Research progress of omics in the diagnosis of tuberculosis_West China Medical Journal

Authors：

XU Jianwen ,  ZHANG Shu

Department of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding?author：

ZHANG Shu, Email: 5042708@qq.com

Keywords：

Tuberculosis; multiomics; genomics; biomarkers

DOI：

10.7507/1002-0179.202508193

Video：

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Abstract Full text Figures/Tables Video References Cited by

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a serious infectious disease that poses a significant threat to public health security. Rapid, sensitive and accurate diagnosis of TB is of vital importance. Omics, by analyzing genes, proteins, metabolites and other components in biological samples, reveals the functional status and changes of biological systems, providing new perspectives and tools for the diagnosis of TB. This article reviews the application and progress of omics in the diagnosis of TB, including the application of genomics, transcriptomics, proteomics, metabolomics and lipidomics, offering new ideas and directions for omics-based methods in TB diagnosis.

Citation： XU Jianwen, ZHANG Shu. Research progress of omics in the diagnosis of tuberculosis. West China Medical Journal, 2026, 41(6): 1032-1038. doi: 10.7507/1002-0179.202508193 Copy

1.	李媛媛, 謝晶晶, 李樹濤, 等. 2024 年 WHO 全球報告: 全球與中國關鍵數據分析. 新發傳染病電子雜志, 2024, 9(6): 92-98.
2.	Xu H, Zhang X, Cai Z, et al. An isothermal method for sensitive detection of Mycobacterium tuberculosis complex using clustered regularly interspaced short palindromic Repeats/Cas12a Cis and trans cleavage. J Mol Diagn, 2020, 22(8): 1020-1029.
3.	Walzl G, McNerney R, du Plessis N, et al. Tuberculosis: advances and challenges in development of new diagnostics and biomarkers. Lancet Infect Dis, 2018, 18(7): e199-e210.
4.	Mistry R, Cliff JM, Clayton CL, et al. Gene-expression patterns in whole blood identify subjects at risk for recurrent tuberculosis.J Infect Dis, 2007, 195(3): 357-365.
5.	Verhagen LM, Zomer A, Maes M, et al. A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children. BMC Genomics, 2013, 14: 74.
6.	Haas CT, Roe JK, Pollara G, et al. Diagnostic ‘omics’ for active tuberculosis. BMC Med, 2016, 14: 37.
7.	Bloom CI, Graham CM, Berry MP, et al. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers. PloS One, 2013, 8(8): e70630.
8.	Koth LL, Solberg OD, Peng JC, et al. Sarcoidosis blood transcriptome reflects lung inflammation and overlaps with tuberculosis. Am J Respir Crit Care Med, 2011, 184(10): 1153-1163.
9.	Maertzdorf J, Ota M, Repsilber D, et al. Functional correlations of pathogenesis-driven gene expression signatures in tuberculosis. PloS One, 2011, 6(10): e26938.
10.	Maertzdorf J, Weiner J 3rd, Mollenkopf HJ, et al. Common patterns and disease-related signatures in tuberculosis and sarcoidosis. Proc Natl Acad Sci U S A, 2012, 109(20): 7853-7858.
11.	Ottenhoff TH, Dass RH, Yang N, et al. Genome-wide expression profiling identifies type 1 interferon response pathways in active tuberculosis. PloS One, 2012, 7(9): e45839.
12.	Meehan CJ, Goig GA, Kohl TA, et al. Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. Nat Rev Microbiol, 2019, 17(9): 533-545.
