A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosis
ObjectiveThis study aims to screen common immunological markers of lung tissues and blood for diagnosis of tuberculosis (TB).MethodsDifferentially expressed miRNAs (DEmRs) and mRNAs (DEGs) were obtained by whole-transcriptome sequencing profiles on 18F-FDG PET/CT high and low metabolic active region...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-03-01
|
| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2025.1544007/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849344303736815616 |
|---|---|
| author | Yutong Wei Yutong Wei Zilu Wen Qinghua Xue Lin Wang Hui Chen Lei Shi Laiyi Wan Leilei Li Hongwei Li Wentao Hao Shulin Zhang Shulin Zhang Ka-Wing Wong Xiaoli Yu Yanzheng Song Yanzheng Song |
| author_facet | Yutong Wei Yutong Wei Zilu Wen Qinghua Xue Lin Wang Hui Chen Lei Shi Laiyi Wan Leilei Li Hongwei Li Wentao Hao Shulin Zhang Shulin Zhang Ka-Wing Wong Xiaoli Yu Yanzheng Song Yanzheng Song |
| author_sort | Yutong Wei |
| collection | DOAJ |
| description | ObjectiveThis study aims to screen common immunological markers of lung tissues and blood for diagnosis of tuberculosis (TB).MethodsDifferentially expressed miRNAs (DEmRs) and mRNAs (DEGs) were obtained by whole-transcriptome sequencing profiles on 18F-FDG PET/CT high and low metabolic active regions in lung tissues of nine TB patients. Common miRNAs were screened by intersecting with DEmRs, four miRNA GEO datasets, and their target mRNAs were predicted through the miRTarbase and Tarbase databases. Then these mRNAs were intersected with DEGs, mRNAs from blood samples and immune-related genes, to construct a miRNA-mRNA interaction network, and the hub genes were identified by Cytoscape. The relationship between immune infiltration and hub genes were evaluated using Cibersort. Finally, a diagnostic model based on Lasso regression analysis was established and validated by qRT-PCR.ResultsFive common miRNAs were obtained in both blood and tissues. Six immune-related mRNAs (NEDD4, PLTP, RNASEL, SEMA7A, TAPBP, and THBS1) were screened out. A diagnostic model was established and validated in the blood samples of 30 pairs (TB/health volunteers). The AUC for the 6-mRNA combination was 0.79.ConclusionWe screened six mRNAs as a combination for diagnosing tuberculosis. |
| format | Article |
| id | doaj-art-e1b81b67b8b8401181e185891b1c413e |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj-art-e1b81b67b8b8401181e185891b1c413e2025-08-20T03:42:41ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-03-011610.3389/fgene.2025.15440071544007A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosisYutong Wei0Yutong Wei1Zilu Wen2Qinghua Xue3Lin Wang4Hui Chen5Lei Shi6Laiyi Wan7Leilei Li8Hongwei Li9Wentao Hao10Shulin Zhang11Shulin Zhang12Ka-Wing Wong13Xiaoli Yu14Yanzheng Song15Yanzheng Song16School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, ChinaDepartment of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaSchool of Life Science and Technology, Wuhan Polytechnic University, Wuhan, ChinaDepartment of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaSchool of Life Science and Technology, Wuhan Polytechnic University, Wuhan, ChinaDepartment of Scientific Research, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaObjectiveThis study aims to screen common immunological markers of lung tissues and blood for diagnosis of tuberculosis (TB).MethodsDifferentially expressed miRNAs (DEmRs) and mRNAs (DEGs) were obtained by whole-transcriptome sequencing profiles on 18F-FDG PET/CT high and low metabolic active regions in lung tissues of nine TB patients. Common miRNAs were screened by intersecting with DEmRs, four miRNA GEO datasets, and their target mRNAs were predicted through the miRTarbase and Tarbase databases. Then these mRNAs were intersected with DEGs, mRNAs from blood samples and immune-related genes, to construct a miRNA-mRNA interaction network, and the hub genes were identified by Cytoscape. The relationship between immune infiltration and hub genes were evaluated using Cibersort. Finally, a diagnostic model based on Lasso regression analysis was established and validated by qRT-PCR.ResultsFive common miRNAs were obtained in both blood and tissues. Six immune-related mRNAs (NEDD4, PLTP, RNASEL, SEMA7A, TAPBP, and THBS1) were screened out. A diagnostic model was established and validated in the blood samples of 30 pairs (TB/health volunteers). The AUC for the 6-mRNA combination was 0.79.ConclusionWe screened six mRNAs as a combination for diagnosing tuberculosis.https://www.frontiersin.org/articles/10.3389/fgene.2025.1544007/fulltuberculosismiRNAimmune gene signaturediagnosisLASSO regression |
| spellingShingle | Yutong Wei Yutong Wei Zilu Wen Qinghua Xue Lin Wang Hui Chen Lei Shi Laiyi Wan Leilei Li Hongwei Li Wentao Hao Shulin Zhang Shulin Zhang Ka-Wing Wong Xiaoli Yu Yanzheng Song Yanzheng Song A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosis Frontiers in Genetics tuberculosis miRNA immune gene signature diagnosis LASSO regression |
| title | A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosis |
| title_full | A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosis |
| title_fullStr | A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosis |
| title_full_unstemmed | A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosis |
| title_short | A panel of six immune-related mRNAs as biomarkers for tuberculosis diagnosis |
| title_sort | panel of six immune related mrnas as biomarkers for tuberculosis diagnosis |
| topic | tuberculosis miRNA immune gene signature diagnosis LASSO regression |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2025.1544007/full |
| work_keys_str_mv | AT yutongwei apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT yutongwei apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT ziluwen apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT qinghuaxue apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT linwang apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT huichen apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT leishi apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT laiyiwan apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT leileili apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT hongweili apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT wentaohao apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT shulinzhang apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT shulinzhang apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT kawingwong apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT xiaoliyu apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT yanzhengsong apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT yanzhengsong apanelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT yutongwei panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT yutongwei panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT ziluwen panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT qinghuaxue panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT linwang panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT huichen panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT leishi panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT laiyiwan panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT leileili panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT hongweili panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT wentaohao panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT shulinzhang panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT shulinzhang panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT kawingwong panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT xiaoliyu panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT yanzhengsong panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis AT yanzhengsong panelofsiximmunerelatedmrnasasbiomarkersfortuberculosisdiagnosis |