The Role of mir-152 and DNMT1 in Gastric Cancer Cell Proliferation and Invasion

Wang, Fangyu

doi:10.24966/ghr-2566/100011

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…All of these new predicted miRNA–disease associations have been verified in other databases such as mirCancer, mirDB, dbDEMC V2.0 and other literatures. For examples, Wang et al 56 showed that Hsa-mir-152 expression was significantly down regulated in Gastric Cancer cell lines. MicroRNA-338 inhibits growth, invasion and metastasis of Gastric Cancer by Targeting NRP1 Expression 57 .…”

Section: Resultsmentioning

confidence: 99%

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Nguyen

Than

et al. 2021

Sci Rep

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Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA–disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA–disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures.

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Section: Resultsmentioning

confidence: 99%

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Nguyen

Than

et al. 2021

Sci Rep

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Plasmonic biosensor for early gastric cancer detection

Asl¹,

Karamdel²,

Khoshbaten³

et al. 2022

Opt. Continuum

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Early detection of cancer, can greatly reduce mortality and thus increase the life expectancy of patients. In this study, we introduce a plasmonic biosensor platform to detect relevant microRNAs for gastric cancer diagnosis. The proposed sensor uses the LSPR to detect RNA in the human blood. Different geometries of nanostructures were examined, and the results of their resonance peak were analyzed. The proposed nano-flower structure with five petals was considered as the original shape and then was examined in terms of changes, including substrate changes, the type of structure, the presence or absence of holes on the structure, and different thicknesses of the desired biomarkers. It shows the optimal wavelength of LSPR at 652 nm, which is suitable for physiological environments such as blood and plasma. The creation of several holes caused a shift to the wavelength of 663.63 nm, which was about 12.12 nm, but due to the reduction of the peak intensity, the optimization steps were performed without holes. Target miRNAs such as miR-21, miR-221, and miR-153 are selectively trapped on nanostructured surfaces and change λ LSPR . The resonance peak of the LSPR found a 30 nm shift due to the presence of biomarkers.

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The Role of mir-152 and DNMT1 in Gastric Cancer Cell Proliferation and Invasion

Cited by 2 publications

References 47 publications

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Plasmonic biosensor for early gastric cancer detection

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