Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia
<p dir="ltr">Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facil...
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| مؤلفون آخرون: | , , , , , , , , , , , , , , , , , , , , |
| منشور في: |
2023
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| _version_ | 1864513511885897728 |
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| author | Hanan Ehtewish (17149825) |
| author2 | Areej Mesleh (17149822) Georgios Ponirakis (803076) Alberto De la Fuente (18877321) Aijaz Parray (290256) Ilham Bensmail (12204845) Houari Abdesselem (14152914) Marwan Ramadan (17499450) Shafi Khan (17499453) Mani Chandran (17499447) Raheem Ayadathil (14150124) Ahmed Elsotouhy (3853348) Ahmed Own (6677990) Hanadi Al Hamad (12229412) Essam M. Abdelalim (5768072) Julie Decock (44558) Nehad M. Alajez (7397276) Omar Albagha (8977856) Paul J. Thornalley (291723) Abdelilah Arredouani (10914455) Rayaz A. Malik (7372649) Omar M. A. El-Agnaf (8809331) |
| author2_role | author author author author author author author author author author author author author author author author author author author author author |
| author_facet | Hanan Ehtewish (17149825) Areej Mesleh (17149822) Georgios Ponirakis (803076) Alberto De la Fuente (18877321) Aijaz Parray (290256) Ilham Bensmail (12204845) Houari Abdesselem (14152914) Marwan Ramadan (17499450) Shafi Khan (17499453) Mani Chandran (17499447) Raheem Ayadathil (14150124) Ahmed Elsotouhy (3853348) Ahmed Own (6677990) Hanadi Al Hamad (12229412) Essam M. Abdelalim (5768072) Julie Decock (44558) Nehad M. Alajez (7397276) Omar Albagha (8977856) Paul J. Thornalley (291723) Abdelilah Arredouani (10914455) Rayaz A. Malik (7372649) Omar M. A. El-Agnaf (8809331) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hanan Ehtewish (17149825) Areej Mesleh (17149822) Georgios Ponirakis (803076) Alberto De la Fuente (18877321) Aijaz Parray (290256) Ilham Bensmail (12204845) Houari Abdesselem (14152914) Marwan Ramadan (17499450) Shafi Khan (17499453) Mani Chandran (17499447) Raheem Ayadathil (14150124) Ahmed Elsotouhy (3853348) Ahmed Own (6677990) Hanadi Al Hamad (12229412) Essam M. Abdelalim (5768072) Julie Decock (44558) Nehad M. Alajez (7397276) Omar Albagha (8977856) Paul J. Thornalley (291723) Abdelilah Arredouani (10914455) Rayaz A. Malik (7372649) Omar M. A. El-Agnaf (8809331) |
| dc.date.none.fl_str_mv | 2023-05-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/ijms24098117 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Blood-Based_Proteomic_Profiling_Identifies_Potential_Biomarker_Candidates_and_Pathogenic_Pathways_in_Dementia/26095204 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Bioinformatics and computational biology Genetics Biomedical and clinical sciences Neurosciences Information and computing sciences Machine learning dementia MCI plasma proteomics biomarkers Olink assay machine learning |
| dc.title.none.fl_str_mv | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ −0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Molecular Sciences<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/ijms24098117" target="_blank">https://dx.doi.org/10.3390/ijms24098117</a></p><p dir="ltr">Additional institutions affiliated with: Hamad General Hospital - HMC, Cancer Research Center - QBRI.</p><p dir="ltr"><br></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f0ba8f67eea5ee2e82e48f38e768e86d |
| identifier_str_mv | 10.3390/ijms24098117 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26095204 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in DementiaHanan Ehtewish (17149825)Areej Mesleh (17149822)Georgios Ponirakis (803076)Alberto De la Fuente (18877321)Aijaz Parray (290256)Ilham Bensmail (12204845)Houari Abdesselem (14152914)Marwan Ramadan (17499450)Shafi Khan (17499453)Mani Chandran (17499447)Raheem Ayadathil (14150124)Ahmed Elsotouhy (3853348)Ahmed Own (6677990)Hanadi Al Hamad (12229412)Essam M. Abdelalim (5768072)Julie Decock (44558)Nehad M. Alajez (7397276)Omar Albagha (8977856)Paul J. Thornalley (291723)Abdelilah Arredouani (10914455)Rayaz A. Malik (7372649)Omar