A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels.

ABSTRACT:

Objective

Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy.

Methods

Microarray datasets related with FAP were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes were identified by FAP vs. normal samples and FAP and duodenal cancer vs. normal samples. Furthermore, functional enrichment analyses of these differentially expressed genes were performed. A support vector machine (SVM) was performed to train and validate cancer risk prediction model.

Results

A total of 196 differentially expressed genes were identified between FAP compared with normal samples. 177 similarly expressed gene

SUBMITTER: Liu W 

PROVIDER: S-EPMC7315318 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature