Cancer Research

RNA Biomarker Research

Suitable biomarkers are key to improving cancer screening, diagnosis and monitoring. Which biomarker will prove to be the best?

The discovery of RNA biomarkers and their development toward clinical use is challenging. Those challenges we must overcome, because the ultimate goals of better screening, prognosis and support for people who get cancer are what matters. Working together, we can ease your path to cancer biomarker discoveries and get relevant results.

Our portfolio of Sample to Insight workflows for cancer miRNA, cancer gene expression, transcriptome or functionality studies is comprehensive. Here, we give insights into each step of the most common workflows and make it easier to find the information and products you need. From formalin-fixed, paraffin-embedded (FFPE) and liquid biopsy samples to RNA-seq and discovering new cancer pathways, we provide you with proven solutions to increase your efficiency and accelerate your RNA biomarker research. We share a common goal. Let’s work to conquer cancer. Together.

Sample preparation

RNA stabilization and isolation

If you are isolating RNA from liquid biopsy or FFPE samples, you will know that there are many challenges. Storage conditions, pretreatment and the fragile nature of RNA mean that we have to plan and prepare to get the best possible results. We have put some useful resources together to help you get started or take a deeper dive into the details that make all the difference to your sample preparation when working with RNA biomarkers.

Challenges in RNA isolation for biomarker research

Watch this webinar to learn how to get high yields of high-quality RNA from challenging liquid biopsy and FFPE samples.

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cfDNA collection/stabilization and sample preparation

Guides and posters

Guidelines for profiling biofluid miRNAs

Maximize your miRNA sequencing and qPCR success from liquid biopsy samples, including blood, serum/plasma, urine, CSF and exosomes.

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Poster: A new workflow for FFPE samples

New workflows with high degrees of automation for convenience and reproducibility in applications such as biomarker research.

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Goodbye phenol, hello high yields!

Advanced phenol-free chemistry for in-depth miRNA analysis brings safety and convenience to miRNA purification.

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What is the smallest number of cells you can isolate miRNA from?

Technically there should be no lower limit. We have data on our website showing reproducible extraction from as few as cells.

What is DV200?

DV200 is the percentage of RNA fragments longer than 200 nucleotides that is determined by electropherogram. That allows us to compare the preservation of RNA integrity between different FFPE samples. For more details, watch the webinar.

What is the source of cell-free RNA in plasma and other biofluids?

Within extracellular vesicles, the RNA comes from metabolically active cells, while RNA outside of extracellular vesicles comes mainly from dead or dying cells.

What can I use to isolate RNA smaller than 200 nucleotides?

For miRNA isolation from cells and tissues, we have developed the miRNeasy Mini Kit and the miRNeasy 96 Kit. We also have the PAXgene Blood miRNA and Tissue miRNA kits for isolation from blood stored in PAXgene Blood RNA tubes and PAXgene Tissue Containers, respectively. Other miRNA isolation supplementary protocols can also be found by searching our comprehensive protocols.

Small RNA sequencing across diverse biofluids identifies optimal methods for exRNA isolation

Srinivasan S, et al. Cell. 2019; 177(2):446–462.e16.

Phospho-RNA-seq: A modified small RNA-seq method that reveals circulating mRNA and lncRNA fragments as potential biomarkers in human plasma

Giraldez MD, et al. EMBO J. 2019; 38(11):e101695.

Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma

Arroyo JD, et al. Proc Natl Acad Sci USA. 2011; 108(12):5003–8.

Comparison of pre-analytical FFPE sample preparation methods and their impact on massively parallel sequencing in routine diagnostics

Heydt C, et al. PLoS One. 2014; 9(8):e104566.

Ultraviolet C radiation influences the robustness of RNA integrity measurement

Under C, et al. Electrophoresis. 2015; 36(17):2072–81.

RNA analysis of cancer predisposing genes in formalin-fixed paraffin-embedded tissue determines aberrant splicing

Jansen AM, et al. Eur J Hum Genet. 2018; 26(8):1143–1150.

Five technologies for detecting the EGFR T790M mutation in the circulating cell-free DNA of patients with non-small cell Lung cancer: A comparison

Chen YL, et al. Front Oncol. 2019 ; 9:631.

Microfluidics for minute DNA sample analysis: Open challenges for genetic testing of cell-free circulating DNA in blood plasma

Malbec R, et al. Micro Nano Eng. 2018; 1:25–32.

Determinants of RNA quality from FFPE samples

Ahlfen S, et al. PLoS One. 2007; 2(12):e1261.

DV200 index for assessing RNA integrity in next-generation sequencing

Matsubara T, et al. Biomed Res Int. 2020; 2020:9349132.

Quality control

How can you be confident that your RNA analysis will work when you set up your reactions or send your samples to a core lab? Various factors during RNA sample collection, storage and isolation influence the RNA that you get as templates for analysis. And small differences in quality can have a significant impact on your results. That’s where RNA quality control comes in and gives you peace of mind. QC methods can vary and they are explored and explained in the webinar and other resources we have gathered for you here.

RNA quality control

RNA quality control explained: Technologies and their limitations for RNA quality assessment are explained by Daniel Lehmann, Associate Director, Life Science Instruments.

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Guides and posters

Standardized determination of RNA integrity

This technical note compares the RNA integrity score (RIS) system used by QIAxcel technology with the RNA integrity number (RIN) used by the Bioanalyzer 2100 and the RIN equivalent (RINe) of the TapeStation.

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Power up your sample quality control for NGS

Learn about the advantages of cartridge-based capillary gel electrophoresis for quality control of RNA for next-generation sequencing library assessment.

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How can we measure the quantity of RNA we have isolated?

RNA quantities can be measured by different methods including, UV/Vis spectrophotometry, fluorometry, qRT-PCR and digital PCR.

How can we check contamination or measure the purity of our RNA after extraction?

The classic method uses spectrophotometric absorbance reading (A₂₆₀/A₂₈₀, A₂₆₀/A₂₃₀).

PCR can also be used with inhibition assays or by adding spike-in controls. Electrophoresis can show the presence or absence of short fragments and discrete rRNA bands. See our webinar for details.

How can we check degradation or see the integrity of extracted RNA?

RNA integrity can be seen by gel electrophoresis – ribosomal RNA should appear as sharp bands. Alternatively, integrity can be confirmed by measuring RIS, RIN, RINe or RQN (see Technical Note) or DV200.

RT-PCR can also be used to check the quality of isolated RNA – quality control assays can be developed, and the upper limit of the RT-PCR detection checked.

Products

Check out kits for RNA sample prep and quality control

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Next-generation sequencing

RNA and miRNA sequencing

Next-generation sequencing (NGS) is a powerful technique in RNA biomarker discovery and characterization, but it can be complicated and challenging to set up in your lab. If you have a high throughput of samples, we can help you. We are working to make NGS as easy as possible – our aim is for NGS to be as easy as PCR, thereby democratizing NGS and supporting smaller cancer research labs in getting started to ensure great results. Whether you are analyzing gene expression or fusion genes, with any number of samples, we will accelerate your RNA biomarker discovery with NGS solutions.

Tips and tricks for difficult samples

Working with difficult samples? Watch this webinar. Jonathan Schäfer, QIAGEN R&D, discusses strategies to tackle challenges posed by low-input and low-quality samples as well as FFPE and fragmented RNA.

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RNA-seq from FFPE samples

Finding RNA-seq from FFPE samples troublesome? See the case study: “Rescue of low RIN RNA from FFPE samples and improved RNA sequencing using QIAseq RNA-seq solutions.”

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RNA-seq data analysis

Now that you have successfully profiled gene expression using NGS as part of your biomarker discovery project, how should you best analyze the raw data to move forward? One way is to use an online data analysis tool

Expert data analysis – tips and tricks for RNA biomarker research

Listen as our expert George Quellhorst highlights how you can find the most statistically significant differentially expressed genes. He’ll help define the appropriate fold change and p-value filters to apply and which volcano plot and heatmap data visualizations to use. These tips and tricks will allow you to define a subset of the most correlative biomarkers for your follow-up screening and validation studies.

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Expert data analysis – tips and tricks for RNA biomarker research

Struggling to make sense of your RNA-seq data?

Take the stress out of data analysis. Fast-track your path to gene expression insights with our easy, web-based RNA-seq Analysis Portal.

Simply upload your sequence files into the portal and start your analysis. Go from FASTQ files to pathway analysis insights in hours instead of days.

Try our portal

QIAseq FastSelect Custom RNA Removal Kits

How can I assemble a workflow for miRNA analysis by next-generation sequencing?

Our workflow configurator can help you find products that are used in a workflow. Choose your sample type, “miRNA” as the analyte and “next-generation sequencing” to easily select products to cover the entire workflow. To see an example for FFPE samples, go to workflow configator.

Multisite evaluation of next-generation methods for small RNA quantification

Herbert ZT, et al. J Biomol Tech. 2020; 31(2):47–56.

PIWI-interacting RNAs are aberrantly expressed and may serve as novel biomarkers for diagnosis of lung adenocarcinoma

Li J, et al. Thorac Cancer. 2021;12(18):2468–2477.

Epigenetic regulation of triple negative breast cancer (TNBC) by TGF-β signaling

Vishnubalaji R, Alajez NM. Sci Rep. 2021;11(1):15410.

Products

Browse NGS kits and panels for RNA biomarker research

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Digital PCR

miRNA profiling

MicroRNAs (miRNAs) are extensively involved in cancer progression and suppression by regulating thousands of cancer-associated genes and circulating cell-free miRNAs have recently opened new opportunities for a non-invasive test for early cancer detection.

However, miRNA quantification in serum or plasma still lacks consistency and standardization. Turn to digital PCR if you want to overcome analytical difficulties with miRNA quantification. Digital PCR (dPCR) offers absolute quantification without the need for standard curves, with higher precision compared to qPCR. This is particularly important when detecting low-abundance miRNAs compared to qPCR. By accurately measuring circulating miRNA levels, dPCR is a valuable tool on your path to biomarker discovery for cancer detection.

Accurate miRNA quantification

Digital PCR allows you to overcome limitations when trying to quantify miRNA in samples with a high inhibitory burden or low nucleic acid content, In this webinar, our R&D Scientist Dr. Domenica Martorana explores dedicated assays that work in conjunction with the nanoplate-based QIAcuity Digital PCR System, demonstrating superior and reproducible results.

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Assays for dPCR-based miRNA analysis

miRCURY LNA miRNA PCR Assays enable extremely sensitive and specific miRNA quantification and are optimized with LNA technology for absolute quantification of miRNA expression changes with dPCR.

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Experiment setup on the QIAcuity

Get detailed information about setting up your miRNA analysis experiments for dPCR applications in this QIAcuity Applications Guide.

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Gene expression analysis

Changes in gene expression are an invaluable indicator that has greatly expanded our understanding of many biological functions. With dPCR, you can take this approach to the next level and overcome previous limitations. For example, what is the significance of low abundant targets or very small expression differences of 2-fold or less? You can now find out using dPCR.

The dPCR method provides more precise and reproducible data than qPCR due to its endpoint measurement and absolute quantification without the need for standard curves. This has major implications, especially when quantifying very low abundant targets that have either been diluted due to the high content of inhibitors and contaminants in the sample or have very low levels of expression.

Accurate and sensitive mRNA quantification

Performing a successful gene expression analysis on the QIAcuity Digital PCR System requires considering a few parameters, including sample input, dilutions and proper controls. In this webinar, our Senior Scientist Dr. Ronny Kellner explores how dPCR on the QIAcuity system enables highly sensitive and accurate quantification of mRNA targets, detecting even the smallest expression changes at the lowest concentrations. A case study will show how the combination of urine-based liquid biopsy and dPCR could drive the future of molecular characterization of bladder cancer.

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Dedicated assays for dPCR-based gene expression analysis

QuantiNova LNA PCR Assays are setting new standards in dPCR-based gene expression analysis. Explore the broadest selection of predesigned assays for accurate and sensitive detection of any human, mouse or rat mRNA or lncRNA – from general transcript detection to differentiation of specific transcript isoforms.

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High-resolution analysis – detect small changes in gene expression, down to 10%

How to perform a successful gene expression analysis on the QIAcuity Digital PCR System? What are the key considerations when quantifying mRNA and lncRNA targets, trying to detect even the smallest changes in gene expression at the lowest concentrations?

Read article

Experiment setup on the QIAcuity

Get detailed information about setting up your gene expression experiments for dPCR applications in this QIAcuity Applications Guide.

Get the guide

Can I use a different RT kit for cDNA synthesis of miRNAs?

No. The miRCURY LNA miRNA assays are only compatible with cDNA produced with the miRCURY LNA RT Kit.

Can I use the miRCURY SYBR Green chemistry also for dPCR?

The QIAcuity EG master mix is better suited to dPCR applications.

Are all qPCR-validated assays also validated for dPCR?

No. All assays validated for dPCR are flagged in GeneGlobe accordingly. However, all non-validated assays are in-silico-validated designs that might also be suitable for dPCR.

Can custom assay designs also be applied to dPCR?

Yes.

Are the miRCURY panels also applicable to dPCR?

Yes. When ordering panels on GeneGlobe there is the option to get the assays spotted on QIAcuity pre-plates. The master mix and template are directly added to the pre-plate and processed according to the supplementary protocol. The assay concentration when using panels is 0.83x instead of 1x when using tube assays.

When can I use probe or EvaGreen for expression applications? Is there any recommendation to select one over the other?

Both methods are capable of capturing precise gene expression levels in different organisms and sample types. In the case of off-target amplification with the used primers, probe-based assays provide higher specificity as intercalating dyes like EvaGreen bind to the unwanted amplicons resulting in an overestimation of the target molecule. Besides, probe-based assays can be run in multiplex reactions. EvaGreen-based assays are more cost-effective.

Which fold change amount is expected to be significant among samples using dPCR since this technique accurately detects very small fold change variation among samples?

With dPCR on the QIAcuity, fold changes of >5% can be resolved along a range of 40 to 86,000 target molecules per reaction using the 26k Nanoplate.

What is the dynamic range of linear quantification and why does it differ between the 8.5k and the 26k Nanoplates?

The range of linear quantification starts at 10 target molecules per reaction for the 8.5k Nanoplate and can be even lower for the 26k Nanoplate under optimal conditions. The upper limit is determined by Poisson statistics, which requires a certain number of negative partitions to calculate the absolute number of target molecules. The upper limit of average template molecules per partition that still allows robust calculation is 5 copies/partition. This results in upper limits for quantification of 170,000 and 200,000 copies/reaction for the 8.5k and 26k Nanoplates, respectively.

Can custom qPCR assays be used in dPCR?

Yes, this is possible in most cases, but you may need to adapt the cycling conditions to account for shifted melting temperatures. This is due to differences in the master mix chemistry that is used for dPCR on the QIAcuity.

RAB18 is a key regulator of GalNAc-conjugated siRNA-induced silencing in Hep3B cells

Lu J, et al. Mol Ther Nucleic Acids. 2022; 28:423434.

The G3BP1-UPF1-associated long non-coding RNA CALA regulates RNA turnover in the cytoplasm

Kirchhof L, et al. Noncoding RNA. 2022; 8(4):49.

MicroRNA-7 regulates melanocortin circuits involved in mammalian energy homeostasis

LaPierre MP, et al. Nat Commun. 2022; 13(1):5733.

Antisense oligonucleotide therapy for the common Stargardt disease type 1-causing variant in ABCA4

Kaltak M, et al. 2022; Preprint article

Ultra-sensitive molecular detection of gene fusions from RNA using ASPYRE

Gray ER, et al. BMC Med Genomics. 2022; 15(1):215.

TOMM40 RNA transcription in Alzheimer's disease brain and its implication in mitochondrial dysfunction

Lee EG, et al. Genes (Basel). 2021; 12(6):871.

Products

Explore our dPCR assays for RNA biomarker research

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Data analysis

QIAGEN Digital Insights has powerful bioinformatics tools to help you understand the biological meaning hidden within your gene expression data. With QIAGEN OmicSoft, compare gene expression patterns and visually find genes that are down- or up-regulated to identify potential biomarkers. Use QIAGEN OmicSoft, web-based Land Explorer to access hundreds of thousands of curated datasets to examine a target’s expression or mutation status across hundreds of disease categories.

QIAGEN Ingenuity Pathway Analysis (IPA) helps you understand the cause and effect of gene expression changes and identify potential targets for further exploration. Predict which upstream regulators are responsible and whether they are activated or inhibited. Visualize downstream effects to identify which genes are likely to cause an increase or decrease in downstream biological processes.

Single-cell analysis of the tumor microenvironment in high-grade serous ovarian cancer

In this webinar, we show how our QIAGEN Digital Insights bioinformatics tools can help you analyze and interpret whole transcriptome data from a human single-cell sequencing experiment.

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