Skip to content


  • Review
  • Open Access

Biomarkers for hepatocellular carcinoma: progression in early diagnosis, prognosis, and personalized therapy

Biomarker Research20131:10

  • Received: 27 October 2012
  • Accepted: 2 February 2013
  • Published:


Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. Currently, surgical resection, liver transplantation, and local ablation are considered curative therapeutic practices for HCC. The diagnosis of HCC without pathologic confirmation is achieved by analyzing serum alpha-fetoprotein (AFP) levels combined with imaging techniques, including ultrasonography, magnetic resonance imaging, and computerized tomography. Although progress has been made in the diagnosis and management of HCC, its prognosis remains dismal. Various new technologies have identified numerous novel biomarkers with potential diagnostic as well as prognostic value, including Dickkopf-1 and Golgi protein 73. These biomarkers not only help in the early diagnosis and prediction of prognosis, but also assist in identifying potential targets for therapeutic interventions. In this article, we provide an up-to-date review of the biomarkers that are used for early diagnosis, prognosis prediction, and personalized treatment of HCC.


  • Hepatocellular carcinoma
  • Early diagnosis
  • Prognosis
  • Biological markers


Hepatocellular carcinoma (HCC) is one of the most frequently diagnosed cancers worldwide. The disease is predominant in Asia and Africa, but its incidence is steadily increasing throughout the rest of the world [1]. Most HCC develop in patients with a history of chronic hepatitis or cirrhosis in which there is continuous inflammation and regeneration of hepatocytes. Unlike other solid malignancies, the coexistence of inflammation and cirrhosis makes the early diagnosis and prognostic assessment of HCC much more difficult. This complication highlights the need to identify valuable biomarkers for the diagnosis and treatment of HCC.

The proliferation and survival of cancer cells require a process called oncogene addiction, which is the activation of specific oncogenes and inactivation of specific tumor suppressors, such as Rb1 in retinoblastoma [2] and BRCA1 in breast cancer [3]. However, no specific oncogene addictions have been observed in HCC, which is a complex disease with a variety of underlying pathogenic anomalies caused by multiple risk factors. The lack of ideal biomarkers for HCC diagnosis, prognosis, and therapy has posed a major challenge to HCC management.

With advances in the understanding of tumor biology, interest in identifying molecular biomarkers of HCC has increased. Over the last decade, a number of new cutting-edge technologies such as next-generation sequencing [4, 5] and microarray technologies [68] have emerged, leading the search for biomarkers into a new era of “omics” [9, 10]. Using these technologies, it is now quite easy to examine a whole tumor genome (including copy number variations, loss of heterogeneity, aneuploidy, single nucleotide polymorphism) [1114], transcriptome [15, 16], proteome [17, 18], epigenome [19, 20], metabolome [2123], and miRNA profile [24, 25], and the analysis of tens of thousands of molecular targets has become affordable and operable. Currently, numerous circulating markers and tissue markers have been identified [17, 2630]; however, few biomarkers are acceptable for clinical utility because of their low predictive accuracy and/or high cost. Here, we provide an up-to-date review of the biomarkers that are used for early diagnosis, prognosis, and personalized treatment of HCC.


Biomarkers for early diagnosis

The diagnosis of HCC without pathologic confirmation can be achieved by assessing the serum alpha-fetoprotein (AFP) level combined with imaging techniques, including ultrasonography, magnetic resonance imaging, and computerized tomography [31, 32]. However, improvement in early diagnosis is still needed because only 44% of the patients are diagnosed at a localized disease stage, and only 30% of patients with HCC are candidates for potentially curative treatments at the time of diagnosis [33]. Thus, the discovery of an effective, reliable tool for early diagnosis of HCC to increase the number of patients who are suitable for curative treatment will play a pivotal role in improving HCC patients’ prognosis.

A marker for early diagnosis would meet the following requirements: first, it should achieve high accuracy, which would increase the probability of a diagnosis being made prior to spread and thus increase the cure rate; second, specimen collection for detecting the marker should be easily operable and non-invasive; and third, the cost-effectiveness should be considered [34]. Tumor tissue-oriented markers are not highly practical because not all tumor tissues can be obtained at an early stage and the invasive procedure may cause spread of tumor cells. Biomarkers from body fluids such as serum, plasma, urine, and bile are suitable candidates for early diagnosis of HCC because they are easily accessible [35]. In the following section, we list some important circulating (serum or plasma) markers for early diagnosis of HCC.


Since AFP was discovered in the serum of HCC patients in 1964 [36], it has been regarded as the most useful serum protein thus far for patients at risk for HCC [3739]. However, its sensitivity for detecting HCC ranges between 25%-60% [39, 40], and its specificity is also low because serum AFP can also be detected in patients with cirrhosis (11%-47%) and chronic hepatitis (15%-58%).

In addition to AFP, more than 20 serum proteins have clinical significance in early diagnosis of HCC [10, 41], among which several proteins are proved to have advantages over AFP.


DKK1 belongs to a family of secreted proteins that play an important role in HCC progression through the promotion of cytoplasmic/nuclear accumulation of beta-catenin in HCC cells via the Wnt/beta-catenin signaling pathway [42].

Recently, Shen et al. [41] reported that serum DKK1 is a promising candidate for HCC diagnosis. The authors retrospectively assessed serum DKK1 in 1284 patients (633 with HCC, 171 with chronic HBV infection, 168 with cirrhosis, and 312 healthy controls) and found that DKK1 has better diagnostic value for HCC than does AFP, especially for patients with AFP-negative and early stage HCC. Combined testing of serum DKK1 and AFP concentrations improved diagnostic accuracy for HCC versus all controls compared with either test alone. Nevertheless, DKK1 is not overly specific for HCC diagnosis, and a recent study reported that serum DKK1 was also elevated in patients with intrahepatic cholangiocarcinoma [43].

Golgi protein 73 (GP73)

GP73 is a 73 kDa trans-membrane glycoprotein that normally resides within the Golgi complex. It is expressed in normal biliary epithelial cells whereas normal hepatocytes do not express this protein, and its expression is significantly increased in liver diseases such as HCC [44].

Serum GP73 is a valuable biomarker for patients with HCC [45, 46]. Mao et al. [46] compared serum GP73 and AFP in 4217 participants, including 1690 healthy adults, 337 HBV carriers, 512 patients with cirrhosis, 789 patients with HCC, 61 patients with other malignant liver lesions, 206 patients with benign liver lesions and 622 patients with 14 non-liver cancers. The sensitivity and specificity of serum GP73 for HCC were 74.6% and 97.4%, respectively, compared with 58.2% and 85.3% for AFP. The GP73 level significantly increased in patients with HCC compared with healthy controls, decreased following surgical resection of HCC lesions and increased with tumor recurrence. Although the control group included HBV carriers, this group lacked patients with chronic hepatitis, whereas most HCC patients have hepatitis.

Protein induced by vitamin K absence or antagonist II (PIVKA-II)

PIVKA-II, an abnormal prothrombin discovered in 1984, has been widely proposed to be a useful HCC biomarker [47]. Takikawa et al. [48] measured plasma levels of PIVKA-II and AFP in 628 patients with various diseases, including 253 patients with liver cirrhosis and 116 patients with HCC. PIVKA-II was detected in 54.3% of patients with HCC, and the concentration showed a positive correlation with the tumor size. As a screening test for detecting HCC, PIVKA-II yielded sensitivity and specificity values (52.8% and 98.8%, respectively) that were comparable with AFP. Beale et al. [49] assessed AFP and PIVKA-II levels in pre-treatment serum samples from 50 patients with HCC, and the combination of serum AFP and PIVKA-II was better for detecting HCC than using either AFP or PIVKA-II alone.

