Parkinson's disease (PD) is the second most prevalent neurodegenerative disease, and its pathological mechanisms are thought to be closely linked to apoptosis. Anoikis, a specific type of apoptosis, has recently been suggested to play a role in the progression of Parkinson's disease; however, the underlying mechanisms are not well understood. To explore the potential mechanisms involved in PD, we selected genes from the GSE28894 dataset and compared their expression in PD patients and healthy controls to identify differentially expressed genes (DEGs), and selected anoikis-related genes (ANRGs) from the DEGs. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression approach and multivariate logistic regression highlighted five key genes-GSK3B, PCNA, CDC42, DAPK2, and SRC-as biomarker candidates. Subsequently, we developed a nomogram model incorporating these 5 genes along with age and sex to predict and diagnose PD. To evaluate the model's coherence, clinical applicability, and distinguishability, we utilized receiver operating characteristic (ROC) curves, the C-index, and calibration curves and validated it in both the GSE20295 dataset and our center's external clinical data. In addition, we confirmed the differential expression of the 5 model genes in human blood samples through qRT-PCR and Western blotting. Our constructed anoikis-related PD diagnostic model exhibits satisfactory predictive accuracy and offers novel insights into both diagnosis and treatment strategies for Parkinson's disease while facilitating its implementation in clinical practice.
Keywords: Anoikis; Bioinformatics; Model; Nomogram; Parkinson’s disease.
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.