Table 1. miR-100 target genes identified in epithelial cells by microarray analysis and bioinformatics prediction.
| Gene a | Genes down-regulated by miR-100 treatment (microarray analysis) | Bioinformatics predicted targets of miR-100 f | ||||
| HaCaT b | 1386Ln b | C4-2 c | SCC29 d | 4T1 e | ||
| CTDSPL | 1 | 1 | 1 | 1 | 7 | |
| NMT1 | 1 | 1 | 1 | 1 | 2 | |
| TMEM30A | 1 | 1 | 1 | 1 | 4 | |
| ATP6AP1 | 1 | 1 | 1 | 4 | ||
| BMPR2 | 1 | 1 | 1 | 4 | ||
| CDK6 | 1 | 1 | 1 | 1 | ||
| DNAJB4 | 1 | 1 | 1 | 2 | ||
| FABP5 | 1 | 1 | 1 | 0 | ||
| HIPK3 | 1 | 1 | 1 | 0 | ||
| HOXA1 | 1 | 1 | 1 | 10 | ||
| KBTBD8 | 1 | 1 | 1 | 8 | ||
| KCTD10 | 1 | 1 | 1 | 3 | ||
| LRRC8B | 1 | 1 | 1 | 1 | ||
| MMP13 | 1 | 1 | 1 | 0 | ||
| mTOR | 1 | 1 | 1 | 12 | ||
| RAB15 | 1 | 1 | 1 | 2 | ||
| REEP3 | 1 | 1 | 1 | 0 | ||
| SLC39A6 | 1 | 1 | 1 | 1 | ||
| SMARCA5 | 1 | 1 | 1 | 11 | ||
| SMEK2 | 1 | 1 | 1 | 5 | ||
| SNX9 | 1 | 1 | 1 | 4 | ||
| TTC30A | 1 | 1 | 1 | 6 | ||
| VLDLR | 1 | 1 | 1 | 7 | ||
| ZDHHC18 | 1 | 1 | 1 | 5 | ||
Gene names in bold font were genes that down-regulated in 4 out of 5 microRNA transfection experiments, or down-regulated in 3 out of 5 microRNA transfection experiments and predicted to be miR-100 targets by 10 out of 12 bioinformatics tools tested.
Human skin keratinocyte HaCaT and head and neck squamous cell carcinoma cell 1386Ln were treated with either miR-100 or control mimic and differential expression analysis was carried out using Affymetrix GeneChip HuGene 1.0 ST arrays. The data was processed using Robust Multi-array Analysis (RMA), and the down-regulated gene was defined as a gene with a microRNA-induced expressional change equal or less than the known target gene mTOR (fold difference = 0.88 and 0.67, respectively for HaCaT and 1386Ln cells).
Microarray data on C4-2 prostate cancer cells treated with either miR-99a or control (GEO accession GSE26332) [7], and processed using Robust Multi-array Analysis (RMA). The down-regulated gene was defined as a gene with a microRNA-induced expressional change equal or less than the known target gene mTOR (fold difference = 0.67).
Data from Henson et al 2009 [23].
Data from Gebeshuber et al 2012 [24].
The candidate targets of miR-100 were predicted using a collection of 12 bioinformatics tools, including DIANAmT, miRanda, microCosm, miRDB, miRWalk, RNAhybrid, PicTar (4-way), PicTar (5-way), PITA, RNA22, TargetScan5, and TargetScanHuman 6.2. The number of bioinformatics tools (out of a total of 12 tools tested here) that predict a gene to be miR-100 target was presented.