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 Table of Contents  
Year : 2018  |  Volume : 14  |  Issue : 8  |  Page : 243-247

Identification of key genes related to high-risk gastrointestinal stromal tumors using bioinformatics analysis

1 Department of Anesthesia, Jinan Central Hospital Affiliated to Shandong University, Jinan, 250013, Shandong Province, China
2 Department of Gastroenterology, Chinese PLA General Hospital, Beijing, 100853, China

Date of Web Publication26-Mar-2018

Correspondence Address:
Shuan Jin
Department of Anesthesia, Jinan Central Hospital Affiliated to Shandong University, 105# Jiefang Road, Jinan, 250013, Shandong Province
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/0973-1482.207068

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 > Abstract 

Aim: The purpose of this study was to identify predictive biomarkers used for clinical therapy and prognostic evaluation of high-risk gastrointestinal stromal tumors (GISTs).
Materials and Methods: In this study, microarray data GSE31802 were used to identify differentially expressed genes (DEGs) between high-risk GISTs and low-risk GISTs. Then, enrichment analysis of DEGs was conducted based on the gene ontology and kyoto encyclopedia of genes and genomes pathway database. In addition, the transcription factors and cancer-related genes in DEGs were screened according to the TRANSFAC, TSGene, and TAG database. Finally, protein–protein interaction (PPI) network was constructed and analyzed to look for critical genes involved in high-risk GISTs.
Results: A total of forty DEGs were obtained and these genes were mainly involved in four pathways, including melanogenesis, neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage. The enriched biological processes were related to the regulation of insulin secretion, integrin activation, and neuropeptide signaling pathway. Transcription factor analysis of DEGs indicated that POU domain, class 2, associating factor 1 (POU2AF1) was significantly downregulated in high-risk GISTs. By constructing the PPI network of DEGs, ten genes with high degrees formed local networks, such as PNOC, P2RY14, and SELP.
Conclusions: Four genes as POU2AF1, PNOC, P2RY14, and SELP might be used as biomarkers for prognosis of high-risk GISTs.

Keywords: Enrichment analysis, gastrointestinal stromal tumors, genes, protein–protein interaction network

How to cite this article:
Jin S, Zhu W, Li J. Identification of key genes related to high-risk gastrointestinal stromal tumors using bioinformatics analysis. J Can Res Ther 2018;14, Suppl S1:243-7

How to cite this URL:
Jin S, Zhu W, Li J. Identification of key genes related to high-risk gastrointestinal stromal tumors using bioinformatics analysis. J Can Res Ther [serial online] 2018 [cited 2021 Dec 4];14:243-7. Available from: https://www.cancerjournal.net/text.asp?2018/14/8/243/207068

 > Introduction Top

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms, which arise throughout the entire gastrointestinal tract [1],[2] and probably originate from interstitial cells of Cajal or their precursor cells.[3] They usually occur in the stomach (50%–60%) and the small intestine (30%–35%), but less frequently arise in the colon and rectum (5%) and the esophagus (<1%).[4] It has been reported that more than 80% patients with GISTs are older than 50 years (median 63 years) despite GISTs can arise at any age.[4],[5]

Complete surgical resection is the primary treatment for GISTs. However, it is not curative for high-risk GISTs due to the high recurrence rate.[6],[7] The risk of relapse is usually estimated based on mitotic rate, tumor size, and tumor site. According to the risk definition system proposed by Fletcher et al.,[8] mitotic rate and tumor size are leading indicators in the risk stratification of tumors, and GISTs are divided into four risk grades: very low risk, low risk, intermediate risk, and high risk. This classification is extended by adding tumor site into the estimation criteria in 2006.[9] Thus, mitotic rate, tumor size, and localization are the key factors used for distinguishing benign from malignant GISTs or at least estimating malignant potential. However, molecular markers are more objective and accurate for identifying different risk grades of GISTs. Most (75%–80%) GISTs have mutations of KIT proto-oncogene, and one-third of GISTs without KIT mutation carry exclusive mutations in the PDGFRA gene.[10],[11],[12] Although the two genes can serve as crucial diagnostic of GISTs, they do not fully explain the progression of malignant GISTs. Therefore, other potential markers are required for the diagnosis and therapy of high-risk GISTs.

