A neural network model to screen feature genes for pancreatic cancer

被引:4
作者
Huang, Jing [1 ]
Zhou, Yuting [2 ]
Zhang, Haoran [1 ]
Wu, Yiming [1 ]
机构
[1] First Hosp Jiaxing, Dept Gastroenterol, Jiaxing 314001, Zhejiang, Peoples R China
[2] Jiangnan Univ, Hosp Joint Logist Support Force PLA 904, Dept Resp, Affiliated Hosp, Wuxi 214000, Jiangsu, Peoples R China
关键词
Pancreatic cancer; Neural network model; Biomarkers; Gene expression profiling; Random forest; CIGARETTE-SMOKING;
D O I
10.1186/s12859-023-05322-z
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
All the time, pancreatic cancer is a problem worldwide because of its high degree of malignancy and increased mortality. Neural network model analysis is an efficient and accurate machine learning method that can quickly and accurately predict disease feature genes. The aim of our research was to build a neural network model that would help screen out feature genes for pancreatic cancer diagnosis and prediction of prognosis. Our study confirmed that the neural network model is a reliable way to predict feature genes of pancreatic cancer, and immune cells infiltrating play an essential role in the development of pancreatic cancer, especially neutrophils. ANO1, AHNAK2, and ADAM9 were eventually identified as feature genes of pancreatic cancer, helping to diagnose and predict prognosis. Neural network model analysis provides us with a new idea for finding new intervention targets for pancreatic cancer.
引用
收藏
页数:14
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