Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study.
<h4>Background</h4>Alzheimer's disease (AD) and Osteoarthritis (OA) have been shown to have a close association in previous studies, but the pathogenesis of both diseases are unclear. This study explores the potential common molecular mechanisms between AD and OA through bioinformat...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316708 |
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| author | Nian Liu Qian Deng Zining Peng Danning Mao Yuanbo Huang Fanyu Meng Xiaoyu Zhang Jiayan Shen Zhaofu Li Weitian Yan Jiangyun Peng |
| author_facet | Nian Liu Qian Deng Zining Peng Danning Mao Yuanbo Huang Fanyu Meng Xiaoyu Zhang Jiayan Shen Zhaofu Li Weitian Yan Jiangyun Peng |
| author_sort | Nian Liu |
| collection | DOAJ |
| description | <h4>Background</h4>Alzheimer's disease (AD) and Osteoarthritis (OA) have been shown to have a close association in previous studies, but the pathogenesis of both diseases are unclear. This study explores the potential common molecular mechanisms between AD and OA through bioinformatics analysis, providing new insights for clinical treatment strategies.<h4>Methods</h4>The AD and OA-related datasets were downloaded from the gene expression database GEO. The datasets were analyzed to obtain differentially expressed gene (DEG) datasets for OA and AD, respectively. The intersection of these DEGs was analyzed to identify common DEGs (Co-DEGs). Subsequently, the Co-DEGs were enriched, and a protein-protein interaction network was constructed to identify core genes. The expression of these genes was validated in a separate dataset, and their diagnostic value for the diseases was analyzed. In addition, the core genes were analyzed using gene set enrichment analysis and single-gene genome variation analysis.<h4>Results</h4>Analysis of DEGs on gene chips from OA and AD patients revealed significant changes in gene expression patterns. Notably, EFEMP2 and TSPO, genes associated with inflammatory responses, showed lower expression levels in both AD and OA patients, suggesting a downregulation in the pathological backgrounds of these diseases. Additionally, GABARAPL1, which is crucial for the maturation of autophagosomes, was found to be upregulated in both conditions. These findings suggest the potential of these genes as diagnostic biomarkers and potential therapeutic targets. However, to confirm the effectiveness of these genes as therapeutic targets, more in-depth mechanistic studies are needed in the future, particularly to explore the feasibility and specific mechanisms of combating disease progression by regulating the expression of these genes.<h4>Conclusions</h4>This study suggests that AD and OA shares common molecular mechanisms. The identification of EFEMP2, GABARAPL1, and TSPO as key target genes highlights potential common factors in both diseases. Further investigation into these findings could lead to new candidate targets and treatment directions for AD and OA, offering promising avenues for developing more effective and targeted therapeutic interventions. |
| format | Article |
| id | doaj-art-7123c9322ef3431c88c35db3f562b887 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-7123c9322ef3431c88c35db3f562b8872025-02-12T05:31:09ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031670810.1371/journal.pone.0316708Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study.Nian LiuQian DengZining PengDanning MaoYuanbo HuangFanyu MengXiaoyu ZhangJiayan ShenZhaofu LiWeitian YanJiangyun Peng<h4>Background</h4>Alzheimer's disease (AD) and Osteoarthritis (OA) have been shown to have a close association in previous studies, but the pathogenesis of both diseases are unclear. This study explores the potential common molecular mechanisms between AD and OA through bioinformatics analysis, providing new insights for clinical treatment strategies.<h4>Methods</h4>The AD and OA-related datasets were downloaded from the gene expression database GEO. The datasets were analyzed to obtain differentially expressed gene (DEG) datasets for OA and AD, respectively. The intersection of these DEGs was analyzed to identify common DEGs (Co-DEGs). Subsequently, the Co-DEGs were enriched, and a protein-protein interaction network was constructed to identify core genes. The expression of these genes was validated in a separate dataset, and their diagnostic value for the diseases was analyzed. In addition, the core genes were analyzed using gene set enrichment analysis and single-gene genome variation analysis.<h4>Results</h4>Analysis of DEGs on gene chips from OA and AD patients revealed significant changes in gene expression patterns. Notably, EFEMP2 and TSPO, genes associated with inflammatory responses, showed lower expression levels in both AD and OA patients, suggesting a downregulation in the pathological backgrounds of these diseases. Additionally, GABARAPL1, which is crucial for the maturation of autophagosomes, was found to be upregulated in both conditions. These findings suggest the potential of these genes as diagnostic biomarkers and potential therapeutic targets. However, to confirm the effectiveness of these genes as therapeutic targets, more in-depth mechanistic studies are needed in the future, particularly to explore the feasibility and specific mechanisms of combating disease progression by regulating the expression of these genes.<h4>Conclusions</h4>This study suggests that AD and OA shares common molecular mechanisms. The identification of EFEMP2, GABARAPL1, and TSPO as key target genes highlights potential common factors in both diseases. Further investigation into these findings could lead to new candidate targets and treatment directions for AD and OA, offering promising avenues for developing more effective and targeted therapeutic interventions.https://doi.org/10.1371/journal.pone.0316708 |
| spellingShingle | Nian Liu Qian Deng Zining Peng Danning Mao Yuanbo Huang Fanyu Meng Xiaoyu Zhang Jiayan Shen Zhaofu Li Weitian Yan Jiangyun Peng Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study. PLoS ONE |
| title | Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study. |
| title_full | Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study. |
| title_fullStr | Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study. |
| title_full_unstemmed | Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study. |
| title_short | Characterization of gene expression profiles in Alzheimer's disease and osteoarthritis: A bioinformatics study. |
| title_sort | characterization of gene expression profiles in alzheimer s disease and osteoarthritis a bioinformatics study |
| url | https://doi.org/10.1371/journal.pone.0316708 |
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