Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research
Abstract Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global public health issue with high mortality rates and challenges posed by drug-resistant strains, emphasizing the continued need for new therapeutic targets and effective treatment strategies. Transcript...
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Nature Portfolio
2025-01-01
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| Online Access: | https://doi.org/10.1038/s41598-025-86951-7 |
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| author | Zhanpeng Chen Qiong Wang Quan Ma Jinyun Chen Xingxing Kong Yuqin Zeng Lanlan Liu Shuihua Lu Xiaomin Wang |
| author_facet | Zhanpeng Chen Qiong Wang Quan Ma Jinyun Chen Xingxing Kong Yuqin Zeng Lanlan Liu Shuihua Lu Xiaomin Wang |
| author_sort | Zhanpeng Chen |
| collection | DOAJ |
| description | Abstract Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global public health issue with high mortality rates and challenges posed by drug-resistant strains, emphasizing the continued need for new therapeutic targets and effective treatment strategies. Transcriptomics is a highly effective tool for the development of novel anti-tuberculosis drugs. However, most studies focus only on changes in gene expression levels at specific time points. This study screened for genes with altered expression patterns from available transcriptomic data and analysed their association with the TB progression. Initially, a total of 1228 genes with altered expression patterns were identified through two-way analysis of variance (ANOVA). We define genes with a P-value less than 0.05 for the combined effect of infection and time on gene expression as those with altered expression patterns. Gene Ontology (GO) enrichment analysis revealed that the biological functions of these genes mainly involve DNA translation, RNA processing, and transcriptional regulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that these genes are primarily associated with fatty acid degradation, pyruvate metabolism, arginine and proline metabolism, as well as cholesterol metabolism signaling pathways. Subsequent Protein-protein interaction (PPI) analysis and Receiver Operating Characteristic (ROC) curve analysis identified four core genes closely associated with TB progression, namely Rac Family Small GTPase 1 (RAC1), Ring-Box 1 (RBX1), Mitochondrial Ribosomal Protein L33 (MRPL33), and ELAV Like RNA Binding Protein 1 (ELAVL1). Q-PCR experiments confirmed that Mtb infection led to changes in the gene expression patterns of RAC1, RBX1, MRPL33, and ELAVL1 in THP-1 cells. These four genes may serve as core biomarkers for TB progression and can be utilized in the development of more effective anti-tuberculosis drugs and host therapy. |
| format | Article |
| id | doaj-art-649eb0a2024b4ab1873350e1fd6506ee |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-649eb0a2024b4ab1873350e1fd6506ee2025-01-26T12:29:36ZengNature PortfolioScientific Reports2045-23222025-01-0115111210.1038/s41598-025-86951-7Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro researchZhanpeng Chen0Qiong Wang1Quan Ma2Jinyun Chen3Xingxing Kong4Yuqin Zeng5Lanlan Liu6Shuihua Lu7Xiaomin Wang8National Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalDepartment of pharmacy, Shenzhen University General HospitalNational Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalNational Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalNational Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalNational Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalNational Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalNational Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalNational Clinical Research Center for Infectious Diseases, Shenzhen Third People’s HospitalAbstract Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global public health issue with high mortality rates and challenges posed by drug-resistant strains, emphasizing the continued need for new therapeutic targets and effective treatment strategies. Transcriptomics is a highly effective tool for the development of novel anti-tuberculosis drugs. However, most studies focus only on changes in gene expression levels at specific time points. This study screened for genes with altered expression patterns from available transcriptomic data and analysed their association with the TB progression. Initially, a total of 1228 genes with altered expression patterns were identified through two-way analysis of variance (ANOVA). We define genes with a P-value less than 0.05 for the combined effect of infection and time on gene expression as those with altered expression patterns. Gene Ontology (GO) enrichment analysis revealed that the biological functions of these genes mainly involve DNA translation, RNA processing, and transcriptional regulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that these genes are primarily associated with fatty acid degradation, pyruvate metabolism, arginine and proline metabolism, as well as cholesterol metabolism signaling pathways. Subsequent Protein-protein interaction (PPI) analysis and Receiver Operating Characteristic (ROC) curve analysis identified four core genes closely associated with TB progression, namely Rac Family Small GTPase 1 (RAC1), Ring-Box 1 (RBX1), Mitochondrial Ribosomal Protein L33 (MRPL33), and ELAV Like RNA Binding Protein 1 (ELAVL1). Q-PCR experiments confirmed that Mtb infection led to changes in the gene expression patterns of RAC1, RBX1, MRPL33, and ELAVL1 in THP-1 cells. These four genes may serve as core biomarkers for TB progression and can be utilized in the development of more effective anti-tuberculosis drugs and host therapy.https://doi.org/10.1038/s41598-025-86951-7MtbTBELAVL1RAC1MRPL33RBX1 |
| spellingShingle | Zhanpeng Chen Qiong Wang Quan Ma Jinyun Chen Xingxing Kong Yuqin Zeng Lanlan Liu Shuihua Lu Xiaomin Wang Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research Scientific Reports Mtb TB ELAVL1 RAC1 MRPL33 RBX1 |
| title | Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research |
| title_full | Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research |
| title_fullStr | Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research |
| title_full_unstemmed | Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research |
| title_short | Identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research |
| title_sort | identification of core biomarkers for tuberculosis progression through bioinformatics analysis and in vitro research |
| topic | Mtb TB ELAVL1 RAC1 MRPL33 RBX1 |
| url | https://doi.org/10.1038/s41598-025-86951-7 |
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