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Copyright ©2014 Baishideng Publishing Group Co.
World J Gastroenterol. Mar 28, 2014; 20(12): 3112-3124
Published online Mar 28, 2014. doi: 10.3748/wjg.v20.i12.3112
Table 1 Gene expression studies of hepatocellular carcinoma for prediction of recurrence
Ref.Sample typeScreening platformVirus typeCharacteristics for distinguishing groupsNumber of marker genesPredictive accuracy for recurrence
Iizuka et al[19] (2003)Tumour tissuesOligonucleotide microarrayHBV < HCVEarly IHR within 1 yr after surgery1225 in 27 independent patients (93%)
Kurokawa et al[20] (2004)Tumour tissuesPCR based arrayHBV < HCVEarly IHR within 2 yr after surgery2029 in 40 independent patients (72.5%)
Budhu et al[35] (2006)Non-tumour tissuescDNA microarrayAlmost all HBVVenous invasion or extrahepatic metastasis1787 of 95 independent patients (92%)
Ho et al[34] (2006)Tumour tissuescDNA microarrayHBV > HCVVenous invasion1426 of 35 independent samples (74.3%)
Lee et al[37] (2006)Tumour tissuesOligonucleotide microarrayHBV > HCVHepatoblast gene signature907P < 0.001 in 66 patients (Probability of recurrence)
Okamoto et al[21] (2006)Non-tumour tissuescDNA microarrayAll HCVSingle nodular HCC vs multicentric HCC3630 of 40 training samples (75%)
Wang et al[22] (2007)Tumour tissues and non-tumour tissuesOligonucleotide microarrayHBV > HCVHCC recurrence5784% in 25 independent samples, sensitivity 86%, specificity 82%
Hoshida et al[23] (2008)FFPE non-tumour tissuesDASL assayHBV < HCVLate recurrence more than 2 yr after resection132P = 0.003 in 224 patients in validation set (Probability of late recurrence)
Somura et al[24] (2008)Tumour tissuesOligonucleotide microarray/qRT-PCRHBV < HCVEarly IHR within 1 yr after surgery335 of 43 independent patients (81.4%)
Tanaka et al[25] (2008)Tumour tissuesOligonucleotide microarrayHBV > HCVAggressive recurrence exceeding Milan Criteria1 (AURKB)54 of 67 independent patients (80.5%)
Woo et al[26] (2008)Tumour tissuesOligonucleotide microarrayAll HBVEarly IHR within 1 yr after surgery628P = 0.0018 in 139 independent patients (Probability of early recurrence)
Yoshioka et al[27] (2009)Tumour tissuesOligonucleotide microarrayHBV < HCVEarly IHR within 2 yr after surgery172P < 0.0001 in 97 independent patients (Probability of RFS)
Roessler et al[28] (2010)Tumour tissuesOligonucleotide microarrayHBV > HCVEarly IHR within 2 yr after surgery161P = 0.0057 for cohort 1, P = 0.017 for cohort 2 (Probability of RFS)
Tsuchiya et al[29] (2010)Non-tumour tissuesOligonucleotide microarrayAll HCVLate recurrence more than 1 yr after resection38P < 0.0001 in 44 training samples (Probability of RFS)
Woo et al[38] (2010)Tumour tissuesOligonucleotide microarrayHBV > HCVCholangiocarcinoma-like signature625P = 0.037 in cohort 1 of 61 patients, P = 0.004 for cohort 2 of 78 patients (Probability of RFS)
Weng et al[30] (2012)Tumour tissues, PBMCOligonucleotide microarrayAll HBVEarly IHR within 1 yr after surgery3P < 0.001 in 80 independent patients (Probability of RFS)
Xieraili et al[31] (2012)Tumour tissuesOligonucleotide microarrayHBV < HCVEarly IHR1 (VIL1)P = 0.025 in 90 independent patients (Probability of RFS)
Tsunedomi et al[32] (2013)Tumour tissuesOligonucleotide microarrayAll HCVEarly IHR within 1 yr after surgery1 (ABCB6)89% sensitivity, 55% specificity, 86% PPV, 62% NPV in 20 independent patients
Table 2 miRNA for prediction of recurrence in hepatocellular carcinoma
Ref.Sample typeScreening platformVirus typeCharacteristics for distinguishing groupsNumber of candidate miRNAPredictive accuracy for recurrence
Fornari et al[53] (2009)Tumour and non-tumour tissuesqRT-PCRHBV < HCVmiR-122 levels1 (miR-122)P = 0.05 for 45 independent patients (Recurrence rate)
Fornari et al[54] (2010)Tumour and non-tumour tissuesqRT-PCRHBV < HCVLate recurrence beyond two years after surgery1 (miR-199a-3p)P = 0.043 for 36 independent patients (Recurrence rate)
Augello et al[47] (2012)FFPE tumour tissuesqRT-PCRAll HCVStages of HCC progression18P = 0.042 for 61 independent patients (Percent Recurrence)
Han et al[52] (2012)FFPE tumour tissuesqRT-PCRMostly HBVRecurrence after OLT1 (miR-155)P < 0.001 for 100 training patients (RFS)
Huang et al[51] (2012)Non-tumour tissuesqRT-PCRHBV > HCVEarly IHR within 6 mo after surgery6P < 0.001 for 216 independent patients
Shih et al[48] (2012)Tumour and non-tumour tissuesqRT-PCRNot mentionedHCC and non-tumour15P = 0.005 for 68 training samples and 13 independent samples (RFS)
Xia et al[50] (2012)Tumour and non-tumour tissuesqRT-PCRMostly HBVearly IHR within 2 years after surgery1 (miR-214)P = 0.009 for 50 independent patients (RFS)
Zhu et al[49] (2012)FFPE tumour and non-tumour tissuesqRT-PCRAll HBVEarly IHR after surgery1 (miR-29a-5p)AUC = 0.708 for 112 independent patients
Table 3 Proteomic studies of hepatocellular carcinoma for prediction of recurrence
Ref.Sample typeScreening platformCharacteristics for distinguishing groupsCandidate biomarkersValidation method
Yokoo et al[55] (2007)Tumour tissues2D-DIGE, MALDI-TOF MSEarly IHR within 6 mo after resection23 protein panel2D-DIGE
Orimo et al[59] (2008)Tumour and non-tumour tissues2D-DIGE, LC-MS/MSHistological differentiation of tumoursAdenomatous polyposis coli-end-binding protein 1 (EB1)IHC
Yi et al[56] (2008)Paired tumour and non-tumour tissues2DE, MALDI-TOF/TOF MSEarly IHR within 1 yr after resectionMortalin (HSPA9)qPCR, Immunoblotting, IHC
Bai et al[58] (2009)Tumour tissuescICAT, 2DLC-MS/MSRecurrence after liver transplantationCalpain small subunit 1 (CAPN4)qRT-PCR, Immunoblotting, TMA
Cheng et al[57] (2011)Tumour tissues2DE, MALDI-TOF/TOF MSRecurrence after liver transplantationN-myc downstream-regulated gene 1 (NDRG1)Immunoblotting, IHC
Kanamori et al[60] (2011)Tumour and non-tumour tissues2DLC-MS/MSTumour and non-tumourTalin-1 (TLN1)IHC
Table 4 Advantages and limitations of the three different omics methodologies
MethodAdvantagesLimitations
TranscriptomicsLarge dataset of dysregulated genes identified, provides insights to biological mechanisms of diseaseAffordable priceHas provided successful example of translation to clinical use (e.g., Oncotype Dx™ in predicting breast cancer recurrence)Differences in platform contribute to lack of overlap in signatures between different studiesHigh possibility of noise present in the gene listPoor correlation between transcript and protein levels
ProteomicsDirect measurement of biological effectorsValidation method (IHC and TMA) routinely performed in pathology labsSmall number of validated targetsPrice, availability and quality of antibodies for validation work
MetabolomicsAmenable to different types of samples which can be obtained in a non-invasive mannerLack of large-scale validation of metabolite signaturesLimited biological information obtained