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- Nordling, Torbjörn E. M.Uppsala universitet,Institutionen för immunologi, genetik och patologi,Stockholm Bioinformat Ctr, Sci Life Lab, S-17121 Solna, Sweden.;Natl Cheng Kung Univ, Dept Mech Engn, Tainan 70101, Taiwan.;Nordron AB, S-17065 Solna, Sweden. (author)
Identification of Biomarkers and Signatures in Protein Data
- Article/chapterEnglish2015
Publisher, publication year, extent ...
- 2015
- printrdacarrier
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- LIBRIS-ID:oai:DiVA.org:uu-303183
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-303183URI
- https://doi.org/10.1109/eScience.2015.46DOI
Supplementary language notes
- Language:English
- Summary in:English
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Notes
- The correct diagnosis of cancer patients conventionally depends on the pathologist's experience and ability to distinguish cancer tissue from normal tissue under a microscope. Advances in technology for measuring the abundance of, e.g., proteins and mRNAs in tissue samples make it interesting to search for an optimal subset of these for classification of samples as cancer or normal. We discuss issues of identification of biomarkers that provide distinct signatures for prediction of tissues as cancer or normal, exemplified by our recent study of cancer signalling signatures in human colon cancer characterised with regards to protein abundance using high sensitivity isoelectric focusing. We show that the optimal subset for separation of cancer tissues from normal tissues does not contain any of the proteins in the top quintile in terms of significant difference between the groups according to Mann-Whitney U-test or correlation to the diagnosis. Actually, one of the proteins belongs to the tertile with the lowest significance and correlation. This highlights the weakness of the practice of only looking for significant differences in the abundance of individual proteins and raises the question of how many lifesaving discoveries that have been missed due to it. We also demonstrate how Monte Carlo simulations of the separation with random class assignment can be used to calculate p-values for observing any specific separation by chance and selection of the optimal number of proteins in the subset based on these p-values. Both selection of the optimal number of biomarkers and calculation of p-values corrected for multiple hypothesis testing are essential to obtain a subset of biomarkers that yield robust predictions for clinical use.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences hsv//eng
- MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Cancer och onkologi hsv//swe
- MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology hsv//eng
- Biomarkers
- Mann-Whitney U test
- Student's t-test
- Spearman's rank correlation
- Subset selection
- Variable selection
- Feature selection
- Monte Carlo simulations
- p-values
- Cancer
- Colon cancer
- Protein abundance
Added entries (persons, corporate bodies, meetings, titles ...)
- Padhan, NarendraUppsala universitet,Vaskulärbiologi(Swepub:uu)narpa396 (author)
- Nelander, SvenUppsala universitet,Neuroonkologi(Swepub:uu)svene843 (author)
- Claesson-Welsh, LenaUppsala universitet,Vaskulärbiologi(Swepub:uu)lcl04207 (author)
- Uppsala universitetInstitutionen för immunologi, genetik och patologi (creator_code:org_t)
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- In:2015 IEEE 11Th International Conference On E-Science, s. 411-4199781467393256
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