Literature DB >> 28603066

Differential gene expression profiles according to the Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histopathological classification in lung adenocarcinoma subtypes.

Camilo Molina-Romero1, Claudia Rangel-Escareño2, Alette Ortega-Gómez1, Gerardo J Alanis-Funes3, Alejandro Avilés-Salas4, Federico Avila-Moreno5, Gabriela E Mercado6, Andrés F Cardona7, Alfredo Hidalgo-Miranda8, Oscar Arrieta9.   

Abstract

The current lung cancer classification from the Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society has considerably changed the pathologic diagnosis of lung invasive adenocarcinoma, identifying disease subtypes with substantial implications for medical practice, such as clinical, radiological, molecular, and prognostic differences. We analyzed the differences in the genetic expression of adenocarcinoma subtypes according to the new classification. Microarray gene expression analysis was performed on a cohort of 29 adenocarcinoma patients treated at the Instituto Nacional de Cancerología of Mexico from 2008 to 2011. All patients had an available biopsy sample and were classified into 4 different subtypes of adenocarcinoma (2015 World Health Organization classification). Lepidic-predominant adenocarcinoma was the only pattern that exhibited a marked gene expression difference compared with other predominant histologic patterns, revealing genes with significant expression (P < .01). Moreover, we identified 13 genes with specific differential expression in the lepidic-predominant adenocarcinoma that could be used as a gene signature. The lepidic-predominant histologic pattern has a differential gene expression profile compared with all predominant histologic patterns. Additionally, we identified a gene expression signature of 13 genes that have a unique behavior in the lepidic histologic pattern; these 13 genes are candidates for follow-up studies for their potential use as biomarkers or therapeutic targets. Results from this study highlight the importance of the new Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification and exemplify the potential clinical implications of correlating histopathology with exclusive molecular beacons.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Acinar; Gene expression; Lepidic; Microarrays; Papillary; Solid

Mesh:

Substances:

Year:  2017        PMID: 28603066     DOI: 10.1016/j.humpath.2017.06.002

Source DB:  PubMed          Journal:  Hum Pathol        ISSN: 0046-8177            Impact factor:   3.466


  5 in total

1.  Automated sample preparation with SP3 for low-input clinical proteomics.

Authors:  Torsten Müller; Mathias Kalxdorf; Rémi Longuespée; Daniel N Kazdal; Albrecht Stenzinger; Jeroen Krijgsveld
Journal:  Mol Syst Biol       Date:  2020-01       Impact factor: 11.429

2.  MLW-gcForest: a multi-weighted gcForest model towards the staging of lung adenocarcinoma based on multi-modal genetic data.

Authors:  Yunyun Dong; Wenkai Yang; Jiawen Wang; Juanjuan Zhao; Yan Qiang; Zijuan Zhao; Ntikurako Guy Fernand Kazihise; Yanfen Cui; Xiaotong Yang; Siyuan Liu
Journal:  BMC Bioinformatics       Date:  2019-11-14       Impact factor: 3.169

3.  Single-cell RNA sequencing reveals distinct tumor microenvironmental patterns in lung adenocarcinoma.

Authors:  Nils Blüthgen; Frederick Klauschen; Philip Bischoff; Alexandra Trinks; Benedikt Obermayer; Jan Patrick Pett; Jennifer Wiederspahn; Florian Uhlitz; Xizi Liang; Annika Lehmann; Philipp Jurmeister; Aron Elsner; Tomasz Dziodzio; Jens-Carsten Rückert; Jens Neudecker; Christine Falk; Dieter Beule; Christine Sers; Markus Morkel; David Horst
Journal:  Oncogene       Date:  2021-10-18       Impact factor: 9.867

4.  Selective Targeting and Eradication of Various Human Non-Small Cell Lung Cancer Cell Lines Using Self-Assembled Aptamer-Decorated Nanoparticles.

Authors:  Daniel Barak; Shira Engelberg; Yehuda G Assaraf; Yoav D Livney
Journal:  Pharmaceutics       Date:  2022-08-08       Impact factor: 6.525

5.  An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.

Authors:  Ying Zhang; Qingchun Deng; Wenbin Liang; Xianchun Zou
Journal:  Biomed Res Int       Date:  2018-08-30       Impact factor: 3.411

  5 in total

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