Skip to main content

Screening and Identification of Molecular Marker for Metastatic Liver Cancer

  • Chapter
  • First Online:
Multidisciplinary Management of Liver Metastases in Colorectal Cancer
  • 937 Accesses

Abstract

Metastastic liver carcinoma, also called as secondary liver cancer, refers to the tumor transferred from other parts of the body to the liver through portal vein, hepatic artery, or lymph. The metastatic liver cancer is generally from the lung, mammary gland, colon, pancreas, and stomach as well as leukemia and other hemocyte cancer. It is said that the stomach cancer, pancreatic cancer, and colon cancer could be transferred to the liver through the portal vein, while the breast cancer and lung cancer could be transferred to the liver through hepatic artery. Generally speaking, the metastatic liver cancer is free from HBV infection, hepatitis, and hepatocirrhosis. Here, AFP is normal, but CEA is raised. As per CT detection, various focal nodes are found inside the liver. They may suffer from necrobiosis, cystic degeneration, bleeding, or calcification. Generally, the metastatic liver cancer is not merged with the portal vein cancer embolus, so that no well-defined symptoms are found at the early phase. In case the symptoms occur, the pathological changes are obvious. At the early phase, it mainly reflects the symptom of primary tumor. However, the symptom of metastatic liver cancer is not obvious. It is mostly found before the primary carcinoma operation, during the follow-up survey after the primary carcinoma operation or exploratory laparotomy. With the disease development, the symptom of metastatic liver cancer gradually appears with the enlargement of tumor. Also, for a minority of patients (mainly transferred from stomach and pancreas), the symptom of metastatic liver cancer is obvious. So, the symptom of metastatic liver cancer is found before the occurrence of primary carcinoma. However the symptom of protopathic tumor is not obvious.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cho JY, Sung HJ. Proteomic approaches in lung cancer biomarker development. Expert Rev Proteomics. 2009;6(1):27–42.

    Article  CAS  PubMed  Google Scholar 

  2. Sun S, Lee NP, Poon RT, et al. Oncoproteomics of hepatocellular carcinoma: from cancer markers’ discovery to functional pathways. Liver Int. 2007;27(8):1021–38.

    Article  CAS  PubMed  Google Scholar 

  3. Gebhart E, Liehr T. Patterns of genomic imbalances in human solid tumors. Int J Oncol. 2000;16(2):383–99.

    CAS  PubMed  Google Scholar 

  4. Kelly L, Clark J, Gilliland DG. Comprehensive genotypic analysis of leukemia: clinical and therapeutic implications. Curr Opin Oncol. 2002;14(1):10–8.

    Article  CAS  PubMed  Google Scholar 

  5. Walther A, Johnstone E, Swanton C, et al. Genetic prognostic and predictive markers in colorectal cancer. Nat Rev Cancer. 2009;9(7):489–99.

    Article  CAS  PubMed  Google Scholar 

  6. Kallioniemi A. CGH microarrays and cancer. Curr Opin Biotechnol. 2008;19(1):36–40.

    Article  CAS  PubMed  Google Scholar 

  7. Shah SP. Computational methods for identification of recurrent copy number alteration patterns by array CGH. Cytogenet Genome Res. 2008;123(1–4):343–51.

    CAS  PubMed  Google Scholar 

  8. Lockwood WW, Chari R, Chi B, et al. Recent advances in array comparative genomic hybridization technologies and their applications in human genetics. Eur J Hum Genet. 2006;14(2):139–48.

    Article  CAS  PubMed  Google Scholar 

  9. Bejjani BA, Shaffer LG. Application of array-based comparative genomic hybridization to clinical diagnostics. J Mol Diagn. 2006;8(5):528–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Shinawi M, Cheung SW. The array CGH and its clinical applications. Drug Discov Today. 2008;13(17–18):760–70.

    Article  CAS  PubMed  Google Scholar 

  11. Costa JL, Meijer G, Ylstra B, et al. Array comparative genomic hybridization copy number profiling: a new tool for translational research in solid malignancies. Semin Radiat Oncol. 2008;18(2):98–104.

    Article  PubMed  Google Scholar 

  12. Harada T, Chelala C, Crnogorac-Jurcevic T, et al. Genome-wide analysis of pancreatic cancer using microarray-based techniques. Pancreatology. 2009;9(1–2):13–24.

    Article  CAS  PubMed  Google Scholar 

  13. Pinkel D, Straume T, et al. Cytogenetic analysis using quantitative, high-sensitivity, fluorescence hybridization. Proc Natl Acad Sci. 1986;83(9):2934–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ried T, Baldini A, Rand TC, et al. Simultaneously visualization of seven different DNA probes by in situ hybridization using fluorescence and digital imaging microscopy. Proc Natl Acad Sci. 1992;89:1388–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Nederlof PM, van de Flier S, Vrolijk J, et al. Fluorescence ratio measurements of double-labeled probes for multiple in situ hybridization by digital imaging microscopy. Cytometry. 1992;13:839–45.

    Article  CAS  PubMed  Google Scholar 

  16. SpeciherM R, Gwyn BS, Ward DC. Karyotyping human chromosomes by combinatorial multi-fluor FISH. Nat Genet. 1996;12:368–75.

    Article  Google Scholar 

  17. Schrock E, du Manoir S, Veldman, et al. Multicolor spectral karyotyping of human chromosomes. Science. 1996;273:494–7.

    Article  CAS  PubMed  Google Scholar 

  18. Kearney L. Multiplex-FISH (M-FISH): technique, developments and applications. Cytogenet Genome Res. 2006;114:189–98.

    Article  CAS  PubMed  Google Scholar 

  19. Eils R, Uhrig S. An optimized, fully automated system for fast and accurate identification of chromosomal rearrangements by multiplex-FISH (M-FISH). Cytogenet Cell Genet. 1998;82:160–71.

    Article  CAS  PubMed  Google Scholar 

  20. Uhrig S, Schuffenhauer S, et al. Multiplex-FISH for pre- and postnatal diagnostic applications. Am J Hum Genet. 1999;65(2):448–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Marshall A, Hodgson J. DNA chips: an array of possibilities. Nat Biotchol. 1998;16:27–8.

