The rectal cancer microRNAome--microRNA expression in rectal cancer and matched normal mucosa

Abstract

Purpose: miRNAs play a prominent role in a variety of physiologic and pathologic biologic processes, including cancer. For rectal cancers, only limited data are available on miRNA expression profiles, whereas the underlying genomic and transcriptomic aberrations have been firmly established. We therefore, aimed to comprehensively map the miRNA expression patterns of this disease.

Experimental design: Tumor biopsies and corresponding matched mucosa samples were prospectively collected from 57 patients with locally advanced rectal cancers. Total RNA was extracted, and tumor and mucosa miRNA expression profiles were subsequently established for all patients. The expression of selected miRNAs was validated using semi-quantitative real-time PCR.

Results: Forty-nine miRNAs were significantly differentially expressed (log(2)-fold difference >0.5 and P < 0.001) between rectal cancer and normal rectal mucosa. The predicted targets for these miRNAs were enriched for the following pathways: Wnt, TGF-beta, mTOR, insulin, mitogen-activated protein kinase, and ErbB signaling. Thirteen of these 49 miRNAs seem to be rectal cancer-specific, and have not been previously reported for colon cancers: miR-492, miR-542-5p, miR-584, miR-483-5p, miR-144, miR-2110, miR-652, miR-375, miR-147b, miR-148a, miR-190, miR-26a/b, and miR-338-3p. Of clinical impact, miR-135b expression correlated significantly with disease-free and cancer-specific survival in an independent multicenter cohort of 116 patients.

Conclusion: This comprehensive analysis of the rectal cancer miRNAome uncovered novel miRNAs and pathways associated with rectal cancer. This information contributes to a detailed view of this disease. Moreover, the identification and validation of miR-135b may help to identify novel molecular targets and pathways for therapeutic exploitation.

Figure 1

A) Heat-map and two-way hierarchical clustering based on 49 miRNAs that were differentially expressed between tumor and normal samples. Normal (green label) and tumor (red label) samples fall in separate clusters. B) PCA plot based on the 100 most variable miRNAs (SD>0.2) showing complete, unsupervised separation of the 114 array samples into 57 tumor (red) and 57 matched normal (green) samples.

Figure 2

Validation of selected miRNAs using semi-quantitative real-time PCR. Overall, there is a good correlation between the microarray data (X-axis, log2 transformed values) and the qRT-PCR data (Y-axis, ΔCt values).

Figure 3

Network interaction map based on combined miRNA expression (Table 1), target prediction and mRNA expression data for all tumor and normal samples. “Red” indicates up-regulation, “green” indicates down-regulation in the tumors compared to the corresponding normal tissue. Precise description of shapes, lines and arrows can be found in Supplementary Figure legend 4-1.

Figure 4

Kaplan-Meier plots showing estimates of DFS (left panel) and CCS (right panel) probabilities grouped by their miR-135b expression levels in a completely independent set of 116 rectal cancer patients from five different institutions. The green curve represents samples with high (above median) miR-135b expression, while the red curve corresponds to samples with low (below median) miR-135b levels. Displayed is the hazard ratio as well as the 95% confidence interval. Abbreviations: DFS, disease free survival; CCS, cancer-specific survival.