Gene signature developed for predicting early relapse and survival in early-stage pancreatic cancer.

ABSTRACT:

Background

The aim of this study was to construct a predictive signature integrating tumour-mutation- and copy-number-variation-associated features using machine learning to precisely predict early relapse and survival in patients with resected stage I-II pancreatic ductal adenocarcinoma.

Methods

Patients with microscopically confirmed stage I-II pancreatic ductal adenocarcinoma undergoing R0 resection at the Chinese PLA General Hospital between March 2015 and December 2016 were enrolled. Whole exosome sequencing was performed, and genes with different mutation or copy number variation statuses between patients with and without relapse within 1 year were identified using bioinformatics analysis. A support vector machine was used to evaluate the importance of the differential

SUBMITTER: Huang L 

PROVIDER: S-EPMC10191445 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature