A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis
Abstract Type 2 diabetes (T2DM) co-existing with pulmonary tuberculosis (PTB) is associated with increased rates of treatment failure and mortality. Therefore, greater understanding of the occurrence and prevalence of this comorbidity and research to address the prevention and treatment of PTB in pa...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-00928-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849326712131682304 |
|---|---|
| author | Yan Liu Yonglan Pu Jie Wang Zhiyong Li Songliang Liu Shenjie Tang |
| author_facet | Yan Liu Yonglan Pu Jie Wang Zhiyong Li Songliang Liu Shenjie Tang |
| author_sort | Yan Liu |
| collection | DOAJ |
| description | Abstract Type 2 diabetes (T2DM) co-existing with pulmonary tuberculosis (PTB) is associated with increased rates of treatment failure and mortality. Therefore, greater understanding of the occurrence and prevalence of this comorbidity and research to address the prevention and treatment of PTB in patients with T2DM (PTB + T2DM) have become paramount. Weighted gene co-expression network analysis (WGCNA) and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were employed to identify key gene modules and functions related to PTB + T2DM. Immune cell infiltration and drug sensitivity were compared between PTB + T2DM patients and healthy controls (HCs), with a bioinformatic approach. Several key genes were chosen for in vitro expression assays using quantitative real-time PCR (qRT-PCR), western blotting (WB), and enzyme-linked immunosorbent assay (ELISA). Compared to HCs and T2DM-only patients, PTB + 2DM patients showed upregulated expression of complement component C1q. WGCNA identified five crucial genes associated with PTB + T2DM: C1QA, CD248, LINC00278, MMP8, and MMP9. Multiscale embedded gene co-expression network analysis further identified FN1. The main KEGG pathways in PTB + T2DM patients were related to extracellular matrix-receptor interaction, the interleukin-17 signaling pathway, the AGE-RAGE signaling pathway in diabetic complications, the PI3K-Akt signaling pathway, and neutrophil extracellular trap formation. Receiver operating characteristic (ROC) analysis indicated that CD248, MMP8, MMP9, LINC00278, and C1QA have potential as diagnostic markers for PTB + T2DM. The expression levels of C1QA, LINC00278, MMP8, and MMP9 were significantly higher, and that of CD248 was significantly lower, in PTB + T2DM patients than in HCs. A network comprising highly correlated hub genes and microRNAs revealed the following interactions: C1QA with hsa-miR-363-5p, hsa-miR-671-5p, and hsa-miR-25-5p; CD248 with COL1 A2, COL1 A1, and COL4 A1; MMP8 with hsa-miR-539-5p, MMP9, and CEACAM8; and MMP9 with FN1, MMP8, hsa-miR-29b-3p, hsa-miR-942-3p, hsa-miR-302-5p, and hsa-miR-133a-5p. Seven drugs (ERK_440_1713, JAK_8517_1739, Palbociclib_1054, PLX.4720_1036, Savolitinib_1936, Selumetinib_1736, and VX.11e_2096) exhibited significant sensitivity in patients with high-expression or low-expression of C1QA. ELISA, qRT-PCR, and WB analyses confirmed the upregulated expression of C1QA, MMP8, and MMP9 in the peripheral blood of PTB + T2DM patients. This study elucidated the intricate molecular connections between PTB and T2DM and identified potential shared targets. Five genes (C1QA, MMP8, MMP9, CD248, and LINC00278) have potential as diagnostic markers for PTB + T2DM, and three genes (C1QA, MMP8, and MMP9) were upregulated in the peripheral blood of PTB + T2DM patients. Our findings may serve as a valuable reference for future research and clinical applications. |
| format | Article |
| id | doaj-art-6344bd7340ef4b34a2d6da96c7e37ed1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6344bd7340ef4b34a2d6da96c7e37ed12025-08-20T03:48:06ZengNature PortfolioScientific Reports2045-23222025-05-0115111610.1038/s41598-025-00928-0A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosisYan Liu0Yonglan Pu1Jie Wang2Zhiyong Li3Songliang Liu4Shenjie Tang5Clinical Medical Center for Tuberculosis, Beijing Chest Hospital, Capital Medical UniversityDepartment of Infectious Diseases, Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of TaicangDepartment of Infectious Diseases, Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of TaicangDepartment of Infectious Diseases, Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of TaicangDepartment of Infectious Diseases, Taicang Affiliated Hospital of Soochow University, The First People’s Hospital of TaicangClinical Medical Center for Tuberculosis & Beijing Tuberculosis Thoracic Tumor Research Institute, Beijing Chest Hospital, Capital Medical UniversityAbstract Type 2 diabetes (T2DM) co-existing with pulmonary tuberculosis (PTB) is associated with increased rates of treatment failure and mortality. Therefore, greater understanding of the occurrence and prevalence of this comorbidity and research to address the prevention and treatment of PTB in patients with T2DM (PTB + T2DM) have become paramount. Weighted gene co-expression network analysis (WGCNA) and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were employed to identify key gene modules and functions related to PTB + T2DM. Immune cell infiltration and drug sensitivity were compared between PTB + T2DM patients and healthy controls (HCs), with a bioinformatic approach. Several key genes were chosen for in vitro expression assays using quantitative real-time PCR (qRT-PCR), western blotting (WB), and enzyme-linked immunosorbent assay (ELISA). Compared to HCs and T2DM-only patients, PTB + 2DM patients showed upregulated expression of complement component C1q. WGCNA identified five crucial genes associated with PTB + T2DM: C1QA, CD248, LINC00278, MMP8, and MMP9. Multiscale embedded gene co-expression network analysis further identified FN1. The main KEGG pathways in PTB + T2DM patients were related to extracellular matrix-receptor interaction, the interleukin-17 signaling pathway, the AGE-RAGE signaling pathway in diabetic complications, the PI3K-Akt signaling pathway, and neutrophil extracellular trap formation. Receiver operating characteristic (ROC) analysis indicated that CD248, MMP8, MMP9, LINC00278, and C1QA have potential as diagnostic markers for PTB + T2DM. The expression levels of C1QA, LINC00278, MMP8, and MMP9 were significantly higher, and that of CD248 was significantly lower, in PTB + T2DM patients than in HCs. A network comprising highly correlated hub genes and microRNAs revealed the following interactions: C1QA with hsa-miR-363-5p, hsa-miR-671-5p, and hsa-miR-25-5p; CD248 with COL1 A2, COL1 A1, and COL4 A1; MMP8 with hsa-miR-539-5p, MMP9, and CEACAM8; and MMP9 with FN1, MMP8, hsa-miR-29b-3p, hsa-miR-942-3p, hsa-miR-302-5p, and hsa-miR-133a-5p. Seven drugs (ERK_440_1713, JAK_8517_1739, Palbociclib_1054, PLX.4720_1036, Savolitinib_1936, Selumetinib_1736, and VX.11e_2096) exhibited significant sensitivity in patients with high-expression or low-expression of C1QA. ELISA, qRT-PCR, and WB analyses confirmed the upregulated expression of C1QA, MMP8, and MMP9 in the peripheral blood of PTB + T2DM patients. This study elucidated the intricate molecular connections between PTB and T2DM and identified potential shared targets. Five genes (C1QA, MMP8, MMP9, CD248, and LINC00278) have potential as diagnostic markers for PTB + T2DM, and three genes (C1QA, MMP8, and MMP9) were upregulated in the peripheral blood of PTB + T2DM patients. Our findings may serve as a valuable reference for future research and clinical applications.https://doi.org/10.1038/s41598-025-00928-0Pulmonary tuberculosisType 2 diabetesWeighted gene co-expression network analysisC1QC1QAMMP8 |
| spellingShingle | Yan Liu Yonglan Pu Jie Wang Zhiyong Li Songliang Liu Shenjie Tang A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis Scientific Reports Pulmonary tuberculosis Type 2 diabetes Weighted gene co-expression network analysis C1Q C1QA MMP8 |
| title | A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis |
| title_full | A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis |
| title_fullStr | A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis |
| title_full_unstemmed | A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis |
| title_short | A bioinformatics-driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis |
| title_sort | bioinformatics driven approach to identify biomarkers and elucidate the pathogenesis of type 2 diabetes concurrent with pulmonary tuberculosis |
| topic | Pulmonary tuberculosis Type 2 diabetes Weighted gene co-expression network analysis C1Q C1QA MMP8 |
| url | https://doi.org/10.1038/s41598-025-00928-0 |
| work_keys_str_mv | AT yanliu abioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT yonglanpu abioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT jiewang abioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT zhiyongli abioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT songliangliu abioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT shenjietang abioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT yanliu bioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT yonglanpu bioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT jiewang bioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT zhiyongli bioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT songliangliu bioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis AT shenjietang bioinformaticsdrivenapproachtoidentifybiomarkersandelucidatethepathogenesisoftype2diabetesconcurrentwithpulmonarytuberculosis |