Table 1.
Upstream regulator analysis
| Master regulator | Molecule type | Prediction | Z Score | p-value | Number of targets |
|---|---|---|---|---|---|
| TCF7L2 | Transcription factor | Inhibited | − 5.186 | 5.21E−08 | 27 |
| BDNF | Growth factor | Inhibited | − 2.381 | 4.23E−06 | 26 |
| CXCL8 | Cytokine | Inhibited | − 1.219 | 8.54E−06 | 8 |
| 1L1B | Cytokine | Activated | 1.349 | 3.26E−05 | 30 |
| IFNG | Cytokine | Activated | 2.541 | 1.26E−04 | 36 |
| TGFB1 | Growth factor | Activated | 1.451 | 1.69E−04 | 55 |
| LIF | Cytokine | Activated | 1.362 | 3.29E−04 | 14 |
| STAT1 | Transcription factor | Activated | 2.592 | 3.95E−04 | 14 |
| IL6R | Receptor | Activated | 2.236 | 6.06E−04 | 7 |
| MKNK1 | Kinase | Activated | 2.714 | 6.47E−04 | 11 |
The over-expression of miR-125a-3p in OPCs caused a statistically significant change in the expression of 1060 transcripts. These differentially expressed genes were analyzed with the Ingenuity Pathway Analysis tool (IPA) by performing an upstream regulator analysis to identify possible alterations in the activity of gene expression master regulators that may be responsible for the changes observed in the experimental dataset. The table shows the most promising master regulators resulting from the analysis, with the relative p-value and number of targets modulated. Cut-off: Z = ± 1. In bold, the transcription factor TCF7L2, selected for subsequent studies is reported.