Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Emerging molecular markers of cancer

Nature Reviews Cancer volume 2, pages 210–219 (2002)Cite this article

Key Points

  • Alterations in gene sequences, expression levels, and protein structure or function can be used as 'molecular markers' to detect cancers at an early stage, determine prognosis, and monitor disease progression or therapeutic response.

  • DNA-based markers of cancer include mutations, loss-of-heterozygosity, microsatellite instability, DNA hypermethylation, mitochondrial DNA mutations and detection of viral DNA.

  • There are various ways of detecting cancer cells by analysing RNA.

  • Techniques to identify alterations in protein structure or function that are associated with cancer include antibody-based detection methods, measurement of enzymatic activity and two-dimensional gel analysis of samples.

  • New approaches to discovering molecular markers include microarray analysis, serial analysis of gene expression and proteomic technologies.

  • Clinical researchers should design future trials to incorporate plans for collecting and analysing molecular markers. Molecular biologists should take advantage of the number of tumour samples now available in tissue banks to identify new molecular markers and to more fully assess existing ones.

Abstract

Alterations in gene sequences, expression levels and protein structure or function have been associated with every type of cancer. These 'molecular markers' can be useful in detecting cancer, determining prognosis and monitoring disease progression or therapeutic response. But what is the best way to identify molecular markers and can they be easily incorporated into the clinical setting?

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Molecular marker detection.
Figure 2: Proteomic detection of cancer.

Similar content being viewed by others

References

  1. Fearon, E. R. & Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell 61, 759–767 (1990).

    CAS  PubMed  Google Scholar 

  2. Feneley, M. R. & Partin, A. W. Diagnosis of localized prostate cancer: 10 years of progress. Curr. Opin. Urol. 10, 319–327 (2000).

    CAS  PubMed  Google Scholar 

  3. Benson, A. B., et al. NCCN practice guidelines for colorectal cancer. Oncology 14, 203–212 (2000).

    PubMed  Google Scholar 

  4. Olsen, O. & Gotzsche, P. C. Cochrane review on screening for breast cancer with mammography. Lancet 358, 1340–1342 (2001).

    CAS  PubMed  Google Scholar 

  5. Pietras, R. J. et al. HER-2 tyrosine kinase pathway targets estrogen receptor and promotes hormone-independent growth in human breast cancer cells. Oncogene 10, 2435–2446 (1995).

    CAS  PubMed  Google Scholar 

  6. Harris, L. N. et al. Comparison of methods of measuring HER-2 in metastatic breast cancer patients treated with high-dose chemotherapy. J. Clin. Oncol. 19, 1698–1706 (2001).

    CAS  PubMed  Google Scholar 

  7. Yancopoulos, G. D., Klagsbrun, M. & Folkman, J. Vasculogenesis, angiogenesis, and growth factors: ephrins enter the fray at the border. Cell 93, 661–664 (1998).

    CAS  PubMed  Google Scholar 

  8. Sawhney, R. S. et al. Differences in sensitivity of biological functions mediated by epidermal growth factor receptor activation with respect to endogenous and exogenous ligands. J. Biol. Chem. 277, 75–86 (2002).

    CAS  PubMed  Google Scholar 

  9. Gold, P. & Freedman, S. O. Tests for carcinoembryonic antigen. Role in diagnosis and management of cancer. JAMA 234, 190–192 (1975).

    CAS  PubMed  Google Scholar 

  10. Shapiro, B. et al. Determination of circulating DNA levels in patients with benign or malignant gastrointestinal disease. Cancer 51, 2116–2120 (1983).

    CAS  PubMed  Google Scholar 

  11. Leon, S. A. et al. Free DNA in the serum of cancer patients and the effect of therapy. Cancer Res. 37, 646–650 (1977).

    CAS  PubMed  Google Scholar 

  12. Anker, P., Lyautey, J., Lederrey, C. & Stroun, M. Circulating nucleic acids in plasma or serum. Clin. Chim. Acta 313, 143–146 (2001).References 10–12 establish and discuss the significance of the presence of free DNA in the serum of patients with cancer.

    CAS  PubMed  Google Scholar 

  13. Sidransky, D. et al. Identification of p53 gene mutations in bladder cancers and urine samples. Science 252, 706–709 (1991).

    CAS  PubMed  Google Scholar 

  14. Sidransky, D. et al. Identification of RAS oncogene mutations in the stool of patients with curable colorectal tumors. Science 256, 102–105 (1992).References 13 and 14 establish the ability to detect oncogene mutations in body fluids.

    CAS  PubMed  Google Scholar 

  15. Mills, N. E. et al. Detection of KRAS oncogene mutations in bronchoalveolar lavage fluid for lung cancer diagnosis. J. Natl Cancer Inst. 87, 1056–1060 (1995).

    CAS  PubMed  Google Scholar 

  16. Tobi, M., Luo, F. C. & Ronai, Z. Detection of KRAS mutation in colonic effluent samples from patients without evidence of colorectal carcinoma. J. Natl Cancer Inst. 86, 1007–1010 (1994).

    CAS  PubMed  Google Scholar 

  17. Dong, S. M. et al. Detecting colorectal cancer in stool with the use of multiple genetic targets. J. Natl Cancer Inst. 93, 858–865 (2001).References 15 – 17 use multiple genetic targets to identify cancer cells in stool DNA.

    CAS  PubMed  Google Scholar 

  18. Ahlquist, D. A. et al. Colorectal cancer screening by detection of altered human DNA in stool: feasibility of a multitarget assay panel. Gastroenterology 119, 1219–1227 (2000).

    CAS  PubMed  Google Scholar 

  19. Traverso, G. et al. Detection of APC mutations in fecal DNA from patients with colorectal tumors. N. Engl. J. Med. 346, 311–320 (2002).

    CAS  PubMed  Google Scholar 

  20. Jackson, P. E. et al. Specific p53 mutations detected in plasma and tumors of hepatocellular carcinoma patients by electrospray ionization mass spectrometry. Cancer Res. 61, 33–35 (2001).

    CAS  PubMed  Google Scholar 

  21. Sun, X., Hung, K., Wu, L., Sidransky, D. & Guo, B. Detection of tumor mutations in the presence of excess amounts of normal DNA. Nature Biotechnol. 20, 186–189 (2002).

    CAS  Google Scholar 

  22. Brennan, J. A. et al. Molecular assessment of histopathological staging in squamous-cell carcinoma of the head and neck. N. Engl. J. Med. 332, 429–435 (1995).

    CAS  PubMed  Google Scholar 

  23. Hayashi, N. et al. Genetic diagnosis identifies occult lymph node metastases undetectable by the histopathological method. Cancer Res. 54, 3853–3856 (1994).References 22 and 23 establish the use of oncogene mutations in the staging of cancer.

    CAS  PubMed  Google Scholar 

  24. Mao, L. et al. Microsatellite alterations as clonal markers for the detection of human cancer. Proc Natl Acad Sci U S A 91, 9871–9875 (1994).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Mao, L. et al. Molecular detection of primary bladder cancer by microsatellite analysis. Science 271, 659–662 (1996).

    CAS  PubMed  Google Scholar 

  26. Steiner, G., Schoenberg, M. P., Linn, J. F., Mao, L. & Sidransky, D. Detection of bladder cancer recurrence by microsatellite analysis of urine. Nature Med. 3, 621–624 (1997).

    CAS  PubMed  Google Scholar 

  27. Spafford, M. F. et al. Detection of head and neck squamous cell carcinoma among exfoliated oral mucosal cells by microsatellite analysis. Clin. Cancer Res. 7, 607–612 (2001).

    CAS  PubMed  Google Scholar 

  28. Nawroz, H. et al. Microsatellite alterations in serum DNA of head and neck cancer patients. Nature Med. 2, 1035–1037 (1996).References 25–28 highlight the use of microsatellite analysis in cancer detection.

    CAS  PubMed  Google Scholar 

  29. Chen, X. Q. Microsatellite alterations in plasma DNA of small cell lung cancer patients. Nature Med. 2, 1033–1035 (1996).

    CAS  PubMed  Google Scholar 

  30. Ahrendt, S. A. et al. Molecular detection of tumor cells in bronchoalveolar lavage fluid from patients with early stage lung cancer. J. Natl Cancer Inst. 91, 332–339 (1999).

    CAS  PubMed  Google Scholar 

  31. Medintz, I. L. et al. Loss of heterozygosity assay for molecular detection of cancer using energy-transfer primers and capillary array electrophoresis. Genome Res. 10, 1211–1218 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Hacia, J. G. et al. Determination of ancestral alleles for human single-nucleotide polymorphisms using high-density oligonucleotide arrays. Nature Genet. 22, 164–167 (1999).

    CAS  PubMed  Google Scholar 

  33. Baylin, S. B. & Herman, J. G. DNA hypermethylation in tumorigenesis: epigenetics joins genetics. Trends Genet. 16, 168–174 (2000).

    CAS  PubMed  Google Scholar 

  34. Merlo, A. et al. 5′ CpG island methylation is associated with transcriptional silencing of the tumour suppressor p16/CDKN2/MTS1 in human cancers. Nature Med. 1, 686–692 (1995).

    CAS  PubMed  Google Scholar 

  35. Herman, J. G., Graff, J. R., Myohanen, S., Nelkin, B. D. & Baylin, S. B. Methylation-specific PCR: a novel PCR assay for methylation status of CpG islands. Proc. Natl Acad. Sci. USA 93, 9821–9826 (1996).A method for detecting methylated markers by PCR.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Rosas, S. L. et al. Promoter hypermethylation patterns of p16, O6-methylguanine-DNA-methyltransferase, and death-associated protein kinase in tumors and saliva of head and neck cancer patients. Cancer Res. 61, 939–942 (2001).

    CAS  PubMed  Google Scholar 

  37. Belinsky, S. A. et al. Aberrant methylation of p16(INK4a) is an early event in lung cancer and a potential biomarker for early diagnosis. Proc. Natl Acad. Sci. USA 95, 11891–11896 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Palmisano, W. A. et al. Predicting lung cancer by detecting aberrant promoter methylation in sputum. Cancer Res. 60, 5954–5958 (2000).

    CAS  PubMed  Google Scholar 

  39. Esteller, M. et al. Detection of aberrant promoter hypermethylation of tumor suppressor genes in serum DNA from non-small cell lung cancer patients. Cancer Res. 59, 67–70 (1999).

    CAS  PubMed  Google Scholar 

  40. Sanchez-Cespedes, M. et al. Gene promoter hypermethylation in tumors and serum of head and neck cancer patients. Cancer Res. 60, 892–895 (2000).

    CAS  PubMed  Google Scholar 

  41. Grady, W. M., Rajput, A., Lutterbaugh, J. D. & Markowitz, S. D. Detection of aberrantly methylated hMLH1 promoter DNA in the serum of patients with microsatellite unstable colon cancer. Cancer Res. 61, 900–902 (2001).

    CAS  PubMed  Google Scholar 

  42. Kawakami, K. et al. Hypermethylated APC DNA in plasma and prognosis of patients with esophageal adenocarcinoma. J. Natl Cancer Inst. 92, 1805–1811 (2000).

    CAS  PubMed  Google Scholar 

  43. Usadel, H. et al. Quantitative adenomatous polyposis coli promoter methylation analysis in tumor tissue, serum, and plasma DNA of patients with lung cancer. Cancer Res. 62, 371–375 (2002).References 42 and 43 highlight the use of quantitative methylation in cancer detection.

    CAS  PubMed  Google Scholar 

  44. Esteller, M., Corn, P. G., Baylin, S. B. & Herman, J. G. A gene hypermethylation profile of human cancer. Cancer Res. 61, 3225–3229 (2001).A comprehensive list of methylated genes in human cancer.

    CAS  PubMed  Google Scholar 

  45. Jeronimo, C. et al. Quantitation of GSTP1 methylation in non-neoplastic prostatic tissue and organ-confined prostate adenocarcinoma. J. Natl Cancer Inst. 93, 1747–1752 (2001).

    CAS  PubMed  Google Scholar 

  46. Warburg, O., Geissler, A. W. & Lorenz, S. [On growth of cancer cells in media in which glucose is replaced by galactose]. Hoppe Seylers Z. Physiol. Chem. 348, 1686–1687 (1967).

    CAS  PubMed  Google Scholar 

  47. Polyak, K. et al. Somatic mutations of the mitochondrial genome in human colorectal tumours. Nature Genet. 20, 291–293 (1998).

    CAS  PubMed  Google Scholar 

  48. Fliss, M. S. et al. Facile detection of mitochondrial DNA mutations in tumors and bodily fluids. Science 287, 2017–2019 (2000).

    CAS  PubMed  Google Scholar 

  49. Sanchez-Cespedes, M. et al. Identification of a mononucleotide repeat as a major target for mitochondrial DNA alterations in human tumors. Cancer Res. 61, 7015–7019 (2001).References 47–49 address the identification of mitochondrial DNA mutations in tumours and clinical samples.

    CAS  PubMed  Google Scholar 

  50. Sherman, M. E. et al. Cervical specimens collected in liquid buffer are suitable for both cytologic screening and ancillary human papillomavirus testing. Cancer 81, 89–97 (1997).

    CAS  PubMed  Google Scholar 

  51. Capone, R. B. et al. Detection and quantitation of human papillomavirus (HPV) DNA in the sera of patients with HPV-associated head and neck squamous cell carcinoma. Clin. Cancer Res. 6, 4171–4175 (2000).

    CAS  PubMed  Google Scholar 

  52. Lo, Y. M. et al. Quantitative and temporal correlation between circulating cell-free Epstein–Barr virus DNA and tumor recurrence in nasopharyngeal carcinoma. Cancer Res. 59, 5452–5455 (1999).

    CAS  PubMed  Google Scholar 

  53. Lo, Y. M. et al. Molecular prognostication of nasopharyngeal carcinoma by quantitative analysis of circulating Epstein–Barr virus DNA. Cancer Res. 60, 6878–6881 (2000).

    CAS  PubMed  Google Scholar 

  54. Datta, Y. H. et al. Sensitive detection of occult breast cancer by the reverse-transcriptase polymerase chain reaction. J. Clin. Oncol. 12, 475–482 (1994).

    CAS  PubMed  Google Scholar 

  55. Gerhard, M. et al. Specific detection of carcinoembryonic antigen-expressing tumor cells in bone marrow aspirates by polymerase chain reaction. J. Clin. Oncol. 12, 725–729 (1994).

    CAS  PubMed  Google Scholar 

  56. Krismann, M. et al. Low specificity of cytokeratin 19 reverse transcriptase-polymerase chain reaction analyses for detection of hematogenous lung cancer dissemination. J. Clin. Oncol. 13, 2769–2775 (1995).

    CAS  PubMed  Google Scholar 

  57. Katz, A. E. et al. Enhanced reverse transcriptase-polymerase chain reaction for prostate specific antigen as an indicator of true pathologic stage in patients with prostate cancer. Cancer 75, 1642–1648 (1995).

    CAS  PubMed  Google Scholar 

  58. Smith, B. et al. Detection of melanoma cells in peripheral blood by means of reverse transcriptase and polymerase chain reaction. Lancet 338, 1227–1229 (1991).

    CAS  PubMed  Google Scholar 

  59. Foss, A. J. et al. The detection of melanoma cells in peripheral blood by reverse transcription-polymerase chain reaction. Br. J. Cancer 72, 155–159 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Brossart, P. et al. A polymerase chain reaction-based semiquantitative assessment of malignant melanoma cells in peripheral blood. Cancer Res. 55, 4065–4068 (1995).

    CAS  PubMed  Google Scholar 

  61. Zippelius, A. et al. Limitations of reverse-transcriptase polymerase chain reaction analyses for detection of micrometastatic epithelial cancer cells in bone marrow. J. Clin. Oncol. 15, 2701–2708 (1997).

    CAS  PubMed  Google Scholar 

  62. Taback, B. et al. Detection of occult metastatic breast cancer cells in blood by a multimolecular marker assay: correlation with clinical state of disease. Cancer Res. 61, 8845–8850 (2001).References 55–62 highlight the clinical issues that surround reverse transcriptase PCR detection of cancer cells.

    CAS  PubMed  Google Scholar 

  63. Chu, E. et al. Induction of thymidylate synthase associated with multidrug resistance in human breast and colon cancer cell lines. Mol. Pharmacol. 39, 136–143 (1991).

    CAS  PubMed  Google Scholar 

  64. Salonga, D. et al. Colorectal tumors responding to 5-fluorouracil have low gene expression levels of dihydropyrimidine dehydrogenase, thymidylate synthase, and thymidine phosphorylase. Clin. Cancer Res. 6, 1322–1327 (2000).

    CAS  PubMed  Google Scholar 

  65. Ohara, K. et al. Utility of serum CA19-9 monitoring in preoperative radiotherapy for pancreatic cancer. Hepatogastroenterology 48, 859–863 (2001).

    CAS  PubMed  Google Scholar 

  66. Slesak, B. et al. Tissue polypeptide specific antigen (TPS), a marker for differentiation between pancreatic carcinoma and chronic pancreatitis. A comparative study with CA19-9. Cancer 89, 83–88 (2001).

    Google Scholar 

  67. Urgell, E. et al. Prospective evaluation of the contribution of KRAS mutational analysis and CA19.9 measurement to cytological diagnosis in patients with clinical suspicion of pancreatic cancer. Eur. J. Cancer 36, 2069–2075 (2000).

    CAS  PubMed  Google Scholar 

  68. Canizares, F. et al. Preoperative values of CA15-3 and CEA as prognostic factors in breast cancer: a multivariate analysis. Tumour Biol. 22, 273–281 (2001).

    CAS  PubMed  Google Scholar 

  69. Sjostrom, J. et al. Serum tumour markers CA15-3, TPA, hCGβ and TATI in the monitoring of chemotherapy response in metastatic breast cancer. Scand. J. Clin. Lab. Invest. 61, 431–441 (2001).

    CAS  PubMed  Google Scholar 

  70. Liefers, G. J. et al. Micrometastases and survival in stage II colorectal cancer. N. Engl. J. Med. 339, 223–228 (1998).

    CAS  PubMed  Google Scholar 

  71. Rosell, R., Green, M. & Gumerlock, P. Advances in the treatment of non-small cell lung cancer: molecular markers take the stage. Semin. Oncol. 28, 28–34 (2001).

    CAS  PubMed  Google Scholar 

  72. Wright, W. E., Shay, J. W. & Piatyszek, M. A. Modifications of a telomeric repeat amplification protocol (TRAP) result in increased reliability, linearity and sensitivity. Nucleic Acids Res. 23, 3794–3795 (1995).This study decribes the TRAP protocol for measuring telomerase activity.

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Nakamura, T. M. et al. Telomerase catalytic subunit homologs from fission yeast and human. Science 277, 955–959 (1997).

    CAS  PubMed  Google Scholar 

  74. Califano, J. et al. Detection of telomerase activity in oral rinses from head and neck squamous cell carcinoma patients. Cancer Res. 56, 5720–5722 (1996).

    CAS  PubMed  Google Scholar 

  75. Yoshida, K. et al. Detection of telomerase activity in exfoliated cancer cells in colonic luminal washings and its related clinical implications. Br. J. Cancer 75, 548–553 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Yoshida, K. et al. Telomerase activity in bladder carcinoma and its implication for noninvasive diagnosis by detection of exfoliated cancer cells in urine. Cancer 79, 362–369 (1997).

    CAS  PubMed  Google Scholar 

  77. Jassem, E. et al. Serum p53 antibodies in small cell lung cancer: the lack of prognostic relevance. Lung Cancer 31, 17–23 (2001).

    CAS  PubMed  Google Scholar 

  78. Yamaguchi, K. et al. Circulating antibodies to p40(AIS) in the sera of respiratory tract cancer patients. Int. J. Cancer 89, 524–528 (2000).

    CAS  PubMed  Google Scholar 

  79. Lubin, R. et al. Serum p53 antibodies as early markers of lung cancer. Nature Med. 1, 701–702 (1995).

    CAS  PubMed  Google Scholar 

  80. Schena, M. et al. Parallel human genome analysis: microarray-based expression monitoring of 1,000 genes. Proc. Natl Acad. Sci. USA 93, 10614–10619 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Pollack, J. R. et al. Genome-wide analysis of DNA copy-number changes using cDNA microarrays. Nature Genet. 23, 41–46 (1999).

    CAS  PubMed  Google Scholar 

  82. Velculescu, V. E., Zhang, L., Vogelstein B. & Kinzler, K. W. Serial analysis of gene expression. Science 270, 368–371 (1995).

    Google Scholar 

  83. Argani, P. et al. Mesothelin is overexpressed in the vast majority of ductal adenocarcinomas of the pancreas: identification of a new pancreatic cancer marker by serial analysis of gene expression (SAGE). Clin Cancer Res 7, 3862–3868 (2001).

    CAS  PubMed  Google Scholar 

  84. Ahrendt, S. A. et al. Rapid p53 sequence analysis in primary lung cancer using an oligonucleotide probe array. Proc. Natl Acad. Sci. USA 96, 7382–7387 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Hanash, S. 2-D or not 2-D: is there a future for 2-D gels in proteomics? Insights from the York proteomics meeting. Proteomics 1, 635–637 (2001).

    CAS  PubMed  Google Scholar 

  86. Paweletz, C. P., Liotta, L. A. & Petricoin, E. F. New technologies for biomarker analysis of prostate cancer progression: laser capture microdissection and tissue proteomics. Urology 57, 160–163 (2001).

    CAS  PubMed  Google Scholar 

  87. Bichsel, V. E., Liotta, L. A. & Petricoin, E. F. Cancer proteomics: from biomarker discovery to signal pathway profiling. Cancer J. 7, 69–78 (2001).

    CAS  PubMed  Google Scholar 

  88. Wall, D. B. et al. Isoelectric focusing nonporous RP HPLC: a two-dimensional liquid-phase separation method for mapping of cellular proteins with identification using MALDI-TOF mass spectrometry. Anal. Chem. 72, 1099–1111 (2000).

    CAS  PubMed  Google Scholar 

  89. Verma, M., Wright, G. L. Jr, Hanash, S. M., Gopal-Srivastava, R. & Srivastava, S. Proteomic approaches within the NCI early detection research network for the discovery and identification of cancer biomarkers. Ann. NY Acad. Sci. 945, 103–115 (2001).

    CAS  PubMed  Google Scholar 

  90. Madoz-Gurpide, J. et al. Protein based microarrays: a tool for probing the proteome of cancer cells and tissues. Proteomics 1, 1279–1287 (2001).

    CAS  PubMed  Google Scholar 

  91. Petricoin, E. F. et al. Use of proteomic patterns in serum to identify ovarian cancer. Lancet 359, 572–577 (2002).The use of proteomics to detect protein patterns in the serum of patients with ovarian cancer.

    CAS  PubMed  Google Scholar 

  92. Le Naour, F. et al. Proteomics-based identification of RS/DJ-1 as a novel circulating tumor antigen in breast cancer. Clin. Cancer Res. 7, 3328–3335 (2001).

    CAS  PubMed  Google Scholar 

  93. Brichory, F., Beer, D., LeNaour, F., Giordano, T. & Hanash, S. Proteomics-based identification of protein gene product 9.5 as a tumor antigen that induces a humoral immune response in lung cancer. Cancer Res. 61, 7908–7912 (2001).

    CAS  PubMed  Google Scholar 

  94. Srinivas, P. R., Srivastava, S., Hanash, S. & Wright, G. L. Jr. Proteomics in early detection of cancer. Clin. Chem. 47, 1901–1911 (2001).

    CAS  PubMed  Google Scholar 

  95. Rawlings, C. J. & Searls, D. B. Computational gene discovery and human disease. Curr. Opin. Genet. Dev. 7, 416–423 (1997).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The author would like to thank S. M. Hanash and G. W. Hart for their critical review. Supported by a National Institute of Health grant, as part of the Early Detection Research Network.

Author information

Authors and Affiliations

  1. The Johns Hopkins University School of Medicine, 720 Rutland Avenue, Ross 818, Baltimore, 21205, Maryland, USA

    David Sidransky

Authors
  1. David Sidransky
    View author publications

    You can also search for this author in PubMed Google Scholar

Related links

Related links

DATABASES

CancerNet:

bladder cancer

breast cancer

cervical carcinoma

colorectal cancer

head and neck cancer

hepatocellular cancer

melanoma

nasopharyngeal cancer

oesophageal cancer

oral cancer

ovarian cancer

pancreatic cancer

prostate cancer

small-cell lung cancers

testicular cancer

 GenBank:

EBV

HPV

 LocusLink:

AFP

annexin

APC

CA125

DAPK

ERBB2

GSTP1

β-HCG

INK4A

mesothelin

MGMT

p63

PSA

RAS

TERT

TP53

WNT

 Medscape DrugInfo:

5-fluorouracil

Herceptin

FURTHER INFORMATION

The DNA Methylation database

The DNA Methylation Society web site

The Expert Protein Analysis System proteomics server of the Swiss Institute of Bioinformatics (SIB)

M.D. Anderson resource on CpG islands

The NCBI Protein Database

The NIH mass spectometry web site

The Proteome Society

SAGE web site

The SNP Consortium web site

Glossary

MICROSATELLITE DNA

Repetitive stretches of short DNA sequences.

SINGLE-NUCLEOTIDE-POLYMORPHISM (SNP) ANALYSIS

Analysis based on single base-pair changes in DNA that differ among individuals. SNPs can be identified by various molecular means.

CpG ISLANDS

A region of DNA with a high density of cytosine–phospho-guanine nucleotides, which are usually located in the promoter region or the first exons of a gene. CpG islands are involved in the regulation of transcription, because their methylation can lead to permanent silencing of the associated gene.

BRONCHOALVEOLAR LAVAGE FLUID

A fluid sample that is obtained by inserting a tube into the lung.

NESTED PCR

Amplification of a DNA sequence that entails one initial amplification with a set of primers, followed by a second amplification with a set of internal (nested) primers, to allow a more robust amplification.

REAL-TIME PCR

The ability to monitor the linear phase of PCR during the actual amplification stages, usually by means of a fluorogenic compound and a laser to detect the accumulation of amplification products.

MITOCHONDRIAL D-LOOP

A section of the mitochondrial genome that is thought to be involved in replication, and contains short poly-pyrimidine tracts.

HOMOPLASMIC

The presence of identical genomes within all of the mitochondrial organelles in a cell.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sidransky, D. Emerging molecular markers of cancer. Nat Rev Cancer 2, 210–219 (2002). https://doi.org/10.1038/nrc755

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc755

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing