Identification and immunological characterization of PLA2G2A and cell death-associated molecular clusters in idiopathic pulmonary fibrosis.

AIMS: Idiopathic pulmonary fibrosis (IPF) is a severe pulmonary interstitial pneumonia. Our study focuses on the role of PLA2 enzyme in the IPF to explore a more effective diagnosis and treatment mechanism of IPF. MAIN METHODS: Transcriptome data of IPF from GEO database and bleomycin-induced pulmonary fibrosis mice were analyzed to identify PLA2 enzyme and their metabolite, lysophosphatidylcholines 18:0, in IPF. Based on PLA2G2A and PLA2G2D / PLA2G2A-associated cell death genes (PCDs), the consensus clustering analysis was used to identify the subtypes of IPF and the correlation between PLA2G2A and prognosis was analyzed. The machine learning (ML) models and artificial neural network (ANN) model was used to validate the diagnostic accuracy of PLA2s and PCDs in diagnosing IPF. The gene and protein expression of NLRP3, GSDMD, and CASP-1 was estimated in recombinant PLA2G2A protein induced MLE-12 cells. KEY FINDINGS: The expression of PLA2G2D, PLA2G2A, and LPC18 significantly changed in IPF. Furtherly, PLA2G2A has a significant correlation with poor patient prognosis, which could predict the 2 or 3-years mortality rates of IPF. Two subtypes of IPF patients, identified based on PCDs, showed significant different immunoinfiltration. Recombinant PLA2G2A protein could induce the pyrotosis in the MLE-12 cell. The generalized linear model and ANN model of PLA2s or PCDs accurate diagnosis IPF. SIGNIFICANCE: PLA2G2A is the most robustly associated gene with IPF among the PLA2s, which demonstrates a potential in diagnosing and prognostic value in IPF, and provides a foundation for further understanding and breakthroughs in IPF diagnosis and treatment.

Identification of telomere-related genes associated with aging-related molecular clusters and the construction of a diagnostic model in Alzheimer's disease based on a bioinformatic analysis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease that is strongly associated with aging. Telomeres are DNA sequences that protect chromosomes from damage and shorten with age. Telomere-related genes (TRGs) may play a role in AD's pathogenesis. OBJECTIVES: To identify TRGs related to aging clusters in AD patients, explore their immunological characteristics, and build a TRG-based prediction model for AD and AD subtypes. METHODS: We analyzed the gene expression profiles of 97 AD samples from the GSE132903 dataset, using aging-related genes (ARGs) as clustering variables. We also assessed immune-cell infiltration in each cluster. We performed a weighted gene co-expression network analysis to identify cluster-specific differentially expressed TRGs. We compared four machine-learning models (random forest, generalized linear model [GLM], gradient boosting model, and support vector machine) for predicting AD and AD subtypes based on TRGs and validated TRGs by conducting an artificial neural network (ANN) analysis and a nomogram model. RESULTS: We identified two aging clusters in AD patients with distinct immunological features: Cluster A had higher immune scores than Cluster B. Cluster A and the immune system are intimately associated, and this association could affect immunological function and result in AD via the digestive system. The GLM predicted AD and AD subtypes most accurately and was validated by the ANN analysis and nomogram model. CONCLUSION: Our analyses revealed novel TRGs associated with aging clusters in AD patients and their immunological characteristics. We also developed a promising prediction model based on TRGs for assessing AD risk.

Ferromagnetic properties in low-doped zigzag graphene nanoribbons.

The temperature-dependent edge magnetic susceptibility [Formula: see text] and the uniform magnetic susceptibility χ in zigzag graphene nanoribbons is studied within the Hubbard model on a honeycomb lattice. By using the determinant quantum Monte Carlo (DQMC) method, it is found that the ferromagnetic fluctuations at the zigzag edge dominate around half-filling, and that the fluctuations are strengthened markedly by the on-site Coulomb interaction U, which may lead to a possible high-temperature edge ferromagnetic behaviour in low-doped zigzag graphene nanoribbons.