In this study, the gene expression profiles GSE31802 were downloaded to screen differentially expressed genes (DEGs) between high-risk GISTs and low-risk GISTs. Then, the gene ontology (GO) enrichment analysis and kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed to study the biological process (BP) Function and pathways related with DEGs. Moreover, analysis of transcription factors and cancer-related genes in DEGs were conducted. Finally, protein–protein interaction (PPI) network was constructed by cytoscape to find key genes involved in high-risk GISTs.

 > Materials and Methods Top

Affymetrix microarray data

The raw microarray data GSE31802 were downloaded from Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), which was deposited by Niinuma et al.[13] A total of 11 chips were available for analysis in our study, including 8 chips of high-risk GIST specimens and 3 chips of low-risk GIST specimens from surgical resections. The GPL4133 Agilent-014850 Whole Human Genome Microarray 4 × 44K G4112F was used as microarray platform.

Data preprocessing and differentially expressed genes screening

The raw data were preprocessed utilizing limma package in bioconductor [14] and affy annotation files from BrainArray Laboratory.[15] Background correction, quantile normalization, and probe summarization of the microarray data were performed by robust multiarray average algorithm [16] to obtain the gene expression matrix.

The Bayesian method was used to identify genes that were significantly, differentially expressed between high-risk GISTs and low-risk GISTs based on limma package. The genes with the cutoff criteria of |log2 FC| >1 and false discovery rate (FDR) <0.05 were selected as DEGs.

Enrichment analysis of differentially expressed genes

The Database for Annotation, Visualization and Integrated Discovery (DAVID)[17] is a comprehensive set of functional annotation tools used for GO enrichment analysis.[18] Based on DAVID online program, GO-BP functional analysis of DEGs was performed for DEGs in this study. KEGG pathway database,[19] which contains graphical representations of cellular processes, was applied to identify the main functional and metabolic pathways of DEGs. P < 0.05 was chosen as the cutoff criterion for both GO and KEGG enrichment analysis.

Functional annotation of differentially expressed genes

To study the transcriptional regulation function of DEGs, the screened genes were analyzed according to the TRANSFAC transcription factor database.[20] Furthermore, we extracted all the proto-oncogenes and cancer suppressor genes in DEGs based on TSGene [21] and TAG database.[22]

Construction of protein–protein interaction network

DEGs were submitted to STRING version 9.1 (http://string-db.org)[23] to search interaction relationships of the proteins, and the confidence score >0.9 was used as the cutoff criterion. Then, the cytoscape [24] was applied to construct the PPI network based on the protein interactions. Finally, the hub nodes of PPI were obtained by connectivity degree analysis in our study.

 > Results Top

Differentially expressed genes analysis

The Bayesian method was used to screen DEGs between high-risk GISTs and low-risk GISTs. A total of 40 DEGs with |log2 FC| >1 and FDR < 0.05 were identified, including 4 upregulated DEGs and 36 downregulated DEGs.

Enrichment and functional analysis of differentially expressed genes

GO and KEGG pathway enrichment analysis were performed for both the upregulated and downregulated DEGs. The GO analysis results showed that upregulated DEGs were not enriched in any BP. The GO terms in BP category of downregulated DEGs included regulation of insulin secretion, integrin activation, and neuropeptide signaling pathway as shown in [Table 1]. The results of KEGG pathway enrichment analysis indicated that upregulated DEGs were enriched in melanogenesis while downregulated DEGs were mainly enriched in three pathways: neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage [Table 2].
Table 1: The enriched gene ontology terms of downregulated differentially expressed genes

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Table 2: The enriched kyoto encyclopedia of genes and genomes pathway of differentially expressed genes

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By analyzing the transcription factors and tumor-associated genes in DEGs, we found that only transcription factor POU domain, class 2, associating factor 1 (POU2AF1) was significantly downregulated in high-risk GISTs.

