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Construction and analysis of protein-protein interaction network for esophageal squamous cell carcinoma.
Wang, Yanfeng; Cao, Yuhan; Wang, Yingcong; Sun, Junwei; Wang, Lidong; Song, Xin; Zhao, Xueke.
Afiliación
  • Wang Y; School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China.
  • Cao Y; School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China.
  • Wang Y; School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China. Electronic address: ying_cong_wang@163.com.
  • Sun J; School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China.
  • Wang L; State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
  • Song X; State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
  • Zhao X; State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of the First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
Comput Biol Med ; 182: 109156, 2024 Sep 13.
Article en En | MEDLINE | ID: mdl-39276610
ABSTRACT
Esophageal squamous cell carcinoma (ESCC) is a prevalent malignant tumor of the digestive tract. Clinical findings reveal that the five-year survival rate for mid-to late-stage ESCC patients is merely around 20 %, whereas those diagnosed at an early stage can achieve up to a 95 % survival rate. Consequently, early detection is paramount to improving ESCC patient survival. Protein markers are essential for diagnosing diseases, and the identification of new candidate proteins associated with ESCC through the protein-protein interaction (PPI) network is aimed for in this paper. The PPI network related to ESCC was constructed using protein data, comprising 2094 nodes and 19,660 edges. To assess the nodes' importance in the network, three metrics-degree centrality, betweenness centrality, and closeness centrality-were employed, leading to the identification of 81 key proteins. Subsequently, the biological significance of these proteins in the network was explored, combining biomedical knowledge from three perspectives network, node, and cluster. The results demonstrated that 52 out of 81 key proteins were confirmed to be linked to ESCC. Among the remaining 29 unreported proteins, 18 displayed significant biological significance, indicating their potential as protein markers related to ESCC.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos