Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning Article Swipe
Jinwu Peng
,
Zhili Duan
,
Guo Yamin
,
Xiaona Li
,
Xiaoqin Luo
,
Xiumin Han
,
Junming Luo
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1177/0300060521993980
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1177/0300060521993980
Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.
Related Topics
Concepts
KEGG
Microarray
Echinococcosis
Gene expression profiling
Medicine
Echinococcus multilocularis
Gene
Disease
Computational biology
Microarray analysis techniques
Liver disease
Gene ontology
Pathology
Gene expression
Biology
Internal medicine
Genetics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1177/0300060521993980
- https://journals.sagepub.com/doi/pdf/10.1177/0300060521993980
- OA Status
- gold
- Cited By
- 4
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3141111741
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3141111741Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1177/0300060521993980Digital Object Identifier
- Title
-
Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-03-01Full publication date if available
- Authors
-
Jinwu Peng, Zhili Duan, Guo Yamin, Xiaona Li, Xiaoqin Luo, Xiumin Han, Junming LuoList of authors in order
- Landing page
-
https://doi.org/10.1177/0300060521993980Publisher landing page
- PDF URL
-
https://journals.sagepub.com/doi/pdf/10.1177/0300060521993980Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://journals.sagepub.com/doi/pdf/10.1177/0300060521993980Direct OA link when available
- Concepts
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KEGG, Microarray, Echinococcosis, Gene expression profiling, Medicine, Echinococcus multilocularis, Gene, Disease, Computational biology, Microarray analysis techniques, Liver disease, Gene ontology, Pathology, Gene expression, Biology, Internal medicine, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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