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MALDI-TOF-MS analysis in discovery and identification of serum proteomic patterns of ovarian cancer

Overview of attention for article published in BMC Cancer, July 2017
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Title
MALDI-TOF-MS analysis in discovery and identification of serum proteomic patterns of ovarian cancer
Published in
BMC Cancer, July 2017
DOI 10.1186/s12885-017-3467-2
Pubmed ID
Authors

Agata Swiatly, Agnieszka Horala, Joanna Hajduk, Jan Matysiak, Ewa Nowak-Markwitz, Zenon J. Kokot

Abstract

Due to high mortality and lack of efficient screening, new tools for ovarian cancer (OC) diagnosis are urgently needed. To broaden the knowledge on the pathological processes that occur during ovarian cancer tumorigenesis, protein-peptide profiling was proposed. Serum proteomic patterns in samples from OC patients were obtained using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF). Eighty nine serum samples (44 ovarian cancer and 45 healthy controls) were pretreated using solid-phase extraction method. Next, a classification model with the most discriminative factors was identified using chemometric algorithms. Finally, the results were verified by external validation on an independent test set of samples. Main outcome of this study was an identification of potential OC biomarkers by applying liquid chromatography coupled with tandem mass spectrometry. Application of this novel strategy enabled the identification of four potential OC serum biomarkers (complement C3, kininogen-1, inter-alpha-trypsin inhibitor heavy chain H4, and transthyretin). The role of these proteins was discussed in relation to OC pathomechanism. The study results may contribute to the development of clinically useful multi-component diagnostic tools in OC. In addition, identifying a novel panel of discriminative proteins could provide a new insight into complex signaling and functional networks associated with this multifactorial disease.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 19%
Researcher 11 14%
Student > Master 10 13%
Student > Bachelor 9 12%
Student > Doctoral Student 5 6%
Other 13 17%
Unknown 15 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 18%
Medicine and Dentistry 13 17%
Chemistry 8 10%
Pharmacology, Toxicology and Pharmaceutical Science 7 9%
Agricultural and Biological Sciences 4 5%
Other 12 15%
Unknown 20 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 April 2018.
All research outputs
#10,201,505
of 12,770,714 outputs
Outputs from BMC Cancer
#3,127
of 4,726 outputs
Outputs of similar age
#206,650
of 273,777 outputs
Outputs of similar age from BMC Cancer
#1
of 1 outputs
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