Abstract
Background and objectivesEach individual studies is limited to multi-factors and potentially lead to a significant difference of results among them. The present study aim to explore the critical genes related to the development of Esophageal squamous cell carcinoma (ESCC) by integrated transcriptomics and to investigate the clinical significance by experimental validation.MethodsDatasets of protein-coding genes expression which involved in ESCC were downloaded from Gene Expression Omnibus (GEO) database. The “Robustrankaggreg” package in language was used for data integration, and the different expression genes (DEGs) were identified based the cut-off criteria as follows: adjust p-value < 0.05, |fold change (FC)| ≥ 1.5; The protein expression of seed gene in 184 cases of primary ESCC tissues and 50 tumor adjacent normal tissues (at least 5 cm away from the tumor, and defind as the controls) were detected by immunohistochemistry; The relationship between the expression level of seed genes and clinical parameter were analyze. Enumeration data were represented by frequency or percentage (%) and were tested by x2 test. The P value of less than 0.05 was considered statistically significant.ResultsA total of 244 DEGs were identified by comparing gene expression patterns between ESCC patients and the controls based on integrating dataset of GSE77861, GSE77861, GSE100942, GSE26886, GSE17351, GSE38129, GSE33426, GSE20347 and GSE23400; The Cyclin-dependent kinase inhibitor 3 (CDKN3) were identified the top 1 seed gene of top cluster by use of protein-protein Interaction network and plug-in Molecular Complex Detection; The level of CDKN3 mRNA was significantly increased in ESCC patients compared to controls; The positive expression rate of CDKN3 protein in ESCC tissue samples was 32 and 61.4% in control, respectively. The correlations between the expression level of CDKN3 and lymph node metastasis or clinical staging of ESCC patients are statistically significant.ConclusionIntegrated transcriptomics is an efficient approach to system biology. By this procedure, our study improved the understanding of the transcriptome status of ESCC.
Highlights
Esophageal squamous cell carcinoma (ESCC) is a dominant malignant tumor, which accounts for mostly 90% of esophageal carcinoma [1]
different expression genes (DEGs) screening A total of 244 DEGs from 9 series of gene expression profiles were found after performing integrated analysis, of which 93 were upregulated and 151 were downregulated P < 0.05 and |Fold Change| > 1.5
protein-protein interaction (PPI) network construction and module mining To explore the biological functions of DEGs, a PPI network included 194 nodes and 864 edges was established via STRING (Fig. 2A)
Summary
Esophageal squamous cell carcinoma (ESCC) is a dominant malignant tumor, which accounts for mostly 90% of esophageal carcinoma [1]. Each individual study is limited to multi-factors such as sample sizes, batch effects, experimental conditions or so on, and potentially lead to a significant result difference among them. This problem implied that an effective in silico method to integrate those individual study could provide a more profound and valuable conclusion to screen the crucial genes of ESCC [5]. For this reason, In this study, robust rank aggregation (RRA) method was performed to integrate ESCC data from different public platforms to obtain different expression genes (DEGs) that were used to construct protein-protein interaction (PPI) and screen the hub genes. The present study aim to explore the critical genes related to the development of Esophageal squamous cell carcinoma (ESCC) by integrated transcriptomics and to investigate the clinical significance by experimental validation
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