Nucleic acids

Nucleic acids, including DNA, RNA, and nucleosomes, can be detected in the circulation of patients with HCC, and changes in their levels have been associated with tumor burden and progression of malignancy [50]. In the past decade, circulating nucleic acids have been extensively studied with regard to their diagnostic significance [5154]. For instance, plasma AFP mRNA [28, 55] is considered to be a diagnostic marker for HCC. Accumulating evidence has shown that microRNAs (miRNAs) play important roles in cancer initiation, propagation, and progression [5658]. MiRNA deregulation occurs at early stages of HCC and increases throughout the various steps of hepatocarcinogenesis [52]. There are multiple studies on the diagnostic function of miRNA in HCC diagnosis [52, 54, 59]. However, the diagnostic value of miRNAs is limited by one or more of the following factors: limited number of screened miRNAs, small sample size, failure to differentiate HCC from hepatitis, and lack of independent validation.

Recently, we measured plasma miRNA expression profiles (723 miRNAs) in a large cohort of 934 participants that included healthy individuals and patients with chronic HBV infection, cirrhosis, or HBV-related HCC. We identified a miRNA panel (miR-122, miR-192, miR-21, miR-223, miR-26a, miR-27a, and miR-801) that provided high diagnostic accuracy for discriminating patients with HCC from the healthy population (AUC = 0.941) and patients with chronic HBV (AUC = 0.842) or cirrhosis (AUC = 0.884). This finding led to the conclusion that the plasma miRNA panel had considerable clinical value for the early diagnosis of HCC and could help patients who might have otherwise missed the curative treatment window benefit from optimal therapy [54].

Prognostic biomarkers

Surgical resection, liver transplantation and local ablation are considered curative therapeutic practices for HCC. Other modalities, such as targeted therapy and transarterial chemoembolization (TACE), are palliative treatments. Despite these curative or palliative treatments, prognosis is still poor due to underlying liver diseases and the unique biology of HCC. As a result, biomarkers that better predict patients who are at higher risk of recurrence and poorer prognosis would help guide their treatment [26].

A number of biomarkers have been reported to predict the outcome of these therapies, including CD151 and CXCL5 for surgical treatment [27, 60], AFP and LDH for TACE [61, 62], PIVKA-II and VEGF for radiofrequency ablation (RFA) [63, 64], and serum AFP and HBeAg for percutaneous ethanol injection (PEI) [6567].

Biomarkers for surgical treatment

Surgical treatment offers a potentially curative option for HCC patients, but patients’ outcomes are varied due to differing tumor characteristics. Additionally, the exact biology of HCC remains poorly understood, thus making prediction of outcome after surgical resection very difficult. The prognosis of HCC patients does not simply reflect the size and number of the tumors; instead, prognosis is affected by a complex interplay between known and unknown factors, including tumor biology, patient condition, etc. [35]. Thus, the ability to predict which patients have a poor prognosis would help to assign risk and guide surgery and other treatments.

Circulating biomarkers

Circulating biomarkers are still preferred for prognostic prediction because they are easily accessible. Serum AFP is commonly used for diagnosis and surveillance of HCC [37, 39] and has been suggested as an independent indicator for prognosis. HCC patients with a high serum AFP level tend to have shorter survival [38, 53].

Other circulating factors such as Ang2 [53], VEGF [53, 68, 69], HGF [70, 71], and TGF-beta [72], are also independent factors for HCC prognosis. A recent study proposed that plasma macrophage migration inhibitory factor (MIF) levels have prognostic value in HCC patients. Plasma MIF levels have a significant association with overall survival (OS) and disease-free survival (DFS) of HCC patients, even in patients with normal serum AFP levels and Tumor Node Metastasis (TNM) stage I HCC [73].

Circulating tumor cells (CTCs) may reflect tumor aggressiveness and serve as a promising candidate for predicting tumor recurrence and metastasis [74]. However, their utility is limited due to the rarity of CTCs in peripheral blood of the patients. Recent technical advances have made it possible to detect CTCs; therefore, their clinical value has been tested in multiple tumor types, including breast cancer [75], lung cancer [76], and prostate cancer [77]. Sun et al. proved that EpCAM-positive CTCs may serve as a prognostic marker in HCC after curative resection [78].

Tumor tissue biomarkers

Research into tumor tissues can provide direct biological information about the tumors; thus, the search for tumor biomarkers is crucial. A plethora of HCC tumor cell-derived biomarkers with potential prognostic significance have been identified in recent decades [9, 17, 26, 35, 7981], but consensus could not be reached.

HCC-related proteins have been extensively explored for use in determining prognosis [911, 82]. For instance, our previous study investigated CXCL5 (epithelial neutrophil-activating peptide-78) expression in a large cohort of 919 HCC patients. The results showed that overexpression of CXCL5 was well correlated with intratumoral neutrophil infiltration and that CXCL5 overexpression alone or in combination with the presence of intratumoral neutrophils was an independent prognostic indicator for OS and cumulative recurrence in HCC patients [60]. In addition, our institute also searched extensively for prognostic biomarkers in HCC patients undergoing liver transplantation [17, 83]. By investigating tumor tissues of 232 HCC patients, we identified calpain small subunit 1 (Capn4) as an independent prognostic factor for recurrence and survival in HCC patients after liver transplantation [17].

Cancer stem cells (CSCs) may play a pivotal role in the progression of tumors [84, 85]. CSCs represent the tumorigenic cells that generate tumors via the stem cell processes of self-renewal and differentiation. CSCs may persist in tumors as a distinct population and cause relapse and metastasis by giving rise to new tumors [86]. Although the existence of CSCs in HCC is still controversial, several studies have demonstrated the clinical significance of CSC markers in HCC patients [10, 79]. These markers include CD90 [87], CD133 [29], CD13 [88], and EpCAM [89].

The role of the microenvironment surrounding tumor cells for the initiation and progression of HCC is becoming increasingly clear [30, 9092]. The tumor microenvironment, also named the tumor stroma, includes the extracellular matrix (ECM) and all other non-tumor cell types within a tumor tissue (e.g. endothelial cells, fibroblasts, and cells of the immune system). Various tumor stroma-associated factors, such as regulatory T cells (Tregs) [93], macrophage colony-stimulating factor (M-CSF) [94], macrophages [95], and hepatic stellate cells [96], have been investigated and exhibit significant prognostic value. For instance, Budhu et al. [97] showed that a unique inflammation/immune response-related signature in the venous metastasis-associated liver microenvironment coincides with elevated expression of M-CSF and can serve as a superior predictor of HCC venous metastases when compared with other clinical prognostic parameters.

Biomarkers for TACE

Although patients with early stage HCC have the chance to undergo curative treatment, most HCC patients are still diagnosed at a late stage when curative treatment is no longer applicable. For these patients, based on randomized, controlled clinical trials, TACE may be an effective treatment option for reducing systemic toxicity, increasing local antitumor effects, and improving survival [98, 99]. However, there are markedly diverse outcomes after TACE in terms of treatment response and survival. Therefore, identifying markers that can predict TACE treatment outcomes before choosing this treatment option is an important endeavor.

