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Cell-based Assay Protocol for the Prognostic Prediction of Idiopathic Scoliosis Using Cellular Dielectric Spectroscopy
Published on: October 16, 2013
Plasma Proteomic of Adolescent Idiopathic Scoliosis
Jiacheng Lyu1, Tianyuan Zhang2, Tao Ji1
1Department of Pediatric Orthopedics, Spine Center, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine, Shanghai, China; Clinical Research Center for Cell-based Immunotherapy of Shanghai Pudong Hospital, Fudan University Pudong Medical Center, State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai, China.
Abstract:
Adolescent idiopathic scoliosis (AIS) is the most common spinal deformity encountered in adolescents. Here we portray the plasma proteomic landscape of 235 AIS samples. Enrichment analysis demonstrate that proteins with the increased level in AIS are significantly enriched in pathways including muscle weakness, disorder of hormone, whereas proteins showed decreased level in healthy controls are mainly involved in pathways related to immune response. The weighted gene correlation network analysis analysis indicates unbalanced lipid and glucose metabolism due to the insulin signaling activation could affect the AIS progression. Molecular subtyping classifies AIS patients into three subtypes that connected with significantly different Cobb angle (the standard radiographic measure of spinal curvature) with the estrogen and glucocorticoid disorder and have effects on the muscle weakness and bone remodeling, respectively. Additional, non-linear associations between Cobb and plasma proteome data reveals that the plasma proteome of 26 degrees and 51 degrees is dramatically differed across these two Cobb ranges. Finally, we construct two proteomics classifiers for the AIS screening and progression state prediction that have the good performance on both discovery and validation cohort (area under the receiver operating characteristic >0.90). This study generates a high-quality data resource that may benefit basic research and provides additional biological insights underlying clinical features of AIS.

