Gene regulatory co-expression networks decipher potential lncRNA-miRNA-mRNA interactions modulating transcription...

Neurodegenerative diseases are complex disorders characterised by progressive neuronal loss and widespread transcriptomic dysregulation. However, understanding the molecular interactions among coding and non-coding RNAs, especially lncRNAs and miRNAs, contributing to disease progression remains elusive. In this study, RNA-Seq datasets from disease-relevant neuronal populations and different brain tissues representing Alzheimer's disease (AD), Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS) are analysed using an integrative network-based framework. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) are combined to identify disease-associated modules and prioritise highly connected hub genes. They are subsequently integrated with curated RNA interaction databases to construct candidate lncRNA-miRNA-mRNA regulatory networks. Functional enrichment and UTR variant analyses are further incorporated to connect the regulatory network framework with biological processes and potential sequence variations. The novelty of the present study lies in the systematic integration of transcriptomic co-expression, post-transcriptional regulation, functional and sequence-level analyses within a single framework. This framework enables the identification of disease-specific and shared regulatory features, and links the candidate regulatory interactions with potential UTR sequence alterations. Across eight tissue-specific samples, 163 hub genes and 4869 candidate lncRNA-miRNA-mRNA interactions are identified. Additionally, we also identify 414 UTR variants associated with the hub genes. Pathways associated with hub genes including axonogenesis, synaptic organization, intracellular transport and RNA regulatory mechanisms are commonly found to be enriched in AD, PD and ALS. These findings provide a quantitative resource for prioritising regulatory interactions between candidate hub genes and associated UTR variants for future experimental validation.