MicroRNAs as modulators of smoking-induced gene expression changes in human airway epithelium.

We have shown that smoking impacts bronchial airway gene expression and that heterogeneity in this response associates with smoking-related disease risk. In this study, we sought to determine whether microRNAs (miRNAs) play a role in regulating the airway gene expression response to smoking. We examined whole-genome miRNA and mRNA expression in bronchial airway epithelium from current and never smokers (n = 20) and found 28 miRNAs to be differentially expressed (P < 0.05) with the majority being down-regulated in smokers. We further identified a number of mRNAs whose expression level is highly inversely correlated with miRNA expression in vivo. Many of these mRNAs contain potential binding sites for the differentially expressed miRNAs in their 3'-untranslated region (UTR) and are themselves affected by smoking. We found that either increasing or decreasing the levels of mir-218 (a miRNA that is strongly affected by smoking) in both primary bronchial epithelial cells and H1299 cells was sufficient to cause a corresponding decrease or increase in the expression of predicted mir-218 mRNA targets, respectively. Further, mir-218 expression is reduced in primary bronchial epithelium exposed to cigarette smoke condensate (CSC), and alteration of mir-218 levels in these cells diminishes the induction of the predicted mir-218 target MAFG in response to CSC. These data indicate that mir-218 levels modulate the airway epithelial gene expression response to cigarette smoke and support a role for miRNAs in regulating host response to environmental toxins.

Identification of methods for use of formalin-fixed, paraffin-embedded tissue samples in RNA expression profiling.

Formalin-fixed paraffin-embedded (FFPE) tissue samples are a potentially valuable resource of expression information for medical research, but are under-utilized due to degradation and modification of the RNA. Using a random primer-based RNA amplification strategy, we have evaluated multiple protocols for the extraction and isolation of RNA from FFPE samples. We found that the RecoverAll RNA isolation procedure with three or four slices (ten-microns in thickness), supplemented with additional DNAse, gave optimal results. RNA integrity as assessed by Agilent Bioanalyzer, and amplification of the 28S ribosomal RNA, were predictive for the number of genes detected on Affymetrix arrays. We obtained expression data for colon and lung tumor and normal FFPE samples and matched frozen samples and found a high correlation between frozen and matched FFPE samples (R(2) between 0.82 and 0.89), while the signature sets in tumor versus normal comparisons were also quite similar. QPCR confirmed all 16 of the differential expression results from the microarrays that we tested. Differentially expressed signature genes from tumor versus matched normal FFPE tissue from colon and lung were identified as cancer-related, with 95 colon tumor and 67 lung tumor genes identified, respectively.

Optimized high-throughput microRNA expression profiling provides novel biomarker assessment of clinical prostate and breast cancer biopsies.

BACKGROUND: Recent studies indicate that microRNAs (miRNAs) are mechanistically involved in the development of various human malignancies, suggesting that they represent a promising new class of cancer biomarkers. However, previously reported methods for measuring miRNA expression consume large amounts of tissue, prohibiting high-throughput miRNA profiling from typically small clinical samples such as excision or core needle biopsies of breast or prostate cancer. Here we describe a novel combination of linear amplification and labeling of miRNA for highly sensitive expression microarray profiling requiring only picogram quantities of purified microRNA. RESULTS: Comparison of microarray and qRT-PCR measured miRNA levels from two different prostate cancer cell lines showed concordance between the two platforms (Pearson correlation R2 = 0.81); and extension of the amplification, labeling and microarray platform was successfully demonstrated using clinical core and excision biopsy samples from breast and prostate cancer patients. Unsupervised clustering analysis of the prostate biopsy microarrays separated advanced and metastatic prostate cancers from pooled normal prostatic samples and from a non-malignant precursor lesion. Unsupervised clustering of the breast cancer microarrays significantly distinguished ErbB2-positive/ER-negative, ErbB2-positive/ER-positive, and ErbB2-negative/ER-positive breast cancer phenotypes (Fisher exact test, p = 0.03); as well, supervised analysis of these microarray profiles identified distinct miRNA subsets distinguishing ErbB2-positive from ErbB2-negative and ER-positive from ER-negative breast cancers, independent of other clinically important parameters (patient age; tumor size, node status and proliferation index). CONCLUSION: In sum, these findings demonstrate that optimized high-throughput microRNA expression profiling offers novel biomarker identification from typically small clinical samples such as breast and prostate cancer biopsies.