Gene Expression Biomarker Analysis

RNA biomarker discovery, profiling, and analytical validation

What is a biomarker in gene expression analysis? Gene expression analysis provides a powerful way to identify changes in gene expression that correlate with a phenotype or risk. We call these changes transcriptional, or RNA, biomarkers. Analysis of gene expression biomarkers involves their discovery and research screening/profiling, and analytical validation/verification. This is becoming increasingly important as studies link gene expression biomarkers to various diseases.

Solutions for each stage of gene expression biomarker analysis ›
Download poster for eQTL analysis workflow ›

Biopharma laboratories process a large volume of data from information management systems, which is why bioinformatic tools and concordance of data across technology platforms are essential components of the biomarker discovery and analytical validation process, ensuring that all systems are connected, accessible, and reliable.

Applied Biosystems solutions enable comprehensive, high-quality, reproducible, and scalable gene- and transcriptome-level analysis across platforms using integrated data tools to help identify accurate biomarker signatures to translate discovery into potential future clinical utility.


Poster: Complete workflow for discovery and verification of eQTLs in lung adenocarcinoma

Genome-wide association studies (GWAS) have revealed associations between gene expression levels in tumors and commonly inherited genetic variants profiled in the same subjects. Since the majority of inherited cancer risk variants as identified by GWAS are in noncoding regions, expression quantitative trait loci (eQTL) analysis of cancer tissues has become extremely important. This poster demonstrates a complete workflow from discovery and research screening to verification of eQTLs in lung adenocarcinoma.

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The importance of concordant data across multiple technology platforms

RNA sequencing (RNA-Seq), gene expression microarrays, and qPCR provide comprehensive solutions for gene expression analysis and biomarker discovery, screening/profiling, and verification. The different RNA analysis platforms measure absolute quantities of transcripts by using very different methods and algorithms, making comparisons of absolute expression levels complicated. However, concordance of data is essential so that results can be compared across platforms and without having to repeat experiments, minimizing error and freeing up time and instruments in the lab. Since qPCR produces relative gene expression measurements, comparing gene expression differences between samples with qPCR is the most relevant approach for benchmarking RNA-Seq and gene expression microarrays.

Applied Biosystems TaqMan Gene Expression assays have long been considered the gold standard for studying gene expression, providing a wide dynamic range of greater than six logarithmic units of expression levels, high sensitivity, and high specificity. Figures 1 and 2 below show concordance of data generated using TaqMan Assays with the Ion AmpliSeq Transcriptome Human Gene Expression Kit and Applied Biosystems Clariom D Assay results for differentially expressed genes in two reference RNA samples. The same set of 70 genes was investigated across all three platforms. Note the high degree of concordance between the two comparisons based on differential expression status (i.e., differentially expressed or not differentially expressed).

Figure 1

Figure 1. Fold-change correlation between the Ion AmpliSeq Transcriptome Human Gene Expression Kit and TaqMan Gene Expression assays. The scatter plot shows that the data sets are highly correlated. Ion AmpliSeq data were analyzed on Torrent Suite Software, and qPCR data were analyzed on Connect, the cloud-based platform from Thermo Fisher Scientific, using the RQ app.

Figure 2

Figure 2. Fold-change correlation between a Clariom D Assay and TaqMan Gene Expression assays. The scatter plot shows that the data sets are highly correlated. Clariom D Assay data were analyzed on TAC 4.0, and qPCR data were analyzed on the Connect platform using the RQ app.

The importance of eQTLs as biomarkers

Expression quantitative trait loci (eQTLs) are genetic variants associated with changes in gene expression that have become increasingly important in connecting genome-wide associated studies (GWAS) results to molecular mechanisms of various diseases. eQTLs are identified by linking variations in transcript abundance with variations in genotypes (Figure 3A). Typically, this involves collecting transcriptomic and genomic variant data from samples in a large cohort, and correlating the difference in gene mRNA levels with SNP genotypes in individuals.

eQTLs can fall into two categories. cis-eQTLs are SNPs that are proximal to a target locus (Figure 3B). Proximity is variably defined, but typically cis-eQTLs fall within 200 kb–1 Mb of the transcriptional start site of a gene. Thus, these are usually thought of as SNPs that fall within the promotor, enhancer, or other transcriptional regulatory sequences. The other type of eQTL, trans-eQTLs, are usually genetically unlinked and are thought to regulate expression of a locus from a distance. For example, a trans-eQTL could be a SNP in a transcriptional activating protein that lowers, but does not eliminate, the DNA-binding affinity of the protein to all binding sites (Figure 3C). eQTLs can also be tissue specific—a SNP that affects the expression of a gene in one tissue may have no effect in a different tissue.

Figure 1

Figure 3. How eQTLs affect expression levels of genes. (A) In a wild type individual, a transcription factor (TF) binds to the controlling region of two genes, resulting in normal transcript levels. (B) An individual with a cis-eQTL can have a SNP in the region bound by the transcription factor that reduces (but may not eliminate) transcript levels of gene B while leaving levels of gene A unaffected. (C) In an individual with a trans-eQTL, a SNP in the DNA-binding motif of the transcription factor reduces (but may not eliminate) its affinity to all binding sites, resulting in reduced expression of multiple genes.

A link between the GWAS-identified SNP locus and eQTL suggests that differential expression of the gene affected by the eQTL could contribute to the trait. Once a putative link has been established, verification of the link is needed. This could be done by research screening the transcriptome and genome of even more samples; however, this could become unwieldy and expensive. Alternatively, an orthogonal technology, such as qPCR, could be used to confirm the association of the SNP genotype and mRNA levels of the affected gene in a targeted manner. This method has the potential to deliver information on targeted associations in a cost-effective approach.

The right tools for the job: find your ideal match

Which gene expression technologies are right for you?

Analysis type


Verified solutions

Biomarker discovery

Transcriptome: exon-level discoveryAnalyze the complete set of RNA transcripts (coding, splice variants, and lncRNA) produced by the genome
Transcriptome: gene-level discovery (transcripts)Analyze complete set of coding RNA transcripts produced by the genome or interrogate well-annotated genes
Alternative splicingEvaluate eukaryotic gene regulation at the RNA processing level in which different mRNA molecules (isoforms/variants) are produced from the same primary transcript
Long noncoding RNA (lncRNA)Study non–protein-coding transcripts (>200 nucleotides), which are abundant in the mammalian transcriptome, have been shown to regulate transcription, and have been implicated in a wide range of developmental processes and diseases 
miRNAProfile small noncoding RNA (~22 nucleotides) generated from a hairpin structure on a precursor RNA transcribed from a particular gene, and that function in RNA silencing and posttranscriptional regulation of gene expression
Fusion genes and/or fusion transcriptsInterrogate hybrid genes formed from two previously separate genes that can give rise to hybrid proteins or to misregulation of transcription

Biomarker profiling

All RNA TypesEvaluate the activity of many genes at once to create a global picture of expression patterns
miRNAProfile small noncoding RNA (~22 nucleotides) generated from a hairpin structure on a precursor RNA transcribed from a particular gene, and that function in RNA silencing and posttranscriptional regulation of gene expression

Biomarker analytical validation/verification

All RNA typesConfirm gene expression profiles of biological samples by reliable complementary techniques 

Featured gene expression technologies

For Research Use Only. Not for use in diagnostic procedures.