Literature DB >> 21622546

Classification of non-small cell lung carcinoma in transthoracic needle specimens using microRNA expression profiling.

Ambrogio Fassina1, Rocco Cappellesso2, Matteo Fassan2.   

Abstract

BACKGROUND: Emerging targeted lung cancer therapies require the accurate morphologic subclassification of non-small cell lung cancer (NSCLC), even in scant and distorted specimens obtained by transthoracic needle aspiration (TTNA). MicroRNAs (miRNAs) are small noncoding genes recently reported as useful in differentiating squamous cell carcinoma (SCC) from adenocarcinoma (AD) in resected tumor specimens. We investigated their ability to do so in TTNA specimens.
METHODS: Smears, immunocytochemistry slides, and corresponding cell blocks of 31 NSCLC TTNA specimens were retrieved and classified as AD or SCC based on their cytologic features and immunocytochemical profiles. Data on EGFR and K-RAS mutational status were available for all cases of AD. We quantified the hsa-let-7 family and hsa-miR-205 by quantitative reverse transcription-polymerase chain reaction and compared the miRNA expression levels in AD and SCC using Student t test.
RESULTS: Eighteen cases were classified as AD and 13 as SCC by light microscopy and immunocytochemistry. miRNA expression profiles demonstrated considerable, statistically significant differences between AD and SCC, showing an upregulation of hsa-let-7a, hsa-let-7b, hsa-let-7c,hsa-let-7f, hsa-let-7g, hsa-let-7i, and hsa-miR-98 and a downregulation of hsa-miR-205 in AD specimens (all P < .05; t test).
CONCLUSIONS: Profiling the hsa-let-7 family and hsa-miR-205 is a promising method for differentiating AD from SCC, even in such small specimens as transthoracic aspirates. Subject to the validation of these findings in further, larger studies, this could prove to be a reliable, standardizable tool for the subclassification of NSCLC.

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Year:  2011        PMID: 21622546     DOI: 10.1378/chest.11-0708

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  33 in total

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2.  MicroRNAs As Biomarkers For Clinical Features Of Lung Cancer.

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4.  Role of deregulated microRNAs in non-small cell lung cancer progression using fresh-frozen and formalin-fixed, paraffin-embedded samples.

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5.  Machine Learning Approach for Predicting Past Environmental Exposures From Molecular Profiling of Post-Exposure Human Serum Samples.

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Journal:  Carcinogenesis       Date:  2012-02-13       Impact factor: 4.944

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Review 8.  Integrating contextual miRNA and protein signatures for diagnostic and treatment decisions in cancer.

Authors:  Lorenzo F Sempere
Journal:  Expert Rev Mol Diagn       Date:  2011-11       Impact factor: 5.225

9.  MicroRNA-34a inhibits the proliferation and promotes the apoptosis of non-small cell lung cancer H1299 cell line by targeting TGFβR2.

Authors:  Zhong-Liang Ma; Pin-Pin Hou; Yan-Li Li; De-Tao Wang; Tian-Wei Yuan; Jia-Li Wei; Bo-Tao Zhao; Jia-Tao Lou; Xin-Tai Zhao; Yan Jin; You-Xin Jin
Journal:  Tumour Biol       Date:  2014-12-13

10.  [Expression of CDC25A in non-small cell lung cancer and its relationship with let-7 gene].

Authors:  Dianming Li; Zhaofei Liu; Guolan Ning
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2020-11-30
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