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. 2023 Dec 10;13(1):21874.
doi: 10.1038/s41598-023-49110-4.

Construction and analysis of pseudogene-related ceRNA network in breast cancer

Hossein Mohebifar  1 , Amir Sabbaghian  1 , Touraj Farazmandfar  1 , Masoud Golalipour  2
Affiliations

Construction and analysis of pseudogene-related ceRNA network in breast cancer

Hossein Mohebifar et al. Sci Rep. .

Abstract

Breast cancer (BC) is one of the leading causes of cancer-related deaths in women. The present study explored the potential role of pseudogenes in BC via construction and analysis of a competing endogenous RNA (ceRNA) network through a three-step process. First, we screened differentially expressed genes in nine BC datasets. Then the gene-pseudogenes pairs (nine hub genes) were selected according to the functional enrichment and correlation analysis. Second, the candidate hub genes and interacting miRNAs were used to construct the ceRNA network. Further analysis of the ceRNA network revealed a crucial ceRNA module with two genes-pseudogene pairs and two miRNAs. The in-depth analysis identified the GBP1/hsa-miR-30d-5p/GBP1P1 axis as a potential tumorigenic axis in BC patients. In the third step, the GBP1/hsa-miR-30d-5p/GBP1P1 axis expression level was assessed in 40 tumor/normal BC patients and MCF-7 cell lines. The expression of GBP1 and GBP1P1 was significantly higher in the tumor compared to the normal tissue. However, the expression of hsa-miR-30d-5p was lower in tumor samples. Then, we introduced the GBP1P1 pseudogene into the MCF-7 cell line to evaluate its effect on GBP1 and hsa-miR-30d-5p expression. As expected, the GBP1 level increased while the hsa-miR-30d-5p level decreased in the GBP1P1-overexprsssing cell line. In addition, the oncogenic properties of MCF-7 (cell viability, clonogenicity, and migration) were improved after GBP1P1 overexpression. In conclusion, we report a ceRNA network that may provide new insight into the role of pseudogenes in BC development.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The flowchart of the study process.
Figure 2
Figure 2
The landscape of gene-pseudogene dysregulation in breast cancer. (A) Flower plot diagram showing common differentially expressed genes across 9 breast cancer datasets. (B) gene-pseudogene pairs with the highest correlation coefficients and best p values. (C) and (D) Functional enrichment analysis of the gene-pseudogene pairs. Several cancer-related pathways and mechanisms were enriched in KEGG pathways and GO terms.
Figure 3
Figure 3
Screening for candidate Hub gene-pseudogene pairs. (A) The Sankey plot of genes that were involved in each enriched pathway. The dot plot shows the number of overlap genes in each pathway. The color of the dots shows the p-value. (B) Survival analysis of candidate genes in breast cancer. The Cox proportional regression analysis of hazard ratio (HZ) with 95% confidence interval. The dot shows HR, and the color shows the p-value. (C) Correlation between gene and pseudogene in breast cancer. The best correlation was observed in the GBP1-GBP1P1 pair. (D) and (E) ROC curves. The AUC was calculated for each gene to evaluate the predictive power to distinguish the breast tumor from normal (D) or responsive to chemotherapy from non-responders (E). GBP1 had the best discriminatory power in both groups.
Figure 4
Figure 4
Construction of competing endogenous RNA (ceRNA) network. (A) Interaction plot of gene-miRNA-pseudogene. The color of the squares shows the correlation coefficient and the size of the squares shows the p-value. (B) Tripartite ceRNA network.
Figure 5
Figure 5
Further screening for core ceRNA module. (A) Matrix plot of gene-pseudogene-miRNA axes. The selection was based on the p-value, correlation size, and concurrent negative correlation of miRNA with gene-pseudogene pairs. (B) The core module of the ceRNA network with two gene-pseudogene pairs and two miRNAs. (C) Association between GBP1 and PDE4DIP with clinical information of breast cancer patients. (D) Correlation between GBP1/hsa-miR-30d-5p/GBP1P1 expressions in breast cancer.
Figure 6
Figure 6
Experimental validation of GBP1/hsa-miR-30d-5p/GBP1P1 axis expression in breast cancer patient and MCF-7 cell line. (A) expression of GBP1/hsa-miR-30d-5p/GBP1P1 axis in breast cancer patients confirmed that GBP1 and GBP1P1 RNA level increases while the hsa-miR-30d-5p level decreases in tumor samples (p < 0.00001). (B) After GBP1P1 introduction into MCF-7, the GBP1 expression increases, and the hsa-miR-30d-5p level decreases in the GBP1P1-MCF7 cell line (p < 0.001).
Figure 7
Figure 7
Tumorigenic properties of GBP1P1-MCF7. When GBP1P1 transfected into MCF7, the tumorigenic potential of GBP1P1-MCF7 increased. (A) The viability of cells was higher in GBP1P1-MCF7 cells (p = 0.004). (B) The rate of early apoptosis was higher in GBP1P1-MCF7 (5.93%), while the rate of late apoptosis was higher in control MCF-7 (4.31%). (C) Wound healing assay. The wound closure rate was higher in GBP1P1-MCF than control (p = 0.0305). (D) Colony formation assay. The higher colony area in GBP1P1-MCF7 cells shows more colony formation potential of GBP1P1-harboring cells.
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