Literature DB >> 25523829

Discovering cancer biomarkers from clinical samples by protein microarrays.

Bin Hu1, Xin Niu, Li Cheng, Li-Na Yang, Qing Li, Yang Wang, Sheng-Ce Tao, Shu-Min Zhou.   

Abstract

Cancer biomarkers are of potential use in early cancer diagnosis, anticancer therapy development, and monitoring the responses to treatments. Protein-based cancer biomarkers are major forms in use, as they are much easier to be monitored in body fluids or tissues. For cancer biomarker discovery, high-throughput techniques such as protein microarrays hold great promises, because they are capable of global unbiased monitoring but with a miniaturized format. In doing so, novel and cancer type specific biomarkers can be systematically discovered at an affordable cost. In this review, we give a relatively complete picture on protein microarrays applied to clinical samples for cancer biomarker discovery, and conclude this review with the future perspectives.
© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Entities:  

Keywords:  Cancer biomarker; Cancer metastasis; Clinical samples; Protein microarray

Mesh:

Substances:

Year:  2015        PMID: 25523829     DOI: 10.1002/prca.201400094

Source DB:  PubMed          Journal:  Proteomics Clin Appl        ISSN: 1862-8346            Impact factor:   3.494


  4 in total

Review 1.  Systematic evaluation of immune regulation and modulation.

Authors:  David F Stroncek; Lisa H Butterfield; Michael A Cannarile; Madhav V Dhodapkar; Tim F Greten; Jean Charles Grivel; David R Kaufman; Heidi H Kong; Firouzeh Korangy; Peter P Lee; Francesco Marincola; Sergio Rutella; Janet C Siebert; Giorgio Trinchieri; Barbara Seliger
Journal:  J Immunother Cancer       Date:  2017-03-21       Impact factor: 13.751

Review 2.  Current strategies and findings in clinically relevant post-translational modification-specific proteomics.

Authors:  Oliver Pagel; Stefan Loroch; Albert Sickmann; René P Zahedi
Journal:  Expert Rev Proteomics       Date:  2015-05-08       Impact factor: 3.940

Review 3.  Biomarkers could facilitate prediction of worse clinical outcome of cancer with special insight to cervical cancer.

Authors:  Bartosz Czerniak; Dorota Olszewska-Słonina
Journal:  Contemp Oncol (Pozn)       Date:  2018-04-03

Review 4.  Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning.

Authors:  Mahaly Baptiste; Sarah Shireen Moinuddeen; Courtney Lace Soliz; Hashimul Ehsan; Gen Kaneko
Journal:  Genes (Basel)       Date:  2021-05-12       Impact factor: 4.096

  4 in total

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