Association between chronic periodontitis and the risk of Alzheimer's disease: combination of text mining and GEO dataset

A review and analysis of key biomarkers in Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

A meta-analysis of microarray datasets to identify biological regulatory networks in Alzheimer's disease

Background: Alzheimer's Disease (AD) is an age-related progressive neurodegenerative disorder characterized by mental deterioration, memory deficit, and multiple cognitive abnormalities, with an overall prevalence of ∼2% among industrialized countries. Although a proper diagnosis is not yet available, identification of miRNAs and mRNAs could offer valuable insights into the molecular pathways underlying AD's prognosis. Method: This study aims to utilize microarray bioinformatic analysis to identify potential biomarkers of AD, by analyzing six microarray datasets (GSE4757, GSE5281, GSE16759, GSE28146, GSE12685, and GSE1297) of AD patients, and control groups. Furthermore, this study conducted gene ontology, pathways analysis, and protein-protein interaction network to reveal major pathways linked to probable biological events. The datasets were meta-analyzed using bioinformatics tools, to identify significant differentially expressed genes (DEGs) and hub genes and their targeted miRNAs'. Results: According to the findings, CXCR4, TGFB1, ITGB1, MYH11, and SELE genes were identified as hub genes in this study. The analysis of DEGs using GO (gene ontology) revealed that these genes were significantly enriched in actin cytoskeleton regulation, ECM-receptor interaction, and hypertrophic cardiomyopathy. Eventually, hsa-mir-122-5p, hsa-mir-106a-5p, hsa-mir-27a-3p, hsa-mir16-5p, hsa-mir-145-5p, hsa-mir-12-5p, hsa-mir-128-3p, hsa-mir 3200-3p, hsa-mir-103a-3p, and hsa-mir-9-3p exhibited significant interactions with most of the hub genes. Conclusion: Overall, these genes can be considered as pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

The Genetic Cross-Talk between Periodontitis and Chronic Kidney Failure Revealed by Transcriptomic Analysis

Periodontitis and chronic kidney failure (CKF) are potentially related to each other. This bioinformatics analysis aimed at the identification of potential cross-talk genes and related pathways between periodontitis and CKF. Based on NCBI Gene Expression Omnibus (GEO), datasets GSE10334, GSE16134, and GSE23586 were extracted for periodontitis. A differential expression analysis (p < 0.05, |log2(FC)| > 0.5) was performed to assess deregulated genes (DEGs). CKF-related genes were extracted from DisGeNET and examined regarding their overlap with periodontitis-related DEGs. Cytoscape was used to construct and analyze a protein-protein interaction (PPI) network. Based on Cytoscape plugin MCODE and a LASSO regression analysis, the potential hub cross-talk genes were identified. Finally, a complex PPI of the hub genes was constructed. A total of 489 DEGs for periodontitis were revealed. With the 805 CKF-related genes, an overlap of 47 cross-talk genes was found. The PPI network of the potential cross-talk genes was composed of 1081 nodes and 1191 edges. The analysis with MCODE resulted in 10 potential hub genes, while the LASSO regression resulted in 22. Finally, five hub cross-talk genes, CCL5, FCGR3B, MMP-9, SAA1, and SELL, were identified. Those genes were significantly upregulated in diseased samples compared to controls (p ≤ 0.01). Furthermore, ROC analysis showed a high predictive value of those genes (AUC ≥ 73.44%). Potentially relevant processes and pathways were primarily related to inflammation, metabolism, and cardiovascular issues. In conclusion, five hub cross-talk genes, i.e., CCL5, FCGR3B, MMP-9, SAA1, and SELL, could be involved in the interplay between periodontitis and CKF, whereby primarily inflammation, metabolic, and vascular issues appear to be of relevance.

Predicting Key Genes and Therapeutic Molecular Modelling to Explain the Association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s Disease (AD)

The association between Porphyromonas gingivalis (P. gingivalis) and Alzheimer’s disease (AD) remains unclear. The major aim of this study was to elucidate the role of genes and molecular targets in P. gingivalis-associated AD. Two Gene Expression Omnibus (GEO) datasets, GSE5281 for AD (n = 84 Alzheimer’s, n = 74 control) and GSE9723 (n = 4 P. gingivalis, n = 4 control), were downloaded from the GEO database. Differentially expressed genes (DEGs) were obtained, and genes common to both diseases were drawn. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis was performed from the top 100 genes (50 upregulated and 50 downregulated genes). We then proceeded with CMap analysis to screen for possible small drug molecules targeting these genes. Subsequently, we performed molecular dynamics simulations. A total of 10 common genes (CALD1, HES1, ID3, PLK2, PPP2R2D, RASGRF1, SUN1, VPS33B, WTH3DI/RAB6A, and ZFP36L1) were identified with a p-value < 0.05. The PPI network of the top 100 genes showed UCHL1, SST, CHGB, CALY, and INA to be common in the MCC, DMNC, and MNC domains. Out of the 10 common genes identified, only 1 was mapped in CMap. We found three candidate small drug molecules to be a fit for PLK2, namely PubChem ID: 24971422, 11364421, and 49792852. We then performed molecular docking of PLK2 with PubChem ID: 24971422, 11364421, and 49792852. The best target, 11364421, was used to conduct the molecular dynamics simulations. The results of this study unravel novel genes to P. gingivalis-associated AD that warrant further validation.

Bioinformatics analysis of gene expression profile and functional analysis in periodontitis and Parkinson’s disease

Periodontitis is a chronic inflammatory disease inextricably linked to both the innate and acquired immune systems of the body. Parkinson’s disease (PD) is a neurodegenerative disease caused by immune system dysfunction. Although recent studies suggest that a clinical relationship exists between PD and periodontitis, the pathogenesis of this relationship is unclear. Therefore, in the present study, we obtained datasets of periodontitis and PD from the Gene Expression Omnibus (GEO) database and extracted 785 differentially expressed genes (DEGs), including 15 common upregulated genes and four common downregulated genes. We performed enrichment analyses of these DEGs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. We found that the genes were mainly enriched in keratinocyte differentiation, neuronal cell bodies, and structural constituents of epidermis terms, and pathways such as immune response and synaptic pathways. In addition, we screened matching hub genes by constructing a protein–protein interaction (PPI) network map and a Molecular Complex Detection (MCODE) map using the Cytoscape software. The hub genes were then subjected to GO enrichment analysis, which revealed that the dopamine biosynthetic process, dopaminergic synapse and dopamine-binding terms, and dopaminergic synapse and serotonergic synapse pathways were primarily where they were expressed. Finally, we selected four of these genes for validation in the periodontitis and PD datasets, and we confirmed that these hub genes were highly sensitive and specific for diagnosing and monitoring PD and periodontitis. In conclusion, the above experimental results indicate that periodontitis is a high-risk factor for PD, and the association between these two conditions is mainly manifested in immune and dopamine-related pathways. Hub genes, such as the CDSN, TH, DDC, and SLC6A3 genes, may serve as potential biomarkers for diagnosing or detecting PD.

Association between the number of remaining teeth and disability-free life expectancy, and the impact of oral self-care in older Japanese adults: a prospective cohort study

Background

Tooth loss has been reportedly associated with shorter disability-free life expectancy (DFLE). However, no study has explored whether oral self-care offsets reduction in DFLE. The present study aimed to assess the association between oral self-care and DFLE in older individuals with tooth loss.

Methods

Data on the 13-year follow-up from a cohort study of 14,206 older Japanese adults aged ≥ 65 years in 2006 were analyzed. Information on the number of remaining teeth was collected using a questionnaire, and the participants were then categorized into three groups (0–9, 10–19, and ≥ 20 teeth). Additionally, “0–9” and “10–19” groups were divided into two subgroups based on whether they practiced oral self-care. DFLE was defined as the average number of years a person could expect to live without disability, and was calculated by the multistate life table method based on a Markov model.

