Identification of important modules and biomarkers in tuberculosis based on WGCNA
BackgroundTuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and...
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1354190/full |
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| author | Jing Dong Jing Dong Jing Dong Ruixue Song Ruixue Song Ruixue Song Xuetian Shang Xuetian Shang Xuetian Shang Yingchao Wang Yingchao Wang Yingchao Wang Qiuyue Liu Qiuyue Liu Zhiguo Zhang Hongyan Jia Hongyan Jia Hongyan Jia Mailing Huang Mailing Huang Chuanzhi Zhu Chuanzhi Zhu Chuanzhi Zhu Qi Sun Qi Sun Qi Sun Boping Du Boping Du Boping Du Aiying Xing Aiying Xing Aiying Xing Zihui Li Zihui Li Zihui Li Lanyue Zhang Lanyue Zhang Lanyue Zhang Liping Pan Liping Pan Liping Pan Zongde Zhang Zongde Zhang Zongde Zhang |
| author_facet | Jing Dong Jing Dong Jing Dong Ruixue Song Ruixue Song Ruixue Song Xuetian Shang Xuetian Shang Xuetian Shang Yingchao Wang Yingchao Wang Yingchao Wang Qiuyue Liu Qiuyue Liu Zhiguo Zhang Hongyan Jia Hongyan Jia Hongyan Jia Mailing Huang Mailing Huang Chuanzhi Zhu Chuanzhi Zhu Chuanzhi Zhu Qi Sun Qi Sun Qi Sun Boping Du Boping Du Boping Du Aiying Xing Aiying Xing Aiying Xing Zihui Li Zihui Li Zihui Li Lanyue Zhang Lanyue Zhang Lanyue Zhang Liping Pan Liping Pan Liping Pan Zongde Zhang Zongde Zhang Zongde Zhang |
| author_sort | Jing Dong |
| collection | DOAJ |
| description | BackgroundTuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.MethodsWe performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.ResultsBased on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.ConclusionThis study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis. |
| first_indexed | 2024-03-08T04:45:48Z |
| format | Article |
| id | doaj.art-701b1824b77b41d68d5127ade07b8adf |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-03-08T04:45:48Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-701b1824b77b41d68d5127ade07b8adf2024-02-08T09:24:50ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2024-02-011510.3389/fmicb.2024.13541901354190Identification of important modules and biomarkers in tuberculosis based on WGCNAJing Dong0Jing Dong1Jing Dong2Ruixue Song3Ruixue Song4Ruixue Song5Xuetian Shang6Xuetian Shang7Xuetian Shang8Yingchao Wang9Yingchao Wang10Yingchao Wang11Qiuyue Liu12Qiuyue Liu13Zhiguo Zhang14Hongyan Jia15Hongyan Jia16Hongyan Jia17Mailing Huang18Mailing Huang19Chuanzhi Zhu20Chuanzhi Zhu21Chuanzhi Zhu22Qi Sun23Qi Sun24Qi Sun25Boping Du26Boping Du27Boping Du28Aiying Xing29Aiying Xing30Aiying Xing31Zihui Li32Zihui Li33Zihui Li34Lanyue Zhang35Lanyue Zhang36Lanyue Zhang37Liping Pan38Liping Pan39Liping Pan40Zongde Zhang41Zongde Zhang42Zongde Zhang43Beijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Intensive Care Unit, Beijing Chest Hospital, Capital Medical University, Beijing, ChinaChangping Tuberculosis Prevent and Control Institute of Beijing, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaDepartment of Tuberculosis, Beijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBeijing Chest Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory for Drug Resistant Tuberculosis Research, Beijing, ChinaBeijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, ChinaBackgroundTuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.MethodsWe performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.ResultsBased on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.ConclusionThis study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.https://www.frontiersin.org/articles/10.3389/fmicb.2024.1354190/fulltuberculosislncRNAmRNAWGCNAdiagnosis |
| spellingShingle | Jing Dong Jing Dong Jing Dong Ruixue Song Ruixue Song Ruixue Song Xuetian Shang Xuetian Shang Xuetian Shang Yingchao Wang Yingchao Wang Yingchao Wang Qiuyue Liu Qiuyue Liu Zhiguo Zhang Hongyan Jia Hongyan Jia Hongyan Jia Mailing Huang Mailing Huang Chuanzhi Zhu Chuanzhi Zhu Chuanzhi Zhu Qi Sun Qi Sun Qi Sun Boping Du Boping Du Boping Du Aiying Xing Aiying Xing Aiying Xing Zihui Li Zihui Li Zihui Li Lanyue Zhang Lanyue Zhang Lanyue Zhang Liping Pan Liping Pan Liping Pan Zongde Zhang Zongde Zhang Zongde Zhang Identification of important modules and biomarkers in tuberculosis based on WGCNA Frontiers in Microbiology tuberculosis lncRNA mRNA WGCNA diagnosis |
| title | Identification of important modules and biomarkers in tuberculosis based on WGCNA |
| title_full | Identification of important modules and biomarkers in tuberculosis based on WGCNA |
| title_fullStr | Identification of important modules and biomarkers in tuberculosis based on WGCNA |
| title_full_unstemmed | Identification of important modules and biomarkers in tuberculosis based on WGCNA |
| title_short | Identification of important modules and biomarkers in tuberculosis based on WGCNA |
| title_sort | identification of important modules and biomarkers in tuberculosis based on wgcna |
| topic | tuberculosis lncRNA mRNA WGCNA diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2024.1354190/full |
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