- Email: [email protected]
Network-based discovery of gene signature for vascular invasion prediction in HCC
JOURNAL OF HEPATOLOGY
Network-based discovery of gene signature for vascular invasion prediction in HCC To the Editor: We read with great interest the paper by Minguez and colleagues [1]. In this study, the authors successfully defined a 35-gene signature of vascular invasion (VI) by gene expression profiling of HCV-related hepatocellular carcinoma (HCC) samples, and validated this gene panel in an independent mixed cohort of patients with various etiologies, including HBV, HCV, and alcohol. It is already known that VI can predict recurrence and survival in HCC patients after tumor resection or liver transplantation [2]. The signature may be of help during candidate selection for liver transplantation, and as a guide to therapeutic intervention. However, the signature genes may be biased as the authors used specific HCV-HCC patients for VI signature discovery. Moreover, some useful information may be lost when using the traditional gene differential expression analysis as described. The largest change in gene expression does not necessarily mean the greatest contribution to VI. We believe that their signature is not a unique one. Therefore, we wanted to explore more generalized candidate genes for VI in HCC using a state-of-the-art systems biology method. Compared to the differential expression, which compares mean expression levels of individual genes between two or more groups, weighted gene co-expression network analysis (WGCNA) can utilize the inherent variability in microarray data that exists among biological samples to uncover higher-order relationships among gene products [3]. Genes that are highly connected are referred to as hubs which have been shown to be important in disease and in controlling module behavior [4]. WGCNA was performed on GSE20017 [1], which contains HCC samples of various etiologies, to eliminate any possible bias caused by sampling. Twelve significant VI related modules (p <0.05) were identified in VI network. Since not every gene contributes equally to VI, 12 hub genes ARID3A, PTTG1, TNPO2, MRPS16, HIST1H2AE, CD86, FYB, C8B, GHR, IFI44, TXNDC12 and GLYAT were used to construct a support vector machine (SVM) classifier using GSE9843 as training set. To obtain a classifier with better performance, recursive feature elimination procedure was run to reject those genes bearing weak correlation with VI. Nine genes
Survival probability
1.0 0.8 0.6 0.4 0.2
N0 = 46 N1 = 34 p = 0.001
0.0 0
5
10 Survival (yr)
15
20
Fig. 1. The 9-gene VI classifier was tested in the GSE10141 dataset. Kaplan– Meier curve for the overall survival of HCC in the GSE10141 test set. N0 and N1 represent numbers of patients classified in VI negative and positive groups respectively. p value refers to the difference in mean survival time between the two groups (Log–Rank test).
(GHR, IFI44 and TXNDC12 were rejected) were retained after this step. Prediction of VI was achieved with an accuracy of 77%, a specificity of 69% and a negative predictive value of 80%, which outweighed accuracy of the original work. To evaluate the clinical value of our 9-gene signature, a previous published HCC dataset [5] with patient survival information was classified using the SVM classifier. The Kaplan–Meier survival plot for the VI negative and positive patients showed that the 9-gene signature had a favorable prognosis function (Fig. 1). Interestingly, only GLYATL1 overlapped with Minguez’s 35-gene signature, indicating that our 9-gene signature derived from an alternative method may complement the 35-gene signature from the study by Minguez et al. In summary, network-based large-scale gene expression analysis provides an alternative powerful tool for liver disease-related target gene discovery. Conflict of interest The authors who have taken part in this study declared that they do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript. Financial support This work was partially supported by Chinese State Key Projects for Basic Research (‘‘973 Program’’) (Nos. 2011CB910601, 2011CB910700, 2010CB912700 and 2011CB505304), National Natural Science Foundation of China (Nos. 30972909, 81000192, 81001470, 81170399 and 81010064), International Scientific Collaboration Program (Nos. 2009DFB33070, 2010DFA31260 and 2011DFB30370) and State Key Laboratory of Proteomics (Nos. SKLP-Y200901 and SKLP-O200901). References [1] Minguez B, Hoshida Y, Villanueva A, Toffanin S, Cabellos L, Thung S, et al. Gene-expression signature of vascular invasion in hepatocellular carcinoma. J Hepatol 2011;55:1325–1331. [2] Thuluvath PJ. Vascular invasion is the most important predictor of survival in HCC, but how do we find it? J Clin Gastroenterol 2009;43:101–102. [3] Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005;4. Article17. [4] Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA 2006;103:17402–17407. [5] Hoshida Y, Villanueva A, Kobayashi M, Peix J, Chiang DY, Camargo A, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 2008;359:1995–2004.
