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The Key Genes for Perineural Invasion in Pancreatic Ductal Adenocarcinoma Identified With Monte-Carlo Feature Selection Method

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is the most aggressive form of pancreatic cancer. Its 5-year survival rate is only 3–5%. Perineural invasion (PNI) is a process of cancer cells invading the surrounding nerves and perineural spaces. It is considered to be associated with the poor p...

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Detalles Bibliográficos
Autores principales: Zhu, Jin-Hui, Yan, Qiu-Liang, Wang, Jian-Wei, Chen, Yan, Ye, Qing-Huang, Wang, Zhi-Jiang, Huang, Tao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7593847/
https://www.ncbi.nlm.nih.gov/pubmed/33193628
http://dx.doi.org/10.3389/fgene.2020.554502
Descripción
Sumario:BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is the most aggressive form of pancreatic cancer. Its 5-year survival rate is only 3–5%. Perineural invasion (PNI) is a process of cancer cells invading the surrounding nerves and perineural spaces. It is considered to be associated with the poor prognosis of PDAC. About 90% of pancreatic cancer patients have PNI. The high incidence of PNI in pancreatic cancer limits radical resection and promotes local recurrence, which negatively affects life quality and survival time of the patients with pancreatic cancer. OBJECTIVES: To investigate the mechanism of PNI in pancreatic cancer, we analyzed the gene expression profiles of tumors and adjacent tissues from 50 PDAC patients which included 28 patients with perineural invasion and 22 patients without perineural invasion. METHOD: Using Monte-Carlo feature selection and Incremental Feature Selection (IFS) method, we identified 26 key features within which 15 features were from tumor tissues and 11 features were from adjacent tissues. RESULTS: Our results suggested that not only the tumor tissue, but also the adjacent tissue, was informative for perineural invasion prediction. The SVM classifier based on these 26 key features can predict perineural invasion accurately, with a high accuracy of 0.94 evaluated with leave-one-out cross validation (LOOCV). CONCLUSION: The in-depth biological analysis of key feature genes, such as TNFRSF14, XPO1, and ATF3, shed light on the understanding of perineural invasion in pancreatic ductal adenocarcinoma.