A 3-Gene-Based Diagnostic Signature in Alzheimer’s Disease

2021 ◽  
pp. 1-8
Author(s):  
Huimin Wang ◽  
Yanqiu Zhang ◽  
Chengyao Zheng ◽  
Songqi Yang ◽  
Xiuju Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Diagnostic Methods ◽  
Sample Type ◽  
Ppi Network

<b><i>Background:</i></b> Alzheimer’s disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD. <b><i>Methods:</i></b> All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type. <b><i>Results:</i></b> Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model. <b><i>Conclusion:</i></b> The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.

A three-gene-based Type 1 diabetes diagnostic signature

2020 ◽  
Vol 26 ◽  
Author(s):  
Rongrong Wang ◽  
Yanan Zhou ◽  
Yan Zhang ◽  
Shaoqing Li ◽  
Runzhou Pan ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Bioinformatic Analysis ◽  
Diagnostic Methods ◽  
Sample Type ◽  
Ppi Network ◽  
Hub Genes

Background:: Type 1 diabetes is a chronic autoimmune disease featured by insulin deprivation caused by pancreatic β-cell loss, followed by hyperglycaemia. Objective: Currently, there is no cure for this disease in clinical treatment, and patients have to accept a lifelong injection of insulin. The exploration of potential diagnosis biomarkers through analysis of mass data by bioinformatic tools and machine learning is important for Type 1 diabetes. Methods: We collected two mRNA expression datasets of Type 1 diabetes peripheral blood samples from GEO, screened out differentially expressed genes (DEGs) by R software, conducted GO and KEGG pathway enrichment using the DEGs. And the STRING database and Cytoscape were used to build PPI network and predict hub genes. We constructed a Logistic regression model by using the hub genes to assess sample type. Results: Bioinformatic analysis of GEO dataset revealed 92 and 75 DEGs in GSE50098 and GSE9006 datasets, separately, and 10 overlapping DEGs. PPI network of these 10 DEGs showed 7 hub genes, namely EGR1, LTF, CXCL1, TNFAIP6, PGLYRP1, CHI3L1 and CAMP. We built a Logistic regression basing on these hub genes and optimized the model to 3 genes (LTF, CAMP and PGLYRP1) based Logistic model. The values of area under curve (AUC) of training set GSE50098 and testing set GSE9006 were 0.8452 and 0.8083, indicating the efficacy of this model. Conclusion: Integrated bioinformatic analysis of gene expression in Type 1 diabetes and the effective Logistic regression model built in our study may provide promising diagnostic methods for Type 1 diabetes.

Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people

2017 ◽  
Vol 29 (9) ◽  
pp. 1535-1541 ◽  
Author(s):  
Shih-Wei Lai ◽  
Cheng-Li Lin ◽  
Kuan-Fu Liao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Logistic Regression ◽  
Older People ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Population Based ◽  
Multivariable Logistic Regression Model ◽  
Retrospective Case ◽  
Taiwan National Health Insurance

ABSTRACTBackground:The purpose of this paper was to examine whether glaucoma could be a non-memory manifestation of Alzheimer's disease in older people.Methods:We conducted a population-based, retrospective, case-control study to analyze the database of the Taiwan National Health Insurance Program. There were 1,351 subjects ≥65 years old with newly diagnosed Alzheimer's disease as the cases, and 5,329 subjects without any type of dementias as the controls during 2000–2011. The odds ratio (OR) and 95% confidence interval (CI) for the risk of Alzheimer's disease associated with glaucoma was estimated by the multivariable unconditional logistic regression model.Results:After controlling for confounders, the multivariable logistic regression model demonstrated that the adjusted OR of Alzheimer's disease was 1.50 in subjects with glaucoma (95% CI 1.19, 1.89), compared to subjects without glaucoma.Conclusions:Older people with glaucoma are associated with 1.5-fold increased odds of Alzheimer's disease in Taiwan. Glaucoma may be a non-memory manifestation of Alzheimer's disease in older people. Further research is needed to confirm this issue.

Screening of Potential Biomarkers and Therapeutic Targets for T2DM-related Alzheimer's Disease Based on Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhengqiang He
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Actin Filament ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Protein Protein Interaction

Abstract More and more studies have suggested that type 2 diabetes mellitus (T2DM) was susceptible to trigger Alzheimer’s disease(AD), but the common underlying mechanism were unclear. We want to perform bioinformatic analysis with public databases. The T2DM dataset GSE95849 and AD dataset GSE97760 were selected from Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) and the communal DEGs between the two diseases, which perform to the enrichment analysis, protein-protein interaction (PPI) network analysis, correlation analysis.We found 255 communal DEGs between T2DM and AD. They were enriched in negative regulation of actin filament depolymerization and regulation of actin filament depolymerization. Top 5 hub genes which identified from the PPI network were enriched in autophagy. The actin filament and autophagy may be the key association between the two diseases.

scholarly journals Identification of the Hub Genes in Alzheimer’s Disease

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huiwen Gui ◽  
Qi Gong ◽  
Jun Jiang ◽  
Mei Liu ◽  
Huanyin Li
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Elderly ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Geo Database

Purpose. Alzheimer’s disease (AD) is considered to be the most common neurodegenerative disease and also one of the major fatal diseases affecting the elderly, thus bringing a huge burden to society. Therefore, identifying AD-related hub genes is extremely important for developing novel strategies against AD. Materials and Methods. Here, we extracted the gene expression profile GSE63061 from the National Center for Biotechnology Information (NCBI) GEO database. Once the unverified gene chip was removed, we standardized the microarray data after quality control. We utilized the Limma software package to screen the differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network using the STRING database. Result. We screened 2169 DEGs, comprising 1313 DEGs with upregulation and 856 DEGs with downregulation. Functional enrichment analysis showed that the response of immune, the degranulation of neutrophils, lysosome, and the differentiation of osteoclast were greatly enriched in DEGs with upregulation; peptide biosynthetic process, translation, ribosome, and oxidative phosphorylation were dramatically enriched in DEGs with downregulation. 379 nodes and 1149 PPI edges were demonstrated in the PPI network constructed by upregulated DEGs; 202 nodes and 1963 PPI edges were shown in the PPI network constructed by downregulated DEGs. Four hub genes, including GAPDH, RHOA, RPS29, and RPS27A, were identified to be the newly produced candidates involved in AD pathology. Conclusion. GAPDH, RHOA, RPS29, and RPS27A are expected to be key candidates for AD progression. The results of this study can provide comprehensive insight into understanding AD’s pathogenesis and potential new therapeutic targets.

scholarly journals A 2 miRNAs-based signature for the diagnosis of atherosclerosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiujiang Han ◽  
Huimin Wang ◽  
Yongjian Li ◽  
Lina Liu ◽  
Sheng Gao
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cross Validation ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Multivariate Regression Analysis ◽  
Functional Enrichment ◽  
Validation Method ◽  
Model Based

Abstract Background Atherosclerosis (AS) is a leading cause of vascular disease worldwide. MicroRNAs (miRNAs) play an essential role in the development of AS. However, the miRNAs-based biomarkers for the diagnosis of AS are still limited. Here, we aimed to identify the miRNAs significantly related to AS and construct the predicting model based on these miRNAs for distinguishing the AS patients from healthy cases. Methods The miRNA and mRNA expression microarray data of blood samples from patients with AS and healthy cases were obtained from the GSE59421 and GSE20129 of Gene Expression Omnibus (GEO) database, respectively. Weighted Gene Co-expression Network Analysis (WGCNA) was performed to evaluate the correlation of the miRNAs and mRNAs with AS and identify the miRNAs and mRNAs significantly associated with AS. The potentially critical miRNAs were further optimized by functional enrichment analysis. The logistic regression models were constructed based on these optimized miRNAs and validated by threefold cross-validation method. Results WGCNA revealed 42 miRNAs and 532 genes significantly correlated with AS. Functional enrichment analysis identified 12 crucial miRNAs in patients with AS. Moreover, 6 miRNAs among the identified 12 miRNAs, were selected using a stepwise regression model, in which four miRNAs, including hsa-miR-654-5p, hsa-miR-409-3p, hsa-miR-485-5p and hsa-miR-654-3p, were further identified through multivariate regression analysis. The threefold cross-validation method showed that the AUC of logistic regression model based on the four miRNAs was 0.7308, 0.8258, and 0.7483, respectively, with an average AUC of 0.7683. Conclusion We identified a total of four miRNAs, including hsa-miR-654-5p and hsa-miR-409-3p, are identified as the potentially critical biomarkers for AS. The logistic regression model based on the identified 2 miRNAs could reliably distinguish the patients with AS from normal cases.

scholarly journals Detecting Depression Using an Ensemble Logistic Regression Model Based on Multiple Speech Features

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Haihua Jiang ◽  
Bin Hu ◽  
Zhenyu Liu ◽  
Gang Wang ◽  
Lan Zhang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Disease Burden ◽  
Logistic Regression Model ◽  
Diagnostic Methods ◽  
Base Classifier ◽  
Speech Features ◽  
Speech Classification ◽  
Sensitivity Specificity ◽  
Chinese Subjects

