Application of a classifier combining bronchial transcriptomics and chest computed tomography features facilitates the diagnostic evaluation of lung cancer in smokers and nonsmokers.
Int J Cancer
; 149(6): 1290-1301, 2021 09 15.
Article
em En
| MEDLINE
| ID: mdl-33963762
ABSTRACT
Lung cancer screening by computed tomography (CT) reduces mortality but exhibited high false-positive rates. We established a diagnostic classifier combining chest CT features with bronchial transcriptomics. Patients with CT-detected suspected lung cancer were enrolled. The sample collected by bronchial brushing was used for RNA sequencing. The e1071 and pROC packages in R software was applied to build the model. Eventually, a total of 283 patients, including 183 with lung cancer and 100 with benign lesions, were included into final analysis. When incorporating transcriptomic data with radiological characteristics, the advanced model yielded 0.903 AUC with 81.1% NPV. Moreover, the classifier performed well regardless of lesion size, location, stage, histologic type or smoking status. Pathway analysis showed enhanced epithelial differentiation, tumor metastasis, and impaired immunity were predominant in smokers with cancer, whereas tumorigenesis played a central role in nonsmokers with cancer. Apoptosis and oxidative stress contributed critically in metastatic lung cancer; by contrast, immune dysfunction was pivotal in locally advanced lung cancer. Collectively, we devised a minimal-to-noninvasive, efficient diagnostic classifier for smokers and nonsmokers with lung cancer, which provides evidence for different mechanisms of cancer development and metastasis associated with smoking. A negative classifier result will help the physician make conservative diagnostic decisions.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Tomografia Computadorizada por Raios X
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Perfilação da Expressão Gênica
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Redes Reguladoras de Genes
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Fumantes
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Não Fumantes
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Neoplasias Pulmonares
Tipo de estudo:
Diagnostic_studies
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Prognostic_studies
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Screening_studies
Limite:
Adult
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Aged
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Aged80
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Female
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Humans
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Male
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Middle aged
Idioma:
En
Revista:
Int J Cancer
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
China