Abstract
Sporadic amyotrophic lateral sclerosis (SALS) is a devastating neurodegenerative disorder. However, the understanding of SALS is still poor. This research aimed to excavate attractor modules for SALS by integrating the systemic module inference and attract method. To achieve this, gene expression data and protein-protein data were recruited and preprocessed. Then, based on the Spearman's correlation coefficient (SCC) of the interactions under these two conditions, two PPI networks separately with 870 nodes (979 interactions) in normal control group and 601 nodes (777 interactions) in SALS group were built. Systemic module inference method was performed to identify the modules, and attract method was used to identify attractor modules. Finally, pathway enrichment analysis was performed to disclose the functional enrichment of these attractor modules. In total 44 and 118 modules were identified for normal control and SALS groups, respectively. Among them, 6 modules were with similar gene composition between the two groups, and all 6 modules were considered as the attractor module via attract method. These attractor modules might be potential biomarkers for early diagnosis and therapy of SALS, which could provide insight into the disease biology and suggest possible directions for drug screening programs.
Keywords:
Spearman's correlation coefficient; attractor module; pathway; protein-protein interaction network; sporadic amyotrophic lateral sclerosis.