Transcriptome Network Analysis Reveals Potential Candidate Genes for Esophageal Squamous Cell Carcinoma

  • Ma, Zheng (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Guo, Wei (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Niu, Hui-Jun (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Yang, Fan (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Wang, Ru-Wen (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Jiang, Yao-Guang (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University) ;
  • Zhao, Yun-Ping (Department of General Thoracic Surgery, Daping Hospital and Institute of Surgery Research, The Third Military Medical University)
  • Published : 2012.03.31


The esophageal squamous cell carcinoma (ESCC) is an aggressive tumor with a poor prognosis. Understanding molecular changes in ESCC should improve identification of risk factors with different molecular subtypes and provide potential targets for early detection and therapy. Our study aimed to obtain a molecular signature of ESCC through the regulation network based on differentially expressed genes (DEGs). We used the GSE23400 series to identify potential genes related to ESCC. Based on bioinformatics we constructed a regulation network. From the results, we could establish that many transcription factors and pathways closely related with ESCC were linked by our method. STAT1 also arose as a hub node in our transcriptome network, along with some transcription factors like CCNB1, TAP1, RARG and IFITM1 proven to be related with ESCC by previous studies. In conclusion, our regulation network provided information on important genes which might be useful in investigating the complex interacting mechanisms underlying the disease.


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