13.	Satta G, Lipman M, Smith GP, et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential?. Clin Microbiol Infect, 2018, 24(6): 604-609.
14.	Morey-León G, Andrade-Molina D, Fernández-Cadena JC, et al. Comparative genomics of drug-resistant strains of Mycobacterium tuberculosis in Ecuador. BMC Genomics, 2022, 23(1): 844.
15.	Walker TM, Ip CL, Harrell RH, et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect Dis, 2013, 13(2): 137-146.
16.	Walker TM, Lalor MK, Broda A, et al. Assessment of Mycobacterium tuberculosis transmission in Oxfordshire, UK, 2007-12, with whole pathogen genome sequences: an observational study. Lancet Respir Med, 2014, 2(4): 285-292.
17.	Bryant JM, Harris SR, Parkhill J, et al. Whole-genome sequencing to establish relapse or re-infection with Mycobacterium tuberculosis: a retrospective observational study. Lancet Respir Med, 2013, 1(10): 786-792.
18.	K?ser CU, Bryant JM, Becq J, et al. Whole-genome sequencing for rapid susceptibility testing of M. tuberculosis. N Engl J Med, 2013, 369(3): 290-292.
19.	Outhred AC, Jelfs P, Suliman B, et al. Added value of whole-genome sequencing for management of highly drug-resistant TB.J Antimicrob Chemother, 2015, 70(4): 1198-1202.
20.	Coll F, McNerney R, Preston MD, et al. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences. Genome Med, 2015, 7(1): 51.
21.	Walker TM, Kohl TA, Omar SV, et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet Infect Dis, 2015, 15(10): 1193-1202.
22.	Miller S, Chiu C. The role of metagenomics and next-generation sequencing in infectious disease diagnosis. Clin Chem, 2021, 68(1): 115-124.
23.	Di Resta C, Galbiati S, Carrera P, et al. Next-generation sequencing approach for the diagnosis of human diseases: open challenges and new opportunities. EJIFCC, 2018, 29(1): 4-14.
24.	Lecuit M, Eloit M. The diagnosis of infectious diseases by whole genome next generation sequencing: a new era is opening. Front Cell Infect Microbiol, 2014, 4: 25.
25.	Zignol M, Cabibbe AM, Dean AS, et al. Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study. Lancet Infect Dis, 2018, 18(6): 675-683.
26.	Vogel M, Utpatel C, Corbett C, et al. Implementation of whole genome sequencing for tuberculosis diagnostics in a low-middle income, high MDR-TB burden country. Sci Rep, 2021, 11(1): 15333.
27.	Ji XC, Zhou LF, Li CY, et al. Reduction of human DNA contamination in clinical cerebrospinal fluid specimens improves the sensitivity of metagenomic next-generation sequencing. J Mol Neurosci, 2020, 70(5): 659-666.
28.	Wang S, Chen Y, Wang D, et al. The feasibility of metagenomic next-generation sequencing to identify pathogens causing tuberculous meningitis in cerebrospinal fluid. Front Microbiol, 2019, 10: 1993.
29.	You Y, Ni YM, Shi G. Diagnostic accuracy of metagenomic next-generation sequencing in pulmonary tuberculosis: a systematic review and meta-analysis. Syst Rev, 2024, 13(1): 317.
30.	Shi CL, Han P, Tang PJ, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. J Infect, 2020, 81(4): 567-574.
31.	王怡婷, 孟祥莉, 付茵, 等. 宏基因組測序應用于結核病防治的研究進展. 中國防癆雜志, 2024, 46(8): 976-981.
32.	Sibandze DB, Kay A, Dreyer V, et al. Rapid molecular diagnostics of tuberculosis resistance by targeted stool sequencing. Genome Med, 2022, 14(1): 52.
33.	Gootenberg JS, Abudayyeh OO, Lee JW, et al. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science, 2017, 356(6336): 438-442.
34.	Chen JS, Ma E, Harrington LB, et al. CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science, 2018, 360(6387): 436-439.
35.	Myhrvold C, Freije CA, Gootenberg JS, et al. Field-deployable viral diagnostics using CRISPR-Cas13. Science, 2018, 360(6387): 444-448.
36.	Eisenstein M. Seven technologies to watch in 2022. Nature, 2022, 601(7894): 658-661.
37.	Qi Y, Li K, Li Y, et al. CRISPR-based diagnostics: a potential tool to address the diagnostic challenges of tuberculosis. Pathogens, 2022, 11(10): 1211.
38.	Liu S, Xiao G, Li P, et al. Plasma-based ultrasensitive detection of Mycobacterium tuberculosis ESAT6/CFP10 fusion antigen using a CRISPR-driven aptamer fluorescence testing (CRAFT). Biosens Bioelectron, 2025, 284: 117566.
39.	Huang Z, LaCourse SM, Kay AW, et al. CRISPR detection of circulating cell-free Mycobacterium tuberculosis DNA in adults and children, including children with HIV: a molecular diagnostics study. Lancet Microbe, 2022, 3(7): e482-e492.
40.	Zein-Eddine R, Refrégier G, Cervantes J, et al. The future of CRISPR in Mycobacterium tuberculosis infection. J Biomed Sci, 2023, 30(1): 34.
41.	Deffur A, Wilkinson RJ, Coussens AK. Tricks to translating TB transcriptomics. Ann Transl Med, 2015, 3(Suppl 1): S43.
42.	Cliff JM, Lee JS, Constantinou N, et al. Distinct phases of blood gene expression pattern through tuberculosis treatment reflect modulation of the humoral immune response. J Infect Dis, 2013, 207(1): 18-29.
43.	Bloom CI, Graham CM, Berry MP, et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PloS One, 2012, 7(10): e46191.
44.	Maertzdorf J, Repsilber D, Parida SK, et al. Human gene expression profiles of susceptibility and resistance in tuberculosis. Genes Immun, 2011, 12(1): 15-22.
45.	Cai Y, Yang Q, Tang Y, et al. Increased complement C1q level marks active disease in human tuberculosis. PloS One, 2014, 9(3): e92340.
46.	Nakiboneka R, Walbaum N, Musisi E, et al. Specific human gene expression in response to infection is an effective marker for diagnosis of latent and active tuberculosis. Sci Rep, 2024, 14(1): 26884.
47.	Dai B, Liu JY, Li DB, et al. RNA-Seq transcriptome profiling reveals distinct immune response landscapes to identifying inflammation-related diagnostic markers in latent endometrial tuberculosis. Sci Rep, 2025, 15(1): 12361.
48.	Jiang Y, Zhang X, Wang B, et al. Single-cell transcriptomic analysis reveals a decrease in the frequency of macrophage-RGS1^high subsets in patients with osteoarticular tuberculosis. Mol Med, 2024, 30(1): 118.
49.	Mulenga H, Zauchenberger CZ, Bunyasi EW, et al. Performance of diagnostic and predictive host blood transcriptomic signatures for tuberculosis disease: a systematic review and meta-analysis. PloS One, 2020, 15(8): e0237574.
50.	Denkinger CM, Kik SV, Cirillo DM, et al. Defining the needs for next generation assays for tuberculosis. J Infect Dis, 2015, 211(Suppl 2): S29-S38.
51.	Chang A, Loy CJ, Eweis-LaBolle D, et al. Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis. Nat Commun, 2024, 15(1): 4949.
52.	Parida SK, Kaufmann SH. The quest for biomarkers in tuberculosis. Drug Discov Today, 2010, 15(3/4): 148-157.
