Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Abstract

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Keywords: GEO data; differentially expressed genes; gastric cancer; integrated bioinformatics.

Figure 1

Differential gene expression from two datasets based on GEO database. A, The standardization (left and middle) and volcano plots (right) of GSE103236 data. B, The standardization (left and middle) and volcano plots (right) of GSE118897 data. The data before normalization were displayed as the blue bar, while the normalized data were shown as the red bar. The red and green points respectively represented upregulated and downregulated genes screened on the basis of |fold change (FC)|>2.0 and a corrected P-value < 0.05. Genes with no significant difference were shown as the black points.

Figure 2

LogFC heatmap of the image data of each expression microarray. A, Venny diagram of intersections of up- and downregulated genes between GSE103236 data and GSE118897 data. B, The abscissa was defined as GEO ID, and the ordinate was defined as the gene name. Red represents logFC >0, blue represents logFC< 0, and the values in the box represent the logFC values.

Figure 3

GO terms and KEGG pathway for common DEGs. A, GO analysis divided DEGs into three functional groups: cell composition, molecular function and biological processes. B, GO enrichment significance items of DEGs in different functional groups. C, DEGs with different GO-enriched functions were distributed in gastric cancer. D, Significant pathway enrichment of DEGs. Red represents the signaling pathway, green represents downregulated genes, purple represents signaling pathway, and yellow represents upregulated genes.

Figure 4

Transcriptional levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in STAD tissues and adjacent normal gastric tissues from TCGA database. Expression panels for OLFM4 (A), IGF2BP3 (B), CLDN1 (C) and MMP1 (D) based on sample types comparing 34 normal individuals and 415 patients with STAD in TCGA database.

Figure 5

The expressions of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC cell lines. A, western blot assay for detecting the protein levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC cell lines. The relative mRNA levels of OLFM4 (B), IGF2BP3 (C), CLDN1 (D) and MMP1 (E) in GC cell lines.

Figure 6

The expressions and 5-year survival rate of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues. The transcriptional levels for OLFM4 (A), IGF2BP3 (C), CLDN1 (E) and MMP1 (G) in GC tissues. The 5-year survival rate of patients with GC based on top and bottom 50% OLFM4 (B), IGF2BP3 (D), CLDN1 (F) and MMP1 (H) expression.