MicroRNA expression differentiates histology and predicts survival of lung cancer

Maria Teresa Landi, Yingdong Zhao, Melissa Rotunno, Jill Koshiol, Hui Liu, Andrew W. Bergen, Maurizia Rubagotti, Alisa M. Goldstein, Ilona Linnoila, Francesco M. Marincola, Margaret A. Tucker, Pier Alberto Bertazzi, Angela C. Pesatori, Neil E. Caporaso, Lisa M. McShane, Ena Wang

Research output: Contribution to journalArticle

257 Citations (Scopus)

Abstract

Purpose: The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histologic subtypes and predict survival for non-small cell lung cancer. Experimental Design: We analyzed miR expression in 165 adenocarcinoma and 125 squamous cell carcinoma (SQ) tissue samples from the Environment 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 adenocarcinoma and SQ (Pglobal < 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 downregulated in SQ. Major findings were confirmed by quantitative real time-polymerase chain reaction (qRT-PCR) in EAGLE samples and in an independent set of lung cancer cases. In SQ, the low expression of miRs that are downregulated in the histology comparison was associated with 1.2- to 3.6-fold increased mortality risk. A five-miR signature significantly predicted survival for SQ. Conclusions: We identified a miR expression profile that strongly differentiated adenocarcinoma from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.

Original languageEnglish
Pages (from-to)430-441
Number of pages12
JournalClinical Cancer Research
Volume16
Issue number2
DOIs
Publication statusPublished - 15 Jan 2010
Externally publishedYes

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MicroRNAs
Lung Neoplasms
Histology
Survival
Adenocarcinoma
Linear Models
Down-Regulation
Non-Small Cell Lung Carcinoma
Real-Time Polymerase Chain Reaction
Squamous Cell Carcinoma
Research Design
Chromosomes
Smoking
Demography
Mortality
Neoplasms

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

MicroRNA expression differentiates histology and predicts survival of lung cancer. / Landi, Maria Teresa; Zhao, Yingdong; Rotunno, Melissa; Koshiol, Jill; Liu, Hui; Bergen, Andrew W.; Rubagotti, Maurizia; Goldstein, Alisa M.; Linnoila, Ilona; Marincola, Francesco M.; Tucker, Margaret A.; Bertazzi, Pier Alberto; Pesatori, Angela C.; Caporaso, Neil E.; McShane, Lisa M.; Wang, Ena.

In: Clinical Cancer Research, Vol. 16, No. 2, 15.01.2010, p. 430-441.

Research output: Contribution to journalArticle

Landi, MT, Zhao, Y, Rotunno, M, Koshiol, J, Liu, H, Bergen, AW, Rubagotti, M, Goldstein, AM, Linnoila, I, Marincola, FM, Tucker, MA, Bertazzi, PA, Pesatori, AC, Caporaso, NE, McShane, LM & Wang, E 2010, 'MicroRNA expression differentiates histology and predicts survival of lung cancer', Clinical Cancer Research, vol. 16, no. 2, pp. 430-441. https://doi.org/10.1158/1078-0432.CCR-09-1736
Landi, Maria Teresa ; Zhao, Yingdong ; Rotunno, Melissa ; Koshiol, Jill ; Liu, Hui ; Bergen, Andrew W. ; Rubagotti, Maurizia ; Goldstein, Alisa M. ; Linnoila, Ilona ; Marincola, Francesco M. ; Tucker, Margaret A. ; Bertazzi, Pier Alberto ; Pesatori, Angela C. ; Caporaso, Neil E. ; McShane, Lisa M. ; Wang, Ena. / MicroRNA expression differentiates histology and predicts survival of lung cancer. In: Clinical Cancer Research. 2010 ; Vol. 16, No. 2. pp. 430-441.
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AU - Landi, Maria Teresa

AU - Zhao, Yingdong

AU - Rotunno, Melissa

AU - Koshiol, Jill

AU - Liu, Hui

AU - Bergen, Andrew W.

AU - Rubagotti, Maurizia

AU - Goldstein, Alisa M.

AU - Linnoila, Ilona

AU - Marincola, Francesco M.

AU - Tucker, Margaret A.

AU - Bertazzi, Pier Alberto

AU - Pesatori, Angela C.

AU - Caporaso, Neil E.

AU - McShane, Lisa M.

AU - Wang, Ena

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N2 - Purpose: The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histologic subtypes and predict survival for non-small cell lung cancer. Experimental Design: We analyzed miR expression in 165 adenocarcinoma and 125 squamous cell carcinoma (SQ) tissue samples from the Environment 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 adenocarcinoma and SQ (Pglobal < 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 downregulated in SQ. Major findings were confirmed by quantitative real time-polymerase chain reaction (qRT-PCR) in EAGLE samples and in an independent set of lung cancer cases. In SQ, the low expression of miRs that are downregulated in the histology comparison was associated with 1.2- to 3.6-fold increased mortality risk. A five-miR signature significantly predicted survival for SQ. Conclusions: We identified a miR expression profile that strongly differentiated adenocarcinoma from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.

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