Identification of Shared Gene Signatures Associated with Alzheimer’sDisease and COVID-19 through Bioinformatics Analysis
Notice bibliographique
Résumé
BACKGROUND: Some studies have shown a link between Alzheimer's disease (AD) and COVID-19. This includes a Mendelian randomization study, which suggests that Alzheimer's disease and COVID-19 may be causally linked in terms of pathogenic mechanisms. However, there are fewer studies related to the two in terms of common pathogenic genes and immune infiltration. We conducted this study to identify key genes in COVID-19 linked to Alzheimer's disease, assess their relevance to immune cell profiles, and explore potential novel biomarkers. METHODS: The RNA datasets GSE157103 and GSE125583 for COVID-19 and Alzheimer's disease, respectively, were acquired via the GEO database and subsequently processed. Through the utilization of differential expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA), genes associated with Alzheimer's disease and COVID-19 were identified. The immune cell signatures were estimated using the xCell algorithm, and correlation analysis identified links between key genes and significantly different immune cell signatures. Finally, we conducted transcription factor (TF) analysis, mRNA analysis, and sensitivity drug analysis. RESULTS: Differential analysis identified 3560 (2099 up-regulated and 1461 down-regulated) and 1456 (640 up-regulated and 816 down-regulated) differential genes for COVID-19 and AD compared to normal controls, respectively. WGCNA analysis revealed 254 key module genes for COVID-19 and 791 for AD. We combined the differential genes and WGCNA key module genes for each disease to obtain two gene sets. The intersection of these two gene sets was examined to obtain intersecting genes. Subsequently, PPI network analysis was conducted, leading to the identification of 12 hub genes. Then, 12 immune-related hub genes were further identified. Immune infiltration patterns and the correlation between 12 hub genes and 64 immune cell types were analyzed. The analysis revealed a significant positive correlation between the two diseases under study. The relationship network between Transcription Factors and mRNA, as well as the predictions of drugs, further illustrate the strong association between the two diseases. This provides valuable information for further target exploration and drug screening. CONCLUSION: Our study suggests potential shared genes, signalling pathways, and common drug candidates that may be associated with COVID-19 and AD. This may provide insights for future studies of AD patients infected with SARS-CoV-2 and help improve diagnostic and therapeutic approaches.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».