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. 2019 May 24:11:4757-4772.
doi: 10.2147/CMAR.S198331. eCollection 2019.

A panel of collagen genes are associated with prognosis of patients with gastric cancer and regulated by microRNA-29c-3p: an integrated bioinformatics analysis and experimental validation

Qiang-Nu Zhang  1 , Hui-Li Zhu  1 , Meng-Ting Xia  2 , Juan Liao  1 , Xiao-Tao Huang  2 , Jiang-Wei Xiao  3 , Cong Yuan  2
Affiliations

A panel of collagen genes are associated with prognosis of patients with gastric cancer and regulated by microRNA-29c-3p: an integrated bioinformatics analysis and experimental validation

Qiang-Nu Zhang et al. Cancer Manag Res. .

Abstract

Background: The systematic expression characteristics and functions of collagen genes in gastric cancer (GC) have not been reported. Through public data integration, combined with bioinformatics analysis, we identified a panel of collagen genes overexpressed in GC. The functions of these genes were analyzed and validated in a GC-related cohort. microRNAs that may potentially target such genes were investigated in vitro. Methods: Four GC-related datasets retrieved from the Gene Expression Omnibus (GEO) were used to extract differentially expressed genes (DEGs) in GC. Functional annotation was performed to identify the potential roles of the identified DEGs. The association of candidate genes involved in the prognosis of GC patients (n=876) was determined using data provided by the Kaplan-Meier-plotter database, The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) repository, and a GC-related dataset (GSE15459). The expression characteristics of candidate genes and their associations with clinical parameters were validated in our in-house cohort (n=58). MicroRNAs able to target the identified candidate genes were predicted and confirmed using qRT-PCR, Western blotting, and dual-luciferase reporter assays in vitro. Results: After the integration of four GEO datasets, 76 DEGs were identified. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated that these DEGs were significantly enriched in ECM-related functions and pathways. A group of collagen genes was significantly upregulated in the GC tissues and constituted a protein-protein interaction network as important nodes. Some of these collagen genes were closely associated with poor prognosis in GC patients. Overexpression of COL1A1 and COL4A1 was confirmed in our in-house cohort, and this was related to prognosis and certain clinicopathological parameters. We found that microRNA-29c-3p could directly target COL1A1 and COL4A1 in BGC-823 cells. Conclusions: Collagen genes identified in this study were associated with patient prognosis in GC and may represent diagnostic markers or potential therapeutic targets. Aberrant expression of such candidate genes may be induced by microRNA-29c-3p.

Keywords: COL1A1; COL4A1; collagen; gastric cancer; microRNA-29c-3p; prognosis.

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Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Differently expressed genes in gastric cancer tissues compared with non-tumor tissues. (A) Seventy-six differently expressed genes were identified after integrating results obtained from four GEO gastric cancer-related dataset; (B) Heatmap displays the expression level of 76 differently expressed genes between tumor tissues and non-tumor tissues in four GEO gastric cancer-related dataset.
Figure 2
Figure 2
Functional enrichment analyses for 76 differently expressed genes in gastric cancer tissues. Extracellular matrix-related terms or pathway were significantly enriched (A) Top ten enriched GO-biological process terms; (B) Top ten enriched GO-molecular function terms; (C) Top ten enriched GO-cellular component terms; (D) Top twelve enriched KEGG pathways. z-score=(fold change of upregulated genes- fold change of downregulated genes)/square root of gene count numbers. For each gene, the fold change used here was the average value calculated by the results obtained from four GEO datasets.
Figure 3
Figure 3
Protein–protein interaction network for 76 differently expressed genes in gastric cancer tissues. Nodes that have zero degrees value were excluded.
Figure 4
Figure 4
Functional enrichment analyses and level expression of seven differently expressed collagen genes in gastric cancer. (A) Top five enriched Go-terms for COL10A1, COL1A1, COL1A2, COL3A1, COL4A1, COL5A2, and COL6A3. (B) Heatmap showed all of these seven collagen genes were upregulated significantly in gastric cancer among four GEO dataset.
Figure 5
Figure 5
Association of seven candidate collagen genes with the overall survival rate of 876 patients with gastric cancer. The data were collected from KM plotter database. (A) COL1A1; (B) COL1A2; (C) COL3A1; (D) COL1A1; (E) COL5A2; (F) COL6A3; (G) COL10A1.
Figure 6
Figure 6
Association of seven candidate collagen genes with the overall survival rate of patients in the Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) dataset. (A) COL1A1; (B) COL1A2; (C) COL3A1; (D) COL1A1; (E) COL5A2; (F) COL6A3; (G) COL10A1. Survival curves were provided by online tool GEPIA (http://gepia.cancer-pku.cn/).
Figure 7
Figure 7
The overall survival rate of gastric cancer patients with different (high and low). (A) Data collected from GSE15459; (B) Data collected from TCGA-STAD. The risk score was calculated by the formula: Risk score=0.45COL1A1+0.48COL4A1.
Figure 8
Figure 8
Expression of COL1A1 and COL4A1 in the tissue samples from an independent in-house gastric cancer-related cohort. (A) qRT-PCR showed a higher COL1A1 and COL4A1 level in tumor tissues; (B) Representative images of immunohistochemical staining for COL1A1 and COL4A1 expression in tissue samples (100×). The higher positive rate of COL1A1 and COL4A1 was observed in tumor tissues. *P<0.05 compared with non-tumor tissues; (C) the expression difference folds of COL1A1 and COL4A1 between high expression and low expression groups.
Figure 9
Figure 9
Patients in the in-house cohort were stratified by the median of COL1A1, COL4A1 and risk score. The overall survival rate was analyzed and the diagnostic ability of risk score was analyzed. (A) Meidan of COL1A1 was used as cut-off; (B) Median of COL4A1 was used as cut-off; (C) Median of risk score was used as cut-off. The risk score was calculated by the formula: Risk score=0.45COL1A1+0.48COL4A1; (D) ROC analysis was conducted to further investigate the ability of risk score to distinguish tumor tissues from the non-tumor tissues.
Figure 10
Figure 10
microRNA-29c-3p was decreased in gastric cancer tissue and may target on the candidate collagen genes. (A) microRNAs that may target on COL10A1, COL1A1, COL1A2, COL4A1, and COL6A3 were predicted. Three members of the microRNA-29 family may target on both of candidate collagen genes. (B) The expression changes of three members of the microRNA-29 family in gastric cancer patients from TCGA-STAD dataset. *P<0.05 compared with non-tumor tissues.
Figure 11
Figure 11
microRNA-29c-3p directly target on COL1A1 and COL4A1. (A)Upregulation of microRNA-29c-3p reduced the mRNA level of COL1A1 and COL4A1; (B) Upregulation of microRNA-29c-3p decreased the protein level of COL1A1 and COL4A1; (C) and (D) Dual luciferase reporter assays indicated microRNA-29c-3p could bind to the 3ʹ-UTR of COL1A1 and COL4A1 mRNA directly. *P<0.05 compared with wild-type 3ʹUTR.
Figure 12
Figure 12
The effect of microRNA-29c-3p, COL1A1, and COL4A1 on proliferation of GC cells in vitro. (A) Upregulation of microRNA-29c-3p inhibited (B) knock-down COL1A1and COL4A1 inhibited proliferation of GC cells; (C) overexpression of COL1A1and COL4A1 promoted proliferation of GC cells.
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