Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris
Accurate biomarker-based diagnosis of psoriasis vulgaris has remained a challenge; no reliable disease-specific biomarkers have yet been identified. There are several different chronic inflammatory skin diseases that can present similar clinical and dermoscopy features to psoriasis vulgaris, making...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Medical Journals Sweden
2019-10-01
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| Series: | Acta Dermato-Venereologica |
| Subjects: | |
| Online Access: |
https://www.medicaljournals.se/acta/content/html/10.2340/00015555-3337
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| _version_ | 1819350212632641536 |
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| author | Ene Reimann Freddy Lättekivi Maris Keermann Kristi Abram Sulev Kõks Külli Kingo Alireza Fazeli |
| author_facet | Ene Reimann Freddy Lättekivi Maris Keermann Kristi Abram Sulev Kõks Külli Kingo Alireza Fazeli |
| author_sort | Ene Reimann |
| collection | DOAJ |
| description | Accurate biomarker-based diagnosis of psoriasis vulgaris has remained a challenge; no reliable disease-specific biomarkers have yet been identified. There are several different chronic inflammatory skin diseases that can present similar clinical and dermoscopy features to psoriasis vulgaris, making accurate diagnosis more difficult. Both literature-based and data-driven selection of biomarker was conducted to select candidates for a multicomponent biomarker for psoriasis vulgaris. Support vector machine-based classification models were trained using gene expression data from locally recruited patients and validated on 7 public datasets, which included gene expression data of other inflammatory skin diseases in addition to psoriasis vulgaris. The resulting accuracy of the best classification model based on the expression levels of 4 genes (IL36G, CCL27, NOS2 and C10orf99) was 96.4%, outperforming classification based on other marker gene combinations, which were more affected by variability in gene expression profiles between different datasets and patient groups. This approach has the potential to fill the void of clinically applicable diagnostic biomarkers for psoriasis vulgaris and other inflammatory skin diseases. |
| first_indexed | 2024-12-24T19:12:50Z |
| format | Article |
| id | doaj.art-a97e9b0506af42c1bb677606395359df |
| institution | Directory Open Access Journal |
| issn | 0001-5555 1651-2057 |
| language | English |
| last_indexed | 2024-12-24T19:12:50Z |
| publishDate | 2019-10-01 |
| publisher | Medical Journals Sweden |
| record_format | Article |
| series | Acta Dermato-Venereologica |
| spelling | doaj.art-a97e9b0506af42c1bb677606395359df2022-12-21T16:42:57ZengMedical Journals SwedenActa Dermato-Venereologica0001-55551651-20572019-10-0199131258126510.2340/00015555-33375602Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis VulgarisEne Reimann0Freddy LättekiviMaris KeermannKristi AbramSulev KõksKülli KingoAlireza Fazeli Department of Pathophysiology, University of Tartu, 14b Ravila Str., EE-50411 Tartu, Estonia. Accurate biomarker-based diagnosis of psoriasis vulgaris has remained a challenge; no reliable disease-specific biomarkers have yet been identified. There are several different chronic inflammatory skin diseases that can present similar clinical and dermoscopy features to psoriasis vulgaris, making accurate diagnosis more difficult. Both literature-based and data-driven selection of biomarker was conducted to select candidates for a multicomponent biomarker for psoriasis vulgaris. Support vector machine-based classification models were trained using gene expression data from locally recruited patients and validated on 7 public datasets, which included gene expression data of other inflammatory skin diseases in addition to psoriasis vulgaris. The resulting accuracy of the best classification model based on the expression levels of 4 genes (IL36G, CCL27, NOS2 and C10orf99) was 96.4%, outperforming classification based on other marker gene combinations, which were more affected by variability in gene expression profiles between different datasets and patient groups. This approach has the potential to fill the void of clinically applicable diagnostic biomarkers for psoriasis vulgaris and other inflammatory skin diseases. https://www.medicaljournals.se/acta/content/html/10.2340/00015555-3337 psoriasis transcriptome support vector machine |
| spellingShingle | Ene Reimann Freddy Lättekivi Maris Keermann Kristi Abram Sulev Kõks Külli Kingo Alireza Fazeli Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris Acta Dermato-Venereologica psoriasis transcriptome support vector machine |
| title | Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris |
| title_full | Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris |
| title_fullStr | Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris |
| title_full_unstemmed | Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris |
| title_short | Multicomponent Biomarker Approach Improves the Accuracy of Diagnostic Biomarkers for Psoriasis Vulgaris |
| title_sort | multicomponent biomarker approach improves the accuracy of diagnostic biomarkers for psoriasis vulgaris |
| topic | psoriasis transcriptome support vector machine |
| url |
https://www.medicaljournals.se/acta/content/html/10.2340/00015555-3337
|
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