Biomarkers of Alzheimer's Disease Associated with Programmed Cell Death Reveal Four Repurposed Drugs.

Cell death and survival pathways in Alzheimer's disease: an integrative hypothesis testing approach utilizing -omic data sets

Most neurodegenerative diseases are exacerbated by aging, with symptoms often worsening over time. Programmed cell death (PCD) is a controlled cell suicide mechanism that is essential for the stability, growth, and homeostasis of organisms. Understanding the effects of aging at the level of systems biology could lead to new therapeutic approaches for a broad spectrum of neurodegenerative diseases. In the absence of comprehensive functional studies on the relationship between PCD and aging of the prefrontal cortex, this study provides prefrontal brain biomarkers of aging associated with PCD that could open the way for improved therapeutic techniques for age-related neurodegenerative diseases. To this end, publicly available transcriptome data were subjected to bioinformatic analyses such as differential gene expression, functional enrichment, and the weighted gene coexpression network analysis (WGCNA). The diagnostic utility of the biomarkers was tested using a logistic regression-based prediction model. Three genes, namely BMP4, SGSH, and SLC11A2, were found to be aging biomarkers associated with PCD. Finally, a multifactorial regulatory network with interacting proteins, transcription factors (TFs), competing endogenous RNAs (ceRNAs), and microRNAs (miRNAs) was constructed around these biomarkers. The elements of this multifactorial regulatory network were mainly enriched in BMP signaling. Further exploration of these three biomarkers and their regulatory elements would enable the development of 3PM (predictive, preventive, and personalized) medicine for the treatment of age-related neurodegenerative diseases.

Integrated Bioinformatic Analysis and Validation Identifies Immune Microenvironment-Related Potential Biomarkers in Alzheimer's Disease.

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.

Alzheimer's disease (AD) stands as a widespread neurodegenerative disorder marked by the gradual onset of memory impairment, predominantly impacting the elderly. With projections indicating a substantial surge in AD diagnoses, exceeding 13.8 million individuals by 2050, there arises an urgent imperative to discern novel biomarkers for AD. To accomplish these objectives, we explored immune cell infiltration and the expression patterns of immune cells and immune function-related genes of AD patients. Furthermore, we utilized the consensus clustering method combined with aggrephagy-related genes (ARGs) for typing AD patients and categorized AD specimens into distinct clusters (C1, C2). A total of 272 candidate genes were meticulously identified through a combination of differential analysis and Weighted Gene Co-Expression Network Analysis (WGCNA). Subsequently, we applied three machine learning algorithms-namely random forest (RF), support vector machine (SVM), and generalized linear model (GLM)-to pinpoint a pathogenic signature comprising five genes associated with AD. To validate the predictive accuracy of these identified genes in discerning AD progression, we constructed nomograms. Our analyses uncovered that cluster C2 exhibits a higher immune expression than C1. Based on the ROC(0.956). We identified five characteristic genes (PFKFB4, PDK3, KIAA0319L, CEBPD, and PHC2T) associated with AD immune cells and function. The nomograms constructed on the basis of these five diagnostic genes demonstrated effectiveness. In the validation group, the ROC values were found to be 0.760 and 0.838, respectively. These results validate the robustness and reliability of the diagnostic model, affirming its potential for accurate identification of AD. Our findings not only contribute to a deeper understanding of the molecular mechanisms underlying AD but also offer valuable insights for drug development and clinical analysis. The limitation of our study is the limited sample size, and although AD-related genes were identified and some of the mechanisms elucidated, further experiments are needed to elucidate the more in-depth mechanisms of these characterized genes in the disease.

Alzheimer’s disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurate and objective assessment of disease progression for early detection and intervention to delay its onset. In our study, three AD datasets from the Gene Expression Omnibus (GEO) database were integrated for differential expression analysis, followed by a weighted gene co-expression network analysis (WGCNA), and potential AD biomarkers were screened. Our study identified UBE2N as a promising biomarker for AD. Functional enrichment analysis revealed that UBE2N is associated with synaptic vesicle cycling and T cell/B cell receptor signaling pathways. Notably, UBE2N expression levels were found to be significantly reduced in the cortex and hippocampus of the TauP301S mice. Furthermore, analysis of single-cell data from AD patients demonstrated the association of UBE2N and T cell function. These findings underscore the potential of UBE2N as a valuable biomarker for AD, offering important insights for diagnosis and targeted therapeutic strategies.

Alzheimer’s disease (AD) is a neurodegenerative disorder with a multifactorial pathogenesis, comprising gene expression alterations and abnormal immune cell infiltration. In this study, we aimed at further exploring AD pathogenesis and identifying potential therapeutic targets. We downloaded GSE181279 dataset-derived single-cell data from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, then processed and analyzed it using various bioinformatic methods. We screened, standardized, homogenized, and processed the data using principal component analysis and harmony. We identified subgroups using t-distributed stochastic neighbor embedding analysis. For the single-cell data, we performed cell-type annotation and marker analysis, and compared cell proportions between the groups. After confirming the plasma cell subtype, we screened the disease-associated gene modules via high-dimensional weighted gene co-expression network analysis and identified key genes using pathway, Mendelian randomization (MR), sensitivity, and immune cell infiltration analyses. Finally, we analyzed the transcription factor regulatory network and the correlation between key genes, identifying 21 subgroups. The plasma cell subtype proportion significantly increased in patients with AD. We identified brown- and blue-module disease-associated genes. Further pathway and MR analyses identified four key genes (COX4I1, MAL, RGS1, and RPS5) and confirmed their association with AD. Immune cell infiltration analysis revealed that the key genes are closely related to immune cells, underlining their potentially important roles in AD-related immune regulation. This study explored various AD pathogenesis-related aspects and identified disease-associated key genes and biological pathways. Our results provide important clues for upcoming AD pathophysiological mechanism-related studies and AD therapeutic target assessments.

Alzheimer's disease (AD) is a common progressive neurodegenerative disease. The Ubiquitin-Protease system (UPS), which plays important roles in maintaining protein homeostasis in eukaryotic cells, is involved in the development of AD. This study sought to identify differential UPS-related genes (UPGs) in AD patients by using bioinformatic methods, reveal potential biomarkers for early detection of AD, and investigate the association between the identified biomarkers and immune cell infiltration in AD. The differentially expressed UPGs were screened with bioinformatics analyses using the Gene Expression Omnibus (GEO) database. A weighted gene co-expression network analysis (WGCNA) analysis was performed to explore the key gene modules associated with AD. A Single-sample Gene Set Enrichment Analysis (ssGSEA) analysis was peformed to explore the patterns of immune cells in the brain tissue of AD patients. Real-time quantitative PCR (RT-qPCR) was performed to examine the expression of hub genes in blood samples from healthy controls and AD patients. In this study, we identified four UPGs (USP3, HECW2, PSMB7, and UBE2V1) using multiple bioinformatic analyses. Furthermore, three UPGs (USP3, HECW2, PSMB7) that are strongly correlated with the clinical features of AD were used to construct risk score prediction markers to diagnose and predict the severity of AD. Subsequently, we analyzed the patterns of immune cells in the brain tissue of AD patients and the associations between immune cells and the three key UPGs. Finally, the risk score model was verified in several datasets of AD and showed good accuracy. Three key UPGs are identified as potential biomarker for AD patients. These genes may provide new targets for the early identification of AD patients.

Unveiling manganese metabolism-related biomarkers in Alzheimer's disease: Insights into diagnosis and therapeutic targets.

Previous research has highlighted lysosomal ion channel-related genes (LICRGs) as promising therapeutic targets for neurodegenerative diseases. This study aimed to identify and analyze LICRG-associated biomarkers for Alzheimer’s disease (AD), elucidating their underlying biological mechanisms. Three datasets (GSE63061, GSE63060, GSE181279) were analyzed. In GSE63061, intersecting genes were identified by integrating differentially expressed genes (DEGs) from differential expression analysis with key module genes from Weighted Gene Co-expression Network Analysis (WGCNA). Candidate biomarkers were then selected using the MCODE plugin for PPI analysis (top 30 genes), two machine learning approaches, and cross-validation of gene expression profiles in GSE63061 and GSE63060. Single-cell RNA sequencing (scRNA-seq) analysis of GSE181279 identified key biomarkers and cell populations, followed by pseudo-temporal analysis of these cells. Nomogram construction, functional enrichment analysis, immune infiltration assessment, and RT-qPCR analysis were subsequently performed. scRNA-seq analysis revealed that SRP14, EIF3E, and COX7C were prominently expressed across most cell types, particularly in CD4+ T cells, which were identified as key cells in AD. Pseudo-temporal analysis indicated that CD4+ T cells from control subjects primarily resided in early differentiation stages, whereas those from patients with AD were predominantly found in later stages. The reduced expression of these biomarkers in AD CD4+ T cells was consistent with transcriptomic data and further validated by RT-qPCR. A nomogram incorporating these biomarkers demonstrated strong predictive power for AD risk. Functional analysis linked the biomarkers to pathways such as “ribosome” and “oxidative phosphorylation.” Immune infiltration analysis revealed 23 differentially abundant immune cell types, with significant correlations between all three biomarkers and memory CD4+ T cells, mesangial cells, and other immune cell types. This study identified SRP14, EIF3E, and COX7C as novel biomarkers, underscoring CD4+ T cells as pivotal in AD pathogenesis. These findings offer new mechanistic insights and potential therapeutic strategies for AD.

Back to the future: Alzheimer's disease heterogeneity revisited

Evidence for the Involvement of Apoptosis-Inducing Factor–Mediated Caspase-Independent Neuronal Death in Alzheimer Disease

Alzheimer’s disease (AD) is the leading cause of dementia in aged population. Oxidative stress and neuroinflammation play important roles in the pathogenesis of AD. Investigation of hub genes for the development of potential therapeutic targets and candidate biomarkers is warranted. The differentially expressed genes (DEGs) in AD were screened in GSE48350 dataset. The differentially expressed oxidative stress genes (DEOSGs) were analyzed by intersection of DEGs and oxidative stress-related genes. The immune-related DEOSGs and hub genes were identified by weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) analysis, respectively. Enrichment analysis was performed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The diagnostic value of hub genes was assessed by receiver operating characteristic analysis and validated in GSE1297. The mRNA expression of diagnostic genes was determined by qRT-PCR analysis. Finally, we constructed the drug, transcription factors (TFs), and microRNA network of the diagnostic genes. A total of 1160 DEGs (259 up-regulated and 901 down-regulated) were screened in GSE48350. Among them 111 DEOSGs were identified in AD. Thereafter, we identified significant difference of infiltrated immune cells (effector memory CD8 T cell, activated B cell, memory B cell, natural killer cell, CD56 bright natural killer cell, natural killer T cell, plasmacytoid dendritic cell, and neutrophil) between AD and control samples. 27 gene modules were obtained through WGCNA and turquoise module was the most relevant module. We obtained 66 immune-related DEOSGs by intersecting turquoise module with the DEOSGs and identified 15 hub genes through PPI analysis. Among them, 9 hub genes (CCK, CNR1, GAD1, GAP43, NEFL, NPY, PENK, SST, and TAC1) were identified with good diagnostic values and verified in GSE1297. qRT-PCR analysis revealed the downregulation of SST, NPY, GAP43, CCK, and PENK and upregulation of NEFL in AD. Finally, we identified 76 therapeutic agents, 152 miRNAs targets, and 91 TFs regulatory networks. Our study identified 9 key genes associated with oxidative stress and immune reaction in AD pathogenesis. The findings may help to provide promising candidate biomarkers and therapeutic targets for AD.

BackgroundHuman Immunodeficiency Virus-1 (HIV) infection frequently results in neurocognitive impairment. While the cause remains unclear, recent gene expression studies have identified genes whose transcription is dysregulated in individuals with HIV-association neurocognitive disorder (HAND). However, the methods for interpretation of such data have lagged behind the technical advances allowing the decoding genetic material. Here, we employ systems biology methods novel to the field of NeuroAIDS to further interrogate extant transcriptome data derived from brains of HIV + patients in order to further elucidate the neuropathogenesis of HAND. Additionally, we compare these data to those derived from brains of individuals with Alzheimer’s disease (AD) in order to identify common pathways of neuropathogenesis.MethodsIn Study 1, using data from three brain regions in 6 HIV-seronegative and 15 HIV + cases, we first employed weighted gene co-expression network analysis (WGCNA) to further explore transcriptome networks specific to HAND with HIV-encephalitis (HIVE) and HAND without HIVE. We then used a symptomatic approach, employing standard expression analysis and WGCNA to identify networks associated with neurocognitive impairment (NCI), regardless of HIVE or HAND diagnosis. Finally, we examined the association between the CNS penetration effectiveness (CPE) of antiretroviral regimens and brain transcriptome. In Study 2, we identified common gene networks associated with NCI in both HIV and AD by correlating gene expression with pre-mortem neurocognitive functioning.ResultsStudy 1: WGCNA largely corroborated findings from standard differential gene expression analyses, but also identified possible meta-networks composed of multiple gene ontology categories and oligodendrocyte dysfunction. Differential expression analysis identified hub genes highly correlated with NCI, including genes implicated in gliosis, inflammation, and dopaminergic tone. Enrichment analysis identified gene ontology categories that varied across the three brain regions, the most notable being downregulation of genes involved in mitochondrial functioning. Finally, WGCNA identified dysregulated networks associated with NCI, including oligodendrocyte and mitochondrial functioning. Study 2: Common gene networks dysregulated in relation to NCI in AD and HIV included mitochondrial genes, whereas upregulation of various cancer-related genes was found.ConclusionsWhile under-powered, this study identified possible biologically-relevant networks correlated with NCI in HIV, and common networks shared with AD, opening new avenues for inquiry in the investigation of HAND neuropathogenesis. These results suggest that further interrogation of existing transcriptome data using systems biology methods can yield important information.

Background Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD. Methods In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣log2FoldChange | >0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve. Results In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD (r = 0.64, P = 3e − 20), and it was visualized by establishing a protein–protein interaction network. Moreover, this module was particularly enriched in “pathways of neurodegeneration—multiple diseases,” “Alzheimer disease,” “oxidative phosphorylation,” and “proteasome.” Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve (AUC = 0.940). Conclusions Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.