Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer's Disease.

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Autophagy is closely associated with AD and plays multiple roles in eliminating harmful aggregated proteins and maintaining cell homeostasis. This study identified 1191 differentially expressed genes (DEGs) based on the GSE5281 dataset from the GEO database, intersected them with 325 autophagy-related genes from GeneCards, and screened 26 differentially expressed autophagy-related genes (DEAGs). Subsequently, GO and KEGG enrichment analysis was performed and indicated that these DEAGs were primarily involved in autophagy-lysosomal biological process. Further, eight hub genes were determined by PPI construction, and experimental validation was performed by qRT-PCR on a SH-SY5Y cell model. Finally, three hub genes (TFEB, TOMM20, GABARAPL1) were confirmed to have potential application for biomarkers. A multigenic prediction model with good predictability (AUC = 0.871) was constructed in GSE5281 and validated in the GSE132903 dataset. Hub gene-targeted miRNAs closely associated with AD were also retrieved through the miRDB and HDMM database, predicting potential therapeutic agents for AD. This study provides new insights into autophagy-related genes in brain tissues of AD patients and offers more candidate biomarkers for AD mechanistic research as well as clinical diagnosis.", "claims": [{"id": "claim-method", "statement": "The Limma package in R (4.2.1) was applied for standardization and analysis of DEGs between AD samples and control subjects [30]. Logistic regression was performed to evaluate the diagnostic significance of the hub genes. A response variable was assigned 1 for AD samples and 0 for ND (non-demented) controls. Receiver operating characteristic (ROC) analysis was performed using the pROC (1.18.0) package in R (4.2.1) software, and the results were visualized by ggplot2 [3.3.6]. The diagnostic efficacy was assessed by the area under the curve (AUC).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39202387", "locator": "sections[4] \"2.2. Identification of DEGs and DEAGs\" paragraph 2", "quote": "The Limma package in R (4.2.1) was applied for standardization and analysis of DEGs between AD samples and control subjects [30]."}, {"asset": "source:pmid:39202387", "locator": "sections[7] \"2.5. Diagnostic ROC Curve Construction\" paragraph 2", "quote": "Logistic regression was performed to evaluate the diagnostic significance of the hub genes."}, {"asset": "source:pmid:39202387", "locator": "sections[7] \"2.5. Diagnostic ROC Curve Construction\" paragraph 2", "quote": "A response variable was assigned 1 for AD samples and 0 for ND (non-demented) controls."}, {"asset": "source:pmid:39202387", "locator": "sections[7] \"2.5. 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Real-time PCR was performed with the following procedure:"], "order_basis": "paper_text_order:2058", "id": "assay:2"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The Limma package in R (4.2.1) was applied for standardization and analysis of DEGs between AD samples and control subjects [30]. Logistic regression was performed to evaluate the diagnostic significance of the hub genes. A response variable was assigned 1 for AD samples and 0 for ND (non-demented) controls. Receiver operating characteristic (ROC) analysis was performed using the pROC (1.18.0) package in R (4.2.1) software, and the results were visualized by ggplot2 [3.3.6]. The diagnostic efficacy was assessed by the area under the curve (AUC)."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}