Omics-based biomarkers discovery for Alzheimer's disease

Aerqin, Qiaolifan; Wang, Zuo-Teng; Wu, Kaimin; He, Xiao‐Yu; Dong, Qiang; Yu, Jin‐Tai

doi:10.1007/s00018-022-04614-6

Cited by 19 publications

(13 citation statements)

References 231 publications

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“…Once individuals with specific genetic profiles are identified, it is possible to create customized prediction models, an instrumental step toward individualized, specific disease risk evaluation and treatment. Integrating proteomic data with other omics (including genomics, transcriptomics, and metabolomics) can help to better understand the complex etiology of AD, further improving the discovery of AD biomarkers and their use in clinical management, as reviewed by Aerqin et al (47). In addition, there may be different downstream, and potentially causal, pathways leading to disease in these individuals.…”

Section: Discussionmentioning

confidence: 99%

“…Integrating proteomic data with other omics (including genomics, transcriptomics, and metabolomics) can help to better understand the complex etiology of AD, further improving the discovery of AD biomarkers and their use in clinical management, as reviewed by Aerqin et al. ( 47 ). In addition, there may be different downstream, and potentially causal, pathways leading to disease in these individuals.…”

Section: Discussionmentioning

confidence: 99%

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Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer’s disease

Sung,

Yang,

Norton

et al. 2023

Sci. Transl. Med.

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Proteomic studies for Alzheimer’s disease (AD) are instrumental in identifying AD pathways but often focus on single tissues and sporadic AD cases. Here, we present a proteomic study analyzing 1305 proteins in brain tissue, cerebrospinal fluid (CSF), and plasma from patients with sporadic AD, TREM2 risk variant carriers, patients with autosomal dominant AD (ADAD), and healthy individuals. We identified 8 brain, 40 CSF, and 9 plasma proteins that were altered in individuals with sporadic AD, and we replicated these findings in several external datasets. We identified a proteomic signature that differentiated TREM2 variant carriers from both individuals with sporadic AD and healthy individuals. The proteins associated with sporadic AD were also altered in patients with ADAD, but with a greater effect size. Brain-derived proteins associated with ADAD were also replicated in additional CSF samples. Enrichment analyses highlighted several pathways, including those implicated in AD (calcineurin and Apo E), Parkinson’s disease (α-synuclein and LRRK2), and innate immune responses (SHC1, ERK-1, and SPP1). Our findings suggest that combined proteomics across brain tissue, CSF, and plasma can be used to identify markers for sporadic and genetically defined AD.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer’s disease

Sung,

Yang,

Norton

et al. 2023

Sci. Transl. Med.

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“…156 Moreover, there have been many successful examples in finding cancer biomarkers by multiomics, for example, diffuse large B-cell lymphoma, 157 ovarian cancer, 158 and pancreatic cancer. 159 In addition to the application in the discovery of tumor or cancerrelated disease biomarkers, multi-omics is also applied to find biomarkers for other diseases, such as stroke, 160 obesity, 161 cardiovascular diseases, 162,163 severe COVID-19, [164][165][166] Alzheimer's disease, 167 diabetes, 168 obstructive sleep apnea, 169 and so on. Integrated omics can also be used to investigate the influence of environmental factors on early disease formation in humans, such as sleep deficiency.…”

Section: Disease Biomarkers and Targetsmentioning

confidence: 99%

Applications of multi‐omics analysis in human diseases

Chen

Wang

Pan

et al. 2023

MedComm

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Multi‐omics usually refers to the crossover application of multiple high‐throughput screening technologies represented by genomics, transcriptomics, single‐cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi‐omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi‐omics. This review outlines the existing technical categories of multi‐omics, cautions for experimental design, focuses on the integrated analysis methods of multi‐omics, especially the approach of machine learning and deep learning in multi‐omics data integration and the corresponding tools, and the application of multi‐omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open‐source analysis tools and databases, and finally, discusses the challenges and future directions of multi‐omics integration and application in precision medicine. With the development of high‐throughput technologies and data integration algorithms, as important directions of multi‐omics for future disease research, single‐cell multi‐omics and spatial multi‐omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi‐omics medical research.

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“…Proteomics and metabolomics represent fields that have grown significantly in the last decades, thanks to the important technological advances that allow accurate and sensitive analyses. Both approaches have been extensively used for biomarker discovery in various disorders (26)(27)(28)(29)(30)(31).…”

Section: Proteomic and Metabolomic Approaches For Biomarker Discoverymentioning

confidence: 99%

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

et al. 2023

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Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

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Omics-based biomarkers discovery for Alzheimer's disease

Cited by 19 publications

References 231 publications

Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer’s disease

Proteomics of brain, CSF, and plasma identifies molecular signatures for distinguishing sporadic and genetic Alzheimer’s disease

Applications of multi‐omics analysis in human diseases

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

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