Gene signature associated with neuro-endocrine activity predicting prognosis of pancreatic carcinoma.

ABSTRACT:

Background

Genomic analysis is the promising tool to clear understanding of the tumorigenesis and guide molecular classification for pancreatic cancer. Our purpose was to develop a critical predictive model for prognosis in pancreatic carcinoma, based on the genomic data.

Methods

The online The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were queried as training and validation cohorts for comprehensive bioinformatic analysis. We applied Lasso and multivariate Cox regression to shrink genes and construct predictive model.

Results

A four genes model (DNAH10: HR = 0.71, 95% CI = 0.57-0.88, HSBP1L1: HR = 1.51, 95% CI = 1.18-1.92, KIAA0513: HR = 0.69, 95% CI = 0.50-0.96, and MRPL3: HR = 3.73, 95% CI = 2.03-6.86), was proposed and v

SUBMITTER: Chen K 

PROVIDER: S-EPMC6625361 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature