Prediction of disease-free survival in hepatocellular carcinoma by gene expression profiling.

Published

Journal Article

BACKGROUND: Progression of hepatocellular carcinoma (HCC) often leads to vascular invasion and intrahepatic metastasis, which correlate with recurrence after surgical treatment and poor prognosis. The molecular prognostic model that could be applied to the HCC patient population in general is needed for effectively predicting disease-free survival (DFS). METHODS: A cohort of 286 HCC patients from South Korea and a second cohort of 83 patients from Hong Kong, China, were used as training and validation sets, respectively. RNA extracted from both tumor and adjacent nontumor liver tissues was subjected to microarray gene expression profiling. DFS was the primary clinical end point. Gradient lasso algorithm was used to build prognostic signatures. RESULTS: High-quality gene expression profiles were obtained from 240 tumors and 193 adjacent nontumor liver tissues from the training set. Sets of 30 and 23 gene-based DFS signatures were developed from gene expression profiles of tumor and adjacent nontumor liver, respectively. DFS gene signature of tumor was significantly associated with DFS in an independent validation set of 83 tumors (P = 0.002). DFS gene signature of nontumor liver was not significantly associated with DFS in the validation set (P = 0.827). Multivariate analysis in the validation set showed that DFS gene signature of tumor was an independent predictor of shorter DFS (P = 0.018). CONCLUSIONS: We developed and validated survival gene signatures of tumor to successfully predict the length of DFS in HCC patients after surgical resection.

Full Text

Duke Authors

Cited Authors

  • Lim, H-Y; Sohn, I; Deng, S; Lee, J; Jung, SH; Mao, M; Xu, J; Wang, K; Shi, S; Joh, JW; Choi, YL; Park, C-K

Published Date

  • November 2013

Published In

Volume / Issue

  • 20 / 12

Start / End Page

  • 3747 - 3753

PubMed ID

  • 23800896

Pubmed Central ID

  • 23800896

Electronic International Standard Serial Number (EISSN)

  • 1534-4681

Digital Object Identifier (DOI)

  • 10.1245/s10434-013-3070-y

Language

  • eng

Conference Location

  • United States