Construction of protein–protein interaction network

To systemically analyze the functions of DEGs in GISTs samples, the PPI network of DEGs was constructed using cytoscape in accordance with protein interactions. From [Figure 1], we discovered that the DEGs of PNOC (degree = 51), P2RY14 (degree = 48), KCNA5 (degree = 43), GPC5 (degree = 26), BLK (degree = 26), PTGIR (degree = 26), FZD8 (degree = 20), SELP (degree = 17), PDE1B (degree = 13), and NDST4 (degree = 10) formed local networks with high degrees. However, the degrees of other proteins were <10.
Figure 1: Protein–protein interaction network of differentially expressed genes. Red nodes represent upregulated differentially expressed genes; green nodes represent downregulated differentially expressed genes; yellow nodes represent normal genes

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 > Discussion Top

In this study, we used bioinformatics methods to investigate the molecular mechanism of high-risk GISTs and find potential biomarkers for clinical therapy and prognostic evaluation of malignant GISTs. The results showed that the expression of forty genes was altered in high-risk GISTs compared with low-risk GISTs. These genes were mainly enriched in insulin secretion, integrin activation, and neuropeptide signaling pathway by GO analysis. Pathways of melanogenesis, neuroactive ligand-receptor interaction, malaria, and hematopoietic cell lineage were involved in high-risk GISTs. By transcription factor analysis of DEGs, only POU2AF1 was significantly downregulated in high-risk GISTs. In addition, genes with high degrees were obtained according to the PPI network, such as PNOC, P2RY14 and SELP.

The previous studies indicate that metastasis often arises in malignant tumors, such as colorectal cancer,[25] breast cancer,[26] and GISTs.[27] It has been reported that about 40% of localized GISTs will give rise to metastases.[4] Metastases of GISTs have a predilection to the liver, peritoneum, and other intra-abdominal sites.[28] Our study showed that integrin activation was one of the main enriched BPs involved in high-risk GISTs. The research of Felding-Habermann et al.[29] suggested that integrin activation controlled breast cancer metastasis. Integrins can activate focal adhesion kinase (FAK) which is an important molecule signal for tumor progression and metastasis.[30],[31],[32] Reports showed that FAK was activated by integrins in GISTs with imatinib treatment and inhibition of FAK was used as a potential therapeutic strategy for imatinib-resistant GISTs.[33],[34] Therefore, integrin activation might be closely associated with GISTs. These evidence also demonstrated that the analysis of our study was reliable.

POU2AF1 that constitutively expressed in B lymphocytes is a B-cell-specific transcriptional coactivator.[35] The functions of POU2AF1 in B cell proliferation and differentiation have been extensively studied.[36],[37],[38] Moreover, POU2AF1 plays an important role in malignancy, such as lymphocytic leukemia,[39] T-cell neoplasms,[40] and multiple myeloma.[41] Experimental results showed that the activation of POU2AF1 contributed to the progression of multiple myeloma by directly regulating expression of TNFRSF17 (B-cell maturation factor).[42] Consequently, we speculated that POU2AF1 might be a crucial transcription factor involved in the progression of GISTs.

The results of PPI network indicated that PNOC, P2RY14, and SELP were the key nodes with high degrees. PNOC (prepronociceptin) is the precursor protein of nociceptin and has been proved to be associated with neuroblastoma.[43] Zaveri et al.[44] revealed that the mRNA expression of PNOC was upregulated in the NS20Y neuroblastoma cells, and PNOC was closely related to cellular differentiation. However, there was no report about the function of PNOC in GISTs. Further study is needed to demonstrate the relationship between PNOC and GISTs. P2RY14 belongs to the family of G-protein coupled receptors for UDP-glucose and related sugar nucleotides.[45],[46] It has been showed that P2RY14 can trigger innate mucosal immune responses in human and mouse female reproductive tract.[47] In 2012, Wu et al.[48] discovered that P2RY14 might be one of the lung adenocarcinoma marker genes by bioinformatics analysis, but lack of experimental evidence. P2RY14 was one of the important genes that might be related to GISTs in our study; further experiments were needed as well. SELP (P-selectin, CD62, GMP 140) is a member of the selectin family of adhesion receptors that may be involved in tumor metastasis.[49] Early researches have shown that SELP has an essential role in different tumors, such as breast cancer, neuroblastoma, and lung cancer.[50],[51] Recent studies indicated that SELP could be used to explore the effective treatment of lung cancer.[52],[53] However, SELP has not been reported to be involved in GISTs. In our study, SELP was found to interact with CD34 based on PPI network. It has been proved that CD34 is expressed in roughly 70% of GISTs [54] and can be used as a biomarker for the diagnosis of GISTs.[55],[56] Therefore, SELP might play an important role in GISTs.

In summary, the genes identified in this study were potential biomarkers of high-risk GISTs, which might contribute to the therapy and prognostic evaluation of this disease. However, these genes were only analyzed by bioinformatics methods. Experiments are still needed to validate the relationship between the genes and GISTs.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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