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. 2020 Oct;9(10):6246-6262.
doi: 10.21037/tcr-20-1726.

Collagen family genes and related genes might be associated with prognosis of patients with gastric cancer: an integrated bioinformatics analysis and experimental validation

Kongyan Weng  1   2 , Yinger Huang  1   2 , Hao Deng  1   2 , Ruixue Wang  3 , Shuhong Luo  3 , Hongfeng Wu  1   2 , Jialing Chen  1   2 , Mingjian Long  4 , Wenbo Hao  1   2
Affiliations

Collagen family genes and related genes might be associated with prognosis of patients with gastric cancer: an integrated bioinformatics analysis and experimental validation

Kongyan Weng et al. Transl Cancer Res. 2020 Oct.

Abstract

Background: Gastric cancer (GC) is disease with a high morbidity. The purpose of this study was to identify genes essential to GC development in patients and to reveal the underlying mechanisms of progression.

Methods: Bioinformatics analysis is an effective tool for discovering essential genes of different disease states. We used the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs), the DAVID online tool to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of DEGs, the STRING database to construct the protein-protein interaction (PPI) network of DEGs, the Oncomine and the Cancer Genome Atlas-Stomach Adenocarcinoma (TCGA-STAD) databases to analyze the gene expression differences, the Human pan-Cancer Methylation database (MethHC) to compare the DNA methylation of genes, and the Kaplan-Meier plotter to show the survival analysis of DEGs. We performed Real-Time quantitative PCR (RT-qPCR) experiment to confirm our analysis results.

Results: After the integration of four Gene Expression Series (GSEs), we identified 407 DEGs. GO and KEGG pathway analysis indicated that the upregulated DEGs were significantly enriched in Extracellular Matrix (ECM) related functions and pathways. The main DEGs were collagens (COLs). Moreover, the downregulated DEGs were enriched in ethanol oxidation. Several groups of DEGs, such as insulin-like growth factor binding protein (IGFBP), collagen (COL) and serpin peptidase inhibitors (SERPIN) gene families, constituted several PPI networks. In the Oncomine database, all of the collagen genes were highly expressed in breast cancer, esophageal cancer, GC, head and neck cancer and pancreatic cancer, compared with normal tissues. Consistently, from the TCGA-STAD database, most of the collagens (COLs) were highly expressed and exhibited methylated variation in GC patients. In GC patients, some of these collagen (COL) genes related to worse prognosis, as evidenced by the results from the Kaplan-Meier plotter database analysis. Our RT-qPCR results showed that collagen type III α1 chain (COL3A1) was highly expressed in GC cells. Collagen type V α1 chain (COL5A1) was highly expressed, except in AGS cells, which was consistent with our analysis.

Conclusions: Collagen (COL) family genes might serve as progression and prognosis markers of GC.

Keywords: Gastric cancer (GC); bioinformatics analysis; collagens; experimental validation; prognosis.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr-20-1726). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The distribution of differentially expressed genes between Gene Expression Series, GSE26899, GSE29272, GSE79973 and GSE54129. (A) The distribution of upregulated genes. (B) The distribution of down regulated genes.
Figure 2
Figure 2
Module (A) and 5 submodules (B-F) of protein-protein interaction (PPI) network. Line color indicates the type of interaction evidence. The number of lines between two genes indicate the level of interaction between the two genes.
Figure 3
Figure 3
mRNA levels of collagen isoforms in different cancers (Oncomine). The counts of datasets with statistically significant collagens mRNA down-regulation (blue) or up-regulation (red) (normal tissues versus corresponding different cancers) are shown. Threshold setting: gene rank, top 10%; fold change, 2; P value, 0.01. The figures in the colored box represent the numbers of datasets meeting the threshold.
Figure 4
Figure 4
Uaclan database showed that mRNA expression of collagen family genes differed between primary tumor and corresponding normal tissues in gastric cancer patients using (A-J). The blue box represents normal tissue; red box represents tumor tissue. Only P<0.05 was shown.
Figure 5
Figure 5
The methylation of collagen isoforms in gastric cancer and normal tissues (MethHC). Box plots in red color represent cancer samples and those in green color represent normal samples. **, indicates P<0.005. GC, gastric cancer.
Figure 6
Figure 6
Different mRNA levels of collagen genes prognostic values in gastric cancer patients (Kaplan-Meier plotter). Kaplan-Meier plots show the relationship between OS (A), FP (B) and PPS (C) and the expression of collagens in gastric cancer patients, with hazard ratio (HR) and statistical significance.
Figure 7
Figure 7
The expression of COL3A1 and COL5A1 mRNA in different gastric cancer cells. *, indicates folds change from 2 to 10; ***, indicates folds change from 100 to 500; ****, indicates folds higher than 1,000.
Figure S1
Figure S1
Different mRNA level of collagens’ prognostic values in diffuse subtype gastric cancer patients (Kaplan-Meier plotter). Notes: Kaplan-Meier plots show the relationship between OS (A), FP (B) and PPS (C) and the expression of collagens in gastric cancer patients, respectively, with hazard ratio (HR) and statistical significance.
Figure S2
Figure S2
Different mRNA level of collagens’ prognostic values in intestinal subtype gastric cancer patients (Kaplan-Meier plotter). Notes: Kaplan-Meier plots show the relationship between OS (A), FP (B) and PPS (C) and the expression of collagens in gastric cancer patients, respectively, with hazard ratio (HR) and statistical significance.
Figure S3
Figure S3
Different mRNA level of collagens’ prognostic values in mixed subtype gastric cancer patients (Kaplan-Meier plotter). Notes: Kaplan-Meier plots show the relationship between OS (A), FP (B) and PPS (C) and the expression of collagens in gastric cancer patients, respectively, with hazard ratio (HR) and statistical significance.
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References

    1. Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394-424. 10.3322/caac.21492 - DOI - PubMed
    1. Zhang H, Wang X, Huang H, et al. Hsa_circ_0067997 promotes the progression of gastric cancer by inhibition of miR-515-5p and activation of X chromosome-linked inhibitor of apoptosis (XIAP). Artif Cells Nanomed Biotechnol 2019;47:308-18. 10.1080/21691401.2018.1553787 - DOI - PubMed
    1. Wang YN, Xu F, Zhang P, et al. MicroRNA-575 regulates development of gastric cancer by targeting PTEN. Biomed Pharmacother 2019;113:108716. 10.1016/j.biopha.2019.108716 - DOI - PubMed
    1. Shah MA, Xu RH, Bang YJ, et al. HELOISE: Phase IIIb Randomized Multicenter Study Comparing Standard-of-Care and Higher-Dose Trastuzumab Regimens Combined With Chemotherapy as First-Line Therapy in Patients With Human Epidermal Growth Factor Receptor 2-Positive Metastatic Gastric or Gastroesophageal Junction Adenocarcinoma. J Clin Oncol 2017;35:2558-67. 10.1200/JCO.2016.71.6852 - DOI - PubMed
    1. Sedlazeck FJ, Lee H, Darby CA, et al. Piercing the dark matter: bioinformatics of long-range sequencing and mapping. Nat Rev Genet 2018;19:329-46. 10.1038/s41576-018-0003-4 - DOI - PubMed