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• W4309729072 abstract "Abstract Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases’, ‘amyotrophic lateral sclerosis’, and ‘endocytosis’ KEGG terms. Further, some of other modules related to ALS were enriched in ‘autophagy’ and ‘amyotrophic lateral sclerosis’. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value." @default.
• W4309729072 created "2022-11-29" @default.
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• W4309729072 date "2022-11-22" @default.
• W4309729072 modified "2023-10-01" @default.
• W4309729072 title "Identification of potentially functional modules and diagnostic genes related to amyotrophic lateral sclerosis based on the WGCNA and LASSO algorithms" @default.
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• W4309729072 doi "https://doi.org/10.1038/s41598-022-24306-2" @default.
• W4309729072 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36418457" @default.
• W4309729072 hasPublicationYear "2022" @default.
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