Wei Liu Fuchu He ⇑ Ying Jiang State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China ⇑ Corresponding author: Tel.: +86 10 80705299; fax: +86 10 80705002 E-mail address: [email protected]
Journal of Hepatology 2012 vol. 56 j 1420–1429
1423
Network-based discovery of gene signature for vascular invasion prediction in HCC To the Editor: We read with great interest the paper by Minguez and colleagues [1]. In this study, the authors successfully defined a 35-gene signature of vascular invasion (VI) by gene expression profiling of HCV-related hepatocellular carcinoma (HCC) samples, and validated this gene panel in an independent mixed cohort of patients with various etiologies, including HBV, HCV, and alcohol. It is already known that VI can predict recurrence and survival in HCC patients after tumor resection or liver transplantation [2]. The signature may be of help during candidate selection for liver transplantation, and as a guide to therapeutic intervention. However, the signature genes may be biased as the authors used specific HCV-HCC patients for VI signature discovery. Moreover, some useful information may be lost when using the traditional gene differential expression analysis as described. The largest change in gene expression does not necessarily mean the greatest contribution to VI. We believe that their signature is not a unique one. Therefore, we wanted to explore more generalized candidate genes for VI in HCC using a state-of-the-art systems biology method. Compared to the differential expression, which compares mean expression levels of individual genes between two or more groups, weighted gene co-expression network analysis (WGCNA) can utilize the inherent variability in microarray data that exists among biological samples to uncover higher-order relationships among gene products [3]. Genes that are highly connected are referred to as hubs which have been shown to be important in disease and in controlling module behavior [4]. WGCNA was performed on GSE20017 [1], which contains HCC samples of various etiologies, to eliminate any possible bias caused by sampling. Twelve significant VI related modules (p <0.05) were identified in VI network. Since not every gene contributes equally to VI, 12 hub genes ARID3A, PTTG1, TNPO2, MRPS16, HIST1H2AE, CD86, FYB, C8B, GHR, IFI44, TXNDC12 and GLYAT were used to construct a support vector machine (SVM) classifier using GSE9843 as training set. To obtain a classifier with better performance, recursive feature elimination procedure was run to reject those genes bearing weak correlation with VI. Nine genes
Survival probability
1.0 0.8 0.6 0.4 0.2
N0 = 46 N1 = 34 p = 0.001
0.0 0
5
10 Survival (yr)
15
20
Fig. 1. The 9-gene VI classifier was tested in the GSE10141 dataset. Kaplan– Meier curve for the overall survival of HCC in the GSE10141 test set. N0 and N1 represent numbers of patients classified in VI negative and positive groups respectively. p value refers to the difference in mean survival time between the two groups (Log–Rank test).
(GHR, IFI44 and TXNDC12 were rejected) were retained after this step. Prediction of VI was achieved with an accuracy of 77%, a specificity of 69% and a negative predictive value of 80%, which outweighed accuracy of the original work. To evaluate the clinical value of our 9-gene signature, a previous published HCC dataset [5] with patient survival information was classified using the SVM classifier. The Kaplan–Meier survival plot for the VI negative and positive patients showed that the 9-gene signature had a favorable prognosis function (Fig. 1). Interestingly, only GLYATL1 overlapped with Minguez’s 35-gene signature, indicating that our 9-gene signature derived from an alternative method may complement the 35-gene signature from the study by Minguez et al. In summary, network-based large-scale gene expression analysis provides an alternative powerful tool for liver disease-related target gene discovery. Conflict of interest The authors who have taken part in this study declared that they do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript. Financial support This work was partially supported by Chinese State Key Projects for Basic Research (‘‘973 Program’’) (Nos. 2011CB910601, 2011CB910700, 2010CB912700 and 2011CB505304), National Natural Science Foundation of China (Nos. 30972909, 81000192, 81001470, 81170399 and 81010064), International Scientific Collaboration Program (Nos. 2009DFB33070, 2010DFA31260 and 2011DFB30370) and State Key Laboratory of Proteomics (Nos. SKLP-Y200901 and SKLP-O200901). References [1] Minguez B, Hoshida Y, Villanueva A, Toffanin S, Cabellos L, Thung S, et al. Gene-expression signature of vascular invasion in hepatocellular carcinoma. J Hepatol 2011;55:1325–1331. [2] Thuluvath PJ. Vascular invasion is the most important predictor of survival in HCC, but how do we find it? J Clin Gastroenterol 2009;43:101–102. [3] Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005;4. Article17. [4] Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, et al. Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. Proc Natl Acad Sci USA 2006;103:17402–17407. [5] Hoshida Y, Villanueva A, Kobayashi M, Peix J, Chiang DY, Camargo A, et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 2008;359:1995–2004.
Wei Liu Fuchu He ⇑ Ying Jiang State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 102206, China ⇑ Corresponding author: Tel.: +86 10 80705299; fax: +86 10 80705002 E-mail address: [email protected]
Journal of Hepatology 2012 vol. 56 j 1420–1429
1423