Simulation-selection-extrapolation: Estimation in high-dimensional errors-in-variables models

Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models

Background

Despite tremendous progress in diagnosis and prediction of Alzheimer's disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.

Methods

By differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.

Results

Screened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.

Conclusion

The LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

Simulation extrapolation method for measurement error: A review

Measurement error is pervasive in statistics due to the non-availability of authentic data. The reasons for measurement error mainly relate to cost, convenience, and human error. Measurement error can result in non-negligible bias due to attenuated estimates, reduced power of statistical tests, and lower coverage probabilities of the coefficient estimators in a regression model. Several methods have been proposed to correct for measurement error, all of which can be grouped into two broad categories based on the underlying model-functional and structural. Functional models provide flexibility and robustness to estimators by placing minimal or no assumptions on the distribution of the mismeasured covariate or by treating them as a fixed entity, as opposed to a structural model which treats the underlying mismeasured covariates as random with a specified structure. The simulation extrapolation method is one method that is used for the partial correction of measurement error in both structural and functional models. Reviews of measurement error correction techniques are available in the literature. However, none of the previously conducted reviews has exclusively focused on simulation extrapolation and its application in continuous measurement error models, despite its widespread use and ease of application. We attempt to close this gap in the literature by highlighting its development over the past two and a half decades.

Screening methods for linear errors-in-variables models in high dimensions

Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a small number of subjects. One common approach to analyzing such high-dimensional data is to use linear errors-in-variables models; however, current methods for fitting such models are computationally expensive. In this paper, we present two efficient screening procedures, namely corrected penalized marginal screening and corrected sure independence screening, to reduce the number of variables for final model building. Both screening procedures are based on fitting corrected marginal regression models relating the outcome to each contaminated covariate separately, which can be computed efficiently even with a large number of features. Under mild conditions, we show that these procedures achieve screening consistency and reduce the number of features substantially, even when the number of covariates grows exponentially with sample size. Additionally, if the true covariates are weakly correlated, we show that corrected penalized marginal screening can achieve full variable selection consistency. Through a simulation study and an analysis of gene expression data for bone mineral density of Norwegian women, we demonstrate that the two new screening procedures make estimation of linear errors-in-variables models computationally scalable in high dimensional settings, and improve finite sample estimation and selection performance compared with estimators that do not employ a screening stage. This article is protected by copyright. All rights reserved.

Classifiers for Predicting Coronary Artery Disease Based on Gene Expression Profiles in Peripheral Blood Mononuclear Cells

Objective

Coronary artery disease (CAD) is a serious global health concern. Current diagnostic methods for CAD involve risk to the patient and are costly, so better diagnostic tools are needed. We defined four classifiers based on gene expression profiles in peripheral blood mononuclear cells and determined their potential for CAD detection.

Methods

We downloaded a CAD-related data set (GSE113079) from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in peripheral blood mononuclear cells between CAD samples and healthy controls. DEGs were analyzed for functional enrichment. To create a robust CAD classifier, DEGs were identified by feature selection using the principal component analysis. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, and support vector machine (SVM) models were created. Gene set variation analysis (GSVA) score and gene set enrichment analysis (GSEA) were also conducted. The performance of the models was evaluated in terms of the area under receiver operating characteristic curves (AUC).

Results

In the training set, we found 135 up-regulated genes and 104 down-regulated genes in CAD patients compared with controls. The DEGs were involved in some pathways associated with CAD, such as pathways involving calcium and interleukin-17 signaling. Twenty genes were identified as optimal features and used to generate the logistic classifier based on LASSO. The AUC for the classifier was 1.00 in the training set and 0.997 in the test set. Using the 20 DEGs, SVM and random forest classifiers were also generated and showed high diagnostic efficacy, with respective AUCs of 0.997 and 1.00 against the training set. A GSVA score was also established using the top 20 significant DEGs, which showed an AUC of 0.971 in the training set and 0.989 in the test set. Furthermore, GSEA showed autophagy and the proteasome to be major pathways involving the DEGs.

Conclusion

We identified a set of genes specific for CAD whose expression can be measured non-invasively. Using these genes, we defined four diagnostic classifiers using multiple methods.