Hunting for robust gene signature from cancer profiling data: sources of variability, different interpretations, and...

Gene microarray is a powerful platform to investigate the expression patterns of thousands of genes simultaneously. One central objective of such analysis is to select sets of genes (i.e., gene signatures) which correlate with clinical characteristics, such as disease subtype diagnosis, response to drug treatment and prognosis. However, previous studies have found that mRNA signatures are highly unstable and strongly depend on the selection of patient samples. Based on five large microRNA profiling datasets, we empirically found that microRNA signatures are also generally unstable. Therefore, concerns arise regarding the reproducibility and clinical applicability of these derived gene signatures. Here, we first provide a brief review on the sources of variability and different interpretations of multiple distinct gene signatures. We then focus on those recent methodological progresses aimed at developing more stable gene signatures.