Identification of Prognostic Biomarkers by Combined mRNA and miRNA Expression Microarray Analysis in Pancreatic Cancer

Abstract

Pancreatic cancer is the fourth leading cause for cancer-related death, and early diagnosis is one key to improve the survival rate of this disease. Molecular biomarkers are an important method for diagnostic use in pancreatic cancer. We used data from three mRNA microarray datasets and a microRNA dataset (GSE16515, GSE15471, GSE28735, and GSE41372) to identify potential key genes. Differentially expressed genes (DEGs) and microRNAs (DEMs) were identified. Functional, pathway enrichment, and protein-protein interaction analyses were performed on common DEGs across all datasets. The target genes of the DEMs were identified. DEMs targets that were also DEGs were further scrutinized using overall survival analysis. A total of 236 DEGs and 21 DEMs were identified. There were a total of four DEGs (ECT2, NR5A2, NRP2, and TGFBI), which were also predicted target genes of DEMs. Overall survival analysis showed that high expression levels of three of these genes (ECT2, NRP2, and TGFBI) were associated with poor overall survival for pancreatic cancer patients. The basic expression of DEGs in pancreas stood lower level in various organ tissues. The expression of ECT2 and NRP2 was higher in different pancreatic cancer cell lines than normal pancreas cell line. Knockout of ECT2 by Crispr Cas9 gene editing system decreased proliferation and migration ability in pancreatic cancer cell line MiaPaCa2. In conclusion, we think that data mining method can do well in biomarker screening, and ECT2 and NRP2 can play as potential biomarker or therapy target by Crispr Cas9 in pancreatic cancer.

Figure 1

Identification of DEGs in mRNA expression profiling datasets GSE15471, GSE16515, and GSE28735. (A) The upregulated genes in the profiling datasets. (B) The downregulated genes in the profiling datasets.

Figure 2

PPI network and a significant module. (A) PPI network of total DEGs in the three mRNA expression profiling datasets. (B) A significant module selected from PPI network. All the genes are upregulated genes. The line represents interaction relationship between nodes.

Figure 3

Identification of DEGs and paired miRNAs in the three mRNA expression and one miRNA expression profiling datasets. We found four specially DEGs and their miRNA, including ECT2-miR302c, NR5A2-miR27a, NRP2-miR27a, and miR331-3p, TGFBI-miR21.

Figure 4

Prognostic value of four DEGs and their miRNA in pancreatic cancer patients. Prognostic value of (A) ETC2 (log-rank P value = .00124), (B) NR5A2 (log-rank P value = .139), (C) NRP2 (log-rank P value = .0177), (D) TGFBI (log-rank P value = .0199), (E) miR21 (log-rank P value = .00157), and (F) miR27a (log-rank P value = .00117).

Figure 5

The basic expression of DEGs in different organs and cancer. The basic expression of DEGs in pancreas stood lower level in 37 kinds of organs (A). The expressions of ECT2 and NRP2 were significant higher in pancreatic cancer tissues (B).

Figure 6

The basic expression of DEGs in normal pancreas and pancreatic cancer cell lines. The protein expression of DEGs in normal pancreas and pancreatic cancer cell lines (A). The mRNA expression of DEGs in normal pancreas and pancreatic cancer cell lines (B, C, D). *P value < .05.

Figure 7

Knockout of ECT2 decreased proliferation and migration ability in MiaPaCa2 cells. Knockout of ECT2 by Crispr Cas9 gene editing system inhibited protein expression of ECT2 in MiaPaCa2 cells (A). ECT2 knocked out cells grew slower than control groups (B). ECT2 knocked out cells migration slower than control groups (C and D). *P value < .05.