Biomarker detection and categorization in ribonucleic acid sequencing meta-analysis using Bayesian hierarchical models

Microarray meta-analysis reveals IL6 and p38β/MAPK11 as potential targets of hsa-miR-124 in endothelial progenitor cells: Implications for stent re-endothelization in diabetic patients

Circulating endothelial progenitor cells (EPCs) play an important role in the repair processes of damaged vessels, favoring re-endothelization of stented vessels to minimize restenosis. EPCs number and function is diminished in patients with type 2 diabetes, a known risk factor for restenosis. Considering the impact of EPCs in vascular injury repair, we conducted a meta-analysis of microarray to assess the transcriptomic profile and determine target genes during the differentiation process of EPCs into mature ECs. Five microarray datasets, including 13 EPC and 12 EC samples were analyzed, using the online tool ExpressAnalyst. Differentially expressed genes (DEGs) analysis was done by Limma method, with an | log2FC| > 1 and FDR < 0.05. Combined p-value by Fisher exact method was computed for the intersection of datasets. There were 3,267 DEGs, 1,539 up-regulated and 1,728 down-regulated in EPCs, with 407 common DEGs in at least four datasets. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed enrichment for terms related to “AGE-RAGE signaling pathway in diabetic complications.” Intersection of common DEGs, KEGG pathways genes and genes in protein-protein interaction network (PPI) identified four key genes, two up-regulated (IL1B and STAT5A) and two down-regulated (IL6 and MAPK11). MicroRNA enrichment analysis of common DEGs depicted five hub microRNA targeting 175 DEGs, including STAT5A, IL6 and MAPK11, with hsa-miR-124 as common regulator. This group of genes and microRNAs could serve as biomarkers of EPCs differentiation during coronary stenting as well as potential therapeutic targets to improve stent re-endothelization, especially in diabetic patients.

Fused inverse-normal method for integrated differential expression analysis of RNA-seq data

Background

Use of next-generation sequencing technologies to transcriptomics (RNA-seq) for gene expression profiling has found widespread application in studying different biological conditions including cancers. However, RNA-seq experiments are still small sample size experiments due to the cost. Recently, an increased focus has been on meta-analysis methods for integrated differential expression analysis for exploration of potential biomarkers. In this study, we propose a p-value combination method for meta-analysis of multiple independent but related RNA-seq studies that accounts for sample size of a study and direction of expression of genes in individual studies.

Results

The proposed method generalizes the inverse-normal method without an increase in statistical or computational complexity and does not pre- or post-hoc filter genes that have conflicting direction of expression in different studies. Thus, the proposed method, as compared to the inverse-normal, has better potential for the discovery of differentially expressed genes (DEGs) with potentially conflicting differential signals from multiple studies related to disease. We demonstrated the use of the proposed method in detection of biologically relevant DEGs in glioblastoma (GBM), the most aggressive brain cancer. Our approach notably enabled the identification of over-expressed tumour suppressor gene RAD51 in GBM compared to healthy controls, which has recently been shown to be a target for inhibition to enhance radiosensitivity of GBM cells during treatment. Pathway analysis identified multiple aberrant GBM related pathways as well as novel regulators such as TCF7L2 and MAPT as important upstream regulators in GBM.

Conclusions

The proposed meta-analysis method generalizes the existing inverse-normal method by providing a way to establish differential expression status for genes with conflicting direction of expression in individual RNA-seq studies. Hence, leading to further exploration of them as potential biomarkers for the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04859-9.

Meta-Analysis of Transcriptome-Wide Association Studies across 13 Brain Tissues Identified Novel Clusters of Genes Associated with Nicotine Addiction

Genome-wide association studies (GWAS) have identified and reproduced thousands of diseases associated loci, but many of them are not directly interpretable due to the strong linkage disequilibrium among variants. Transcriptome-wide association studies (TWAS) incorporated expression quantitative trait loci (eQTL) cohorts as a reference panel to detect associations with the phenotype at the gene level and have been gaining popularity in recent years. For nicotine addiction, several important susceptible genetic variants were identified by GWAS, but TWAS that detected genes associated with nicotine addiction and unveiled the underlying molecular mechanism were still lacking. In this study, we used eQTL data from the Genotype-Tissue Expression (GTEx) consortium as a reference panel to conduct tissue-specific TWAS on cigarettes per day (CPD) over thirteen brain tissues in two large cohorts: UK Biobank (UKBB; number of participants (N) = 142,202) and the GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN; N = 143,210), then meta-analyzing the results across tissues while considering the heterogeneity across tissues. We identified three major clusters of genes with different meta-patterns across tissues consistent in both cohorts, including homogenous genes associated with CPD in all brain tissues; partially homogeneous genes associated with CPD in cortex, cerebellum, and hippocampus tissues; and, lastly, the tissue-specific genes associated with CPD in only a few specific brain tissues. Downstream enrichment analyses on each gene cluster identified unique biological pathways associated with CPD and provided important biological insights into the regulatory mechanism of nicotine dependence in the brain.

Biomarker Categorization in Transcriptomic Meta-Analysis by Concordant Patterns With Application to Pan-Cancer Studies

With the increasing availability and dropping cost of high-throughput technology in recent years, many-omics datasets have accumulated in the public domain. Combining multiple transcriptomic studies on related hypothesis via meta-analysis can improve statistical power and reproducibility over single studies. For differential expression (DE) analysis, biomarker categorization by DE pattern across studies is a natural but critical task following biomarker detection to help explain between study heterogeneity and classify biomarkers into categories with potentially related functionality. In this paper, we propose a novel meta-analysis method to categorize biomarkers by simultaneously considering the concordant pattern and the biological and statistical significance across studies. Biomarkers with the same DE pattern can be analyzed together in downstream pathway enrichment analysis. In the presence of different types of transcripts (e.g., mRNA, miRNA, and lncRNA, etc.), integrative analysis including miRNA/lncRNA target enrichment analysis and miRNA-mRNA and lncRNA-mRNA causal regulatory network analysis can be conducted jointly on all the transcripts of the same category. We applied our method to two Pan-cancer transcriptomic study examples with single or multiple types of transcripts available. Targeted downstream analysis identified categories of biomarkers with unique functionality and regulatory relationships that motivate new hypothesis in Pan-cancer analysis.

A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data

As the sequencing cost continued to drop in the past decade, RNA sequencing (RNA-seq) has replaced microarray to become the standard high-throughput experimental tool to analyze transcriptomic profile. As more and more datasets are generated and accumulated in the public domain, meta-analysis to combine multiple transcriptomic studies to increase statistical power has received increasing popularity. In this article, we propose a Bayesian hierarchical model to jointly integrate microarray and RNA-seq studies. Since systematic fold change differences across RNA-seq and microarray for detecting differentially expressed genes have been previously reported, we replicated this finding in several real datasets and showed that incorporation of a normalization procedure to account for the bias improves the detection accuracy and power. We compared our method with the popular two-stage Fisher's method using simulations and two real applications in a histological subtype (invasive lobular carcinoma) of breast cancer comparing PR+ versus PR- and early-stage versus late-stage patients. The result showed improved detection power and more significant and interpretable pathways enriched in the detected biomarkers from the proposed Bayesian model.