Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis

Introduction

The aim of this study is to establish a prognostic risk model based on ferroptosis to prognosticate the severity of Alzheimer's disease (AD) through gene expression changes.

Methods

The GSE138260 dataset was initially downloaded from the Gene expression Omnibus database. The ssGSEA algorithm was used to evaluate the immune infiltration of 28 kinds of immune cells in 36 samples. The up-regulated immune cells were divided into Cluster 1 group and Cluster 2 group, and the differences were analyzed. The LASSO regression analysis was used to establish the optimal scoring model. Cell Counting Kit-8 and Real Time Quantitative PCR were used to verify the effect of different concentrations of Aβ_1-42 on the expression profile of representative genes in vitro.

Results

Based on the differential expression analysis, there were 14 up-regulated genes and 18 down-regulated genes between the control group and Cluster 1 group. Cluster 1 and Cluster 2 groups were differentially analyzed, and 50 up-regulated genes and 101 down-regulated genes were obtained. Finally, nine common differential genes were selected to establish the optimal scoring model. In vitro, CCK-8 experiments showed that the survival rate of cells decreased significantly with the increase of Aβ_1-42 concentration compared with the control group. Moreover, RT-qPCR showed that with the increase of Aβ_1-42 concentration, the expression of POR decreased first and then increased; RUFY3 was firstly increased and then decreased.

Discussion

The establishment of this research model can help clinicians make decisions on the severity of AD, thus providing better guidance for the clinical treatment of Alzheimer's disease.

Identifying Immune Cell Infiltration and Effective Diagnostic Biomarkers in Lung Adenocarcinoma by Comprehensive Bioinformatics Analysis and In Vitro Study

Family with sequence similarity 72B (FAM72B) has been characterized in the regulation of neuronal development. Nevertheless, the prognostic value of FAM72B expression and its function in the immune microenvironment of lung adenocarcinoma (LUAD) currently remains elusive. In this study, by adopting bioinformatics methodology and experimental verification, we found that FAM72B was upregulated in lung cancer tissues and cell lines, and a higher FAM72B level predicted an unfavorable clinical outcome in LUAD patients. The knockdown of FAM72B significantly inhibited cell proliferation, cell migration, and induced cell apoptosis in LUAD. The receiver operating characteristic curve suggested that FAM72B had a high predictive accuracy for the outcomes of LUAD. Kyoto Encyclopedia of Genes and Genomes and Gene Set Enrichment Analyses confirmed that FAM72B-related genes were involved in cell proliferation and immune-response signaling pathway. Moreover, upregulated FAM72B expression was significantly associated with immune cell infiltration in the LUAD tumor microenvironment. Meanwhile, a potential ceRNA network was constructed to identify the lncRNA-AL360270.2/TMPO-AS1/AC125807.2/has-let-7a/7b/7c/7e/7f/FAM72B regulatory axis that regulates FAM72B overexpression in LUAD and is associated with a poor prognosis. We also confirmed that AL360270.2, TMPO-AS1, and AC125807.2 were significantly upregulated in LUAD cell lines than in human bronchial epithelial cells. In conclusion, FAM72B may serve as a novel biomarker in predicting the clinical prognosis and immune status for lung adenocarcinoma.