    Article  CAS  Google Scholar 

  22. Schena M, Shalon D, Davis RW, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science. 1995;270:467–70.

    Article  CAS  PubMed  Google Scholar 

  23. Lipshutz RJ, Fodor SP, Gingeras TR, et al. High density synthetic oligonucleotide arrays. Nat Genet. 1999;21:20–4.

    Article  CAS  PubMed  Google Scholar 

  24. Ye QH, Qin LX, Forgues M, et al. Predicting hepatitis B virus-positive metastatic hepatocellular carcinomas using gene expression profiling and supervised machine learning. Nat Med. 2003;9:416–23.

    Article  CAS  PubMed  Google Scholar 

  25. Alizadeh AA, Eisen MB, Davis RE, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;40:503–11.

    Article  CAS  Google Scholar 

  26. Okabe H, Satoh S, Kato T, et al. Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis and tumor progression. Cancer Res. 2001;61:2129–37.

    CAS  PubMed  Google Scholar 

  27. Seliger B, Dressler SP, Wang E, et al. Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma. Proteomics. 2009;9:1567–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Mandoiu II, Prajescu C. High-throughput SNP genotyping by SBE/SBH. IEEE Trans Nanobioscience. 2007;6:28–35.

    Article  PubMed  Google Scholar 

  29. Cunha BA, Esrick MD, Larusso M. Staphylococcus hominis native mitral valve bacterial endocarditis (SBE) in a patient with hypertrophic obstructive cardiomyopathy. Heart Lung. 2007;36:380–2.

    Article  PubMed  Google Scholar 

  30. Shen R, Fan JB, Campbell D, et al. High-throughput SNP genotyping on universal bead arrays. Mutat Res. 2005;573:70–82.

    Article  CAS  PubMed  Google Scholar 

  31. Van Heek NT, Clayton SJ, Sturm PD, et al. Comparison of the novel quantitative ARMS assay and an enriched PCR-ASO assay for K-ras mutations with conventional cytology on endobiliary brush cytology from 312 consecutive extrahepatic biliary stenoses. J Clin Pathol. 2005;58:1315–20.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Dalma-Weiszhausz DD, Murphy Jr GM. Single nucleotide polymorphisms and their characterization with oligonucleotide microarrays. Psychiatr Genet. 2002;12:97–107.

    Article  PubMed  Google Scholar 

  33. Haihui S, Huasheng X. Polymorphyism and drug metabolism of cytochrome P450gene. Int Genet. 2008;31(3):206–12.

    Google Scholar 

  34. Frommer M, McDonald LE, Millar DS, et al. A genomic sequencing protocol that yields a positive display of 5-methylcytosine residues in individual DNA strands. Proc Natl Acad Sci U S A. 1992;89(5):1827–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gitan RS, Shi H, Chen CM, et al. Methylation-specific oligonucleotide microarray: a new potential for high-throughput methylation analysis. Genome Res. 2002;12(1):158–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Gao L, Cheng L, Zhou JN, et al. DNA microarray: a high throughput approach for methylation detection. Colloids Surf B Biointerfaces. 2005;40(3–4):127–31.

    Article  CAS  PubMed  Google Scholar 

  37. Bibikova M, Chudin E, Wu B, et al. Human embryonic stem cells have a unique epigenetic signature. Genome Res. 2006;16(9):1075–83.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Cross SH, Charlton JA, Nan X, et al. Purification of CpG islands using a methylated DNA binding column. Nat Genet. 1994;6(3):236–44.

    Article  CAS  PubMed  Google Scholar 

  39. Versmold B, Felsberg J, Mikeska T, et al. Epigenetic silencing of the candidate tumor suppressor gene PROX1 in sporadic breast cancer. Int J Cancer. 2007;121(3):547–54.

    Article  CAS  PubMed  Google Scholar 

  40. Rush L, Plass C. Restriction landmark genomic scanning for DNA methylation in cancer: past, present and future applications. Anal Biochem. 2002;307(2):191–201.

    Article  CAS  PubMed  Google Scholar 

  41. Songfa Z, Feng Y, Cheng H, et al. Research on genome CpG methylation detection. Int J Genet. 2006;29(3):201–17.

    Google Scholar 

  42. Hatada I, Hayashizaki Y, Hirotsune S, et al. A genomic scanning method for higher organisms using restriction sites as landmarks. Proc Natl Acad Sci U S A. 1991;88(21):9523–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Hyashizaki Y, Watanabe S, editors. Restriction landmark genomic scanning (RLGS). Tokyo: Springer; 1997.

    Google Scholar 

  44. Matsuyama T, Kimura MT, Koike K, et al. Global methylation screening in the Arabidopsis thaliana and Mus musculus genome: applications of virtual image restriction landmark genomic scanning (Vi-RLGS). Nucleic Acids Res. 2003;31(15):4490–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Nobuyasu S. Characterization of circulating DNA in healthy human plasma. Clin Chim Acta. 2008;387:55–8.

    Article  CAS  Google Scholar 

  46. Sabine J. DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res. 2001;61:1659–65.

    Google Scholar 

  47. Ugur D, et al. Frequent copresence of methylated DNA and fragmented nucleosomal DNA in plasma of lymphoma patients. Clin Chim Acta. 2003;335:89–94.

    Article  CAS  Google Scholar 

  48. Ning R, et al. The prognostic value of circulating plasma DNA level and its allelic imbalance on chromosome 8p in patients with hepatocellular carcinoma. J Cancer Res Clin Oncol. 2006;32:399–407.

    Google Scholar 

  49. Gabriella S, et al. Analysis of circulating tumor DNA in plasma at diagnosis and during follow-up of lung cancer patients. Cancer Res. 2001;61:4675–8.

    Google Scholar 

  50. Oliver G, et al. Circulating deoxyribonucleic acid as prognostic marker in Non-small-cell lung cancer patients undergoing chemotherapy. Clin Oncol. 2004;22:4157–64.

    Article  CAS  Google Scholar 

  51. Chao CH, et al. Quantification of circulating cell-free DNA in the plasma of cancer patients during radiation therapy. Cancer Sci. 2009;100:303–9.

    Article  CAS  Google Scholar 

  52. Mayrhofer C, Krieger S, Allmaier, et al. DIGE compatible labeling of surface proteins on vital cells in vitro and in vivo. Proteomics. 2006;6(2):579–85.

    Article  CAS  PubMed  Google Scholar 

  53. Choi KS, Song L, Park YM, et al. Analysis of human plasma proteome by 2DE- and 2D nanoLC-based mass spectrometry. Prep Biochem Biotechnol. 2006;36(1):3–17.

    Article  CAS  PubMed  Google Scholar 

  54. Hutchens TW, Yip TT. New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Commun Mass Spectrom. 1993;7:576–80.

    Article  CAS  Google Scholar 

  55. Seibert V, Wiesner A, Buschmann T, et al. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI TOF-MS) and proteinchip technology in proteomics research. Pathol Res Pract. 2004;200:83–94.

    Article  CAS  PubMed  Google Scholar 

  56. Fauq AH, Kache R, Khan MA, et al. Synthesis of acid-cleavable light isotope-coded affinity tags (ICAT-L) for potential use in proteomic expression profiling analysis. Bioconjug Chem. 2006;17(1):248–54.

    Article  CAS  PubMed  Google Scholar 

  57. Shui WQ, Liu YK, Fan HZ, et al. Enhancing TOF-TOF-based novo sequencing for high throughput identification with amino acid coded mass tagging. J Proteome Res. 2005;4:83–90.

    Article  CAS  PubMed  Google Scholar 

  58. Ross PL, Huang YN, Marchese JN, et al. Multiplexed protein quantitation in saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomice. 2004;3(12):1154–69.

    Article  CAS  Google Scholar 

  59. Ledue TB, Garfin D, et al. Immunofixation an dimmunoblotting. In: Rose NR, de Conway ME, Folds JD, editors. Manual of clinic laboratory microbiology. 5th ed. Washington, DC: American Society for Microbiology; 1997. p. 54–64.

    Google Scholar 

  60. Puig O, Caspary F, Rigaut G, et al. The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods. 2001;24:218–29.

    Article  CAS  PubMed  Google Scholar 

  61. Naour FL, Brichory F, Beretta L, et al. Identification of tumor-associated antigens using proteomics. Technol Cancer Res Treat. 2002;1:257–62.

    Article  PubMed  Google Scholar 

  62. Lichtenfels R, Kellner R, Bukur J, et al. Heat shock protein expression and anti-heat shock protein reactivity in renal cell carcinoma. Proteomics. 2002;2:561–70.

    Article  CAS  PubMed  Google Scholar 

  63. Brichory FM, Misek DE, Yim AM, et al. An immune response manifested by the common occurrence of annexins I and II autoantibodies and high circulating levels of IL-6 in lung cancer. Proc Natl Acad Sci U S A. 2001;98:9824–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Le Naour F, Misek DE, Krause MC, et al. Proteomics-based identification of RS/DJ-1 as a novel circulating tumor antigen in breast cancer. Clin Cancer Res. 2001;7:3328–35.

    PubMed  Google Scholar 

  65. Le Naour F, Brichory F, Misek DE, et al. A distinct repertoire of autoantibodies in hepatocellular carcinoma identified by proteomic analysis. Mol Cell Proteomics. 2002;1:197–203.

    Article  PubMed  CAS  Google Scholar 

  66. Jutao F, Yinkun L, Zhi D. Screening of spontaneous antibody of liver cancer via serum proteomics. China Hepatopathy Mag. 2005;13(11):832–5.

    Google Scholar 

  67. Petricoin EF, Ardekani A, Hitt P, et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359(2):572–7.

    Article  CAS  PubMed  Google Scholar 

  68. Yanagisawa K, Yu S, Xu BJ, et al. Proteomic patterns of tumor subsets in non-small-cell lung cell. Lancet. 2003;362(9382):433–9.

    Article  CAS  PubMed  Google Scholar 

  69. Ornstein DK, Rayford W, Fusaro VA, et al. Serum proteomic profiling can discriminate prostate cancer from benign prostates in men with total prostate specific antigen levels between 2.5 and 15.0ng/ml. J Urol Oncol. 2004;172:1302–5.

    Article  CAS  Google Scholar 

  70. Junker K, Gneist J, Melle C, et al. Identification of protein pattern in kidney cancer using proteinchip arrays and bioinformatics. Int J Mol Med. 2005;15(2):285–90.

    CAS  PubMed  Google Scholar 

  71. Hudelist G, Margit P-Z, Christian SF, et al. Use of high-throughput protein array for profiling of differentially expressed proteins in normal and malignant breast tissue. Breast Cancer Res Treat. 2004;86(3):281–91.

    Google Scholar 

  72. Scott G, Quynh-Thu L, et al. The use of plasma surface-enhanced laser desorption/ ionization time-of-flight mass spectrometry proteomic patterns for detection of head and neck squamous cell cancers. Clin Cancer Res. 2004;10:4806–12.

    Article  Google Scholar 

  73. Chen YD, Zheng S, Yu JK, et al. Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population. Clin Cancer Res. 2004;10:8380–5.

    Article  CAS  PubMed  Google Scholar 

  74. Huangcheng FJ, Jian Z. Research on serum protein molecular markers related to the cancer embolus formation of portal vein of hepatocellular carcinoma. Chin Med J (Engl). 2005;85(11):781–5.

    CAS  Google Scholar 

  75. Song HY, Liu YK, Feng JT, et al. Proteomic analysis on metastasis-associated proteins of human hepatocellular carcinoma tissues. J Cancer Res Clin Oncol. 2006;132(2):92–8.

    Article  CAS  PubMed  Google Scholar 

  76. Feng JT, Liu YK, Song HY, et al. Heat shock protein 27: a potential biomarker for hepatocellular carcinoma identified by serum proteome analysis. Proteomics. 2005;5(17):4581–8.

    Article  CAS  PubMed  Google Scholar 

  77. Wong J, Cagney G, Cartwright H. SpecAlign-processing and alignment of mass spectra datasets. Bioinformatics. 2005;21(9):2088–90.

    Article  CAS  PubMed  Google Scholar 

  78. Shin H, Mutlu M, Koomen JM, et al. Parametric power spectral density analysis of noise from instrumentation in MALDI TOF mass spectrometry. Cancer Inform. 2007;3:317–28.

    Google Scholar 

  79. Shin H, Markey MK. A machine learning perspective on the development of clinical decision support systems utilizing mass spectra of blood samples. J Biomed Inform. 2006;39(2):227–48.

    Article  CAS  PubMed  Google Scholar 

  80. Cruz-Marcelo A, Guerra R, Vannucci M, et al. Comparison of algorithms for pre-processing of SELDI-TOF mass spectrometry data. Bioinformatics. 2008;24(19):2129–36.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Cui J, Kang X, Dai Z, Huang C, et al. Prediction of chronic hepatitis B, liver cirrhosis and hepatocellular carcinoma by SELDI-based serum decision tree classification. J Cancer Res Clin Oncol. 2007;133(11):825–34.

    Article  PubMed  Google Scholar 

  82. Schwegler EE, Cazares L, Steel LF, et al. SELDI-TOF-MS profiling of serum for detection of the progression of chronic hepatitis C to hepatocellular carcinoma. Hepatology. 2005;41(3):634–42.

    Article  PubMed  Google Scholar 

  83. Scarlett CJ, Saxby AJ, Nielsen A, et al. Diagnostic potential of SELDI-TOF MS in malignant bile duct stricture. Hepatology. 2006;44(3):658–66.

    Article  CAS  PubMed  Google Scholar 

  84. Lim JY, Cho JY, Paik YH, et al. Diagnostic application of serum proteomic patterns in gastric cancer patients by ProteinChip surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. Int J Biol Markers. 2007;22(4):281–6.

    CAS  PubMed  Google Scholar 

  85. Liu XP, Shen J, Li ZF, et al. A serum proteomic pattern for the detection of colorectal adenocarcinoma using surface enhanced laser desorption and ionization mass spectrometry. Cancer Invest. 2006;24(8):747–53.

    Article  CAS  PubMed  Google Scholar 

  86. Yang SY, Xiao XY, Zhang WG, et al. Application of serum SELDI proteomic patterns in diagnosis of lung cancer. BMC Cancer. 2005;20(5):83.

    Article  CAS  Google Scholar 

  87. Xu G, Xiang CQ, Lu Y, et al. SELDI-TOF-MS-based serum proteomic screening in combination with CT scan distinguishes renal cell carcinoma from benign renal tumors and healthy persons. Technol Cancer Res Treat. 2009;8(3):225–30.

    Article  CAS  PubMed  Google Scholar 

  88. Navaglia F, Fogar P, Basso D, Tonidandel L, Fadi E, Zambon CF, Bozzato D, Moz S, Seraglia R, Pedrazzoli S, Plebani M. Pancreatic cancer biomarkers discovery by surface-enhanced laser desorption and ionization time-of-flight mass spectrometry. Clin Chem Lab Med. 2009;47(6):713–23.

    Article  CAS  PubMed  Google Scholar 

  89. Cheng L, Zhou L, Tao L, et al. SELDI-TOF MS profiling of serum for detection of laryngeal squamous cell carcinoma and the progression to lymph node metastasis. J Cancer Res Clin Oncol. 2008;134(7):769–76.

    Article  PubMed  Google Scholar 

  90. Wei YS, Zheng YH, Liang WB, et al. Identification of serum biomarkers for nasopharyngeal carcinoma by proteomic analysis. Cancer. 2008;112(3):544–51.

    Article  CAS  PubMed  Google Scholar 

  91. Zhou L, Cheng L, Tao L, et al. Detection of hypopharyngeal squamous cell carcinoma using serum proteomics. Acta Otolaryngol. 2006;126(8):853–60.

    Article  CAS  PubMed  Google Scholar 

  92. Ho DW, Yang ZF, Wong BY, et al. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry serum protein profiling to identify nasopharyngeal carcinoma. Cancer. 2006;107(1):99–107.

    Article  CAS  PubMed  Google Scholar 

  93. Ward DG, Cheng Y, N’Kontchou G, et al. Changes in the serum proteome associated with the development of hepatocellular carcinoma in hepatitis C-related cirrhosis. Br J Cancer. 2006;94(2):287–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Cao SM, Guo X, Chen FJ, et al. Serum diagnosis of head and neck squamous cell carcinoma using surface-enhanced desorption ionization mass spectrometry and artificial neural network analyses. Ai Zheng. 2007;26(7):767–70.

    CAS  PubMed  Google Scholar 

  95. Au JS, Cho WC, Yip TT, et al. Deep proteome profiling of sera from never-smoked lung cancer patients. Biomed Pharmacother. 2007;61(9):570–7.

    Article  CAS  PubMed  Google Scholar 

  96. Qi XN. Support vector machines and application research overview. Comput Eng. 2004;30:10.

    Google Scholar 

  97. Shen Q, Shi WM, Kong W. New gene selection method for multiclass tumor classification by class centroid. J Biomed Inform. 2009;42(1):59–65.

    Article  CAS  PubMed  Google Scholar 

  98. Oberthuer A, Berthold F, Warnat P, et al. Customized oligonucleotide microarray gene expression-based classification of neuroblastoma patients outperforms current clinical risk stratification. J Clin Oncol. 2006;24(31):5070–8.

    Article  CAS  PubMed  Google Scholar 

  99. Roepman P, Schuurman A, Delahaye LJ, et al. A gene expression profile for detection of sufficient tumour cells in breast tumour tissue: microarray diagnosis eligibility. BMC Med Genomics. 2009;2(1):52.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  100. Rosenfeld N, Aharonov R, Meiri E, et al. MicroRNAs accurately identify cancer tissue origin. Nat Biotechnol. 2008;26(4):400–1.

    Article  CAS  Google Scholar 

  101. Kawamura T, Mutoh H, Tomita Y, et al. Cancer DNA microarray analysis considering multi-subclass with graph-based clustering method. J Biosci Bioeng. 2008;106(5):442–8.

    Article  CAS  PubMed  Google Scholar 

  102. Trolet J, Hupé P, Huon I, et al. Genomic profiling and identification of high-risk uveal melanoma by array CGH analysis of primary tumors and liver metastasis. Invest Ophthalmol Vis Sci. 2009;50(6):2572–80.

    Article  PubMed  Google Scholar 

  103. Hewett R, Kijsanayothin P. Tumor classification ranking from microarray data. BMC Genomics. 2008;9(2):21.

    Article  Google Scholar 

  104. Botting SK, Trzeciakowski JP, Benoit MF, et al. Sample entropy analysis of cervical neoplasia gene-expression signatures. BMC Bioinforma. 2009;10:66.

    Article  CAS  Google Scholar 

  105. Murakami Y, Yasuda T, Saigo K, et al. Comprehensive analysis of microRNA expression patterns in hepatocellular carcinoma and non-tumorous tissues. Oncogene. 2006;25(17):2537–45.

    Article  CAS  PubMed  Google Scholar 

  106. Jiang H, Deng Y, Chen HS, et al. Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes. BMC Bioinforma. 2004;5:81.

    Article  Google Scholar 

  107. Jiang DF, Gao J, Zhao NQ (2005) Microarray data analysis for breast cancer. Fudan Univ J Med Sci 32(2):167–72.

    Google Scholar 

  108. Patwa TH, Li C, Poisson LM, et al. The identification of phosphoglycerate kinase-1 and histone H4 autoantibodies in pancreatic cancer patient serum using a natural protein microarray. Electrophoresis. 2009;30(12):2215–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Moriya Y, Iyoda A, Kasai Y, et al. Prediction of lymph node metastasis by gene expression profiling in patients with primary resected lung cancer. Lung Cancer. 2009;64(1):86–91.

    Article  PubMed  Google Scholar 

  110. Shiwa M, Nishimura Y, Wakatabe R, et al. Rapid discovery and identification of a tissue specific tumor biomarker from39 human cancer cell lines using the SELDI protein chip platform[J]. Biochem Biophys Res Commun. 2003;309(1):18–25.

    Article  CAS  PubMed  Google Scholar 

  111. Lawrie LC, Curran S, McLeod HL, et al. Application of laser capture microdissection and proteomics in colon cancer [J]. MolPat hol. 2001;54(4):253–8.

    CAS  Google Scholar 

  112. Simpson RJ, Connolly LM, Eddes JS, et al. Proteomic analysis of the human colon carcinoma cell line (LIM1215): development of a membrane protein database. Electrophoresis. 2000;21(9):1707–32.

    Article  CAS  PubMed  Google Scholar 

  113. Ahmed N, Oliva K, Wang Y, et al. Proteomic profiling of proteins associated wit h urokinase plasminogen activator receptor in a colon cancer cell line using an antisense approach[J]. Proteomics. 2003;3(3):288–98.

    Article  CAS  PubMed  Google Scholar 

  114. Stierum R, Gaspari M, Dommels Y, et al. Proteome analysis reveals novel proteins associated wit h proliferation and differentiation of t he colorectal cancer cell line Caco22[J]. Biochim Biophys Acta. 2003;1650(1–2):73–91.

    Article  CAS  PubMed  Google Scholar 

  115. Xu WH, Chen YD, Hu Y, et al. Preoperatively molecular staging with CM10 ProteinChip and SELD I2TOF2MS for colorectal cancer patients. J Zhejiang Univ Sci B. 2006;7(3):235–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Roboz J, Mal H, Sung M, et al. Protein profiles of serum in colon cancer by SEIDL – TOF mass spectrometry [R]. Proeomic: Poster Session AACR; 2002.

    Google Scholar 

  117. Petricoin EF, Liotta LA. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer [J]. Curr OpinBiotechnol. 2004;15(1):24–30.

    CAS  Google Scholar 

  118. Friedman D, Hill S, Keller J, et al. Proteome analysis of human colon cancer by two-dimensional difference gel electrophoresis and mass spectrometry. Proteomics. 2004;4(3):793–811.

    Article  CAS  PubMed  Google Scholar 

  119. Chaurand P, DaGue BB, Pearsall RS, et al. Profiling proteins from azoxymethane induced colon tumors at the molecular level by matrix assisted laser desorption/ ionization mass spectrometry. Proc Natl Acad Sci U S A. 2001;1(10):1320–6.

    CAS  Google Scholar 

  120. Stulik J, Koupilova K, Osterreicher J, et al. Protein abundance alterations in matched sets of macroscopically normal colon mucosa and colorectal carcinoma. Electrophoresis. 1999;20(18):3638–46.

    Article  CAS  PubMed  Google Scholar 

  121. Stulik J, Hernychova L, Porkertova S, et al. Proteome study of colorectal carcinogenesis. Electrophoresis. 2001;22(14):3019–25.

    Article  CAS  PubMed  Google Scholar 

  122. Roblick UJ, Hirschberg D, Habermann JK, et al. Sequential proteome alterations during genesis and progression of colon cancer [J]. Cell Mol Life Sci. 2004;61(10):1246.

    Article  CAS  PubMed  Google Scholar 

  123. Haiping P, Zhu H, Liang Z, et al. Application of two dimension electrophoresis and mass-spectrometric technique to sort out the differential protein expression between carcinoma of large intestine and normal intestinal tissue [J]. China Gen Surg. 2005;10(14):7482752.

    Google Scholar 

  124. Ping A, Yu B, Shiyong L. Proteomics research on occurrence and hepatic metastasis of carcinoma of large intestine [J]. China Surg Dep Mag. 2004;42(11):668–71.

    Google Scholar 

  125. Tachibana M, Ohkura Y, Kobayashi Y, et al. Expression of apolipoprotein A1 in colonic adenocarcinoma [J]. Anticancer Res. 2003;23(5b):4161–7.

    CAS  PubMed  Google Scholar 

  126. Lin HM, Chatterjee A, Lin YH, et al. Genome wide expression profiling identifies genes associated with colorectal liver metastasis. Oncol Rep. 2007;17(6):1541–9.

    CAS  PubMed  Google Scholar 

  127. Fritzmann J, Morkel M, Besser D, et al. A colorectal cancer expression profile that includes transforming growth factor β inhibitor BAMBI predicts metastatic potential. Gastroenterology. 2009;137(1):165–75.

    Article  CAS  PubMed  Google Scholar 

  128. Sato T, Oshima T, Yoshihara K, et al. Overexpression of the fibroblast growth factor receptor-1 gene correlates with liver metastasis in colorectal cancer. Oncol Rep. 2009;21(1):211–6.

    CAS  PubMed  Google Scholar 

  129. Oshima T, Akaike M, Yoshihara K, et al. Clinicopathological significance of the gene expression of matrix metalloproteinase-7, insulin-like growth factor-1, insulin-like growth factor-2 and insulin-like growth factor-1 receptor in patients with colorectal cancer: insulin-like growth factor-1 receptor gene expression is a useful predictor of liver metastasis from colorectal cancer. Oncol Rep. 2008;20(2):359–64.

    CAS  PubMed  Google Scholar 

  130. Oshima T, Akaike M, Yoshihara K, et al. Overexpression of EphA4 gene and reduced expression of EphB2 gene correlates with liver metastasis in colorectal cancer. Int J Oncol. 2008;33(3):573–7.

    CAS  PubMed  Google Scholar 

  131. Akashi A, Komuta K, Haraguchi M, et al. Carcinoembryonic antigen mRNA in the mesenteric vein is not a predictor of hepatic metastasis in patients with resectable colorectal cancer: a long-term study. Dis Colon Rectum. 2003;46(12):1653–8.

    Article  PubMed  Google Scholar 

  132. Rohde F, Rimkus C, Friederichs J, et al. Holzmann B,Siewert JR, Janssen KP. Expression of osteopontin, a target gene of de-regulated Wnt signaling, predicts survival in colon cancer. Int J Cancer. 2007;121(8):1717–23.

    Article  CAS  PubMed  Google Scholar 

  133. Rubie C, Frick VO, Pfeil S, et al. Schilling MK Correlation of IL-8 with induction, progression and metastatic potential of colorectal cancer. World J Gastroenterol. 2007;13(37):4996–5002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Miyagawa S, Soeda J, Takagi S, et al. Prognostic significance of mature dendritic cells and factors associated with their accumulation in metastatic liver tumors from colorectal cancer. Hum Pathol. 2004;35(11):1392–6.

    Article  PubMed  Google Scholar 

  135. Shi HJ, Stubbs R, Hood K. Characterization of de novo synthesized proteins released from human colorectal tumour explants. Electrophoresis. 2009;30(14):2442–53.

    Article  CAS  PubMed  Google Scholar 

  136. Katayama M, Nakano H, Ishiuchi A, et al. Protein pattern difference in the colon cancer cell lines examined by two-dimensional differential in-gel electrophoresis and mass spectrometry. Surg Today. 2006;36(12):1085–93.

    Article  CAS  PubMed  Google Scholar 

  137. Pei H, Zhu H, Zeng S, et al. Proteome analysis and tissue microarray for profiling protein markers associated with lymph node metastasis in colorectal cancer. J Proteome Res. 2007;6(7):2495–501.

    Article  CAS  PubMed  Google Scholar 

  138. Kang B, Hao C, Wang H, et al. Evaluation of hepatic-metastasis risk of colorectal cancer upon the protein signature of PI3K/AKT pathway. J Proteome Res. 2008;7(8):3507–15.

    Article  CAS  PubMed  Google Scholar 

  139. Pierobon M, Calvert V, Belluco C, et al. Multiplexed cell signaling analysis of metastatic and nonmetastatic colorectal cancer reveals COX2-EGFR signaling activation as a potential prognostic pathway biomarker. Clin Colorectal Cancer. 2009;8(2):110–7.

    Article  CAS  Google Scholar 

  140. Nakamoto RH, Uetake H, Iida S, et al. Correlations between cyclooxygenase-2 expression and angiogenic factors in primary tumors and liver metastasis in colorectal cancer. Jpn J Clin Oncol. 2007;37(9):679–85.

    Article  PubMed  Google Scholar 

  141. Melle C, Ernst G, Schimmel B, et al. Colon-derived liver metastasis, colorectal carcinoma, and hepatocellular carcinoma can be discriminated by the Ca(2+)-binding proteins S100A6 and S100A11. PLoS One. 2008;3(12):3767.

    Article  CAS  Google Scholar 

  142. Fang YJ, Lu ZH, Wang GQ, Pan ZZ, et al. Elevated expressions of MMP7, TROP2, and survivin are associated with survival, disease recurrence, and liver metastasis of colon cancer. Int J Colorectal Dis. 2009;24(8):875–84.

    Article  CAS  PubMed  Google Scholar 

  143. Ochiai H, Nakanishi Y, Fukasawa Y, et al. A new formula for predicting liver metastasis in patients with colorectal cancer: immunohistochemical analysis of a large series of 439 surgically resected cases. Oncology. 2008;75(1–2):32–41.

    Article  CAS  PubMed  Google Scholar 

  144. Choi HN, Kim KR, Lee JH, et al. Serum response factor enhances liver metastasis of colorectal carcinoma via alteration of the E-cadherin/β-catenin complex. Oncol Rep. 2009;21(1):57–63.

    CAS  PubMed  Google Scholar 

  145. Pancione M, Forte N, Sabatino L, et al. Reduced β-catenin and peroxisome proliferator-activated receptor-gamma expression levels are associated with colorectal cancer metastatic progression: correlation with tumor-associated macrophages, cyclooxygenase 2, and patient outcome. Hum Pathol. 2009;40(5):714–25.

    Article  CAS  PubMed  Google Scholar 

  146. Delektorskaya VV, Perevoshchikov AG, Golovkov DA, et al. Expression of E-cadherin, β-catenin, and CD-44v6 cell adhesion molecules in primary tumors and metastasis of colorectal adenocarcinoma. Bull Exp Biol Med. 2005;139(6):706–10.

    Article  CAS  PubMed  Google Scholar 

  147. de Heer P, Koudijs MM, van de Velde CJ, et al. Combined expression of the non-receptor protein tyrosine kinases FAK and Src in primary colorectal cancer is associated with tumor recurrence and metastasis formation. Eur J Surg Oncol. 2008;34(11):1253–61.

    Article  PubMed  Google Scholar 

  148. Peeters CF, Ruers TJ, Westphal JR, et al. Progressive loss of endothelial P-selectin expression with increasing malignancy in colorectal cancer. Lab Invest. 2005;85(2):248–56.

    Article  CAS  PubMed  Google Scholar 

  149. Noike T, Miwa S, Soeda J, et al. Increased expression of thioredoxin-1, vascular endothelial growth factor, and redox factor-1 is associated with poor prognosis in patients with liver metastasis from colorectal cancer. Hum Pathol. 2008;39(2):201–8.

    Article  CAS  PubMed  Google Scholar 

  150. Wang M, Vogel I, Kalthoff H. Correlation between metastatic potential and variants from colorectal tumor cell line HT-29. World J Gastroenterol. 2003;9(11):2627–31.

    PubMed  PubMed Central  Google Scholar 

  151. Wang S, Zhou J, Wang XY, et al. Down- regulated expression of SATB2 is associated with metastasis and poor prognosis in colorectal cancer. J Pathol. 2009;219(1):114–22.

    Article  CAS  PubMed  Google Scholar 

  152. Oue N, Kuniyasu H, Noguchi T, et al. Serum concentration of Reg IV in patients with colorectal cancer: overexpression and high serum levels of Reg IV are associated with liver metastasis. Oncology. 2007;72(5–6):3713–80.

    Google Scholar 

  153. Peng L, Ning J, Meng L, et al. The association of the expression level of protein tyrosine phosphatase PRL-3 protein with liver metastasis and prognosis of patients with colorectal cancer. J Cancer Res Clin Oncol. 2004;130(9):521–6.

    Article  CAS  PubMed  Google Scholar 

  154. Li J, Guo K, Koh VW, Tang JP, et al. Generation of PRL-3 and PRL-1 specific monoclonal antibodies as potential diagnostic markers for cancer metastasis. Clin Cancer Res. 2005;11(6):2195–204.

    Article  CAS  PubMed  Google Scholar 

  155. Hatate K, Yamashita K, Hirai K, et al. Liver metastasis of colorectal cancer by protein-tyrosine phosphatase type 4A, 3 (PRL-3) is mediated through lymph node metastasis and elevated serum tumor markers such as CEA and CA19-9. Oncol Rep. 2008;20(4):737–43.

    PubMed  Google Scholar 

  156. Tsuboi K, Shimura T, Masuda N, et al. Galectin-3 expression in colorectal cancer: relation to invasion and metastasis. Anticancer Res. 2007;27(4B):2289–96.

    CAS  PubMed  Google Scholar 

  157. Zheng H, Tsuneyama K, Cheng C, et al. Maspin expression was involved in colorectal adenoma-adenocarcinoma sequence and liver metastasis of tumors. Anticancer Res. 2007;27(1A):259–65.

    CAS  PubMed  Google Scholar 

  158. Lin BR, Chang CC, Che TF, et al. Connective tissue growth factor inhibits metastasis and acts as an independent prognostic marker in colorectal cancer. Gastroenterology. 2005;128(1):9–23.

    Article  CAS  PubMed  Google Scholar 

  159. Saito N, Kameoka S. Serum laminin is an independent prognostic factor in colorectal cancer. Int J Colorectal Dis. 2005;20(3):238–44.

    Article  PubMed  Google Scholar 

  160. Yoshidome H, Kohno H, Shida T, et al. Significance of monocyte chemoattractant protein-1 in angiogenesis and survival in colorectal liver metastases. Int J Oncol. 2009;34(4):923–30.

    Article  CAS  PubMed  Google Scholar 

  161. Ochiumi T, Tanaka S, Oka S, et al. Clinical significance of angiopoietin-2 expression at the deepest invasive tumor site of advanced colorectal carcinoma. Int J Oncol. 2004;24(3):539–47.

    CAS  PubMed  Google Scholar 

  162. Yokomizo H, Yoshimatsu K, Ishibashi K, et al. Fas ligand expression is a risk factor for liver metastasis in colorectal cancer with venous invasion. Anticancer Res. 2003;23(6D):5221–4.

    PubMed  Google Scholar 

  163. Fujimoto Y, Nakanishi Y, Sekine S, et al. CD10 expression in colorectal carcinoma correlates with liver metastasis. Dis Colon Rectum. 2005;48(10):1883–9.

    Article  PubMed  Google Scholar 

  164. Hayashi H, Kohno H, Ono T, et al. Transforming growth factor-β1 induced hepatocyte apoptosis; a possible mechanism for growth of colorectal liver metastasis. Acta Oncol. 2004;43(1):91–7.

    Article  CAS  PubMed  Google Scholar 

  165. Auguste P, Fallavollita L, Wang N, et al. The host inflammatory response promotes liver metastasis by increasing tumor cell arrest and extravasation. Am J Pathol. 2007;170(5):1781–92.

    Article  PubMed  PubMed Central  Google Scholar 

  166. Kawahara A, Akagi Y, Hattori S, et al. Higher expression of deoxyuridine triphosphatase (dUTPase) may predict the metastasis potential of colorectal cancer. J Clin Pathol. 2009;62(4):364–9.

    Article  CAS  PubMed  Google Scholar 

  167. Zhou ZW, Ren JQ, Wan DS, et al. Multivariate regressive analysis of prognosis of liver metastasis from colorectal cancer. Ai Zheng. 2006;25(9):1149–52.

