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Table 2 Current findings in the study of miRNA-regulated PPI networks

From: Computational developments in microRNA-regulated protein-protein interactions

Research area Description
Correlation between protein connectivity and miRNA regulation complexity A. There is positive correlation between miRNA target site types and its regulated protein connectivity. B. MiRNA target propensity may be due to high protein connectivity. C. MiRNA regulation propensity changes due to different hub proteins [72].
miRNA targeted proteins have short distance and higher modularity than randomly selected proteins [73].
MiRNA-regulated specific proteins in PPI networks A. MiRNAs that target a lower number genes have the propensity to regulate commonly expressed proteins rather than tissue-specific proteins. B. Commonly expressed proteins and tissue-specific proteins are always regulated together by a miRNA, and the numbers of protein expressed are close in both proteins [44].
The coordination role of MicroRNAs: miRNA clusters regulate PPI networks miRNAs in the same clusters have the tendency to coordinate to regulate protein functions in protein-protein interaction networks [74].
The coordination role of MicroRNAs: miRNAs coordinate to regulate protein complex A. MiRNAs coordinate to regulate protein complexes in posttranscriptional level. B. Correlations between the proteins exist in the same complex regulated by miRNAs [75].
The coordination role of MicroRNAs: miRNA crossingtalking with transcription factors Crosstalk motifs between miRNAs and transcription factors motif demonstrate higher network properties in miRNA-regulated PPI networks [7]
Identifying miRNA-regulated PPI networks in special diseases A. In gastric cancer [76]: six miRNA-regulated protein networks are identified in gastric cancer based on the human PPI network; it is suggested that miR-148a may resist tumor extension. B. In human ovarian cancer [77]: six miRNAs (hsa-mir-20a, hsa-mir-24-2, hsa-mir-34a, hsa-mir-21, hsamir-17 and hsa-mir-hsa-mir-155) and six TFs (BRCA1, SP1, ESR1, SMAD3, PO2F1 and TFE2) play key roles in ovarian cancer progression. C. In aging-related diseases [78]: 35 genes related to diseases associated with aging were identified.