These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: The PANoptosis-related hippocampal molecular subtypes and key biomarkers in Alzheimer's disease patients.
    Author: Li CL, Wang Q, Wu L, Hu JY, Gao QC, Jiao XL, Zhang YX, Tang S, Yu Q, He PF.
    Journal: Sci Rep; 2024 Oct 11; 14(1):23851. PubMed ID: 39394418.
    Abstract:
    Alzheimer's Disease (AD) is a neurodegenerative disorder, and various molecules associated with PANoptosis are involved in neuroinflammation and neurodegenerative diseases. This work aims to identify key genes, and characterize PANoptosis-related molecular subtypes in AD. Moreover, we establish a scoring system for distinguishing PANoptosis molecular subtypes and constructing diagnostic models for AD differentiation. A total of 5 hippocampal datasets were obtained from the Gene Expression Omnibus (GEO) database. In total, 1324 protein-encoding genes associated with PANoptosis (1313 apoptosis genes, 11 necroptosis genes, and 31 pyroptosis genes) were extracted from the GeneCards database. The Limma package was used to identify differentially expressed genes. Weighted Gene Co-Expression Network Analysis (WGCNA) was conducted to identify gene modules significantly associated with AD. The ConsensusClusterPlus algorithm was used to identify AD subtypes. Gene Set Variation Analysis (GSVA) was used to assess functional and pathway differences among the subtypes. The Boruta, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithms were used to select the three PANoptosis-related Key AD genes (PKADg). A scoring model was constructed based on the Boruta algorithm. PANoptosis diagnostic models were developed using the RF, SVM-RFE, and Logistic Regression (LR) algorithms. The ROC curves were used to assess the model performance. A total of 48 important genes were identified by intersecting 725 differentially expressed genes and 2127 highly correlated module genes from WGCNA with 1324 protein-encoding genes related to PANoptosis. Machine learning algorithms identified 3 key AD genes related to PANoptosis, including ANGPT1, STEAP3, and TNFRSF11B. These genes had strong discriminatory capacities among samples, with Receiver Operating Characteristic Curve (ROC) analysis indicating Area Under the Curve (AUC) values of 0.839, 0.8, and 0.868, respectively. Using the 48 important genes, the ConsensusClusterPlus algorithm identified 2 PANoptosis subtypes among AD patients, i.e., apoptosis subtype and mild subtype. Apoptosis subtype patients displayed evident cellular apoptosis and severe functionality damage in the hippocampal tissue. Meanwhile, mild subtype patients showed milder functionality damage. These two subtypes had significant differences in apoptosis and necroptosis; however, there was no apparent variation in pyroptosis functionality. The scoring model achieved an AUC of 100% for sample differentiation. The RF PANoptosis diagnostic model demonstrated an AUC of 100% in the training set and 85.85% in the validation set for distinguishing AD. This study identified two PANoptosis-related hippocampal molecular subtypes of AD, identified key genes, and established machine learning models for subtype differentiation and discrimination of AD. We found that in the context of AD, PANoptosis may influence disease progression through the modulation of apoptosis and necrotic apoptosis.
    [Abstract] [Full Text] [Related] [New Search]