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Gene function analysis and underlying mechanism of esophagus cancer based on microarray gene expression profiling

2017; Impact Journals LLC; Volume: 8; Issue: 62 Linguagem: Inglês

10.18632/oncotarget.22160

1949-2553

Ying Yue, Mengjia Song, Yamin Qiao, Pupu Li, Yiqiang Yuan, Jingyao Lian, Suying Wang, Yi Zhang,

Esophageal Cancer Research and Treatment

// Ying Yue 1, 2, 3, * , Mengjia Song 1, 2, * , Yamin Qiao 1, 2, * , Pupu Li 1, 2 , Yiqiang Yuan 3 , Jingyao Lian 1, 2 , Suying Wang 4 and Yi Zhang 1, 2, 5, 6 1 Biotherapy Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China 2 Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450052, China 3 The No.7. People's Hospital of Zhengzhou, Zhengzhou, Henan 450016, China 4 Clinical Laboratory, Hebi People's Hospital, Hebi 458030, China 5 School of Life Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China 6 Key Laboratory for Tumor Immunology and Biotherapy of Henan Province, Zhengzhou, Henan 450052, China * These authors have contributed equally to this work Correspondence to: Yi Zhang, email: yizhang@zzu.edu.cn Keywords: esophagus cancer; gene function analysis; microarray gene expression profiling Received: February 23, 2017 Accepted: August 28, 2017 Published: October 30, 2017 ABSTRACT Esophageal cancer (EC) is one of the most common digestive malignant tumors worldwide. Over the past decades, there have been minimal improvements in outcomes for patients with EC. New targets and novel therapies are needed to improve outcomes for these patients. This study aimed to explore the molecular mechanisms of EC by integrated bioinformatic analyses of the feature genes associated with EC and correlative gene functions which can distinguish cancerous tissues from non-cancerous tissues. Gene expression profile GSE20347 was downloaded from Gene Expression Omnibus (GEO) database, including 17 EC samples and their paired adjacent non-cancerous samples. The differentially expressed genes (DEGs) between EC and normal specimens were identified and then applied to analyze the GO enrichment on gene functions and KEGG pathways. Corresponding Pathway Relation Network (Pathway-net) and Gene Signal Network (signal-net) of DEGs were established based on the data collected from GCBI datasets. The results showed that DEGs mainly participated in the process of cell adhesion, cell proliferation, survival, invasion, metastasis and angiogenesis. Aberrant expression of PTK2, MAPK signaling pathway, PI3K-Akt signaling pathway, p53 signaling pathway and MET were closely associated with EC carcinogenesis. Importantly, Interleukin 8 (IL8) and C-X-C chemokine receptor type 7 (CXCR-7) were predicted to be significantly related to EC. These findings were further validated by analyzing both TCGA database and our clinical samples of EC. Our discovery provides a registry of genes and pathways that are disrupted in EC, which has the potential to be used in clinic for diagnosis and target therapy of EC in future.