Early intervention for depression is very important to ease the disease burden, but current diagnostic methods are still limited. This study investigated automatic depressed speech classification in a sample of 170 native Chinese subjects (85 healthy controls and 85 depressed patients). The classification performances of prosodic, spectral, and glottal speech features were analyzed in recognition of depression. We proposed an ensemble logistic regression model for detecting depression (ELRDD) in speech. The logistic regression, which was superior in recognition of depression, was selected as the base classifier. This ensemble model extracted many speech features from different aspects and ensured diversity of the base classifier. ELRDD provided better classification results than the other compared classifiers. A technique for identifying depression based on ELRDD, ELRDD-E, was here suggested and tested. It offered encouraging outcomes, revealing a high accuracy level of 75.00% for females and 81.82% for males, as well as an advantageous sensitivity/specificity ratio of 79.25%/70.59% for females and 78.13%/85.29% for males.

scholarly journals Identification of Potential miRNA-mRNA Regulatory Network Contributing to Hypertrophic Cardiomyopathy (HCM)

2021 ◽  
Vol 8 ◽  
Author(s):  
Lin Wang ◽  
Fengmin Lu ◽  
Jing Xu
Keyword(s):  
Logistic Regression ◽  
Hypertrophic Cardiomyopathy ◽  
Regression Model ◽  
Regulatory Network ◽  
Logistic Regression Model ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Expression Omnibus ◽  
Diagnostic Methods ◽  
Go Annotation

Background: Hypertrophic cardiomyopathy (HCM) is a myocardial disease with unidentified pathogenesis. Increasing evidence indicated the potential role of microRNA (miRNA)-mRNA regulatory network in disease development. This study aimed to explore the miRNA-mRNA axis in HCM.Methods: The miRNA and mRNA expression profiles obtained from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed miRNAs (DEMs) and genes (DEGs) between HCM and normal samples. Target genes of DEMs were determined by miRTarBase. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify biological functions of the DEGs and DEMs. miRNA-mRNA regulatory network was constructed to identify the hub genes and miRNAs. Logistic regression model for HCM prediction was established basing on the network.Results: A total of 224 upregulated and 366 downregulated DEGs and 10 upregulated and 14 downregulated DEMs were determined. We identified 384 DEM-targeted genes, and 20 of them were overlapped with the DEGs. The enriched functions include extracellular structure organization, organ growth, and phagosome and melanoma pathways. The four miRNAs and three mRNAs, including hsa-miR-373, hsa-miR-371-3p, hsa-miR-34b, hsa-miR-452, ARHGDIA, SEC61A1, and MYC, were identified through miRNA-mRNA regulatory network to construct the logistic regression model. The area under curve (AUC) values over 0.9 suggested the good performance of the model.Conclusion: The potential miRNA-mRNA regulatory network and established logistic regression model in our study may provide promising diagnostic methods for HCM.

scholarly journals A Five-Genes Based Diagnostic Signature for Sepsis-Induced ARDS

2021 ◽  
Vol 27 ◽  
Author(s):  
Ning Xu ◽  
Hui Guo ◽  
Xurui Li ◽  
Qian Zhao ◽  
Jianguo Li
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Support Vector ◽  
Pathway Enrichment Analysis ◽  
Blood Samples ◽  
Svm Model

Background: Acute respiratory distress syndrome (ARDS) is a frequent and serious complication of sepsis without specific and sensitive diagnostic signatures.Methods: The mRNA profiles, including 60 blood samples with sepsis-induced ARDS and 86 blood samples with sepsis alone, were obtained from the Gene Expression Omnibus (GEO). The differently expressed genes (DEGs) were analyzed by limma package of R language. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out using the clusterProfiler package of R. Eventually, multivariate logistic regression model was established through the glm function of R, and support vector machine (SVM) model was constructed via the e1071 package of R.Results: A total of 242 DEGs in GSE32707 and 102 DEGs in GSE66890 were identified. Notably, five genes exhibited significant differences between the two datasets and were considered to be closely associated with the occurrence of ARDS induced by sepsis. Furthermore, functional enrichment analysis based on the DEGs showed there were 80 overlapped GO terms and one KEGG pathway which were significantly enriched in the two datasets. The logistic regression model and SVM model constructed could efficiently distinguish sepsis patients with or without ARDS.Conclusion: In brief, our study suggested that NKG7, SPTA1, FGL2, RGS2, and IFI27 might be potential diagnostic signatures for sepsis-induced ARDS, which contributed to the future exploration in mechanism of ARDS occurrence and development.

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