The most promising prognostic candidates for TACE are circulating biomarkers. Some studies have reported that serum AFP [61], circulating nucleosomes [100], blood neutrophil-to-lymphocyte ratio [101], and lactate dehydrogenase [62], are prognostic factors for TACE. As an example, Wang et al. [61] retrospectively studied the survival of 441 HCC patients (including 139 patients with normal AFP levels and 302 patients with elevated AFP levels) after TACE, and found that patients with normal AFP levels had a better treatment response and prognosis after TACE than patients with elevated AFP levels.

Personalized therapy

The recent discovery of new therapeutic targets based on the molecular pathways that are involved in hepatocarcinogenesis has led to exciting results in targeted treatment of HCC patients. Investigators have attempted to select therapeutic options for patients according to their tumor’s molecular profile, and this treatment modality will pave the way for personalized treatment of HCC.

Targeted therapy

Targeted therapy that specifically inhibits molecular abnormalities has emerged as an effective therapeutic option for malignancies [102, 103]. Small molecule tyrosine kinase inhibitors have great potential for the treatment of HCC through targeting several growth factors and their associated signaling pathways (e.g. EGF/EGFR, VEGF/VEGFR, IGF/IGFR, PDGF, FGF, RAS/RAF/ERK/MAPK, PI3K/AKT/mTOR, Wnt/beta-catenin) [104, 105]. Currently, nearly 60 reagents are being investigated for treatment of HCC, but only sorafenib have been proven effective in patients with advanced HCC [106].

Sorafenib is an oral multi-kinase inhibitor that competitively inhibits ATP binding to the catalytic domains of various kinases, such as Raf kinase, VEGFR-2, -3, and PDGFR, thereby increasing apoptosis and decreasing angiogenesis and cell proliferation [79, 106, 107]. However, no specific marker can guide the use of sorafenib in HCC; in contrast, HER2 and EGFR expression can positively predict the therapeutic response rate of trastuzumab in breast cancer and cetuximab in non-small cell lung cancer, respectively.

Other oral tyrosine-kinase inhibitors including sunitinib, linifanib, brivanib, and regorafenib block a number of angiogenesis-related signaling pathways, such as VEGFR, PDGFR, and FGFR [35, 104, 107]. Although many clinical trials have been discontinued because of poor effectiveness or severe adverse effects, these approaches provide critical insight into the mechanisms of targeted therapy for HCC and may finally allow us to optimize the current therapies for this fatal disease.


Interferon-alpha is a multifunctional cytokine that postpones recurrence of HCC and improves OS in HCC patients after curative resection [108110]. However, the benefit of interferon-alpha therapy is usually modest because it is not effective for all patients, and it is difficult to determine which patients will respond well to interferon-alpha [108, 111]. A recent study analyzed the miRNA profiles of 455 patients with HCC who had undergone curative tumor resection and assessed the association of the miRNA profiles with survival and response to therapy with interferon-alpha. The study showed that HCC patients whose tumors express low levels of miR-26 have a better response to interferon-alpha therapy than patients with high levels, suggesting that miR-26 expression status could be used as a predictor of the response to interferon-alpha therapy [112]. At present, a multicenter, randomized controlled trial assessing the impact of low miR-26 expression on interferon-alpha adjuvant therapy for HCC patients is ongoing in China (NCT01681446,


New technologies have identified numerous novel biomarkers with potential diagnostic and prognostic value. Recent advances in identification, isolation, and capture of tumor-derived microvesicles will reveal new insights into HCC diagnosis and personalized therapy [113]. Nevertheless, most of these markers have been studied retrospectively; few prospective trials have evaluated their clinical significance, or clinical application.

Because HCC is a complex disease with multiple underlying pathogenic mechanisms caused by a variety of risk factors, it is difficult to characterize HCC with a single biomarker. Thus, signatures of a combination of biomarkers may be more valuable for the diagnosis, staging, and prognosis of HCC. In the near future, identifying non-invasive and cost-effective biomarkers for early diagnosis and personalized treatment of HCC will be one of the most promising fields of biomarker research.

Grant support

This study was jointly supported by the National Key Sci-Tech Special Project of China (Grant No. 2012ZX10002-016), National Science Fund for Distinguished Young Scholars (81225019) and National Natural Science Funds of China (No. 812250125; No. 81172277; No. 81272724).



Hepatocellular carcinoma




Golgi protein 73


Protein induced by vitamin K absence or antagonist II




Transarterial chemoembolization


Radiofrequency ablation


Percutaneous ethanol injection


Migration inhibitory factor


Overall survival


Disease-free survival


Tumor Node Metastasis


Circulating tumor cell


Calpain small subunit 1


Cancer stem cell


Extracellular matrix


Regulatory T cell


Macrophage colony-stimulating factor.


Authors’ Affiliations

Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Fudan University, Shanghai, 200032, China
Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, 200032, China


