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1.  MicroRNA expression differentiates histology and predicts survival of lung cancer 
Purpose
The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histological subtypes and predict survival for non-small cell lung cancer.
Experimental design
We analyzed miR expression in 165 adenocarcinoma (AD) and 125 squamous cell carcinoma (SQ) tissue samples from the Environmental And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using t-tests and F-tests and accounted for multiple testing using global permutation tests. We assessed the association of miR expression with tobacco smoking using Spearman correlation coefficients and linear regression models, and with clinical outcome using log-rank tests, Cox proportional hazards and survival risk prediction models, accounting for demographic and tumor characteristics.
Results
MiR expression profiles strongly differed between AD and SQ (global p<0.0001), particularly in the early stages, and included miRs located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Most miRs, including all members of the let-7 family, were down-regulated in SQ. Major findings were confirmed by QRT-PCR in EAGLE samples and in an independent set of lung cancer cases. In SQ, low expression of miRs down-regulated in the histology comparison was associated with 1.2 to 3.6-fold increased mortality risk. A 5-miR signature significantly predicted survival for SQ.
Conclusions
We identified a miR expression profile that strongly differentiated AD from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.
doi:10.1158/1078-0432.CCR-09-1736
PMCID: PMC3163170  PMID: 20068076

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