M. A. El-Agnaf (8809331)Biological sciencesBioinformatics and computational biologyGeneticsBiomedical and clinical sciencesNeurosciencesInformation and computing sciencesMachine learningdementiaMCIplasma proteomicsbiomarkersOlink assaymachine learning<p dir="ltr">Dementia is a progressive and debilitating neurological disease that affects millions of people worldwide. Identifying the minimally invasive biomarkers associated with dementia that could provide insights into the disease pathogenesis, improve early diagnosis, and facilitate the development of effective treatments is pressing. Proteomic studies have emerged as a promising approach for identifying the protein biomarkers associated with dementia. This pilot study aimed to investigate the plasma proteome profile and identify a panel of various protein biomarkers for dementia. We used a high-throughput proximity extension immunoassay to quantify 1090 proteins in 122 participants (22 with dementia, 64 with mild cognitive impairment (MCI), and 36 controls with normal cognitive function). Limma-based differential expression analysis reported the dysregulation of 61 proteins in the plasma of those with dementia compared with controls, and machine learning algorithms identified 17 stable diagnostic biomarkers that differentiated individuals with AUC = 0.98 ± 0.02. There was also the dysregulation of 153 plasma proteins in individuals with dementia compared with those with MCI, and machine learning algorithms identified 8 biomarkers that classified dementia from MCI with an AUC of 0.87 ± 0.07. Moreover, multiple proteins selected in both diagnostic panels such as NEFL, IL17D, WNT9A, and PGF were negatively correlated with cognitive performance, with a correlation coefficient (r2) ≤ −0.47. Gene Ontology (GO) and pathway analysis of dementia-associated proteins implicated immune response, vascular injury, and extracellular matrix organization pathways in dementia pathogenesis. In conclusion, the combination of high-throughput proteomics and machine learning enabled us to identify a blood-based protein signature capable of potentially differentiating dementia from MCI and cognitively normal controls. Further research is required to validate these biomarkers and investigate the potential underlying mechanisms for the development of dementia.</p><h2>Other Information</h2><p dir="ltr">Published in: International Journal of Molecular Sciences<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/ijms24098117" target="_blank">https://dx.doi.org/10.3390/ijms24098117</a></p><p dir="ltr">Additional institutions affiliated with: Hamad General Hospital - HMC, Cancer Research Center - QBRI.</p><p dir="ltr"><br></p>2023-05-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/ijms24098117https://figshare.com/articles/journal_contribution/Blood-Based_Proteomic_Profiling_Identifies_Potential_Biomarker_Candidates_and_Pathogenic_Pathways_in_Dementia/26095204CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/260952042023-05-01T00:00:00Z |
| spellingShingle | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia Hanan Ehtewish (17149825) Biological sciences Bioinformatics and computational biology Genetics Biomedical and clinical sciences Neurosciences Information and computing sciences Machine learning dementia MCI plasma proteomics biomarkers Olink assay machine learning |
| status_str | publishedVersion |
| title | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia |
| title_full | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia |
| title_fullStr | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia |
| title_full_unstemmed | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia |
| title_short | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia |
| title_sort | Blood-Based Proteomic Profiling Identifies Potential Biomarker Candidates and Pathogenic Pathways in Dementia |
| topic | Biological sciences Bioinformatics and computational biology Genetics Biomedical and clinical sciences Neurosciences Information and computing sciences Machine learning dementia MCI plasma proteomics biomarkers Olink assay machine learning |