ArticleClinical cancer research : an official journal of the American Association for Cancer Research2016
An Expression Signature as an Aid to the Histologic Classification of Non-Small Cell Lung Cancer.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers, 1 of them a synthesis that pooled it.
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Who cites it
112 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- RNA‑binding protein MBNL1 regulates tumor growth, chemosensitivity and antitumor immunity in lung adenocarcinoma by controlling the expression of tumor suppressor RNF125.Oncology reports · 2025Article
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- Construction of ubiquitination-related risk model for predicting prognosis in lung adenocarcinoma.Scientific reports · 2025Article
- Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework.Discover oncology · 2025Article
- Genomic and Transcriptomic Profiles in Smokers and Never-Smokers Lung Squamous Cell Carcinoma Patients.Lung Cancer (Auckland, N.Z.) · 2025Article
- Pan-cancer analysis identifies tRNA modification enzyme CTU2 as a novel tumor biomarker and its role in immune microenvironment.Frontiers in immunology · 2025Article
- Macrophage abundance in oncocytic thyroid cancer is associated with PD-L1 expression.Endocrine oncology (Bristol, England) · 2025Article
52 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
purposeMost non-small cell lung cancers (NSCLC) are now diagnosed from small specimens, and classification using standard pathology methods can be difficult. This is of clinical relevance as many therapy regimens and clinical trials are histology dependent. The purpose of this study was to develop an mRNA expression signature as an adjunct test for routine histopathologic classification of NSCLCs. EXPERIMENTAL
designA microarray dataset of resected adenocarcinomas (ADC) and squamous cell carcinomas (SCC) was used as the learning set for an ADC-SCC signature. The Cancer Genome Atlas (TCGA) lung RNAseq dataset was used for validation. Another microarray dataset of ADCs and matched nonmalignant lung was used as the learning set for a tumor versus nonmalignant signature. The classifiers were selected as the most differentially expressed genes and sample classification was determined by a nearest distance approach.
resultsWe developed a 62-gene expression signature that contained many genes used in immunostains for NSCLC typing. It includes 42 genes that distinguish ADC from SCC and 20 genes differentiating nonmalignant lung from lung cancer. Testing of the TCGA and other public datasets resulted in high prediction accuracies (93%-95%). In addition, a prediction score was derived that correlates both with histologic grading and prognosis. We developed a practical version of the Classifier using the HTG EdgeSeq nuclease protection-based technology in combination with next-generation sequencing that can be applied to formalin-fixed paraffin-embedded (FFPE) tissues and small biopsies.
conclusionsOur RNA classifier provides an objective, quantitative method to aid in the pathologic diagnosis of lung cancer. Clin Cancer Res; 22(19); 4880-9. ©2016 AACR.
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