  1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D: Global cancer statistics. CA Cancer J Clin 2011, 61: 69–90. 10.3322/caac.20107PubMedView ArticleGoogle Scholar
  2. Sachdeva UM, O'Brien JM: Understanding pRb: toward the necessary development of targeted treatments for retinoblastoma. J Clin Invest 2012, 122: 425–434. 10.1172/JCI57114PubMed CentralPubMedView ArticleGoogle Scholar
  3. Miki Y, Swensen J, Shattuck-Eidens D, Futreal PA, Harshman K, Tavtigian S, Liu Q, Cochran C, Bennett LM, Ding W, et al.: A strong candidate for the breast and ovarian cancer susceptibility gene BRCA1. Science 1994, 266: 66–71. 10.1126/science.7545954PubMedView ArticleGoogle Scholar
  4. Cho W, Ziogas DE, Katsios C, Roukos DH: Emerging personalized oncology: sequencing and systems strategies. Future Oncol 2012, 8: 637–641. 10.2217/fon.12.44PubMedView ArticleGoogle Scholar
  5. Meyerson M, Gabriel S, Getz G: Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet 2010, 11: 685–696. 10.1038/nrg2841PubMedView ArticleGoogle Scholar
  6. Bostjancic E, Zidar N, Glavac D: MicroRNA microarray expression profiling in human myocardial infarction. Dis Markers 2009, 27: 255–268.PubMed CentralPubMedView ArticleGoogle Scholar
  7. Leivonen SK, Makela R, Ostling P, Kohonen P, Haapa-Paananen S, Kleivi K, Enerly E, Aakula A, Hellstrom K, Sahlberg N, Kristensen VN, Borresen-Dale AL, Saviranta P, Perala M, Kallioniemi O: Protein lysate microarray analysis to identify microRNAs regulating estrogen receptor signaling in breast cancer cell lines. Oncogene 2009, 28: 3926–3936. 10.1038/onc.2009.241PubMedView ArticleGoogle Scholar
  8. Dexlin L, Ingvarsson J, Frendeus B, Borrebaeck CA, Wingren C: Design of recombinant antibody microarrays for cell surface membrane proteomics. J Proteome Res 2008, 7: 319–327. 10.1021/pr070257xPubMedView ArticleGoogle Scholar
  9. Aravalli RN, Steer CJ, Cressman EN: Molecular mechanisms of hepatocellular carcinoma. Hepatology 2008, 48: 2047–2063. 10.1002/hep.22580PubMedView ArticleGoogle Scholar
  10. Marquardt JU, Galle PR, Teufel A: Molecular diagnosis and therapy of hepatocellular carcinoma (HCC): an emerging field for advanced technologies. J Hepatol 2012, 56: 267–275. 10.1016/j.jhep.2011.07.007PubMedView ArticleGoogle Scholar
  11. Villanueva A, Newell P, Chiang DY, Friedman SL, Llovet JM: Genomics and signaling pathways in hepatocellular carcinoma. Semin Liver Dis 2007, 27: 55–76. 10.1055/s-2006-960171PubMedView ArticleGoogle Scholar
  12. Kumar V, Kato N, Urabe Y, Takahashi A, Muroyama R, Hosono N, Otsuka M, Tateishi R, Omata M, Nakagawa H, Koike K, Kamatani N, Kubo M, Nakamura Y, Matsuda K: Genome-wide association study identifies a susceptibility locus for HCV-induced hepatocellular carcinoma. Nat Genet 2011, 43: 455–458. 10.1038/ng.809PubMedView ArticleGoogle Scholar
  13. Krawczyk M, Mullenbach R, Weber SN, Zimmer V, Lammert F: Genome-wide association studies and genetic risk assessment of liver diseases. Nat Rev Gastroenterol Hepatol 2010, 7: 669–681. 10.1038/nrgastro.2010.170PubMedView ArticleGoogle Scholar
  14. You JS, Jones PA: Cancer genetics and epigenetics: two sides of the same coin. Cancer Cell 2012,22(1):9–20. 10.1016/j.ccr.2012.06.008PubMed CentralPubMedView ArticleGoogle Scholar
  15. Hoshida Y, Nijman SM, Kobayashi M, Chan JA, Brunet JP, Chiang DY, Villanueva A, Newell P, Ikeda K, Hashimoto M, Watanabe G, Gabriel S, Friedman SL, Kumada H, Llovet JM, Golub TR: Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res 2009, 69: 7385–7392. 10.1158/0008-5472.CAN-09-1089PubMed CentralPubMedView ArticleGoogle Scholar
  16. Scott KL, Nogueira C, Heffernan TP, van Doorn R, Dhakal S, Hanna JA, Min C, Jaskelioff M, Xiao Y, Wu CJ, Cameron LA, Perry SR, Zeid R, Feinberg T, Kim M, Vande Woude G, Granter SR, Bosenberg M, Chu GC, Depinho RA, Rimm DL, Chin L: Proinvasion metastasis drivers in early-stage melanoma are oncogenes. Cancer Cell 2011,20(1):92–103. 10.1016/j.ccr.2011.05.025PubMed CentralPubMedView ArticleGoogle Scholar
  17. Bai DS, Dai Z, Zhou J, Liu YK, Qiu SJ, Tan CJ, Shi YH, Huang C, Wang Z, He YF, Fan J: Capn4 overexpression underlies tumor invasion and metastasis after liver transplantation for hepatocellular carcinoma. Hepatology 2009, 49: 460–470. 10.1002/hep.22638PubMedView ArticleGoogle Scholar
  18. Dai Z, Zhou J, Qiu SJ, Liu YK, Fan J: Lectin-based glycoproteomics to explore and analyze hepatocellular carcinoma-related glycoprotein markers. Electrophoresis 2009, 30: 2957–2966. 10.1002/elps.200900064PubMedView ArticleGoogle Scholar
  19. Gao W, Kondo Y, Shen L, Shimizu Y, Sano T, Yamao K, Natsume A, Goto Y, Ito M, Murakami H, Osada H, Zhang J, Issa JP, Sekido Y: Variable DNA methylation patterns associated with progression of disease in hepatocellular carcinomas. Carcinogenesis 2008, 29: 1901–1910. 10.1093/carcin/bgn170PubMedView ArticleGoogle Scholar
  20. Um TH, Kim H, Oh BK, Kim MS, Kim KS, Jung G, Park YN: Aberrant CpG island hypermethylation in dysplastic nodules and early HCC of hepatitis B virus-related human multistep hepatocarcinogenesis. J Hepatol 2011, 54: 939–947. 10.1016/j.jhep.2010.08.021PubMedView ArticleGoogle Scholar
  21. Claudino WM, Quattrone A, Biganzoli L, Pestrin M, Bertini I, Di LA: Metabolomics: available results, current research projects in breast cancer, and future applications. J Clin Oncol 2007, 25: 2840–2846. 10.1200/JCO.2006.09.7550PubMedView ArticleGoogle Scholar
  22. Ward PS, Thompson CB: Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell 2012, 21: 297–308. 10.1016/j.ccr.2012.02.014PubMed CentralPubMedView ArticleGoogle Scholar
  23. Zhang A, Sun H, Wang X: Power of metabolomics in diagnosis and biomarker discovery of hepatocellular carcinoma. LID. Hepatology 2012. 10.1002/hep.26130Google Scholar
  24. Varnholt H, Drebber U, Schulze F, Wedemeyer I, Schirmacher P, Dienes HP, Odenthal M: MicroRNA gene expression profile of hepatitis C virus-associated hepatocellular carcinoma. Hepatology 2008, 47: 1223–1232.PubMedView ArticleGoogle Scholar
  25. Budhu A, Ji J, Wang XW: The clinical potential of microRNAs. J Hematol Oncol 2010, 3: 37. 10.1186/1756-8722-3-37PubMed CentralPubMedView ArticleGoogle Scholar
