Fig 1.
Flow chart of in vitro processes.
In vitro experiments were performed to further verify the tumor-suppressive role of miR-132 and to assess its cellular functions in HCC.
Fig 2.
Flow chart of bioinformatic processes.
A series of tasks, i.e. natural language processing (NLP) analysis of HCC, prediction of miRNA-132 target genes, comprehensive gene analyses and analytical integration was conducted successively.
Fig 3.
Flow chart of NLP analysis for HCC.
The NLP analysis procedure of HCC includes document mining, data processing and statistical analysis.
Table 1.
Three classes of relationships are mentioned in the genome-wide interaction analysis.
Fig 4.
Time-dependent effects of miR-132 were assessed on viability in various HCC cell lines, i.e. HepG2 (A), SMMC-7221 (B), HepB3 (C) and SNU449 (D). Columns represent the averages of sets of three single, independent experiments while bars stand for the standard deviations. *P < 0.05, ** P < 0.01 and ***P < 0.001, compared to blank and negative controls at the same time point.
Fig 5.
Time-dependent effects of miR-132 were assessed on proliferation in various HCC cell lines, i.e. HepG2 (A), SMMC-7221 (B), HepB3 (C) and SNU449 (D). Points represent the averages of sets of three single, independent experiments while bars stand for the standard deviations. *P < 0.05, ** P < 0.01 and ***P < 0.001, compared to blank and negative controls at the same time point.
Fig 6.
Time-dependent effects of miR-132 were assessed on the caspase-3/7 activities in various HCC cell lines, i.e. HepG2 (A), SMMC-7221 (B), HepB3 (C) and SNU449 (D). Points represent the averages of sets of three single, independent experiments while bars stand for the standard deviations. *P < 0.05, compared to blank and negative controls at the same time point.
Fig 7.
In NLP analysis, a network of multiple genes was established for HCC.
Fig 8.
Connectivity analysis for HCC.
Connectivity analysis demonstrated that the top connectivities of PIK3CA and PIK3R2.
Table 2.
GO analysis classified all the HCC-related genes obtained from NLP in accordance with molecular function, cellular component and biological process.
Table 3.
Twenty-four pathways were identified to be statistically significant for the NLP analysis of HCC (P< = 0.05).
Fig 9.
Network analysis for miR-132 predicted target genes.
Network analysis provided insights into the potential interacting and regulatory networks of miR-132.
Fig 10.
Connectivity analysis for miR-132 predicted target genes.
The additional connectivity analysis revealed that KRAS harbored the highest connectivity and MAPK1 the second highest, interacting with sixteen and fifteen genes respectively.
Table 4.
All the miR-132 predicted target genes were sorted out according to molecular function, cellular component and biological process by GO analysis.
Fig 11.
Network analysis for the overlapped genes in the analytical integration.
A network analysis was performed among the fifty-nine genes identified in the analytical integration so as to better comprehend the possible underlying mechanisms.
Table 5.
The integration systematically analyzed the overlaps and featured 59 genes that were both potentially HCC-related and probably regulated by miR-132.