Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer. (9th December 2020)

• Record Type: Journal Article
• Authors: Yue, Taohua; Liu, Cheng; Zhu, Jing; Huang, Zhihao; Guo, Shihao; Zhang, Yuyang; Xu, Hao; Liu, Yucun; Wang, Pengyuan; Chen, Shanwen
• Other Names: Ulasoglu Celal Academic Editor.
• Is Part Of: BioMed research international. Volume 2020(2020)
• Publication Date: 2020-12-09
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2020/6135060
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Background. Colorectal cancer (CRC) is the second most common cause of cancer death in the United States and the third most common cancer globally. The incidence of CRC tends to be younger, and we urgently need a reliable prognostic assessment strategy. Methods. Protein expression profile and clinical information of 390 CRC patients/samples were downloaded from the TCPA and TCGA database, respectively. The Kaplan-Meier, Cox regression, and Pearson correlation analysis were applied in this study. Results. Based on the TCPA and TCGA database, we screened 6 hub proteins and first constructed protein risk signature, all of which were significantly associated with CRC patients' overall survival (OS). The risk score was an independent prognostic factor and significantly related with the size of the tumor in situ (T ). 6 hub proteins were differentially expressed in cancer and normal tissues and in different CRC stages, which were validated at the ONCOMINE database. Next, 40 coexpressed proteins of 6 hub proteins were extracted from the TCPA database. In the protein-protein interaction (PPI) network, HER1, HER2, and CTNNB1 were at the center. Function enrichment analysis illustrated that 46 proteins were mainly involved in the EGFR (HER1) tyrosine kinase inhibitor resistance pathway. Conclusion. Studies indicated that 6 hub proteins might be considered as new targets for CRC therapies, and the protein risk signature can be used to predict the OS of CRC patients.