The blood transcriptional signature for active and latent tuberculosis
Min Deng,1 Xiao-Dong Lv,2 Zhi-Xian Fang,2 Xin-Sheng Xie,1 Wen-Yu Chen2 1Department of Infectious Diseases, The First Hospital of Jiaxing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China; 2Department of Respiration, The First Hospital of Jiaxing, The First Affiliated Hospit...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2019-01-01
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| Series: | Infection and Drug Resistance |
| Subjects: | |
| Online Access: | https://www.dovepress.com/the-blood-transcriptional-signature-for-active-and-latent-tuberculosis-peer-reviewed-article-IDR |
| _version_ | 1818647596576538624 |
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| author | Deng M Lv XD Fang ZX Xie XS Chen WY |
| author_facet | Deng M Lv XD Fang ZX Xie XS Chen WY |
| author_sort | Deng M |
| collection | DOAJ |
| description | Min Deng,1 Xiao-Dong Lv,2 Zhi-Xian Fang,2 Xin-Sheng Xie,1 Wen-Yu Chen2 1Department of Infectious Diseases, The First Hospital of Jiaxing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China; 2Department of Respiration, The First Hospital of Jiaxing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China Background: Although the incidence of tuberculosis (TB) has dropped substantially, it still is a serious threat to human health. And in recent years, the emergence of resistant bacilli and inadequate disease control and prevention has led to a significant rise in the global TB epidemic. It is known that the cause of TB is Mycobacterium tuberculosis infection. But it is not clear why some infected patients are active while others are latent.Methods: We analyzed the blood gene expression profiles of 69 latent TB patients and 54 active pulmonary TB patients from GEO (Transcript Expression Omnibus) database.Results: By applying minimal redundancy maximal relevance and incremental feature selection, we identified 24 signature genes which can predict the TB activation. The support vector machine predictor based on these 24 genes had a sensitivity of 0.907, specificity of 0.913, and accuracy of 0.911, respectively. Although they need to be validated in a large independent dataset, the biological analysis of these 24 genes showed great promise.Conclusion: We found that cytokine production was a key process during TB activation and genes like CYBB, TSPO, CD36, and STAT1 worth further investigation. Keywords: tuberculosis, blood gene expression, support vector machine, minimal redundancy maximal relevance, incremental feature selection
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| first_indexed | 2024-12-17T01:05:03Z |
| format | Article |
| id | doaj.art-95fed569fb934a169adb2c9e04525f57 |
| institution | Directory Open Access Journal |
| issn | 1178-6973 |
| language | English |
| last_indexed | 2024-12-17T01:05:03Z |
| publishDate | 2019-01-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Infection and Drug Resistance |
| spelling | doaj.art-95fed569fb934a169adb2c9e04525f572022-12-21T22:09:18ZengDove Medical PressInfection and Drug Resistance1178-69732019-01-01Volume 1232132843840The blood transcriptional signature for active and latent tuberculosisDeng MLv XDFang ZXXie XSChen WYMin Deng,1 Xiao-Dong Lv,2 Zhi-Xian Fang,2 Xin-Sheng Xie,1 Wen-Yu Chen2 1Department of Infectious Diseases, The First Hospital of Jiaxing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China; 2Department of Respiration, The First Hospital of Jiaxing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China Background: Although the incidence of tuberculosis (TB) has dropped substantially, it still is a serious threat to human health. And in recent years, the emergence of resistant bacilli and inadequate disease control and prevention has led to a significant rise in the global TB epidemic. It is known that the cause of TB is Mycobacterium tuberculosis infection. But it is not clear why some infected patients are active while others are latent.Methods: We analyzed the blood gene expression profiles of 69 latent TB patients and 54 active pulmonary TB patients from GEO (Transcript Expression Omnibus) database.Results: By applying minimal redundancy maximal relevance and incremental feature selection, we identified 24 signature genes which can predict the TB activation. The support vector machine predictor based on these 24 genes had a sensitivity of 0.907, specificity of 0.913, and accuracy of 0.911, respectively. Although they need to be validated in a large independent dataset, the biological analysis of these 24 genes showed great promise.Conclusion: We found that cytokine production was a key process during TB activation and genes like CYBB, TSPO, CD36, and STAT1 worth further investigation. Keywords: tuberculosis, blood gene expression, support vector machine, minimal redundancy maximal relevance, incremental feature selection https://www.dovepress.com/the-blood-transcriptional-signature-for-active-and-latent-tuberculosis-peer-reviewed-article-IDRTuberculosisBlood Gene ExpressionSupport Vector Machineminimal Redundancy Maximal RelevanceIncremental Feature Selection |
| spellingShingle | Deng M Lv XD Fang ZX Xie XS Chen WY The blood transcriptional signature for active and latent tuberculosis Infection and Drug Resistance Tuberculosis Blood Gene Expression Support Vector Machine minimal Redundancy Maximal Relevance Incremental Feature Selection |
| title | The blood transcriptional signature for active and latent tuberculosis |
| title_full | The blood transcriptional signature for active and latent tuberculosis |
| title_fullStr | The blood transcriptional signature for active and latent tuberculosis |
| title_full_unstemmed | The blood transcriptional signature for active and latent tuberculosis |
| title_short | The blood transcriptional signature for active and latent tuberculosis |
| title_sort | blood transcriptional signature for active and latent tuberculosis |
| topic | Tuberculosis Blood Gene Expression Support Vector Machine minimal Redundancy Maximal Relevance Incremental Feature Selection |
| url | https://www.dovepress.com/the-blood-transcriptional-signature-for-active-and-latent-tuberculosis-peer-reviewed-article-IDR |
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