  26. Zhu K, Dai Z, Pan Q, Wang Z, Yang GH, Yu L, Ding ZB, Shi GM, Ke AW, Yang XR, Tao ZH, Zhao YM, Qin Y, Zeng HY, Tang ZY, Fan J, Zhou J: Metadherin promotes hepatocellular carcinoma metastasis through induction of epithelial-mesenchymal transition. Clin Cancer Res 2011, 17: 7294–7302. 10.1158/1078-0432.CCR-11-1327PubMedView ArticleGoogle Scholar
  27. Ke AW, Shi GM, Zhou J, Huang XY, Shi YH, Ding ZB, Wang XY, Devbhandari RP, Fan J: CD151 amplifies signaling by integrin alpha6beta1 to PI3K and induces the epithelial-mesenchymal transition in HCC cells. Gastroenterology 2011, 140: 1629–1641. e15 10.1053/j.gastro.2011.02.008PubMedView ArticleGoogle Scholar
  28. Ijichi M, Takayama T, Matsumura M, Shiratori Y, Omata M, Makuuchi M: Alpha-Fetoprotein mRNA in the circulation as a predictor of postsurgical recurrence of hepatocellular carcinoma: a prospective study. Hepatology 2002, 35: 853–860. 10.1053/jhep.2002.32100PubMedView ArticleGoogle Scholar
  29. Yin S, Li J, Hu C, Chen X, Yao M, Yan M, Jiang G, Ge C, Xie H, Wan D, Yang S, Zheng S, Gu J: CD133 positive hepatocellular carcinoma cells possess high capacity for tumorigenicity. Int J Cancer 2007, 120: 1444–1450. 10.1002/ijc.22476PubMedView ArticleGoogle Scholar
  30. Sund M, Kalluri R: Tumor stroma derived biomarkers in cancer. Cancer Metastasis Rev 2009, 28: 177–183. 10.1007/s10555-008-9175-2PubMed CentralPubMedView ArticleGoogle Scholar
  31. Trinchet JC, Chaffaut C, Bourcier V, Degos F, Henrion J, Fontaine H, Roulot D, Mallat A, Hillaire S, Cales P, Ollivier I, Vinel JP, Mathurin P, Bronowicki JP, Vilgrain V, N'Kontchou G, Beaugrand M, Chevret S: Ultrasonographic surveillance of hepatocellular carcinoma in cirrhosis: a randomized trial comparing 3- and 6-month periodicities. Hepatology 2011, 54: 1987–1997. 10.1002/hep.24545PubMedView ArticleGoogle Scholar
  32. Aghoram R, Cai P, Dickinson JA: Alpha-foetoprotein and/or liver ultrasonography for screening of hepatocellular carcinoma in patients with chronic hepatitis B. Cochrane Database Syst Rev 2012, 9: CD002799.PubMedGoogle Scholar
  33. Bruix J, Llovet JM: Prognostic prediction and treatment strategy in hepatocellular carcinoma. Hepatology 2002, 35: 519–524. 10.1053/jhep.2002.32089PubMedView ArticleGoogle Scholar
  34. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM: Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005, 97: 1180–1184. 10.1093/jnci/dji237PubMedView ArticleGoogle Scholar
  35. Singhal A, Jayaraman M, Dhanasekaran DN, Kohli V: Molecular and serum markers in hepatocellular carcinoma: predictive tools for prognosis and recurrence. Crit Rev Oncol Hematol 2012, 82: 116–140. 10.1016/j.critrevonc.2011.05.005PubMedView ArticleGoogle Scholar
  36. IuS T: Detection of embryo-specific alpha-globulin in the blood serum of a patient with primary liver cancer. Vopr Med Khim 1964, 10: 90–91.Google Scholar
  37. Nagasue N, Inokuchi K, Kobayashi M, Saku M: Serum alpha-fetoprotein levels after hepatic artery ligation and postoperative chemotherapy: correlation with clinical status in patients with hepatocellular carcinoma. Cancer 1977, 40: 615–618. 10.1002/1097-0142(197708)40:2<615::AID-CNCR2820400204>3.0.CO;2-TPubMedView ArticleGoogle Scholar
  38. Tangkijvanich P, Anukulkarnkusol N, Suwangool P, Lertmaharit S, Hanvivatvong O, Kullavanijaya P, Poovorawan Y: Clinical characteristics and prognosis of hepatocellular carcinoma: analysis based on serum alpha-fetoprotein levels. J Clin Gastroenterol 2000, 31: 302–308. 10.1097/00004836-200012000-00007PubMedView ArticleGoogle Scholar
  39. Zhou L, Liu J, Luo F: Serum tumor markers for detection of hepatocellular carcinoma. World J Gastroenterol 2006, 12: 1175–1181.PubMed CentralPubMedGoogle Scholar
  40. El-Serag HB, Marrero JA, Rudolph L, Reddy KR: Diagnosis and treatment of hepatocellular carcinoma. Gastroenterology 2008, 134: 1752–1763. 10.1053/j.gastro.2008.02.090PubMedView ArticleGoogle Scholar
  41. Shen Q, Fan J, Yang XR, Tan Y, Zhao W, Xu Y, Wang N, Niu Y, Wu Z, Zhou J, Qiu SJ, Shi YH, Yu B, Tang N, Chu W, Wang M, Wu J, Zhang Z, Yang S, Gu J, Wang H, Qin W: Serum DKK1 as a protein biomarker for the diagnosis of hepatocellular carcinoma: a large-scale, multicentre study. Lancet Oncol 2012, 13: 817–826. 10.1016/S1470-2045(12)70233-4PubMedView ArticleGoogle Scholar
  42. Yu B, Yang X, Xu Y, Yao G, Shu H, Lin B, Hood L, Wang H, Yang S, Gu J, Fan J, Qin W: Elevated expression of DKK1 is associated with cytoplasmic/nuclear beta-catenin accumulation and poor prognosis in hepatocellular carcinomas. J Hepatol 2009, 50: 948–957.PubMedView ArticleGoogle Scholar
  43. Shi RY YXR, Shen QJ YLX, Xu YQSJ, Sun YF ZX, Wang ZZK, Qin WX TZ, Fan JZJ: High expression of dickkopf-related protein 1 is related to lymphatic metastasis and indicates poor prognosis in intrahepatic cholangiocarcinoma patients after surgery. Cancer 2012. in pressGoogle Scholar
  44. Kladney RD, Cui X, Bulla GA, Brunt EM, Fimmel CJ: Expression of GP73, a resident Golgi membrane protein, in viral and nonviral liver disease. Hepatology 2002, 35: 1431–1440. 10.1053/jhep.2002.32525PubMedView ArticleGoogle Scholar
  45. Riener MO, Stenner F, Liewen H, Soll C, Breitenstein S, Pestalozzi BC, Samaras P, Probst-Hensch N, Hellerbrand C, Mullhaupt B, Clavien PA, Bahra M, Neuhaus P, Wild P, Fritzsche F, Moch H, Jochum W, Kristiansen G: Golgi phosphoprotein 2 (GOLPH2) expression in liver tumors and its value as a serum marker in hepatocellular carcinomas. Hepatology 2009, 49: 1602–1609. 10.1002/hep.22843PubMedView ArticleGoogle Scholar
  46. Mao Y, Yang H, Xu H, Lu X, Sang X, Du S, Zhao H, Chen W, Xu Y, Chi T, Yang Z, Cai J, Li H, Chen J, Zhong S, Mohanti SR, Lopez-Soler R, Millis JM, Huang J, Zhang H: Golgi protein 73 (GOLPH2) is a valuable serum marker for hepatocellular carcinoma. Gut 2010, 59: 1687–1693. 10.1136/gut.2010.214916PubMedView ArticleGoogle Scholar
  47. Liebman HA, Furie BC, Tong MJ, Blanchard RA, Lo KJ, Lee SD, Coleman MS, Furie B: Des-gamma-carboxy (abnormal) prothrombin as a serum marker of primary hepatocellular carcinoma. N Engl J Med 1984, 310: 1427–1431. 10.1056/NEJM198405313102204PubMedView ArticleGoogle Scholar
