| Literature DB >> 27633132 |
Peng Wang1, Jing-An Ye1, Chong-Xian Hou1, Dong Zhou1, Sheng-Quan Zhan1.
Abstract
Temozolomide (TMZ) is approved for use as first-line treatment for glioblastoma multiforme (GBM). However, GBM shows chemoresistance shortly after the initiation of treatment. In order to detect whether silencing of human protein phosphatase 1D magnesium dependent (PPM1D) gene could increase the effects of TMZ in glioma cells, glioma cells U87-MG were infected with lentiviral shRNA vector targeting PPM1D silencing. After PPM1D silencing was established, cells were treated with TMZ. The multiple functions of human glioma cells after PPM1D silencing and TMZ chemotherapy were detected by flow cytometry and MTT assay. Significantly differentially expressed genes were distinguished by microarray-based gene expression profiling and analyzed by gene pathway enrichment analysis and ontology assessment. Western blotting was used to establish the protein expression of the core genes. PPM1D gene silencing improves TMZ induced cell proliferation and induces cell apoptosis and cell cycle arrest. When PPM1D gene silencing combined with TMZ was performed in glioma cells, 367 genes were upregulated and 444 genes were downregulated compared with negative control. The most significant differential expression pathway was pathway in cancer and IGFR1R, PIK3R1, MAPK8 and EP300 are core genes in the network. Western blotting showed that MAPK8 and PIK3R1 protein expression levels were upregulated and RB1 protein expression was decreased. It was consistent with that detected in gene expression profiling. In conclusion, PPM1D gene silencing combined with TMZ eradicates glioma cells through cell apoptosis and cell cycle arrest. PIK3R1/AKT pathway plays a role in the multiple functions of glioma cells after PPM1D silencing and TMZ chemotherapy.Entities:
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Year: 2016 PMID: 27633132 PMCID: PMC5055212 DOI: 10.3892/or.2016.5089
Source DB: PubMed Journal: Oncol Rep ISSN: 1021-335X Impact factor: 3.906
Figure 1PPM1D mRNA expression levels in U87-MG cells. The relative copies of PPM1D mRNA were significantly decreased after infected with RNAi lentivirus compared with negative control. Means ± SD (bars) are shown. *P<0.05.
Figure 2The effect of PPM1D silencing combined with TMZ on proliferation of U87-MG cells. A MTT analysis of the cell viability of PPM1D silencing and negative control cells were treated with TMZ. Cell numbers were counted at 24, 48, 72, 96 and 120 h after TMZ administration. Statistically significant (P<0.05) differences are marked with an asterisk. Error bars indicate ± SD of five replicates.
Figure 3The effect of PPM1D silencing combined with TMZ on apoptosis of U87-MG cells. The number of apoptotic cells was determined on day 4 after TMZ treatment using flow cytometry. (A) CON group, (B) NC group, (C) KD group. (D) Summary of the experiment illustrated in (A–C). (E) To exactly analyze the influences induced by PPM1D silencing, cells in NC and KD groups with positive expression of GFP were analyzed. Apoptotic cells significantly increased in KD group with PPM1D silencing when treated with TMZ. Error bars indicate ± SD of three replicates. *P<0.05 vs. NC.
Figure 4The effect of PPM1D silencing combined with TMZ on cell cycle distribution of U87-MG cells. Cell cycle distributions were determined on day 4 after TMZ treatment using flow cytometry. (A) CON group, (B) NC group, (C) KD group. (D) Summary of the experiment illustrated in (A–C). G1 and G2/M cells in KD vs. NC, respectively, *P<0.05, **P<0.01.
Figure 5Pathway enrichment analysis. The differentially expressed genes were confirmed with gene pathway enrichment analysis. The ten most significant differential expression pathways and genes are listed, based on an ascending P-value. Cancer in KEGG pathway was the most significant differential expression pathway. The level of significance of the difference between the two groups is indicated with a P-value of 4.36E-08 in cancer pathway.
The ten most significant differential expression pathways and genes between KD and NC.
| Gene set name | Pathway analysis
| ||
|---|---|---|---|
| No. of genes | P-value | Gene names | |
| KEGG_PATHWAYS_IN_CANCER | 26 | 4.36E-08 | RB1, ABL1, CDK6, SKP2, CBL, CBLB, PIK3R1, RAC1, AKT3, ITGA6, FN1, LAMB2, MAX, RUNX1, MAPK8, MMP2, FGF2, FGF5, FGF14, LEF1, TCF7, WNT5A, FZD6, RALA, GLI2, MMP1 |
| KEGG_CELL_CYCLE | 12 | 3.78E-04 | RB1, ABL1, CDK6, SKP2, CDC25B, MCM2, MCM3, MCM4, CDC20, ESPL1, E2F4, RBL2 |
| KEGG_ENDOCYTOSIS | 14 | 3.78E-04 | CBL, CBLB, PIP5K1C, PIP5K1A, AP2M1, AP2A1, EEA1, SMURF2, NEDD4, PARD6B, ACAP3, LDLRAP1, LDLR, VPS36 |
| KEGG_CYTOKINE_CYTOKINE_ RECEPTOR_INTERAERACTION | 17 | 3.78E-04 | IL1A, IL6R, IL6ST, IL11, LEPR, PRLR, TNFRSF10D, TNFRSF10C, CCL2, CXCL2, CXCL3, BMPR2, TNFRSF9, TNFRSF21, TNFRSF11B, TNFSF14, CD70 |
| KEGG_PHOSPHATIDYLINOSITOL_ SIGNALING_SY_SYSTEM | 9 | 4.51E-04 | PIK3R1, PIP5K1C, PIP5K1A, ITPR1, SYNJ2, INPP5A, OCRL, INPP4B, DGKE |
| BIOCARTA_ARF_PATHWAY | 5 | 4.79E-04 | RB1, ABL1, PIK3R1, RAC1, POLR1A |
| KEGG_SMALL_CELL_LUNG_CANCER | 9 | 7.38E-04 | RB1, CDK6, SKP2, PIK3R1, AKT3, ITGA6, FN1, LAMB2, MAX |
| KEGG_CHRONIC_MYELOID_LEUKEMIA | 8 | 1.66E-03 | RB1, ABL1, CDK6, CBL, CBLB, PIK3R1, AKT3, RUNX1 |
| KEGG_FOCAL_ADHESION | 13 | 1.93E-03 | PIK3R1, RAC1, AKT3, ITGA6, FN1, LAMB2, MAPK8, PIP5K1C, ELK1, THBS1, ARHGAP5, COL1A1, COL5A2 |
| KEGG_GNRH_SIGNALING_PATHWAY | 9 | 2.05E-03 | MAPK8, MMP2, ITPR1, ELK1, MAP2K4, ADCY8, ADCY7, ADCY6, GNA11 |
Figure 6Gene network construction. Pathway in cancer in KEGG was confirmed in gene network construction. The relationship between PPM1D and the 26 differential genes is shown. Green and red round dots represent downregulated gene and upregulated gene, respectively. The gray represent added gene. The size of the dot represents the significance of the difference.
Figure 7Western blot analysis of some differential genes. When administered with TMZ, glioma cells with PPM1D silencing showed significantly increased protein expression levels in MAPK8 and PIK3R1, while RB1 protein expression was decreased. The expression of AKT3 was not significantly different. The immunoblots were quantified with software and the data were repeated for at least three times in different experiments (n=3, P<0.05).