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Integrated analysis in cancer diagnostics: why miRNA is better than mRNA?

Cancer networks are specific, contextual, dynamic and interactive! The best way to delineate complex cancer networks is to integrate both transcriptomics (mRNA) expression profile as well as miRNA expression profile – this imparts both the functional and regulatory messages in a cancer context.  However, recent spate of miRNA analysis in the elucidation of cancer networks suggests specific and distinct advantages of miRNA profiling over mRNA signatures – both biological and technical.

Biological advantages:

  • Robust and consistent expression in a localized manner and in a specific time frame.
  • A critical difference is the lack of transcript variants in miRNA (mRNA is alternatively spliced into splicing variants and isoforms) – this endows miRNAs with greater reliability and clarity in deciphering the cancer code.
  • Since miRNA directly influences both mRNA expression and stability, it offers better insight into important gene regulatory pathways. It has been demonstrated that dysregulation of  miRNA expression patterns – as compared to that of mRNA  expression patterns - are better able to identify the origin of tumors, suggesting that tumors maintain a unique tissue miRNA signature.
  • miRNAs exhibit both spatial and temporal specificity – these are expressed in a tissue-specific manner and are expressed in a given developmental stage. This is the reason why miRNA profiles can be informative to trace the origin of poorly defined cancers. Additionally, several large scale tumor miRNA profiling studies have shown that miRNA signatures are significantly different between cancerous and matched non-cancerous tissue. 

    1. miRNAs have greater diagnostic capacity than mRNAs in defining poorly characterized tumors. One study delineated a classifier of 48 miRNAs from a sample of 336 primary and metastatic tumors, and utilized this classifier to predict tissue of origin in 86% of an independent test set, including 77% of the metastatic tumors.
    2. 12 out of 17 histologically undefined tumors were accurately diagnosed through the use of a miRNA-based classifier developed from the miRNA profiles of 68 tumors. By contrast, mRNA-based classifiers were only able to identify the tissue of origin in 1 out of these 17 samples. This demonstrates that miRNAs have greater diagnostic implication in identifying undifferentiated /poorly differentiated tumors.



  • Gene expression profiling has already demonstrated its effectiveness for subtyping various cancers and offers insights into the intricate biological pathways beyond conventional histopathological methods. For example, breast cancer is generally classified into one of 4 subtypes; basal-like, luminal A, luminal B and HER2 positive, and gene expression profiling suggest that each subtype develops from separate cell lineages which are thought to be molecularly distinct with different morphological features. It has been demonstrated that miRNA profiles are equally differential and is richly informative as changes in their expression can provide insights into the host of gene combinations observed in various cancer subtypes. Many miRNAs have been shown to associate with various cancer subtypes, or to correlate with the presence or absence of specific oncogenes.  Because of the extreme specificity (sometimes down to individual cell types), miRNA profiling can identify the subtypes of cancer (say breast cancer) in contrast to hundreds of mRNAs required to do the same again highlighting the greater predictive power of miRNAs compared to mRNAs.
  • Tumor biology along with the tumor microenvironment is highly regulated and dynamic – fewer miRNAs participate in the dynamics than the mRNAs indicating the greater predictive power of miRNAs compared to mRNAs. miRNA profiles can distinguish between similar tumor microenvironments.
  • Greater depth of information prevalent in miRNA than in mRNA as host of gene mutations and single nucleotide polymorphisms (SNPs) are predominant that correlate with cancer signatures.

Technical advantages:

  • miRNAs – due to their increased stability – can tolerate harsh sample processing and handling, particularly when  extracted from samples that are usually difficult to process.
  • Techniques have been refined and improved for studying miRNAs – this also helps in improved handling and manipulation.
  • There is a strong correlation of miRNA expression levels between paired FFPE and frozen tissues irrespective of fixation time and storage time (upto 10 years).
  • miRNA is a far more robust analyte than mRNA for the investigation of FFPE tissue. These results underscore the vigor of miRNA expression profiling and more critically, support the advantages of FFPE tissues as important resources to characterize important molecular pathways and targets in various human cancers.


  1. Chan E, Prado DE, Weidhaas JB. Cancer microRNAs: from subtype profiling to predictors of response to therapy. Trends Mol Med. 2011; 17(5):235-43.
  2. Aihua Liu, Michael T. Tetzlaff, Pat VanBelle, David Elder, Michael Feldman, John W. Tobias, Antonia R. Sepulveda, Xiaowei Xu. MicroRNA Expression Profiling Outperforms mRNA Expression Profiling in Formalin-fixed Paraffin-embedded Tissues Int J Clin Exp Pathol. 2009; 2(6): 519–527.

Topics: BioGenex, Biomarkers, Cancer research, in situ hybridisation, ISH, miRNA, miRNA biomarkers, Cancer diagnostics

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