  48. Takikawa Y, Suzuki K, Yamazaki K, Goto T, Madarame T, Miura Y, Yoshida T, Kashiwabara T, Sato S: Plasma abnormal prothrombin (PIVKA-II): a new and reliable marker for the detection of hepatocellular carcinoma. J Gastroenterol Hepatol 1992, 7: 1–6. 10.1111/j.1440-1746.1992.tb00925.xPubMedView ArticleGoogle Scholar
  49. Beale G, Chattopadhyay D, Gray J, Stewart S, Hudson M, Day C, Trerotoli P, Giannelli G, Manas D, Reeves H: AFP, PIVKAII, GP3, SCCA-1 and follisatin as surveillance biomarkers for hepatocellular cancer in non-alcoholic and alcoholic fatty liver disease. BMC Cancer 2008, 8: 200. 10.1186/1471-2407-8-200PubMed CentralPubMedView ArticleGoogle Scholar
  50. Zhou J, Shi YH, Fan J: Circulating cell-free nucleic acids: promising biomarkers of hepatocellular carcinoma. Semin Oncol 2012, 39: 440–448. 10.1053/j.seminoncol.2012.05.013PubMedView ArticleGoogle Scholar
  51. Cho WC: Circulating microRNAs as minimally invasive biomarkers for cancer theragnosis and prognosis. Front Genet 2011, 2: 7.PubMed CentralPubMedGoogle Scholar
  52. Gao P, Wong CC, Tung EK, Lee JM, Wong CM, Ng IO: Deregulation of microRNA expression occurs early and accumulates in early stages of HBV-associated multistep hepatocarcinogenesis. J Hepatol 2011, 54: 1177–1184. 10.1016/j.jhep.2010.09.023PubMedView ArticleGoogle Scholar
  53. Llovet JM, Pena CE, Lathia CD, Shan M, Meinhardt G, Bruix J: Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma. Clin Cancer Res 2012, 18: 2290–2300. 10.1158/1078-0432.CCR-11-2175PubMedView ArticleGoogle Scholar
  54. Zhou J, Yu L, Gao X, Hu J, Wang J, Dai Z, Wang JF, Zhang Z, Lu S, Huang X, Wang Z, Qiu S, Wang X, Yang G, Sun H, Tang Z, Wu Y, Zhu H, Fan J: Plasma microRNA panel to diagnose hepatitis B virus-related hepatocellular carcinoma. J Clin Oncol 2011, 29: 4781–4788. 10.1200/JCO.2011.38.2697PubMedView ArticleGoogle Scholar
  55. Minata M, Nishida N, Komeda T, Azechi H, Katsuma H, Nishimura T, Kuno M, Ito T, Yamamoto Y, Ikai I, Yamaoka Y, Fukuda Y, Nakao K: Postoperative detection of alpha-fetoprotein mRNA in blood as a predictor for metastatic recurrence of hepatocellular carcinoma. J Gastroenterol Hepatol 2001, 16: 445–451. 10.1046/j.1440-1746.2001.02461.xPubMedView ArticleGoogle Scholar
  56. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, Sweet-Cordero A, Ebert BL, Mak RH, Ferrando AA, Downing JR, Jacks T, Horvitz HR, Golub TR: microRNA expression profiles classify human cancers. Nature 2005, 435: 834–838. 10.1038/nature03702PubMedView ArticleGoogle Scholar
  57. Ventura A, Jacks T: microRNAs and cancer: short RNAs go a long way. Cell 2009, 136: 586–591. 10.1016/j.cell.2009.02.005PubMed CentralPubMedView ArticleGoogle Scholar
  58. Calin GA, Croce CM: microRNA signatures in human cancers. Nat Rev Cancer 2006, 6: 857–866. 10.1038/nrc1997PubMedView ArticleGoogle Scholar
  59. Qu KZ, Zhang K, Li H, Afdhal NH, Albitar M: Circulating microRNAs as biomarkers for hepatocellular carcinoma. J Clin Gastroenterol 2011, 45: 355–360. 10.1097/MCG.0b013e3181f18ac2PubMedView ArticleGoogle Scholar
  60. Zhou SL, Dai Z, Zhou ZJ, Wang XY, Yang GH, Wang Z, Huang XW, Fan J, Zhou J: Overexpression of CXCL5 mediates neutrophil infiltration and indicates poor prognosis for hepatocellular carcinoma. LID. Hepatology 2012. 10.1002/hep.25907Google Scholar
  61. Wang Y, Chen Y, Ge N, Zhang L, Xie X, Zhang J, Chen R, Wang Y, Zhang B, Xia J, Gan Y, Ren Z, Ye S: Prognostic significance of alpha-fetoprotein status in the outcome of hepatocellular carcinoma after treatment of transarterial chemoembolization. Ann Surg Oncol 2012,19(11):3540. 10.1245/s10434-012-2368-5PubMedView ArticleGoogle Scholar
  62. Scartozzi M, Faloppi L, Bianconi M, Giampieri R, Maccaroni E, Bittoni A, Del PM, Loretelli C, Belvederesi L, Svegliati BG, Cascinu S: The role of LDH serum levels in predicting global outcome in HCC patients undergoing TACE: implications for clinical management. PLoS One 2012, 7: e32653. 10.1371/journal.pone.0032653PubMed CentralPubMedView ArticleGoogle Scholar
  63. Kobayashi M, Ikeda K, Kawamura Y, Yatsuji H, Hosaka T, Sezaki H, Akuta N, Suzuki F, Suzuki Y, Saitoh S, Arase Y, Kumada H: High serum des-gamma-carboxy prothrombin level predicts poor prognosis after radiofrequency ablation of hepatocellular carcinoma. Cancer 2009, 115: 571–580. 10.1002/cncr.24031PubMedView ArticleGoogle Scholar
  64. Poon RT, Lau C, Pang R, Ng KK, Yuen J, Fan ST: High serum vascular endothelial growth factor levels predict poor prognosis after radiofrequency ablation of hepatocellular carcinoma: importance of tumor biomarker in ablative therapies. Ann Surg Oncol 2007, 14: 1835–1845. 10.1245/s10434-007-9366-zPubMedView ArticleGoogle Scholar
  65. Pompili M, Rapaccini GL, de Luca F, Caturelli E, Astone A, Siena DA, Villani MR, Grattagliano A, Cedrone A, Gasbarrini G: Risk factors for intrahepatic recurrence of hepatocellular carcinoma in cirrhotic patients treated by percutaneous ethanol injection. Cancer 1997, 79: 1501–1508. 10.1002/(SICI)1097-0142(19970415)79:8<1501::AID-CNCR9>3.0.CO;2-DPubMedView ArticleGoogle Scholar
  66. Chung GE, Kim W, Lee JH, Kim YJ, Yoon JH, Lee JM, Lee JY, Kim SH, Kim D, Lee HS: Negative hepatitis B envelope antigen predicts intrahepatic recurrence in hepatitis B virus-related hepatocellular carcinoma after ablation therapy. J Gastroenterol Hepatol 2011, 26: 1638–1645. 10.1111/j.1440-1746.2011.06777.xPubMedView ArticleGoogle Scholar
  67. Ishii H, Okada S, Nose H, Okusaka T, Nagahama H, Nakayama H, Nakasuka H, Yoshimori M: Predictive factors for recurrence after percutaneous ethanol injection for solitary hepatocellular carcinoma. Hepatogastroenterology 1996, 43: 938–943.PubMedGoogle Scholar
  68. Poon RT, Ng IO, Lau C, Zhu LX, Yu WC, Lo CM, Fan ST, Wong J: Serum vascular endothelial growth factor predicts venous invasion in hepatocellular carcinoma: a prospective study. Ann Surg 2001, 233: 227–235. 10.1097/00000658-200102000-00012PubMed CentralPubMedView ArticleGoogle Scholar
  69. Poon RT, Ho JW, Tong CS, Lau C, Ng IO, Fan ST: Prognostic significance of serum vascular endothelial growth factor and endostatin in patients with hepatocellular carcinoma. Br J Surg 2004, 91: 1354–1360. 10.1002/bjs.4594PubMedView ArticleGoogle Scholar
