transporters, and targets, these resulting chemoresponse communities furnished novel models to interpret multiple kinds of potential regulatory mechanism, such as dysregulation of cancer cell apoptosis or disturbance of drug metabolism. Moreover, compounds were linked based on the enrichment of their common chemoresponse communities to uncover undetected response patterns and possible multidrug resistance phenotype. Finally, an omnibus repository named ChemoCommunity (http://www.jianglab.cn/ChemoCommunity/) was constructed, which furnished a user-friendly interface for a convenient retrieval of the detailed information on chemoresponse communities. Taken together, our method, and the accompanying database, improved the performance of classifiers for drug resistance and provided novel model to uncover the possible regulatory mechanism of individual response to drug.

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“…We performed LOOCV based on the known SMiR associations in the SM2miRdatabase (Liu et al, 2012) to measure the performance of RWNS. RWNS was compared with two stateof-the-art SMiR prediction methods: SMiR-NBI (Li et al, 2016) and TLHNSMMA (Qu et al, 2018) in LOOCV. SMiR-NBI designed a network-based inference method to identify new SMiR associations.…”

Section: Performance Evaluation Under Loocvmentioning

confidence: 99%

“…smiRN-AD integrated gene expression data from bioactive small molecule perturbation and Alzheimer's disease-related miRNA regulation. Li et al (2016) designed a network-based miRNA pharmacogenomic model, SMiR-NBI, integrating relevant biological information, including drugs, miRNAs, genes, and a network-based inference approach into a unified framework. SMiR-NBI effectively discovered potential response mechanisms of anticancer drugs targeting miRNAs and found that miRNAs may be underlying pharmacogenomic biomarkers in cancers.…”

Section: Introductionmentioning

confidence: 99%

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

Liu

Peng

Tian

et al. 2020

Front. Bioeng. Biotechnol.

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Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.

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Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs

Cited by 80 publications

References 57 publications

A review of computational approaches detecting microRNAs involved in cancer

A review of computational approaches detecting microRNAs involved in cancer

Accurate prediction and elucidation of drug resistance based on the robust and reproducible chemoresponse communities

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

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