Results

DFLE (95% confidence interval) was 19.0 years (18.7–19.4) for 0–9 teeth, 20.1 (19.7–20.5) for 10–19 teeth, and 21.6 (21.2–21.9) for ≥ 20 teeth for men. For women, DFLE was 22.6 (22.3–22.9), 23.5 (23.1–23.8), and 24.7 (24.3–25.1), respectively. Practicing oral self-care was associated with longer DFLE, by 1.6–1.9 years with brushing ≥ 2 times a day in people with 0–9 and 10–19 teeth, and by 3.0–3.1 years with the use of dentures in those with 0–9 teeth.

Conclusions

Practicing oral self-care is associated with an increase in DFLE in older people with tooth loss.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-022-03541-2.

Identification of feature genes and pathways for Alzheimer's disease via WGCNA and LASSO regression

While Alzheimer's disease (AD) can cause a severe economic burden, the specific pathogenesis involved is yet to be elucidated. To identify feature genes associated with AD, we downloaded data from three GEO databases: GSE122063, GSE15222, and GSE138260. In the filtering, we used AD for search keywords, Homo sapiens for species selection, and established a sample size of > 20 for each data set, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a training group to build a model, and GSE122063 was used as a test group to verify the model's accuracy. The genes with differential expression found in the combined datasets were used for analysis through Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genome Pathways (KEGG). Then, AD-related module genes were identified using the combined dataset through a weighted gene co-expression network analysis (WGCNA). Both the differential and AD-related module genes were intersected to obtain AD key genes. These genes were first filtered through LASSO regression and then AD-related feature genes were obtained for subsequent immune-related analysis. A comprehensive analysis of three AD-related datasets in the GEO database revealed 111 common differential AD genes. In the GO analysis, the more prominent terms were cognition and learning or memory. The KEGG analysis showed that these differential genes were enriched not only in In the KEGG analysis, but also in three other pathways: neuroactive ligand-receptor interaction, cAMP signaling pathway, and Calcium signaling pathway. Three AD-related feature genes (SST, MLIP, HSPB3) were finally identified. The area under the ROC curve of these AD-related feature genes was greater than 0.7 in both the training and the test groups. Finally, an immune-related analysis of these genes was performed. The finding of AD-related feature genes (SST, MLIP, HSPB3) could help predict the onset and progression of the disease. Overall, our study may provide significant guidance for further exploration of potential biomarkers for the diagnosis and prediction of AD.

Identification of Ferroptosis-Related Biomarkers for Prognosis and Immunotherapy in Patients With Glioma

Ferroptosis is a novel type of iron- and ROS-dependent cell death and is involved in various diseases. LncRNAs are involved and play important roles in the occurrence and development of several cancers. However, researches about the role of ferroptosis-related lncRNAs in glioma are relatively rare. Here, we identified nine ferroptosis-related lncRNAs and then constructed a prognostic model by the LASSO and Cox analysis. The model could predict overall survival with high sensitivity and specificity according to ROC curves. In addition, the cell cycle, p53 signaling, apoptosis, and oxidative phosphorylation pathways were obviously enriched in the pathogenesis of glioma by gene set enrichment analysis. A nomogram was constructed by integrating several independent prognostic clinicopathological features, and it could provide a valuable predictive tool for overall survival. Furthermore, a strong correlation between these nine lncRNAs and immunotherapy was found. Glioma patients in the high-risk group had higher TMB using somatic mutation data, different immune infiltration, and higher expression of immune checkpoints, indicating these patients might benefit from immune checkpoint inhibitor therapy. In summary, these nine ferroptosis-related lncRNAs were promising biomarkers for predicting overall survival and guiding immunotherapy or future immune checkpoint inhibitor development for glioma patients.