High-throughput proteomics and AI for cancer biomarker discovery

被引：73

作者：

Xiao, Qi ^{[1
,2
]}

Zhang, Fangfei ^{[1
,2
]}

Xu, Luang ^{[1
,2
]}

Yue, Liang ^{[1
,2
]}

Kon, Oi Lian ^{[3
]}

Zhu, Yi ^{[1
,2
]}

Guo, Tiannan ^{[1
,2
]}

机构：

[1] Westlake Univ, Sch Life Sci, Key Lab Struct Biol Zhejiang Prov, Westlake Lab Life Sci & Biomed, 18 Shilongshan Rd, Hangzhou 310024, Zhejiang, Peoples R China

[2] Westlake Inst Adv Study, Inst Basic Med Sci, 18 Shilongshan Rd, Hangzhou 310024, Zhejiang, Peoples R China

[3] Natl Canc Ctr Singapore, Div Med Sci, Singapore, Singapore

来源：

ADVANCED DRUG DELIVERY REVIEWS | 2021年 / 176卷

基金：

国家重点研发计划; 中国国家自然科学基金;

关键词：

High-throughput proteomics; Mass spectrometry; AI; Cancer biomarker; SAMPLE PREPARATION METHOD; INTEGRATED PROTEOGENOMIC CHARACTERIZATION; ORBITRAP MASS-SPECTROMETER; COMPLEX PROTEIN MIXTURES; GENE-EXPRESSION; BREAST-CANCER; QUANTITATIVE PROTEOMICS; PATHWAY ACTIVATION; HIGH-RESOLUTION; TISSUE;

D O I：

10.1016/j.addr.2021.113844

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods. (c) 2021 Elsevier B.V. All rights reserved.

引用

页数：15

共 158 条

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