  70. Wright LM, Kreikemeier JT, Fimmel CJ: A concise review of serum markers for hepatocellular cancer. Cancer Detect Prev 2007, 31: 35–44. 10.1016/j.cdp.2006.11.003PubMedView ArticleGoogle Scholar
  71. Yamagamim H, Moriyama M, Matsumura H, Aoki H, Shimizu T, Saito T, Kaneko M, Shioda A, Tanaka N, Arakawa Y: Serum concentrations of human hepatocyte growth factor is a useful indicator for predicting the occurrence of hepatocellular carcinomas in C-viral chronic liver diseases. Cancer 2002, 95: 824–834. 10.1002/cncr.10732PubMedView ArticleGoogle Scholar
  72. Song BC, Chung YH, Kim JA, Choi WB, Suh DD, Pyo SI, Shin JW, Lee HC, Lee YS, Suh DJ: Transforming growth factor-beta1 as a useful serologic marker of small hepatocellular carcinoma. Cancer 2002, 94: 175–180. 10.1002/cncr.10170PubMedView ArticleGoogle Scholar
  73. Zhao YM, Wang L, Dai Z, Wang DD, Hei ZY, Zhang N, Fu XT, Wang XL, Zhang SC, Qin LX, Tang ZY, Zhou J, Fan J: Validity of plasma macrophage migration inhibitory factor for diagnosis and prognosis of hepatocellular carcinoma. Int J Cancer 2011, 129: 2463–2472. 10.1002/ijc.25918PubMedView ArticleGoogle Scholar
  74. Sun YF, Yang XR, Zhou J, Qiu SJ, Fan J, Xu Y: Circulating tumor cells: advances in detection methods, biological issues, and clinical relevance. J Cancer Res Clin Oncol 2011, 137: 1151–1173. 10.1007/s00432-011-0988-yPubMedView ArticleGoogle Scholar
  75. Andreopoulou E, Cristofanilli M: Circulating tumor cells as prognostic marker in metastatic breast cancer. Expert Rev Anticancer Ther 2010, 10: 171–177. 10.1586/era.09.105PubMedView ArticleGoogle Scholar
  76. Chen TF, Jiang GL, Fu XL, Wang LJ, Qian H, Wu KL, Zhao S: CK19 mRNA expression measured by reverse-transcription polymerase chain reaction (RT-PCR) in the peripheral blood of patients with non-small cell lung cancer treated by chemo-radiation: an independent prognostic factor. Lung Cancer 2007, 56: 105–114. 10.1016/j.lungcan.2006.11.006PubMedView ArticleGoogle Scholar
  77. Attard G, Swennenhuis JF, Olmos D, Reid AH, Vickers E, A'Hern R, Levink R, Coumans F, Moreira J, Riisnaes R, Oommen NB, Hawche G, Jameson C, Thompson E, Sipkema R, Carden CP, Parker C, Dearnaley D, Kaye SB, Cooper CS, Molina A, Cox ME, Terstappen LW, de Bono JS: Characterization of ERG, AR and PTEN gene status in circulating tumor cells from patients with castration-resistant prostate cancer. Cancer Res 2009, 69: 2912–2918. 10.1158/0008-5472.CAN-08-3667PubMedView ArticleGoogle Scholar
  78. Yun-Fan Sun YX, Xin-Rong Yang WG, Xin Zhang SQ, Ruo-Yu Shi BH, Jian Zhou JF: Circulating stem cell-like EpCAM + tumor cells indicate poor prognosis of hepatocellular carcinoma after curative resection. Hepatology 2013. in pressGoogle Scholar
  79. Sengupta B, Siddiqi SA: Hepatocellular Carcinoma: Important Biomarkers and their Significance in Molecular Diagnostics and Therapy. Curr Med Chem 2012,19(22):3722. 10.2174/092986712801661059PubMedView ArticleGoogle Scholar
  80. Behne T, Copur MS: Biomarkers for hepatocellular carcinoma. Int J Hepatol 2012, 2012: 859076.PubMed CentralPubMedView ArticleGoogle Scholar
  81. Shi GM, Ke AW, Zhou J, Wang XY, Xu Y, Ding ZB, Devbhandari RP, Huang XY, Qiu SJ, Shi YH, Dai Z, Yang XR, Yang GH, Fan J: CD151 modulates expression of matrix metalloproteinase 9 and promotes neoangiogenesis and progression of hepatocellular carcinoma. Hepatology 2010, 52: 183–196.PubMedView ArticleGoogle Scholar
  82. Farazi PA, DePinho RA: Hepatocellular carcinoma pathogenesis: from genes to environment. Nat Rev Cancer 2006, 6: 674–687. 10.1038/nrc1934PubMedView ArticleGoogle Scholar
  83. Hu J, Wang Z, Fan J, Dai Z, He YF, Qiu SJ, Huang XW, Sun J, Xiao YS, Song K, Shi YH, Sun QM, Yang XR, Shi GM, Yu L, Yang GH, Ding ZB, Gao Q, Tang ZY, Zhou J: Genetic variations in plasma circulating DNA of HBV-related hepatocellular carcinoma patients predict recurrence after liver transplantation. PLoS One 2011, 6: e26003. 10.1371/journal.pone.0026003PubMed CentralPubMedView ArticleGoogle Scholar
  84. Morrison SJ, Kimble J: Asymmetric and symmetric stem-cell divisions in development and cancer. Nature 2006, 441: 1068–1074. 10.1038/nature04956PubMedView ArticleGoogle Scholar
  85. Reya T, Morrison SJ, Clarke MF, Weissman IL: Stem cells, cancer, and cancer stem cells. Nature 2001, 414: 105–111. 10.1038/35102167PubMedView ArticleGoogle Scholar
  86. Bjerkvig R, Johansson M, Miletic H, Niclou SP: Cancer stem cells and angiogenesis. Semin Cancer Biol 2009, 19: 279–284. 10.1016/j.semcancer.2009.09.001PubMedView ArticleGoogle Scholar
  87. Yang ZF, Ho DW, Ng MN, Lau CK, Yu WC, Ngai P, Chu PW, Lam CT, Poon RT, Fan ST: Significance of CD90+ cancer stem cells in human liver cancer. Cancer Cell 2008, 13: 153–166. 10.1016/j.ccr.2008.01.013PubMedView ArticleGoogle Scholar
  88. Haraguchi N, Ishii H, Mimori K, Tanaka F, Ohkuma M, Kim HM, Akita H, Takiuchi D, Hatano H, Nagano H, Barnard GF, Doki Y, Mori M: CD13 is a therapeutic target in human liver cancer stem cells. J Clin Invest 2010, 120: 3326–3339. 10.1172/JCI42550PubMed CentralPubMedView ArticleGoogle Scholar
  89. Yamashita T, Ji J, Budhu A, Forgues M, Yang W, Wang HY, Jia H, Ye Q, Qin LX, Wauthier E, Reid LM, Minato H, Honda M, Kaneko S, Tang ZY, Wang XW: EpCAM-positive hepatocellular carcinoma cells are tumor-initiating cells with stem/progenitor cell features. Gastroenterology 2009, 136: 1012–1024. 10.1053/j.gastro.2008.12.004PubMed CentralPubMedView ArticleGoogle Scholar
  90. Bremnes RM, Donnem T, Al-Saad S, Al-Shibli K, Andersen S, Sirera R, Camps C, Marinez I, Busund LT: The role of tumor stroma in cancer progression and prognosis: emphasis on carcinoma-associated fibroblasts and non-small cell lung cancer. J Thorac Oncol 2011, 6: 209–217. 10.1097/JTO.0b013e3181f8a1bdPubMedView ArticleGoogle Scholar
  91. Tlsty TD, Coussens LM: Tumor stroma and regulation of cancer development. Annu Rev Pathol 2006, 1: 119–150. 10.1146/annurev.pathol.1.110304.100224PubMedView ArticleGoogle Scholar
  92. George AL, Bangalore-Prakash P, Rajoria S, Suriano R, Shanmugam A, Mittelman A, Tiwari RK: Endothelial progenitor cell biology in disease and tissue regeneration. J Hematol Oncol 2011, 4: 24. 10.1186/1756-8722-4-24PubMed CentralPubMedView ArticleGoogle Scholar
  93. Gao Q, Qiu SJ, Fan J, Zhou J, Wang XY, Xiao YS, Xu Y, Li YW, Tang ZY: Intratumoral balance of regulatory and cytotoxic T cells is associated with prognosis of hepatocellular carcinoma after resection. J Clin Oncol 2007, 25: 2586–2593. 10.1200/JCO.2006.09.4565PubMedView ArticleGoogle Scholar
  94. Zhu XD, Zhang JB, Zhuang PY, Zhu HG, Zhang W, Xiong YQ, Wu WZ, Wang L, Tang ZY, Sun HC: High expression of macrophage colony-stimulating factor in peritumoral liver tissue is associated with poor survival after curative resection of hepatocellular carcinoma. J Clin Oncol 2008, 26: 2707–2716. 10.1200/JCO.2007.15.6521PubMedView ArticleGoogle Scholar
  95. Li YW, Qiu SJ, Fan J, Gao Q, Zhou J, Xiao YS, Xu Y, Wang XY, Sun J, Huang XW: Tumor-infiltrating macrophages can predict favorable prognosis in hepatocellular carcinoma after resection. J Cancer Res Clin Oncol 2009, 135: 439–449. 10.1007/s00432-008-0469-0PubMedView ArticleGoogle Scholar
  96. Kang N, Gores GJ, Shah VH: Hepatic stellate cells: Partners in crime for liver metastases. Hepatology 2011, 54: 707–713. 10.1002/hep.24384PubMed CentralPubMedView ArticleGoogle Scholar
  97. Budhu A, Forgues M, Ye QH, Jia HL, He P, Zanetti KA, Kammula US, Chen Y, Qin LX, Tang ZY, Wang XW: Prediction of venous metastases, recurrence, and prognosis in hepatocellular carcinoma based on a unique immune response signature of the liver microenvironment. Cancer Cell 2006, 10: 99–111. 10.1016/j.ccr.2006.06.016PubMedView ArticleGoogle Scholar
  98. Llovet JM, Real MI, Montana X, Planas R, Coll S, Aponte J, Ayuso C, Sala M, Muchart J, Sola R, Rodes J, Bruix J: Arterial embolisation or chemoembolisation versus symptomatic treatment in patients with unresectable hepatocellular carcinoma: a randomised controlled trial. Lancet 2002, 359: 1734–1739. 10.1016/S0140-6736(02)08649-XPubMedView ArticleGoogle Scholar
  99. Lo CM, Ngan H, Tso WK, Liu CL, Lam CM, Poon RT, Fan ST, Wong J: Randomized controlled trial of transarterial lipiodol chemoembolization for unresectable hepatocellular carcinoma. Hepatology 2002, 35: 1164–1171. 10.1053/jhep.2002.33156PubMedView ArticleGoogle Scholar
  100. Kohles N, Nagel D, Jungst D, Durner J, Stieber P, Holdenrieder S: Relevance of circulating nucleosomes and oncological biomarkers for predicting response to transarterial chemoembolization therapy in liver cancer patients. BMC Cancer 2011, 11: 202. 10.1186/1471-2407-11-202PubMed CentralPubMedView ArticleGoogle Scholar
  101. Pinato DJ, Sharma R: An inflammation-based prognostic index predicts survival advantage after transarterial chemoembolization in hepatocellular carcinoma. Transl Res 2012, 160: 146–152. 10.1016/j.trsl.2012.01.011PubMedView ArticleGoogle Scholar
  102. Overdevest JB, Theodorescu D, Lee JK: Utilizing the molecular gateway: the path to personalized cancer management. Clin Chem 2009, 55: 684–697. 10.1373/clinchem.2008.118554PubMedPubMed CentralView ArticleGoogle Scholar
  103. Firer MA, Gellerman G: Targeted drug delivery for cancer therapy: the other side of antibodies. J Hematol Oncol 2012, 5: 70. 10.1186/1756-8722-5-70PubMed CentralPubMedView ArticleGoogle Scholar
  104. Chau GY, Lui WY, Chi CW, Chau YP, Li AF, Kao HL, Wu CW: Significance of serum hepatocyte growth factor levels in patients with hepatocellular carcinoma undergoing hepatic resection. Eur J Surg Oncol 2008, 34: 333–338. 10.1016/j.ejso.2006.12.007PubMedView ArticleGoogle Scholar
  105. Hopfner M, Schuppan D, Scherubl H: Growth factor receptors and related signalling pathways as targets for novel treatment strategies of hepatocellular cancer. World J Gastroenterol 2008, 14: 1–14. 10.3748/wjg.14.1PubMed CentralPubMedView ArticleGoogle Scholar
  106. Wilhelm SM, Adnane L, Newell P, Villanueva A, Llovet JM, Lynch M: Preclinical overview of sorafenib, a multikinase inhibitor that targets both Raf and VEGF and PDGF receptor tyrosine kinase signaling. Mol Cancer Ther 2008, 7: 3129–3140. 10.1158/1535-7163.MCT-08-0013PubMedView ArticleGoogle Scholar
  107. Villanueva A, Llovet JM: Targeted therapies for hepatocellular carcinoma. Gastroenterology 2011, 140: 1410–1426. 10.1053/j.gastro.2011.03.006PubMed CentralPubMedView ArticleGoogle Scholar
  108. Sun HC, Tang ZY, Wang L, Qin LX, Ma ZC, Ye QH, Zhang BH, Qian YB, Wu ZQ, Fan J, Zhou XD, Zhou J, Qiu SJ, Shen YF: Postoperative interferon alpha treatment postponed recurrence and improved overall survival in patients after curative resection of HBV-related hepatocellular carcinoma: a randomized clinical trial. J Cancer Res Clin Oncol 2006, 132: 458–465. 10.1007/s00432-006-0091-yPubMedView ArticleGoogle Scholar
  109. Mazzaferro V, Romito R, Schiavo M, Mariani L, Camerini T, Bhoori S, Capussotti L, Calise F, Pellicci R, Belli G, Tagger A, Colombo M, Bonino F, Majno P, Llovet JM: Prevention of hepatocellular carcinoma recurrence with alpha-interferon after liver resection in HCV cirrhosis. Hepatology 2006, 44: 1543–1554. 10.1002/hep.21415PubMedView ArticleGoogle Scholar
  110. Lo CM, Liu CL, Chan SC, Lam CM, Poon RT, Ng IO, Fan ST, Wong J: A randomized, controlled trial of postoperative adjuvant interferon therapy after resection of hepatocellular carcinoma. Ann Surg 2007, 245: 831–842. 10.1097/01.sla.0000245829.00977.45PubMed CentralPubMedView ArticleGoogle Scholar
  111. Cao B, Chen XP, Zhu P, Ding L, Guan J, Shi ZL: Inhibitory effect of interferon-alpha-2b on expression of cyclooxygenase-2 and vascular endothelial growth factor in human hepatocellular carcinoma inoculated in nude mice. World J Gastroenterol 2008, 14: 6802–6807. 10.3748/wjg.14.6802PubMed CentralPubMedView ArticleGoogle Scholar
  112. Ji J, Shi J, Budhu A, Yu Z, Forgues M, Roessler S, Ambs S, Chen Y, Meltzer PS, Croce CM, Qin LX, Man K, Lo CM, Lee J, Ng IO, Fan J, Tang ZY, Sun HC, Wang XW: MicroRNA expression, survival, and response to interferon in liver cancer. N Engl J Med 2009, 361: 1437–1447. 10.1056/NEJMoa0901282PubMed CentralPubMedView ArticleGoogle Scholar
  113. D'Souza-Schorey C, Clancy JW: Tumor-derived microvesicles: shedding light on novel microenvironment modulators and prospective cancer biomarkers. Genes Dev 2012, 26: 1287–1299. 10.1101/gad.192351.112PubMed CentralPubMedView ArticleGoogle Scholar


© Zhu et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.