MicroRNA expression differentiates histology and predicts survival of lung cancer
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ABSTRACT: Abstract. Background. Treatment for lung adenocarcinoma (AD) and squamous cell carcinoma (SQ) is similar and shows comparable poor efficacy. We investigated whether microRNA (miR) expression profiles can differentiate histological subtypes, predict survival, and suggest histology-specific targets for treatment. Methods. We analyzed miR expression in 165 AD and 125 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 categorized miR-targeted genes using Gene Ontology. We assessed the association of miR expression with clinical outcome using log-rank tests, Cox proportional hazards and survival risk prediction models, accounting for demographic and tumor characteristics. Findings. MiR expression profiles strongly differed between AD and SQ (global p<0.0001), particularly in the early stages. All members of the let-7 family and other oncosuppressor miRs were down-regulated in SQ, while oncomiRs like miR-21 were up-regulated in AD. The miRs with the strongest expression differences are located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Major findings were confirmed by QRT-PCR in EAGLE samples and in an independent cohort. 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. Interpretation. 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.
ORGANISM(S): human gammaherpesvirus 4 Homo sapiens
PROVIDER: GSE15201 | GEO | 2010/03/11
SECONDARY ACCESSION(S): PRJNA114959
REPOSITORIES: GEO
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