Amyotrophic lateral sclerosis & frontotemporal degeneration · 2025
Serum small non-coding RNA define molecular subtypes in amyotrophic lateral sclerosis.
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Baindoor Sharada, Gibriel Hesham A Y, Kool Lindy, Su Junyi, Demaegd Koen C, Venø Morten T, Van Den Berg Leonard H, Kjems Jørgen, Van Es Michael A, Prehn Jochen H M
Аннотация
OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with variable site of onset, disease progression rates and survival times. Early-stage ALS characteristics are shared with other conditions, posing diagnostic challenges and resulting in diagnosis delays. We investigated tRNA-derived small RNAs (tsRNAs) and microRNAs (miRNAs) which are stable and abundantly expressed small non-coding RNAs (sncRNAs) as potential diagnostic serum biomarkers, comparing them to healthy controls and ALS mimics, and gained pathophysiological insights from dysregulated sncRNAs.
METHODS: We analyzed small RNA-seq data from 158 patients with ALS, 60 healthy controls and 39 patients with neurological conditions that mimic ALS to identify differentially expressed sncRNAs. A classifier was built to evaluate their diagnostic potential, followed by hierarchical clustering to identify ALS molecular subtypes. Finally, we performed gene ontology and pathway analysis to identify pathways disrupted within subtypes.
RESULTS: We identified several dysregulated tsRNAs and miRNAs and assessed their diagnostic potential using an extreme gradient boosting (XGBoost) classifier. Our models achieved an accuracy of 87.16% and 82.23% in classifying patients with ALS from healthy controls and ALS mimics, respectively. We identified four sncRNA expression-based ALS molecular subtypes with one C9orf72 enriched cluster. Further analysis of identified differentially expressed sncRNAs showed their involvement in neuronal pathways.
CONCLUSION: Our study identified potential sncRNA-based diagnostic serum biomarkers and associated molecular subtypes which can be further studied to match clinical parameters and develop subtype specific biomarkers and therapeutic strategies for ALS.
METHODS: We analyzed small RNA-seq data from 158 patients with ALS, 60 healthy controls and 39 patients with neurological conditions that mimic ALS to identify differentially expressed sncRNAs. A classifier was built to evaluate their diagnostic potential, followed by hierarchical clustering to identify ALS molecular subtypes. Finally, we performed gene ontology and pathway analysis to identify pathways disrupted within subtypes.
RESULTS: We identified several dysregulated tsRNAs and miRNAs and assessed their diagnostic potential using an extreme gradient boosting (XGBoost) classifier. Our models achieved an accuracy of 87.16% and 82.23% in classifying patients with ALS from healthy controls and ALS mimics, respectively. We identified four sncRNA expression-based ALS molecular subtypes with one C9orf72 enriched cluster. Further analysis of identified differentially expressed sncRNAs showed their involvement in neuronal pathways.
CONCLUSION: Our study identified potential sncRNA-based diagnostic serum biomarkers and associated molecular subtypes which can be further studied to match clinical parameters and develop subtype specific biomarkers and therapeutic strategies for ALS.