Selection of the most stable reference genes by SBS technology.

<p>(<b>A</b>) Sera from cancer patients and healthy participants were pooled separately as described above, and miRNA levels were determined using SBS technology. SBS reads were converted to the log₂ scale. The average log₂-transformed read of each miRNA was plotted against the standard deviation of the log₂-transformed read. MiRNAs highlighted in red are those with higher abundance (log₂-transformed reads > 10) and lower standard deviations (< 1) in the dataset. (<b>B</b>) The average expression values (SBS reads ± standard errors) of the selected miRNAs were plotted. (<b>C</b>) Selection of the most stable reference genes from a panel of 25 genes using geNorm. The geNorm program calculates the average expression stability value (M) for each gene. Genes with the lowest M values are considered the most stable. The least stable gene with the highest M value was automatically excluded for the next calculation round. The x-axis from left to right indicates the ranking of the reference genes according to their expression stability from the least to the most stable, and the y-axis represents the M values of the remaining reference genes. (<b>D</b>) Identification of the optimal number of reference genes for accurate normalization using geNorm. V is the pairwise variation (V_n/V_n+1) between two sequential normalization factors (NF_n and NF_n+1). The magnitude of the change in the normalization factor after the inclusion of an additional reference gene reflects the improvement that is obtained. The authors of geNorm suggest that V > 0.15 should be considered the threshold for including an extra reference gene in the assay, and the least number of genes for each V < 0.15 is selected as the optimal set of genes for normalization. (<b>E</b>) Selection of the most stable reference gene or gene combinations using geNorm. In this case, geNorm indicated that the combination of let-7d, let-7g and let-7i was statistically superior to other combinations or each individually. (<b>F</b>) Identification of the most stable reference genes using NormFinder. The NormFinder algorithm ranks the set of candidate normalization genes according to their expression stability in different groups (e.g., disease versus normal). According to this algorithm, lower stability values of the individual genes indicate greater gene stability. In this case, 23 samples were divided into two groups (12 normal controls and 11 cancer patients). Blue bars represent the stability values of the candidate genes.