Discovering microRNA biomarkers in cancer


In recent years, research into microRNA biomarkers for cancer has expanded, yielding many potential signatures for a variety of cancer types. MicroRNAs are promising biomarker candidates due to their stability in circulation in association with Ago2, HDL or extracellular vesicles, and their important gene regulation roles in multiple cancer-related processes.

Multiple methods are available for miRNA biomarker discovery. MicroRNA detection by qPCR continues to be a major technique for this purpose, but while adding microRNA sequencing to the mix offers unique advantages for scalability and novel discovery, technological challenges have thus far hindered its use. Recent advances, however, now prime miRNA-seq to step into the spotlight as an effective method for identifying biomarkers in cancer. Let’s take a look at a few recent studies discussing biomarkers in various cancers, and then I’ll explain how new technology has opened up access to miRNA sequencing for every personal and core lab.

Want to learn more? Join our webinar on miRNA-seq from biofluids in June! 

Recent miRNA biomarker discoveries in CRC and cervical cancer

Colorectal cancer (CRC) is one of the most common cancers worldwide, and non-invasive detection methods that could catch cancer early may help improve outcomes. Zekri et al. (2016) explored the roles of 14 miRNAs using a custom miScript miRNA PCR Array for miR-17, -18a, -19a, -19b, -20a, -21, -146a, -223, -24, -454, -183, -135a, -135b and -92a and serum samples from individuals with various colonic diseases. They found that miR-17, -19a, -20a and -223 could be used for detection of CRC, whereas -19b and -18a could be useful for detecting colonic polyps (miR-19b) and inflammatory bowel disease (miR-18a).

About 50% of locally advanced cervical cancers (LACC) don’t respond to the standard combination of radiotherapy and cisplatin-based chemotherapy, so Pedroza-Torres et al. (2016) sought a signature to distinguish responders from non-responders. They profiled miRNA in LACC tumor samples using the miScript miRNome array. They identified 101 miRNAs that could classify the response of LACC to therapy, confirming 7 via probe PCR assays (miR-31-3p, -3676, -125a-5p, -100-5p, -125b-5p, -200a-5p and miR-342) in an independent cohort. With further validation, the team suggested that this signature may be useful to identify radio- and chemo-resistant tumors.

As demonstrated in the papers above, qPCR is a highly effective tool for biomarker discovery, offering the gold standard of quantification. Traditionally, using NGS has been tricky for miRNA profiling, but the scalability and potential for novel transcript discovery continue to make NGS a highly appealing option. An emerging strategy in RNA-seq is to combine the two for discovery and verification, but is NGS ready for prime-time when it comes to quantification of potential microRNA biomarkers? In the next two sections, we’ll discuss how large miRNA-seq datasets are helping researchers find miRNA biomarkers, and how new technology enables you to bring the power of miRNA-seq into your own lab or core facility.

Large miRNA-seq datasets help identify biomarkers in OPSCC and CRC

In recent years, one fruitful avenue for identifying potential biomarkers has been to use publicly available cancer datasets. The Cancer Genome Atlas, a public dataset compiled by the National Cancer Institute and National Human Genome Research Institute that includes miRNA-seq, RNA-seq, genotyping data, and more, has been an excellent resource for initial identification of potential biomarkers.

Many studies using TCGA data within the last year have yielded interesting results. For example, Wong et al. (2017) discovered 4 microRNAs that could predict survival in oropharyngeal squamous cell carcinoma (OPSCC); miR-193-3p and miR-455-5p were positively associated and miR-92a-3p and miR-497-5p were negatively associated. They were able to validate these results with an independent cohort as well (3). Similarly, Xue et al. (2017) explored the ability of miR-483-5p to predict survival in esophageal squamous cell carcinoma and found that elevated levels were associated with poor survival (4).

To explore biomarkers for CRC recurrence or metastasis, Miyoshi et al. (2017) used a combination of miRNA microarray and TCGA data for initial discovery, then moved to CRC FFPE tissues, serum samples, and primary CRC tissue plus matched liver metastasis samples from individuals with CRC at various stages. The team identified miR-139-5p as a marker for recurrence and metastasis, and also showed that it promoted peritoneal dissemination when overexpressed in a mouse model (5).

miRNA-seq data is clearly promising for biomarker discovery, but the technique has been plagued by workflow and data quality issues for many researchers. How can you efficiently use it in your lab?

NGS: New miRNA-seq technology for fast, cost-effective biomarker discovery in your lab!

The ease and cost-efficiency of scaling up your experiments with NGS is a major advantage for this technique in biomarker discovery. These experiments typically require high sample numbers, which can be challenging with qPCR. However, miRNA-seq has historically been hindered by a tedious, gel-dependent workflow and low data quality due to adapter dimers and contaminating RNA species. miRNA-seq has been particularly tricky with regards to circulating miRNAs because of contaminating hY4 RNA and prohibitive sample input requirements.

The good news is, those limitations are a thing of the past. The QIAseq miRNA Library Kit overcomes each of these challenges, with the multiplexing capacity to make high-throughput miRNA-seq experiments convenient for any personal lab with an NGS instrument or any NGS core laboratory. Want to know more? Check out our flyer below! It has all the data you need to make an informed choice about what technology to use for your next miRNA biomarker discovery experiment.

Download your copy of the flyer!


  1. 1. Zekri, A-R.N. et al. (2016) Circulating serum miRNAs as diagnostic markers for colorectal cancer. PLoS One 11(5): e0154130. Link

2. Pedroza-Torres, A. et al. (2016) A microRNA expression signature for clinical response in locally advanced cervical cancer. Gynec. Oncol. 142(3), 557–565. Link

3. Wong, N. et al. (2017) Prognostic microRNA signatures derived from The Cancer Genome Atlas for head and neck squamous cell carcinomas. Cancer Med. 5(7): 1619–28. Link

4. Xue, L. et al. (2017) Upregulated miR-483-5p expression as a prognostic biomarker for esophageal squamous cell carcinoma. Cancer Biomark. Epub ahead of print. Link

5. Miyoshi, J. et al. (2017) miR-139-5p as a novel serum biomarker for recurrence and metastasis in colorectal cancer. Sci. Rep. 7: 43393. Link

Ali Bierly

Ali Bierly, PhD is a Global Market Manager in Translational Sciences at QIAGEN, and has written on a number of scientific topics in the biotech industry as the author of QIAGEN's Reviews Online. She received her PhD from Cornell University in 2009, studying the immune response to a protozoan parasite, Toxoplasma gondii. Ali has a keen interest in the emerging importance of microRNA and other circulating nucleic acids as biomarkers for disease.

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