53.	Zhang J, Wu X, Shi L, et al. Diagnostic serum proteomic analysis in patients with active tuberculosis. Clin Chim Acta, 2012, 413(9/10): 883-887.
54.	Tanaka T, Sakurada S, Kano K, et al. Identification of tuberculosis-associated proteins in whole blood supernatant. BMC Infect Dis, 2011, 11: 71.
55.	Yang Q, Chen Q, Zhang M, et al. Identification of eight-protein biosignature for diagnosis of tuberculosis. Thorax, 2020, 75(7): 576-583.
56.	Liu J, Jiang T, Jiang F, et al. Comparative proteomic analysis of serum diagnosis patterns of sputum smear-positive pulmonary tuberculosis based on magnetic bead separation and mass spectrometry analysis. Int J Clin Exp Med, 2015, 8(2): 2077-2085.
57.	De Groote MA, Sterling DG, Hraha T, et al. Discovery and validation of a six-marker serum protein signature for the diagnosis of active pulmonary tuberculosis. J Clin Microbiol, 2017, 55(10): 3057-3071.
58.	Young BL, Mlamla Z, Gqamana PP, et al. The identification of tuberculosis biomarkers in human urine samples. Eur Respir J, 2014, 43(6): 1719-1729.
59.	Pollock N, Dhiman R, Daifalla N, et al. Discovery of a unique Mycobacterium tuberculosis protein through proteomic analysis of urine from patients with active tuberculosis. Microbes Infect, 2018, 20(4): 228-235.
60.	唐靈通, 涂祥俊, 鐘濤, 等. 代謝組學在結核病研究中的應用進展. 中國防癆雜志, 2024, 46(S2): 522-527.
61.	Beukes D, van Reenen M, Loots DT, et al. Tuberculosis is associated with sputum metabolome variations, irrespective of patient sex or HIV status: an untargeted GCxGC-TOFMS study. Metabolomics, 2023, 19(6): 55.
62.	Collins JM, Bobosha K, Narayanan N, et al. A plasma metabolic signature to diagnose pulmonary tuberculosis and monitor treatment response. J Infect Dis, 2025, 232(3): 578-587.
63.	Anh NK, Phat NK, Thu NQ, et al. Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning. Sci Rep, 2024, 14(1): 15312.
64.	Liu Y, Wang R, Zhang C, et al. Automated diagnosis and phenotyping of tuberculosis using serum metabolic fingerprints. Adv Sci (Weinh), 2024, 11(39): e2406233.
65.	Wood PL, Tippireddy S, Feriante J. Plasma lipidomics of tuberculosis patients: altered phosphatidylcholine remodeling. Future Sci OA, 2018, 4(1): FSO255.
66.	Collins JM, Walker DI, Jones DP, et al. High-resolution plasma metabolomics analysis to detect Mycobacterium tuberculosis-associated metabolites that distinguish active pulmonary tuberculosis in humans. PloS one, 2018, 13(10): e0205398.
67.	Han YS, Chen JX, Li ZB, et al. Identification of potential lipid biomarkers for active pulmonary tuberculosis using ultra-high-performance liquid chromatography-tandem mass spectrometry. Exp Biol Med (Maywood), 2021, 246(4): 387-399.
68.	Lyu L, Jia H, Liu Q, et al. Individualized lipid profile in urine-derived extracellular vesicles from clinical patients with Mycobacterium tuberculosis infections. Front Microbiol, 2024, 15: 1409552.
69.	Jiang J, Li Z, Chen C, et al. Metabolomics strategy assisted by transcriptomics analysis to identify potential biomarkers associated with tuberculosis. Infect Drug Resist, 2021, 14: 4795-4807.
70.	Krishnan S, Queiroz ATL, Gupta A, et al. Integrative multi-omics reveals serum markers of tuberculosis in advanced HIV. Front Immunol, 2021, 12: 676980.

1. 李媛媛, 謝晶晶, 李樹濤, 等. 2024 年 WHO 全球報告: 全球與中國關鍵數據分析. 新發傳染病電子雜志, 2024, 9(6): 92-98.
2. Xu H, Zhang X, Cai Z, et al. An isothermal method for sensitive detection of Mycobacterium tuberculosis complex using clustered regularly interspaced short palindromic Repeats/Cas12a Cis and trans cleavage. J Mol Diagn, 2020, 22(8): 1020-1029.
3. Walzl G, McNerney R, du Plessis N, et al. Tuberculosis: advances and challenges in development of new diagnostics and biomarkers. Lancet Infect Dis, 2018, 18(7): e199-e210.
4. Mistry R, Cliff JM, Clayton CL, et al. Gene-expression patterns in whole blood identify subjects at risk for recurrent tuberculosis.J Infect Dis, 2007, 195(3): 357-365.
5. Verhagen LM, Zomer A, Maes M, et al. A predictive signature gene set for discriminating active from latent tuberculosis in Warao Amerindian children. BMC Genomics, 2013, 14: 74.
6. Haas CT, Roe JK, Pollara G, et al. Diagnostic ‘omics’ for active tuberculosis. BMC Med, 2016, 14: 37.
7. Bloom CI, Graham CM, Berry MP, et al. Transcriptional blood signatures distinguish pulmonary tuberculosis, pulmonary sarcoidosis, pneumonias and lung cancers. PloS One, 2013, 8(8): e70630.
8. Koth LL, Solberg OD, Peng JC, et al. Sarcoidosis blood transcriptome reflects lung inflammation and overlaps with tuberculosis. Am J Respir Crit Care Med, 2011, 184(10): 1153-1163.
9. Maertzdorf J, Ota M, Repsilber D, et al. Functional correlations of pathogenesis-driven gene expression signatures in tuberculosis. PloS One, 2011, 6(10): e26938.
10. Maertzdorf J, Weiner J 3rd, Mollenkopf HJ, et al. Common patterns and disease-related signatures in tuberculosis and sarcoidosis. Proc Natl Acad Sci U S A, 2012, 109(20): 7853-7858.
11. Ottenhoff TH, Dass RH, Yang N, et al. Genome-wide expression profiling identifies type 1 interferon response pathways in active tuberculosis. PloS One, 2012, 7(9): e45839.
12. Meehan CJ, Goig GA, Kohl TA, et al. Whole genome sequencing of Mycobacterium tuberculosis: current standards and open issues. Nat Rev Microbiol, 2019, 17(9): 533-545.
13. Satta G, Lipman M, Smith GP, et al. Mycobacterium tuberculosis and whole-genome sequencing: how close are we to unleashing its full potential?. Clin Microbiol Infect, 2018, 24(6): 604-609.
14. Morey-León G, Andrade-Molina D, Fernández-Cadena JC, et al. Comparative genomics of drug-resistant strains of Mycobacterium tuberculosis in Ecuador. BMC Genomics, 2022, 23(1): 844.
15. Walker TM, Ip CL, Harrell RH, et al. Whole-genome sequencing to delineate Mycobacterium tuberculosis outbreaks: a retrospective observational study. Lancet Infect Dis, 2013, 13(2): 137-146.
16. Walker TM, Lalor MK, Broda A, et al. Assessment of Mycobacterium tuberculosis transmission in Oxfordshire, UK, 2007-12, with whole pathogen genome sequences: an observational study. Lancet Respir Med, 2014, 2(4): 285-292.
17. Bryant JM, Harris SR, Parkhill J, et al. Whole-genome sequencing to establish relapse or re-infection with Mycobacterium tuberculosis: a retrospective observational study. Lancet Respir Med, 2013, 1(10): 786-792.
18. K?ser CU, Bryant JM, Becq J, et al. Whole-genome sequencing for rapid susceptibility testing of M. tuberculosis. N Engl J Med, 2013, 369(3): 290-292.
19. Outhred AC, Jelfs P, Suliman B, et al. Added value of whole-genome sequencing for management of highly drug-resistant TB.J Antimicrob Chemother, 2015, 70(4): 1198-1202.
20. Coll F, McNerney R, Preston MD, et al. Rapid determination of anti-tuberculosis drug resistance from whole-genome sequences. Genome Med, 2015, 7(1): 51.
21. Walker TM, Kohl TA, Omar SV, et al. Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance: a retrospective cohort study. Lancet Infect Dis, 2015, 15(10): 1193-1202.
22. Miller S, Chiu C. The role of metagenomics and next-generation sequencing in infectious disease diagnosis. Clin Chem, 2021, 68(1): 115-124.
23. Di Resta C, Galbiati S, Carrera P, et al. Next-generation sequencing approach for the diagnosis of human diseases: open challenges and new opportunities. EJIFCC, 2018, 29(1): 4-14.
24. Lecuit M, Eloit M. The diagnosis of infectious diseases by whole genome next generation sequencing: a new era is opening. Front Cell Infect Microbiol, 2014, 4: 25.
25. Zignol M, Cabibbe AM, Dean AS, et al. Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study. Lancet Infect Dis, 2018, 18(6): 675-683.
26. Vogel M, Utpatel C, Corbett C, et al. Implementation of whole genome sequencing for tuberculosis diagnostics in a low-middle income, high MDR-TB burden country. Sci Rep, 2021, 11(1): 15333.
27. Ji XC, Zhou LF, Li CY, et al. Reduction of human DNA contamination in clinical cerebrospinal fluid specimens improves the sensitivity of metagenomic next-generation sequencing. J Mol Neurosci, 2020, 70(5): 659-666.
28. Wang S, Chen Y, Wang D, et al. The feasibility of metagenomic next-generation sequencing to identify pathogens causing tuberculous meningitis in cerebrospinal fluid. Front Microbiol, 2019, 10: 1993.
29. You Y, Ni YM, Shi G. Diagnostic accuracy of metagenomic next-generation sequencing in pulmonary tuberculosis: a systematic review and meta-analysis. Syst Rev, 2024, 13(1): 317.
30. Shi CL, Han P, Tang PJ, et al. Clinical metagenomic sequencing for diagnosis of pulmonary tuberculosis. J Infect, 2020, 81(4): 567-574.
31. 王怡婷, 孟祥莉, 付茵, 等. 宏基因組測序應用于結核病防治的研究進展. 中國防癆雜志, 2024, 46(8): 976-981.
32. Sibandze DB, Kay A, Dreyer V, et al. Rapid molecular diagnostics of tuberculosis resistance by targeted stool sequencing. Genome Med, 2022, 14(1): 52.
33. Gootenberg JS, Abudayyeh OO, Lee JW, et al. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science, 2017, 356(6336): 438-442.
34. Chen JS, Ma E, Harrington LB, et al. CRISPR-Cas12a target binding unleashes indiscriminate single-stranded DNase activity. Science, 2018, 360(6387): 436-439.
35. Myhrvold C, Freije CA, Gootenberg JS, et al. Field-deployable viral diagnostics using CRISPR-Cas13. Science, 2018, 360(6387): 444-448.
36. Eisenstein M. Seven technologies to watch in 2022. Nature, 2022, 601(7894): 658-661.
37. Qi Y, Li K, Li Y, et al. CRISPR-based diagnostics: a potential tool to address the diagnostic challenges of tuberculosis. Pathogens, 2022, 11(10): 1211.
38. Liu S, Xiao G, Li P, et al. Plasma-based ultrasensitive detection of Mycobacterium tuberculosis ESAT6/CFP10 fusion antigen using a CRISPR-driven aptamer fluorescence testing (CRAFT). Biosens Bioelectron, 2025, 284: 117566.
39. Huang Z, LaCourse SM, Kay AW, et al. CRISPR detection of circulating cell-free Mycobacterium tuberculosis DNA in adults and children, including children with HIV: a molecular diagnostics study. Lancet Microbe, 2022, 3(7): e482-e492.
40. Zein-Eddine R, Refrégier G, Cervantes J, et al. The future of CRISPR in Mycobacterium tuberculosis infection. J Biomed Sci, 2023, 30(1): 34.
41. Deffur A, Wilkinson RJ, Coussens AK. Tricks to translating TB transcriptomics. Ann Transl Med, 2015, 3(Suppl 1): S43.
42. Cliff JM, Lee JS, Constantinou N, et al. Distinct phases of blood gene expression pattern through tuberculosis treatment reflect modulation of the humoral immune response. J Infect Dis, 2013, 207(1): 18-29.
43. Bloom CI, Graham CM, Berry MP, et al. Detectable changes in the blood transcriptome are present after two weeks of antituberculosis therapy. PloS One, 2012, 7(10): e46191.
44. Maertzdorf J, Repsilber D, Parida SK, et al. Human gene expression profiles of susceptibility and resistance in tuberculosis. Genes Immun, 2011, 12(1): 15-22.
45. Cai Y, Yang Q, Tang Y, et al. Increased complement C1q level marks active disease in human tuberculosis. PloS One, 2014, 9(3): e92340.
46. Nakiboneka R, Walbaum N, Musisi E, et al. Specific human gene expression in response to infection is an effective marker for diagnosis of latent and active tuberculosis. Sci Rep, 2024, 14(1): 26884.
47. Dai B, Liu JY, Li DB, et al. RNA-Seq transcriptome profiling reveals distinct immune response landscapes to identifying inflammation-related diagnostic markers in latent endometrial tuberculosis. Sci Rep, 2025, 15(1): 12361.
48. Jiang Y, Zhang X, Wang B, et al. Single-cell transcriptomic analysis reveals a decrease in the frequency of macrophage-RGS1^high subsets in patients with osteoarticular tuberculosis. Mol Med, 2024, 30(1): 118.
49. Mulenga H, Zauchenberger CZ, Bunyasi EW, et al. Performance of diagnostic and predictive host blood transcriptomic signatures for tuberculosis disease: a systematic review and meta-analysis. PloS One, 2020, 15(8): e0237574.
50. Denkinger CM, Kik SV, Cirillo DM, et al. Defining the needs for next generation assays for tuberculosis. J Infect Dis, 2015, 211(Suppl 2): S29-S38.
51. Chang A, Loy CJ, Eweis-LaBolle D, et al. Circulating cell-free RNA in blood as a host response biomarker for detection of tuberculosis. Nat Commun, 2024, 15(1): 4949.
52. Parida SK, Kaufmann SH. The quest for biomarkers in tuberculosis. Drug Discov Today, 2010, 15(3/4): 148-157.
53. Zhang J, Wu X, Shi L, et al. Diagnostic serum proteomic analysis in patients with active tuberculosis. Clin Chim Acta, 2012, 413(9/10): 883-887.
54. Tanaka T, Sakurada S, Kano K, et al. Identification of tuberculosis-associated proteins in whole blood supernatant. BMC Infect Dis, 2011, 11: 71.
55. Yang Q, Chen Q, Zhang M, et al. Identification of eight-protein biosignature for diagnosis of tuberculosis. Thorax, 2020, 75(7): 576-583.
56. Liu J, Jiang T, Jiang F, et al. Comparative proteomic analysis of serum diagnosis patterns of sputum smear-positive pulmonary tuberculosis based on magnetic bead separation and mass spectrometry analysis. Int J Clin Exp Med, 2015, 8(2): 2077-2085.
57. De Groote MA, Sterling DG, Hraha T, et al. Discovery and validation of a six-marker serum protein signature for the diagnosis of active pulmonary tuberculosis. J Clin Microbiol, 2017, 55(10): 3057-3071.
58. Young BL, Mlamla Z, Gqamana PP, et al. The identification of tuberculosis biomarkers in human urine samples. Eur Respir J, 2014, 43(6): 1719-1729.
59. Pollock N, Dhiman R, Daifalla N, et al. Discovery of a unique Mycobacterium tuberculosis protein through proteomic analysis of urine from patients with active tuberculosis. Microbes Infect, 2018, 20(4): 228-235.
60. 唐靈通, 涂祥俊, 鐘濤, 等. 代謝組學在結核病研究中的應用進展. 中國防癆雜志, 2024, 46(S2): 522-527.
61. Beukes D, van Reenen M, Loots DT, et al. Tuberculosis is associated with sputum metabolome variations, irrespective of patient sex or HIV status: an untargeted GCxGC-TOFMS study. Metabolomics, 2023, 19(6): 55.
62. Collins JM, Bobosha K, Narayanan N, et al. A plasma metabolic signature to diagnose pulmonary tuberculosis and monitor treatment response. J Infect Dis, 2025, 232(3): 578-587.
63. Anh NK, Phat NK, Thu NQ, et al. Discovery of urinary biosignatures for tuberculosis and nontuberculous mycobacteria classification using metabolomics and machine learning. Sci Rep, 2024, 14(1): 15312.
64. Liu Y, Wang R, Zhang C, et al. Automated diagnosis and phenotyping of tuberculosis using serum metabolic fingerprints. Adv Sci (Weinh), 2024, 11(39): e2406233.
65. Wood PL, Tippireddy S, Feriante J. Plasma lipidomics of tuberculosis patients: altered phosphatidylcholine remodeling. Future Sci OA, 2018, 4(1): FSO255.
66. Collins JM, Walker DI, Jones DP, et al. High-resolution plasma metabolomics analysis to detect Mycobacterium tuberculosis-associated metabolites that distinguish active pulmonary tuberculosis in humans. PloS one, 2018, 13(10): e0205398.
67. Han YS, Chen JX, Li ZB, et al. Identification of potential lipid biomarkers for active pulmonary tuberculosis using ultra-high-performance liquid chromatography-tandem mass spectrometry. Exp Biol Med (Maywood), 2021, 246(4): 387-399.
68. Lyu L, Jia H, Liu Q, et al. Individualized lipid profile in urine-derived extracellular vesicles from clinical patients with Mycobacterium tuberculosis infections. Front Microbiol, 2024, 15: 1409552.
69. Jiang J, Li Z, Chen C, et al. Metabolomics strategy assisted by transcriptomics analysis to identify potential biomarkers associated with tuberculosis. Infect Drug Resist, 2021, 14: 4795-4807.
70. Krishnan S, Queiroz ATL, Gupta A, et al. Integrative multi-omics reveals serum markers of tuberculosis in advanced HIV. Front Immunol, 2021, 12: 676980.

West China Medical Journal

Research progress of omics in the diagnosis of tuberculosis

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