Integrating a microRNA signature as a liquid biopsy-based tool for the early diagnosis and prediction of potential therapeutic targets in pancreatic cancer.

ABSTRACT:

Introduction

Pancreatic cancer is a highly aggressive cancer, and early diagnosis significantly improves patient prognosis due to the early implementation of curative-intent surgery. Our study aimed to implement machine-learning algorithms to aid in early pancreatic cancer diagnosis based on minimally invasive liquid biopsies.

Materials and methods

The analysis data were derived from nine public pancreatic cancer miRNA datasets and two sequencing datasets from 26 pancreatic cancer patients treated in our medical center, featuring small RNAseq data for patient-matched tumor and non-tumor samples and serum. Upon batch-effect removal, systematic analyses for differences between paired tissue and serum samples were performed. The robust rank aggregation (RRA) algorithm was used

SUBMITTER: Shi W 

PROVIDER: S-EPMC10781694 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature