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Cited by 254

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Proteomics in Early Detection of Cancer

Open access
Srinivas, Pothur R, Srivastava, Sudhir, Hanash, Sam, Wright, George L·2001·Clinical Chemistry

Abstract

Early detection is critical in cancer control and prevention. Biomarkers help in this process by providing valuable information about a the status of a cell at any given point in time. As a cell transforms from nondiseased to neoplastic, distinct changes occur that could be potentially detected through the identification of the appropriate biomarkers. Biomarker research has benefited from advances in technology such as proteomics. We discuss here ongoing research in this field, focusing on proteomic technologies. The advances in two-dimensional electrophoresis and mass spectrometry are discussed in light of their contribution to biomarker research. Chip-based techniques, such as surface-enhanced laser desorption, and ionization and emerging methods, such as tissue and antibody arrays, are also discussed. The development of bioinformatic tools that have and are being developed in parallel to proteomics is also addressed. This report brings into focus the efforts of the Early Detection Research Network at the National Cancer Institute in harnessing scientific expertise from leading institutions to identify and validate biomarkers for early detection and risk assessment.

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Metadata

Authors
Srinivas, P. R., Srivastava, S., Hanash, S., & Wright, G. L.
Venue
Clinical Chemistry
Published
2001-10-01
Volume
47
Issue
10
Pages
1901-1911
ISSN
  • 0009-9147 (ISSN-L)
  • 1530-8561
OpenAlex
W2160658234
Cited by
254
References
2
FWCI
15.72
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Proteomics in Early Detection of Cancer. https://app.cochat.ai/papers/10.1093/clinchem/47.10.1901
The paper
Srinivas, P. R., Srivastava, S., Hanash, S., & Wright, G. L. (2001). Proteomics in Early Detection of Cancer. Clinical Chemistry. https://doi.org/10.1093/clinchem/47.10.1901

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.