Skip to content
2000
Volume 28, Issue 19
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Objectives

Latent tuberculosis infection (LTBI) population is the principle source of active tuberculosis (ATB) patients. The identification of reliable diagnostic biomarkers is critical to the prevention and control of progression from LTBI to ATB. The aim of this study is to screen biomarkers that can distinguish LTBI from ATB patients by a comprehensive bioinformatic analysis strategy.

Methods

The transcriptomic datasets were obtained from GEO database. Hub genes and critical signal pathways for differentiating latent and active TB were identified by a comprehensive bioinformatic analysis strategy comprising weighted gene co-expression network analysis (WGCNA), differentially expressed gene (DEG), protein-protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and hub genes were verified by RT-qPCR in this study.

Results

The transcriptome profiles of GSE193777, GSE157657, GSE168519, GSE107991, and GSE107992 were extracted from the GEO database, in which a total of 18,397 protein-coding genes from 206 samples were included in the bioinformatics analysis. Combined with weighted gene co-expression network, differentially expressed gene, functional enrichment and protein-protein interaction analyses, six hub genes were identified. RT-qPCR confirmed significantly lower expression of (p=0.002), (p=7.19e-8), (p=9.22e-9), and (p=6.81e-7) in LTBI versus ATB.

Conclusions

Our findings may provide crucial clues to potential biomarkers that can distinguish patients with LTBI from those with ATB, aiding the understanding of the mechanism of LTBI progression to ATB.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cchts/10.2174/0113862073401054250526094910
2025-05-27
2026-02-24

  • From This Site

    /content/journals/cchts/10.2174/0113862073401054250526094910
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Global tuberculosis report 2023.2023Available from: lung-health/tb-reports/global-tuberculosis-report-2023
  2. MangioneC.M. BarryM.J. NicholsonW.K. CabanaM. ChelmowD. CokerT.R. DavisE.M. DonahueK.E. JaénC.R. LiL. OgedegbeG. RaoG. RuizJ.M. StevermerJ. UnderwoodS.M. WongJ.B. Screening for latent tuberculosis infection in adults.JAMA2023329171487149410.1001/jama.2023.4899 37129649
     [Google Scholar]
  3. Global tuberculosis report 2019.2019Available from: https://www.who.int/publications/i/item/global-tuberculosis-report-2019
  4. HoubenR.M.G.J. DoddP.J. The global burden of latent tuberculosis infection: A re-estimation using mathematical modelling.PLoS Med.20161310100215210.1371/journal.pmed.1002152 27780211
     [Google Scholar]
  5. PaiM. BehrM. Latent Mycobacterium tuberculosis infection and interferon-gamma release assays.Microbiol. Spectr2016454.5.2410.1128/microbiolspec.TBTB2‑0023‑201627763261
     [Google Scholar]
  6. GualanoG. MencariniP. LauriaF.N. PalmieriF. MfinangaS. MwabaP. ChakayaJ. ZumlaA. IppolitoG. Tuberculin skin test - Outdated or still useful for Latent TB infection screening?Int. J. Infect. Dis.201980sS20S22
     [Google Scholar]
  7. BoomW.H. SchaibleU.E. AchkarJ.M. The knowns and unknowns of latent Mycobacterium tuberculosis infection.J. Clin. Invest.2021131313622210.1172/JCI136222 33529162
     [Google Scholar]
  8. LuL. WeiR. BhaktaS. WaddellS.J. BoixE. Weighted gene co-expression network analysis identifies key modules and hub genes associated with mycobacterial infection of human macrophages.Antibiotics20211029710.3390/antibiotics10020097 33498280
     [Google Scholar]
  9. ChenL. HuaJ. HeX. Coexpression network analysis-based identification of critical genes differentiating between latent and active tuberculosis.Dis. Markers2022202211110.1155/2022/2090560 36411825
     [Google Scholar]
  10. CoppolaM. OttenhoffT.H.M. Genome wide approaches discover novel Mycobacterium tuberculosis antigens as correlates of infection, disease, immunity and targets for vaccination.Semin. Immunol.2018398810110.1016/j.smim.2018.07.001 30327124
     [Google Scholar]
  11. CaiY. DaiY. WangY. YangQ. GuoJ. WeiC. ChenW. HuangH. ZhuJ. ZhangC. ZhengW. WenZ. LiuH. ZhangM. XingS. JinQ. FengC.G. ChenX. Single-cell transcriptomics of blood reveals a natural killer cell subset depletion in tuberculosis.EBioMedicine20205310268610.1016/j.ebiom.2020.102686 32114394
     [Google Scholar]
  12. Martínez-PérezA. IgeaA. EstévezO. FerreiraC.M. TorradoE. CastroA.G. FernándezC. SpetzA.L. AdamL. López GonzálezM. SinghM. ReljicR. González-FernándezÁ. Changes in the immune phenotype and gene expression profile driven by a novel tuberculosis nanovaccine: Short and long-term post-immunization.Front. Immunol.20211158986310.3389/fimmu.2020.589863 33584654
     [Google Scholar]
  13. ZhaoR. QiS. CuiY. GaoY. JiangS. ZhaoJ. ZhangJ. KongL. Transcriptomic and physiological analysis identifies a gene network module highly associated with brassinosteroid regulation in hybrid sweetgum tissues differing in the capability of somatic embryogenesis.Hortic. Res.20229uhab04710.1093/hr/uhab047 35031801
     [Google Scholar]
  14. LiuS.L. SunX.S. ChenQ.Y. LiuZ.X. BianL.J. YuanL. XiaoB.B. LuZ.J. LiX.Y. YanJ.J. YanS.M. LiJ.M. BeiJ.X. MaiH.Q. TangL.Q. Development and validation of a transcriptomics-based gene signature to predict distant metastasis and guide induction chemotherapy in locoregionally advanced nasopharyngeal carcinoma.Eur. J. Cancer2022163263410.1016/j.ejca.2021.12.017 35032814
     [Google Scholar]
  15. Torto-AlaliboT. CollmerC.W. Gwinn-GiglioM. LindebergM. MengS. ChibucosM.C. TsengT.T. LomaxJ. BiehlB. IrelandA. BirdD. DeanR.A. GlasnerJ.D. PernaN. SetubalJ.C. CollmerA. TylerB.M. Unifying themes in microbial associations with animal and plant hosts described using the gene ontology.Microbiol. Mol. Biol. Rev.201074447950310.1128/MMBR.00017‑10 21119014
     [Google Scholar]
  16. YiZ. LiJ. GaoK. FuY. Identifcation of differentially expressed long non-coding RNAs in CD4+ T cells response to latent tuberculosis infection.J. Infect.201469655856810.1016/j.jinf.2014.06.016 24975173
     [Google Scholar]
  17. WeinerJ. DomaszewskaT. DonkorS. KaufmannS.H.E. HillP.C. SutherlandJ.S. Changes in transcript, metabolite, and antibody reactivity during the early protective immune response in humans to Mycobacterium tuberculosis infection.Clin. Infect. Dis.2020711304010.1093/cid/ciz785 31412355
     [Google Scholar]
  18. PennB.H. NetterZ. JohnsonJ.R. Von DollenJ. JangG.M. JohnsonT. OholY.M. MaherC. BellS.L. GeigerK. GolovkineG. DuX. ChoiA. ParryT. MohapatraB.C. StorckM.D. BandH. ChenC. JägerS. ShalesM. PortnoyD.A. HernandezR. CoscoyL. CoxJ.S. KroganN.J. An Mtb-human protein-protein interaction map identifies a switch between host antiviral and antibacterial responses.Mol. Cell2018714637648.e510.1016/j.molcel.2018.07.010 30118682
     [Google Scholar]
  19. StuartJ.M. SegalE. KollerD. KimS.K. A gene-coexpression network for global discovery of conserved genetic modules.Science2003302564324925510.1126/science.1087447 12934013
     [Google Scholar]
  20. LangfelderP. HorvathS. WGCNA: An R package for weighted correlation network analysis.BMC Bioinformatics20089155910.1186/1471‑2105‑9‑559 19114008
     [Google Scholar]
  21. StiensJ. TanY.Y. JoyceR. ArnvigK.B. KendallS.L. NobeliI. Using a whole genome co‐expression network to inform the functional characterisation of predicted genomic elements from Mycobacterium tuberculosis transcriptomic data.Mol. Microbiol.2023119438140010.1111/mmi.15055 36924313
     [Google Scholar]
  22. WenZ. WuL. WangL. OuQ. MaH. WuQ. ZhangS. SongY. Comprehensive genetic analysis of tuberculosis and identification of candidate biomarkers.Front. Genet.20221383273910.3389/fgene.2022.832739 35345666
     [Google Scholar]
  23. XuY. TanY. ZhangX. ChengM. HuJ. LiuJ. ChenX. ZhuJ. Comprehensive identification of immuno-related transcriptional signature for active pulmonary tuberculosis by integrated analysis of array and single cell RNA-seq.J. Infect.202285553454410.1016/j.jinf.2022.08.017 36007657
     [Google Scholar]
  24. DaiQ. ChenH. YiW.J. ZhaoJ.N. ZhangW. HeP.A. LiuX.Q. ZhengY.F. ShiZ.X. Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.Brief. Bioinform.2024256bbae59610.1093/bib/bbae596 39541192
     [Google Scholar]
  25. KongR. XuX. LiuX. HeP. ZhangM.Q. DaiQ. 2SigFinder: the combined use of small-scale and large-scale statistical testing for genomic island detection from a single genome.BMC Bioinforma.202021115910.1186/s12859‑020‑3501‑2 32349677
     [Google Scholar]
  26. GuoX. GuoY. ChenH. LiuX. HeP. LiW. ZhangM.Q. DaiQ. Systematic comparison of genome information processing and boundary recognition tools used for genomic island detection.Comput. Biol. Med.202316610755010.1016/j.compbiomed.2023.107550 37826950
     [Google Scholar]
  27. YangS. WangY. ChenY. DaiQ. MASQC: Next generation sequencing assists third generation sequencing for quality control in N6-methyladenine DNA identification.Front. Genet.20201126910.3389/fgene.2020.00269 32269589
     [Google Scholar]
  28. YangZ. YiW. TaoJ. LiuX. ZhangM.Q. ChenG. DaiQ. HPVMD-C: A disease-based mutation database of human papillomavirus in China.Database20222022baac01810.1093/database/baac018
     [Google Scholar]
  29. DaiQ. BaoC. HaiY. MaS. ZhouT. WangC. WangY. HuoW. LiuX. YaoY. XuanZ. ChenM. ZhangM.Q. MTGIpick allows robust identification of genomic islands from a single genome.Brief. Bioinform.2018193361373 28025178
     [Google Scholar]
  30. WangY. XuY. YangZ. LiuX. DaiQ. Using recursive feature selection with random forest to improve protein structural class prediction for low-similarity sequences.Comput. Math. Methods Med.202120211910.1155/2021/5529389 34055035
     [Google Scholar]
  31. YangB. ZhaiF. LiZ. WangX. DengX. CaoZ. LiuY. WangR. JiangJ. ChengX. Identification of ferroptosis-related gene signature for tuberculosis diagnosis and therapy efficacy.iScience202427711018210.1016/j.isci.2024.110182 38989455
