Recurrence-associated pathways in hepatitis B virus-positive hepatocellular carcinoma.

BACKGROUND: Despite the recent identification of several prognostic gene signatures, the lack of common genes among experimental cohorts has posed a considerable challenge in uncovering the molecular basis underlying hepatocellular carcinoma (HCC) recurrence for application in clinical purposes. To overcome the limitations of individual gene-based analysis, we applied a pathway-based approach for analysis of HCC recurrence. RESULTS: By implementing a permutation-based semi-supervised principal component analysis algorithm using the optimal principal component, we selected sixty-four pathways associated with hepatitis B virus (HBV)-positive HCC recurrence (p < 0.01), from our microarray dataset composed of 142 HBV-positive HCCs. In relation to the public HBV- and public hepatitis C virus (HCV)-positive HCC datasets, we detected 46 (71.9%) and 18 (28.1%) common recurrence-associated pathways, respectively. However, overlap of recurrence-associated genes between datasets was rare, further supporting the utility of the pathway-based approach for recurrence analysis between different HCC datasets. Non-supervised clustering of the 64 recurrence-associated pathways facilitated the classification of HCC patients into high- and low-risk subgroups, based on risk of recurrence (p < 0.0001). The pathways identified were additionally successfully applied to discriminate subgroups depending on recurrence risk within the public HCC datasets. Through multivariate analysis, these recurrence-associated pathways were identified as an independent prognostic factor (p < 0.0001) along with tumor number, tumor size and Edmondson's grade. Moreover, the pathway-based approach had a clinical advantage in terms of discriminating the high-risk subgroup (N = 12) among patients (N = 26) with small HCC (<3 cm). CONCLUSIONS: Using pathway-based analysis, we successfully identified the pathways involved in recurrence of HBV-positive HCC that may be effectively used as prognostic markers.

Integrated analysis of prognostic gene expression profiles from hepatitis B virus-positive hepatocellular carcinoma and adjacent liver tissue.

BACKGROUND: The tissue environment in the region of hepatocellular carcinoma (HCC) influences both vascular invasion and recurrence. Thus, HCC patient prognosis depends on the characteristics not only of the tumor but also those of adjacent surrounding liver tissue. MATERIALS AND METHODS: Expression profiles of both tumor and adjacent liver tissue following curative resection were measured to discriminate 56 hepatitis B virus-positive HCC patients into subgroups based on survival risk. This approach was further tested in 40 patients. RESULTS: Expression profiles of both tumor and adjacent liver tissue successfully discriminated 56 training samples into 2 subgroups, those at low- or high-risk for survival and recurrence. However, the prognostic gene set selected for tumor tissue was quite different from that for adjacent tissues. This variation in prognostic genes resulted in a change in allocation of patients within each low- or high-risk group. Combination of survival subgroups from tumor and adjacent liver tissue significantly improved the prediction of prognostic outcome. This integrative approach was confirmed to be effective in a further 40 test patients. A clinicopathological study showed that survival subgroups divided by tumor and adjacent liver tissue gene expression were also statistically associated with UICC stage and extent of cell differentiation, respectively. CONCLUSIONS: Variation in gene expression during the nontumor stage as well as the tumor stage may affect the prognosis of HCC patients, and integration of the gene expression profiles of HCC and adjacent liver tissue increases discriminatory effectiveness between patient groups, predicting clinical outcomes with enhanced statistical reliability.

Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray.

Recent introduction of a learning algorithm for cDNA microarray analysis has permitted to select feature set to accurately distinguish human cancers according to their pathological judgments. Here, we demonstrate that hepatitis B virus-positive hepatocellular carcinoma (HCC) could successfully be identified from non-tumor liver tissues by supervised learning analysis of gene expression profiling. Through learning and cross-validating HCC sample set, we could identify an optimized set of 44 genes to discriminate the status of HCC from non-tumor liver tissues. In an analysis of other blind-tested HCC sample sets, this feature set was found to be statistically significant, indicating the reproducibility of our molecular discrimination approach with the defined genes. One prominent finding was an asymmetrical distribution pattern of expression profiling in HCC, in which the number of down-regulated genes was greater than that of up-regulated genes. In conclusion, the present findings indicate that application of learning algorithm to HCC may establish a reliable feature set of genes to be useful for therapeutic target of HCC, and that the asymmetric expression pattern may emphasize the importance of suppressed genes in HCC.