    PubMed  Google Scholar 

  168. Takagawa R, Fujii S, Ohta M, et al. Preoperative serum carcinoembryonic antigen level as a predictive factor of recurrence after curative resection of colorectal cancer. Ann Surg Oncol. 2008;15(12):3433–59.

    Article  PubMed  Google Scholar 

  169. Mehrkhani F, Nasiri S, Donboli K, et al. Prognostic factors in survival of colorectal cancer patients after surgery. Colorectal Dis. 2009;11(2):157–61.

    Article  CAS  PubMed  Google Scholar 

  170. Waas ET, Wobbes T, Ruers T, et al. Circulating gelatinases and tissue inhibitor of metalloproteinase-1 in colorectal cancer metastatic liver disease. Eur J Surg Oncol. 2006;32(7):756–63.

    Article  CAS  PubMed  Google Scholar 

  171. Sasaki A, Kawano K, Inomata M, et al. Value of serum carbohydrate antigen 19-9 for predicting extrahepatic metastasis in patients with liver metastasis from colorectal carcinoma. Hepatogastroenterology. 2005;52(66):1814–9.

    PubMed  Google Scholar 

  172. Iwasaki A, Shirakusa T, Yamashita Y, et al. Characteristic differences between patients who have undergone surgical treatment for lung metastasis or hepatic metastasis from colorectal cancer. Thorac Cardiovasc Surg. 2005;53(6):358–64.

    Article  CAS  PubMed  Google Scholar 

  173. Katoh H, Yamashita K, Kokuba Y, et al. Surgical resection of stage IV colorectal cancer and prognosis. World J Surg. 2008;32(6):1130–7.

    Article  PubMed  Google Scholar 

  174. Delektorskaya VV, Golovkov DA, Kushlinskii NE. Clinical significance of levels of molecular biological markers in zones of invasive front-line of colorectal cancer. Bull Exp Biol Med. 2008;146(5):616–9.

    Article  CAS  PubMed  Google Scholar 

  175. Cambien B, Karimdjee BF, Richard-Fiardo P, et al. Organ-specific inhibition of metastatic colon carcinoma by CXCR3 antagonism. Br J Cancer. 2009;100(11):1755–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Rubie C, Kollmar O, Frick VO, et al. Differential CXC receptor expression in colorectal carcinomas. Scand J Immunol. 2008;68(6):635–44.

    CAS  PubMed  Google Scholar 

  177. Murata K, Miyoshi E, Ihara S, et al. Attachment of human colon cancer cells to vascular endothelium is enhanced by N-acetylglucosaminyltransferase V. Oncology. 2004;66(6):492–501.

    Article  CAS  PubMed  Google Scholar 

  178. St Hill CA, Farooqui M, Mitcheltree G, et al. The high affinity selectin glycan ligand C2-O-SLeX and mRNA transcripts of the core 2 β-1,6-N-acetylglucosaminyltransferase (C2GnT1) gene are highly expressed in human colorectal adenocarcinomas. BMC Cancer. 2009;9:79.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  179. Uner A, Akcali Z, Unsal D. Serum levels of soluble E-selectin in colorectal cancer. Neoplasma. 2004;51(4):269–74.

    CAS  PubMed  Google Scholar 

  180. Uemura T, Shiozaki K, Yamaguchi K, et al. Contribution of sialidase NEU1 to suppression of metastasis of human colon cancer cells through desialylation of integrin β4. Oncogene. 2009;28(9):1218–29.

    Article  CAS  PubMed  Google Scholar 

  181. Toiyama Y, Miki C, Inoue Y, et al. Circulating form of human vascular adhesion protein-1 (VAP-1): decreased serum levels in progression of colorectal cancer and predictive marker of lymphatic and hepatic metastasis. J Surg Oncol. 2009;99(6):368–72.

    Article  CAS  PubMed  Google Scholar 

  182. Illemann M, Bird N, Majeed A, et al. Two distinct expression patterns of urokinase, urokinase receptor and plasminogen activator inhibitor-1 in colon cancer liver metastasis. Int J Cancer. 2009;124(8):1860–70.

    Article  CAS  PubMed  Google Scholar 

  183. Halder SK, Rachakonda G, Deane NG, et al. Smad7 induces hepatic metastasis in colorectal cancer. Br J Cancer. 2008;99(6):957–65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Yamada M, Ichikawa Y, Yamagishi S, et al. Amphiregulin is a promising prognostic marker for liver metastasis of colorectal cancer. Clin Cancer Res. 2008;14(8):2351–6.

    Article  CAS  PubMed  Google Scholar 

  185. Murad JC, Ribeiro Jr U, Safatle-Ribeiro AV, et al. Evaluation of molecular markers in hepatic metastasis of colorectal adenocarcinoma. Hepatogastroenterology. 2007;54(76):1029–33.

    CAS  PubMed  Google Scholar 

  186. Wagner P, Koch M, Nummer D, et al. Detection and functional analysis of tumor infiltrating T-lymphocytes (TIL) in liver metastasis from colorectal cancer. Ann Surg Oncol. 2008;15(8):2310–7.

    Article  PubMed  Google Scholar 

  187. Sasaki A, Kai S, Endo Y, Iwaki K, et al. Prognostic value of preoperative peripheral blood monocyte count in patients with colorectal liver metastasis after liver resection. J Gastrointest Surg. 2007;11(5):596–602.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yinkun Liu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Dordrecht and People's Medical Publishing House

About this chapter

Cite this chapter

Liu, Y., Sun, C., Chen, B. (2017). Screening and Identification of Molecular Marker for Metastatic Liver Cancer. In: Qin, X., Xu, J., Zhong, Y. (eds) Multidisciplinary Management of Liver Metastases in Colorectal Cancer. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7755-1_5

Download citation

  • DOI: https://doi.org/10.1007/978-94-017-7755-1_5

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-7753-7

  • Online ISBN: 978-94-017-7755-1

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics