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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| Veröffentlicht in: | PloS one Jg. 15; H. 2; S. e0222552 |
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| Abstract | 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. |
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| AbstractList | 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.BACKGROUNDCigarette 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.METHODSHere, 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.RESULTSExpression 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.CONCLUSIONThe systematic analysis of transcriptomic studies performed here revealed a potential critical link between AKR1B10 expression, smoking and occurrence of lung cancer. BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSION:The systematic analysis of transcriptomic studies performed here revealed a potential critical link between AKR1B10 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 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. Background 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. Methods 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. Results 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. Conclusion The systematic analysis of transcriptomic studies performed here revealed a potential critical link between AKR1B10 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 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. |
| Audience | Academic |
| Author | Fukutani, Kiyoshi F. Andrade, Bruno B. Lima, João Victor Cubillos-Angulo, Juan M. Fukutani, Eduardo R. Queiroz, Artur T. L. Cruz, Luís A. B. Arriaga, María B. |
| AuthorAffiliation | 3 Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Bahia, Brazil 4 Curso de Medicina, Faculdade de Tecnologia e Ciências, Salvador, Bahia, Brazil 6 Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador, Bahia, Brazil 1 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil 5 Universidade Salvador (UNIFACS), Laureate Universities, Salvador, Bahia, Brazil 2 Faculdade de Medicina, Universidade Federal da Bahia, Salvador, Bahia, Brazil King Saud University, SAUDI ARABIA |
| AuthorAffiliation_xml | – name: 5 Universidade Salvador (UNIFACS), Laureate Universities, Salvador, Bahia, Brazil – name: 6 Escola Bahiana de Medicina e Saúde Pública (EBMSP), Salvador, Bahia, Brazil – name: 4 Curso de Medicina, Faculdade de Tecnologia e Ciências, Salvador, Bahia, Brazil – name: 3 Multinational Organization Network Sponsoring Translational and Epidemiological Research (MONSTER) Initiative, Salvador, Bahia, Brazil – name: King Saud University, SAUDI ARABIA – name: 1 Instituto Gonçalo Moniz, Fundação Oswaldo Cruz, Salvador, Bahia, Brazil – name: 2 Faculdade de Medicina, Universidade Federal da Bahia, Salvador, Bahia, Brazil |
| Author_xml | – sequence: 1 givenname: Juan M. surname: Cubillos-Angulo fullname: Cubillos-Angulo, Juan M. – sequence: 2 givenname: Eduardo R. surname: Fukutani fullname: Fukutani, Eduardo R. – sequence: 3 givenname: Luís A. B. surname: Cruz fullname: Cruz, Luís A. B. – sequence: 4 givenname: María B. surname: Arriaga fullname: Arriaga, María B. – sequence: 5 givenname: João Victor surname: Lima fullname: Lima, João Victor – sequence: 6 givenname: Bruno B. orcidid: 0000-0001-6833-3811 surname: Andrade fullname: Andrade, Bruno B. – sequence: 7 givenname: Artur T. L. surname: Queiroz fullname: Queiroz, Artur T. L. – sequence: 8 givenname: Kiyoshi F. surname: Fukutani fullname: Fukutani, Kiyoshi F. |
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| CitedBy_id | crossref_primary_10_1007_s12672_025_03017_w crossref_primary_10_1016_j_fitote_2023_105742 crossref_primary_10_32604_or_2024_049918 crossref_primary_10_3389_fimmu_2021_664474 crossref_primary_10_1002_cbdv_202302053 crossref_primary_10_3390_metabo11060332 crossref_primary_10_3389_fimmu_2024_1462174 crossref_primary_10_3389_fimmu_2023_1063716 crossref_primary_10_1155_2020_1973241 crossref_primary_10_1016_j_hlife_2023_12_002 crossref_primary_10_1124_pharmrev_120_000122 crossref_primary_10_1186_s12935_022_02809_2 |
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| Title | Systems biology analysis of publicly available transcriptomic data reveals a critical link between AKR1B10 gene expression, smoking and occurrence of lung cancer |
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