Application of individualized differential expression analysis in human cancer proteome

Abstract Liquid chromatography–mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is...

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Vydáno v:Briefings in bioinformatics Ročník 23; číslo 3
Hlavní autoři: Liu, Yachen, Lin, Yalan, Yang, Wenxian, Lin, Yuxiang, Wu, Yujuan, Zhang, Zheyang, Lin, Nuoqi, Wang, Xianlong, Tong, Mengsha, Yu, Rongshan
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 13.05.2022
Oxford Publishing Limited (England)
Témata:
Algorithms
Big Data
Biological properties
Biological samples
Biomarkers
Biomedical data
Cancer
Cancer research
Carcinogenesis
Carcinogens
Data science
Deregulation
Liquid chromatography
Mass spectrometry
Mass spectroscopy
Pattern recognition
Population (statistical)
Precision medicine
Proteins
Proteomes
Proteomics
Ribonucleic acid
RNA
Statistical methods
Tumors
individualized differential expression
comparative analysis
prognostic biomarkers
human cancer proteome
ISSN:1467-5463, 1477-4054, 1477-4054
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Shrnutí:Abstract Liquid chromatography–mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbac096