Annals of Surgical Oncology

, Volume 20, Issue 12, pp 3747–3753 | Cite as

Prediction of Disease-free Survival in Hepatocellular Carcinoma by Gene Expression Profiling

  • Ho-Yeong Lim
  • Insuk Sohn
  • Shibing Deng
  • Jeeyun Lee
  • Sin Ho Jung
  • Mao Mao
  • Jiangchun Xu
  • Kai Wang
  • Stephanie Shi
  • Jae Won Joh
  • Yoon La Choi
  • Cheol-Keun Park
Hepatobiliary Tumors



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).


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.


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).


We developed and validated survival gene signatures of tumor to successfully predict the length of DFS in HCC patients after surgical resection.


Intrahepatic Metastasis Nontumor Liver Microarray Gene Expression Profile Multicentric Occurrence Adjacent Nontumor Liver 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by Samsung Medical Center, Korea (PHO-1105301). The authors are grateful to Dr. Soonmyung Paik for scientific discussion.


The authors declare no conflict of interest.

Supplementary material

10434_2013_3070_MOESM1_ESM.doc (42 kb)
Supplementary material 1 (DOC 42 kb)
10434_2013_3070_MOESM2_ESM.tif (68 kb)
Study design. In the training set, tumor and adjacent non-tumor liver were profiled separately, and each was used to generate a prediction model, which was validated in the independent validation set. Supplementary material 2 (TIFF 68 kb)


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Copyright information

© Society of Surgical Oncology 2013

Authors and Affiliations

  • Ho-Yeong Lim
    • 1
  • Insuk Sohn
    • 2
  • Shibing Deng
    • 3
  • Jeeyun Lee
    • 1
  • Sin Ho Jung
    • 2
    • 5
  • Mao Mao
    • 3
  • Jiangchun Xu
    • 3
  • Kai Wang
    • 3
  • Stephanie Shi
    • 4
  • Jae Won Joh
    • 6
  • Yoon La Choi
    • 7
  • Cheol-Keun Park
    • 7
  1. 1.Division of Hematology-Oncology, Department of MedicineSamsung Medical Center, Sungkyunkwan University School of MedicineSeoulKorea
  2. 2.Samsung Cancer Research InstituteSeoulKorea
  3. 3.Oncology Research UnitPfizer IncorporatedSan DiegoUSA
  4. 4.External Research SolutionsPfizer IncorporatedSan DiegoUSA
  5. 5.Department of Biostatistics and BioinformaticsDuke UniversityDurhamUSA
  6. 6.Department of SurgerySamsung Medical Center, Sungkyunkwan University School of MedicineSeoulKorea
  7. 7.Department of PathologySamsung Medical Center, Sungkyunkwan University School of MedicineSeoulKorea

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