: MicroRNAs have altered expression levels in various diseases and may play an important role in the diagnosis and prognosis of colorectal cancer (CRC).
: We systemically reviewed and quantitatively ...synthesized the scientific evidence pertaining to microRNA-20a (miR-20a) as a CRC biomarker. A keyword and reference search in PubMed yielded 32 studies, in which miR-20a was measured in feces, serum, or tumor tissue. Data were extracted from a total of 5014 cancer cases and 2863 controls.
: Twenty out of 21 relevant studies found that miR-20a was upregulated in CRC patients compared to controls. Meta-analysis revealed a pooled miR-20a fold change of 2.45 (95% CI: 2.24-2.66) in CRC patients versus controls. To estimate sensitivity and specificity of miR-20a as a diagnostic biomarker of CRC, a pooled area under the receiver operating characteristic curve (AUROC) was calculated (0.70, 95% CI: 0.63-0.78). The prognostic capacity of miR-20a was assessed using hazard ratios (HRs) for the overall survival (OS). The meta-analysis estimated the pooled HR for OS to be 2.02 (95% CI: 0.90-3.14) in CRC patients with high miR-20a expression.
: miR-20a may be a valid biomarker for CRC detection but may not be a strong predictor of poor prognosis in CRC.
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•Genetic heterogeneity in cancer population requires special statistical methodologies.•Test statistics, clustering methods, and bimodality filtering paradigms are reviewed.•Bimodally ...expressed genes are useful for stratification and personalized medicine.
Standard methods for detecting cancer-associated genes rely on comparison of sample means between cancer patients and healthy controls. While such methods have successfully identified several oncogenes and tumor suppressor genes, they neglect to account for heterogeneity within the cancer population. Genetic mutations, translocations, and amplifications are often inconsistent across tumors, and instead they often affect smaller subsets of patients. This concept gives rise to the idea of bimodally expressed genes, or genes that display two modes of expression within one population. Analysis of bimodal gene expression has been explored via a variety of techniques including test statistics and clustering. In this review, we summarize the methodologies used to quantify bimodal gene expression and address the utility of these genes in patient stratification and specialized therapeutics in breast and lung cancer. Finally we discuss the limitations and future directions for bimodal genes in the era of high-throughput sequencing and personalized medicine.
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