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Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC()

Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non–small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gen...

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Autores principales: Charkiewicz, Radoslaw, Niklinski, Jacek, Claesen, Jürgen, Sulewska, Anetta, Kozlowski, Miroslaw, Michalska-Falkowska, Anna, Reszec, Joanna, Moniuszko, Marcin, Naumnik, Wojciech, Niklinska, Wieslawa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Neoplasia Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408153/
https://www.ncbi.nlm.nih.gov/pubmed/28456114
http://dx.doi.org/10.1016/j.tranon.2017.01.015
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author Charkiewicz, Radoslaw
Niklinski, Jacek
Claesen, Jürgen
Sulewska, Anetta
Kozlowski, Miroslaw
Michalska-Falkowska, Anna
Reszec, Joanna
Moniuszko, Marcin
Naumnik, Wojciech
Niklinska, Wieslawa
author_facet Charkiewicz, Radoslaw
Niklinski, Jacek
Claesen, Jürgen
Sulewska, Anetta
Kozlowski, Miroslaw
Michalska-Falkowska, Anna
Reszec, Joanna
Moniuszko, Marcin
Naumnik, Wojciech
Niklinska, Wieslawa
author_sort Charkiewicz, Radoslaw
collection PubMed
description Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non–small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies.
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spelling pubmed-54081532017-05-01 Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC() Charkiewicz, Radoslaw Niklinski, Jacek Claesen, Jürgen Sulewska, Anetta Kozlowski, Miroslaw Michalska-Falkowska, Anna Reszec, Joanna Moniuszko, Marcin Naumnik, Wojciech Niklinska, Wieslawa Transl Oncol Original article Advances in molecular analyses based on high-throughput technologies can contribute to a more accurate classification of non–small cell lung cancer (NSCLC), as well as a better prediction of both the disease course and the efficacy of targeted therapies. Here we set out to analyze whether global gene expression profiling performed in a group of early-stage NSCLC patients can contribute to classifying tumor subtypes and predicting the disease prognosis. Gene expression profiling was performed with the use of the microarray technology in a training set of 108 NSCLC samples. Subsequently, the recorded findings were validated further in an independent cohort of 44 samples. We demonstrated that the specific gene patterns differed significantly between lung adenocarcinoma (AC) and squamous cell lung carcinoma (SCC) samples. Furthermore, we developed and validated a novel 53-gene signature distinguishing SCC from AC with 93% accuracy. Evaluation of the classifier performance in the validation set showed that our predictor classified the AC patients with 100% sensitivity and 88% specificity. We revealed that gene expression patterns observed in the early stages of NSCLC may help elucidate the histological distinctions of tumors through identification of different gene-mediated biological processes involved in the pathogenesis of histologically distinct tumors. However, we showed here that the gene expression profiles did not provide additional value in predicting the progression status of the early-stage NSCLC. Nevertheless, the gene expression signature analysis enabled us to perform a reliable subclassification of NSCLC tumors, and it can therefore become a useful diagnostic tool for a more accurate selection of patients for targeted therapies. Neoplasia Press 2017-04-26 /pmc/articles/PMC5408153/ /pubmed/28456114 http://dx.doi.org/10.1016/j.tranon.2017.01.015 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original article
Charkiewicz, Radoslaw
Niklinski, Jacek
Claesen, Jürgen
Sulewska, Anetta
Kozlowski, Miroslaw
Michalska-Falkowska, Anna
Reszec, Joanna
Moniuszko, Marcin
Naumnik, Wojciech
Niklinska, Wieslawa
Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC()
title Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC()
title_full Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC()
title_fullStr Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC()
title_full_unstemmed Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC()
title_short Gene Expression Signature Differentiates Histology But Not Progression Status of Early-Stage NSCLC()
title_sort gene expression signature differentiates histology but not progression status of early-stage nsclc()
topic Original article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5408153/
https://www.ncbi.nlm.nih.gov/pubmed/28456114
http://dx.doi.org/10.1016/j.tranon.2017.01.015
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