Analysis of complement system and its related factors in Alzheimer's disease.

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In our study, we conducted a bioinformatics analysis to analyze the role of the complement system and its related factors in AD using Gene Expression Omnibus (GEO) data. We also conducted a functional analysis. Our study verified that 23 genes were closely related to differentially expressed complement system genes in diseases after intersecting the disease-related complement system module genes and differentially expressed genes. The STRING database was used to predict the interactions between the modular gene proteins of the differential complement system. A total of 21 gene proteins and 44 interaction pairs showed close interactions. We screened key genes and created a diagnostic model. The predictive effect of the model was constructed using GSE5281 and our study indicated that the predictive effect of the model was good. Our study also showed enriched negative regulation of Notch signaling, cytokine secretion involved in the immune response pathway, and cytokine secretion involved in immune response hormone-mediated apoptotic signaling pathway. We hope that our study provides a promising target to prevent and delay the onset, diagnosis, and treatment of AD.", "claims": [{"id": "claim-data-access", "statement": "All data during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38114984", "locator": "sections[30] \"notes\" paragraph 1", "quote": "All data during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six-gene LASSO RiskScore model achieved an AUC exceeding 0.85 in the training dataset and an AUC above 0.9 in the GSE5281 validation dataset.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38114984", "locator": "sections[18] \"Key gene screening and diagnostic model construction\" paragraph 4", "quote": "According to the ROC curve, the area under the curve exceeded 0.85: indicating that this model had a good disease prediction effect."}, {"asset": "source:pmid:38114984", "locator": "sections[18] \"Key gene screening and diagnostic model construction\" paragraph 5", "quote": "The area under the curve (AUC) is above 0.9, which is consistent with previous conclusions, indicating that the prediction effect of the model is good."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-gene LASSO RiskScore model achieved an AUC exceeding 0.85 in the training dataset and an AUC above 0.9 in the GSE5281 validation dataset.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38114984"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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