Systems biology analysis of publicly available transcriptomic data reveals a critical link between AKR1B10 gene expression, smoking and occurrence of lung cancer

Cigarette smoking is associated with an increased risk of developing respiratory diseases and various types of cancer. Early identification of such unfavorable outcomes in patients who smoke is critical for optimizing personalized medical care. Here, we perform a comprehensive analysis using Systems...

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Vydané v:	PloS one Ročník 15; číslo 2; s. e0222552
Hlavní autori:	Cubillos-Angulo, Juan M., Fukutani, Eduardo R., Cruz, Luís A. B., Arriaga, María B., Lima, João Victor, Andrade, Bruno B., Queiroz, Artur T. L., Fukutani, Kiyoshi F.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Public Library of Science 25.02.2020 Public Library of Science (PLoS)
Predmet:	Aldo-Keto Reductases - genetics Aldo-Keto Reductases - metabolism Algorithms Alveoli Analysis Biology Biology and Life Sciences Biomarkers Biomarkers, Tumor Cancer genetics Cancer research Cancer screening Cigarette smoking Cigarettes Classification Data mining Datasets Development and progression Diseases DNA methylation Gene expression Gene Expression Profiling Genes Health risks Humans Learning algorithms Lung cancer Lung diseases Lung Neoplasms - etiology Lung Neoplasms - genetics Machine learning Macrophages Medicine and Health Sciences Mutation Physical Sciences Research and Analysis Methods Respiratory diseases Respiratory tract diseases Science Policy Setting (Literature) Smoke Smokers Smoking Smoking - adverse effects Smoking - genetics Social Sciences Systems analysis Systems Biology - methods Tissues Transcriptome Taiwan Brazil
ISSN:	1932-6203, 1932-6203
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Popis
Shrnutí:	Cigarette smoking is associated with an increased risk of developing respiratory diseases and various types of cancer. Early identification of such unfavorable outcomes in patients who smoke is critical for optimizing personalized medical care. Here, we perform a comprehensive analysis using Systems Biology tools of publicly available data from a total of 6 transcriptomic studies, which examined different specimens of lung tissue and/or cells of smokers and nonsmokers to identify potential markers associated with lung cancer. Expression level of 22 genes was capable of classifying smokers from non-smokers. A machine learning algorithm revealed that AKR1B10 was the most informative gene among the 22 differentially expressed genes (DEGs) accounting for the classification of the clinical groups. AKR1B10 expression was higher in smokers compared to non-smokers in datasets examining small and large airway epithelia, but not in the data from a study of sorted alveolar macrophages. Moreover, AKR1B10 expression was relatively higher in lung cancer specimens compared to matched healthy tissue obtained from nonsmoking individuals. Although the overall accuracy of AKR1B10 expression level in distinction between cancer and healthy lung tissue was 76%, with a specificity of 98%, our results indicated that such marker exhibited low sensitivity, hampering its use for cancer screening such specific setting. The systematic analysis of transcriptomic studies performed here revealed a potential critical link between AKR1B10 expression, smoking and occurrence of lung cancer.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: The authors have declared that no competing interests exist. These authors also contributed equally to this work.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0222552