Mining potential biomarkers for active tuberculosis based on bioinformatics_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

 LIU Fei ¹ , PAN Shuo ² , LI Ping ³ , YANG Yuxin ⁴ , INAWATIKIZ Kurban ¹ , BO Geli ¹

1. Department of Infectious Diseases, The People’s Hospital of Bortala Mongol Autonomous Prefecture, Bole, Xinjiang 833400, P. R. China;
2. Department of Medical Affairs The People's Hospital of Bortala Mongol Autonomous Prefecture, Bole, Xinjiang 833400, P. R. China;
3. Department of Dermatology The People's Hospital of Bortala Mongol Autonomous Prefecture, Bole, Xinjiang 833400, P. R. China;
4. Department of Public Health The People's Hospital of Bortala Mongol Autonomous Prefecture, Bole, Xinjiang 833400, P. R. China;

Corresponding?author：

LIU Fei, Email: liufei19900709@163.com

Keywords：

Active pulmonary tuberculosis; biomarkers; bioinformatics; gene expression; diagnostic efficacy; differentially expressed genes.

DOI：

10.7507/1671-6205.202505044

Video：

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Objective To explore potential host-derived biomarkers for active pulmonary tuberculosis and optimize early diagnosis and treatment monitoring strategies for tuberculosis. Methods Integrated analysis of two whole blood gene expression datasets (GSE19444 and GSE42830) from the GEO database (37 tuberculosis patients and 50 healthy controls) was performed. Differentially expressed genes (DEGs) were screened using GEO2R, and gene ontology enrichment, pathway analysis, and protein-protein interaction network construction were conducted using Metascape and STRING to identify key gene modules. Core genes were identified based on MCODE scores (≥10) and T-tests/ANOVA, and their expression trends and diagnostic efficacy were validated in independent datasets (GSE152532, GSE19435). The sensitivity and specificity of candidate biomarkers were assessed using receiver operating characteristic (ROC) curves. Results A total of 70 common DEGs were identified (59 upregulated and 11 downregulated), significantly enriched in type Ⅱ interferon signaling pathways and innate immune responses. Module analysis revealed 16 core genes (e.g., RTP4, GBP4, TRIM22) forming a high-confidence interaction network. Validation showed that the three genes were significantly overexpressed in tuberculosis patients (P<0.05) and dynamically downregulated with treatment. ROC curve analysis indicated that RTP4 had the best diagnostic efficacy with an area under curve (AUC) of 0.952 (sensitivity 100%, specificity 79%), while the three-gene combination (TBscore) had an AUC of 0.933 (sensitivity 100%, specificity 81%). Conclusion RTP4, GBP4, and TRIM22 are potential diagnostic biomarkers for active pulmonary tuberculosis, and their combination model (TBscore) exhibits high sensitivity and specificity, with dynamic expression levels reflecting treatment efficacy, providing new targets for precise diagnosis and treatment of tuberculosis.

Citation： LIU Fei, PAN Shuo, LI Ping, YANG Yuxin, INAWATIKIZ Kurban, BO Geli. Mining potential biomarkers for active tuberculosis based on bioinformatics. Chinese Journal of Respiratory and Critical Care Medicine, 2026, 25(3): 196-204. doi: 10.7507/1671-6205.202505044 Copy

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2.	Singer SN, Ndumnego OC, Kim RS, et al. Plasma host protein biomarkers correlating with increasing Mycobacterium tuberculosis infection activity prior to tuberculosis diagnosis in people living with HIV. EBioMedicine, 2022, 75: 103787.
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4.	李思, 馮銀合, 吉桂宜, 等. 重組結核桿菌融合蛋白在結核感染中診斷價值的研究進展. 中國呼吸與危重監護雜志, 2024, 23(3): 217-220.
5.	李玲, 羅雅文, 杜娟, 等. 外周血γ干擾素釋放試驗假陰性結果的影響因素. 熱帶醫學雜志, 2023, 23(1): 21-24.
6.	姚心怡, 陳兆軍, 徐錦, 等. γ-干擾素釋放試驗聯合血小板及部分炎癥指標檢測在鑒別活動性結核中的臨床應用價值. 臨床個性化醫學, 2024, 3(3): 718-726.
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8.	孔嬌, 陳園園, 蔡青山, 等. 宏基因二代測序技術對非結核分枝桿菌肺病的診斷價值. 中國防癆雜志, 2022, 44(11): 1135-1140.
9.	Sun Y, Chen G, Liu Z, et al. A bioinformatics analysis to identify novel biomarkers for prognosis of pulmonary tuberculosis. BMC Pulm Med, 2020, 20: 279.
10.	Wang Y, Jin F, Mao W, et al. Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning. Front Microbiol, 2024, 15: 1349374.
11.	Zhang T, Rao G, Gao X. Identification of hub genes in tuberculosis via bioinformatics analysis. Comput Math Methods Med, 2021, 8159879.
12.	袁建林, 馮海沜, 羅遠明, 等. 慢性阻塞性肺疾病急性加重合并活動性肺結核的臨床特征, 預測因素及短期預后. 中國呼吸與危重監護雜志, 2024, 23(9): 609-616.
13.	Pang Z, Chong J, Zhou G, et al. MetaboAnalyst 5. 0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res, 2021, 49(W1): W388-W396.
14.	Howell A, Yaros C. Downloading and analysis of metabolomic and lipidomic data from metabolomics workbench using MetaboAnalyst 5. 0. Methods in molecular biology (Clifton, N. J. ), 2023, 2625: 313-321.
15.	Fu L, Zhao L, Li X, et al. Pharmacological mechanism of quercetin in the treatment of colorectal cancer by network pharmacology and molecular simulation. J Biomol Struct Dyn, 2024, 42(13/14): 7065-7076.
16.	Shen Z, Li T, Yang B. Identification of key biomarkers associated with glioma hemorrhage: evidence from bioinformatic analysis and clinical validation. J Mol Neurosci, 2025, 75(1): 1-12.
17.	Lin H, Du X, Wang H, et al. The Potential mechanisms of Qufeng Zhitong capsule against rheumatoid arthritis based on network pharmacology and in vitro experiments. Crit Rev Immunol, 2024, 44(1): 1-16.
18.	Venkatraman ES. A permutation test to compare receiver operating characteristic curves. Biometrics, 2000, 56: 1134-1138.

1. Rodríguez-Silva CN, Prokopczyk IM, Dos Santos JL. The medicinal chemistry of chalcones as anti-Mycobacterium tuberculosis agents. Mini Rev Med Chem, 2022, 22(16): 2068-2080.
2. Singer SN, Ndumnego OC, Kim RS, et al. Plasma host protein biomarkers correlating with increasing Mycobacterium tuberculosis infection activity prior to tuberculosis diagnosis in people living with HIV. EBioMedicine, 2022, 75: 103787.
3. 李思, 馮銀合, 吉桂宜, 等. 重組結核桿菌融合蛋白在結核感染中診斷價值的研究進展. 中國呼吸與危重監護雜志, 2024, 23(3): 217-220.
4. 李思, 馮銀合, 吉桂宜, 等. 重組結核桿菌融合蛋白在結核感染中診斷價值的研究進展. 中國呼吸與危重監護雜志, 2024, 23(3): 217-220.
5. 李玲, 羅雅文, 杜娟, 等. 外周血γ干擾素釋放試驗假陰性結果的影響因素. 熱帶醫學雜志, 2023, 23(1): 21-24.
6. 姚心怡, 陳兆軍, 徐錦, 等. γ-干擾素釋放試驗聯合血小板及部分炎癥指標檢測在鑒別活動性結核中的臨床應用價值. 臨床個性化醫學, 2024, 3(3): 718-726.
7. Mousavian Z, K?llenius G, Sundling C. From simple to complex: protein-based biomarker discovery in tuberculosis. Eur J Immunol, 2023, 53(12): e2350485.
8. 孔嬌, 陳園園, 蔡青山, 等. 宏基因二代測序技術對非結核分枝桿菌肺病的診斷價值. 中國防癆雜志, 2022, 44(11): 1135-1140.
9. Sun Y, Chen G, Liu Z, et al. A bioinformatics analysis to identify novel biomarkers for prognosis of pulmonary tuberculosis. BMC Pulm Med, 2020, 20: 279.
10. Wang Y, Jin F, Mao W, et al. Identification of diagnostic biomarkers correlate with immune infiltration in extra-pulmonary tuberculosis by integrating bioinformatics and machine learning. Front Microbiol, 2024, 15: 1349374.
11. Zhang T, Rao G, Gao X. Identification of hub genes in tuberculosis via bioinformatics analysis. Comput Math Methods Med, 2021, 8159879.
12. 袁建林, 馮海沜, 羅遠明, 等. 慢性阻塞性肺疾病急性加重合并活動性肺結核的臨床特征, 預測因素及短期預后. 中國呼吸與危重監護雜志, 2024, 23(9): 609-616.
13. Pang Z, Chong J, Zhou G, et al. MetaboAnalyst 5. 0: narrowing the gap between raw spectra and functional insights. Nucleic Acids Res, 2021, 49(W1): W388-W396.
14. Howell A, Yaros C. Downloading and analysis of metabolomic and lipidomic data from metabolomics workbench using MetaboAnalyst 5. 0. Methods in molecular biology (Clifton, N. J. ), 2023, 2625: 313-321.
15. Fu L, Zhao L, Li X, et al. Pharmacological mechanism of quercetin in the treatment of colorectal cancer by network pharmacology and molecular simulation. J Biomol Struct Dyn, 2024, 42(13/14): 7065-7076.
16. Shen Z, Li T, Yang B. Identification of key biomarkers associated with glioma hemorrhage: evidence from bioinformatic analysis and clinical validation. J Mol Neurosci, 2025, 75(1): 1-12.
17. Lin H, Du X, Wang H, et al. The Potential mechanisms of Qufeng Zhitong capsule against rheumatoid arthritis based on network pharmacology and in vitro experiments. Crit Rev Immunol, 2024, 44(1): 1-16.
18. Venkatraman ES. A permutation test to compare receiver operating characteristic curves. Biometrics, 2000, 56: 1134-1138.

Chinese Journal of Respiratory and Critical Care Medicine

Mining potential biomarkers for active tuberculosis based on bioinformatics

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