     [Google Scholar]
  32. MolaniaR. ForoutanM. Gagnon-BartschJ.A. GandolfoL.C. JainA. SinhaA. OlshanskyG. DobrovicA. PapenfussA.T. SpeedT.P. Removing unwanted variation from large-scale RNA sequencing data with PRPS.Nat. Biotechnol.2023411829510.1038/s41587‑022‑01440‑w 36109686
     [Google Scholar]
  33. PushkaranA.C. VinodV. VanuopadathM. NairS.S. NairS.V. VasudevanA.K. BiswasR. MohanC.G. Combination of repurposed drug diosmin with amoxicillin-clavulanic acid causes synergistic inhibition of mycobacterial growth.Sci. Rep.201991680010.1038/s41598‑019‑43201‑x 31043655
     [Google Scholar]
  34. DuanH. GeW. ZhangA. XiY. ChenZ. LuoD. ChengY. FanK.S. HorvathS. SofroniewM.V. ChengL. YangZ. SunY.E. LiX. Transcriptome analyses reveal molecular mechanisms underlying functional recovery after spinal cord injury.Proc. Natl. Acad. Sci. USA201511243133601336510.1073/pnas.1510176112 26460053
     [Google Scholar]
  35. SiaJ.K. RengarajanJ. Immunology of Mycobacterium tuberculosis infections.Microbiol. Spectr.2019747.4.610.1128/microbiolspec.GPP3‑0022‑201831298204
     [Google Scholar]
  36. LinP.L. FlynnJ.L. The end of the binary era: Revisiting the spectrum of tuberculosis.J. Immunol.2018201925412548
     [Google Scholar]
  37. HardingC.V. BoomW.H. Regulation of antigen presentation by Mycobacterium tuberculosis: A role for Toll-like receptors.Nat. Rev. Microbiol.20108429630710.1038/nrmicro2321 20234378
     [Google Scholar]
  38. Portal-CelhayC. TufarielloJ.M. SrivastavaS. ZahraA. KlevornT. GraceP.S. MehraA. ParkH.S. ErnstJ.D. JacobsW.R. PhilipsJ.A. Mycobacterium tuberculosis EsxH inhibits ESCRT-dependent CD4+ T-cell activation.Nat. Microbiol.2016221623210.1038/nmicrobiol.2016.232 27918526
     [Google Scholar]
  39. LuL.L. ChungA.W. RosebrockT.R. GhebremichaelM. YuW.H. GraceP.S. SchoenM.K. TafesseF. MartinC. LeungV. MahanA.E. SipsM. KumarM.P. TedescoJ. RobinsonH. TkachenkoE. DraghiM. FreedbergK.J. StreeckH. SuscovichT.J. LauffenburgerD.A. RestrepoB.I. DayC. FortuneS.M. AlterG. A functional role for antibodies in tuberculosis.Cell20161672433443.e1410.1016/j.cell.2016.08.072 27667685
     [Google Scholar]
  40. DengS. ShenS. LiuK. El-AshramS. AlouffiA. Cenci-GogaB.T. YeG. CaoC. LuoT. ZhangH. LiW. LiS. ZhangW. WuJ. ChenC. Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs.Front. Genet.202314104189210.3389/fgene.2023.1041892 36845395
     [Google Scholar]
  41. HasankhaniA. BahramiA. MackieS. MaghsoodiS. AlawamlehH.S.K. SheybaniN. Safarpoor DehkordiF. RajabiF. JavanmardG. KhademH. BarkemaH.W. De DonatoM. RETRACTED: In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection.Front. Microbiol.202213104131410.3389/fmicb.2022.1041314 36532492
     [Google Scholar]
  42. DouekD.C. AltmannD.M. T‐cell apoptosis and differential human leucocyte antigen class II expression in human thymus.Immunology200099224925610.1046/j.1365‑2567.2000.00940.x 10692044
     [Google Scholar]
  43. HornellT.M. BursterT. JahnsenF.L. PashineA. OchoaM.T. HardingJ.J. MacaubasC. LeeA.W. ModlinR.L. MellinsE.D. Human dendritic cell expression of HLA-DO is subset specific and regulated by maturation.J. Immunol.2006176635363547
     [Google Scholar]
  44. KarlssonL. SurhC.D. SprentJ. PetersonP.A. A novel class II MHC molecule with unusual tissue distribution.Nature1991351632648548810.1038/351485a0 1675431
     [Google Scholar]
  45. DenzinL.K. CresswellP. HLA-DM induces clip dissociation from MHC class II αβ dimers and facilitates peptide loading.Cell199582115516510.1016/0092‑8674(95)90061‑6 7606781
     [Google Scholar]
  46. SloanV.S. CameronP. PorterG. GammonM. AmayaM. MellinsE. ZallerD.M. Mediation by HLA-DM of dissociation of peptides from HLA-DR.Nature1995375653480280610.1038/375802a0 7596415
     [Google Scholar]
  47. DenzinL.K. Sant’AngeloD.B. HammondC. SurmanM.J. CresswellP. Negative regulation by HLA-DO of MHC class II-restricted antigen processing.Science1997278533510610910.1126/science.278.5335.106 9311912
     [Google Scholar]
  48. NepalR.M. VesoskyB. TurnerJ. BryantP.DM but not cathepsin L, is required to control an aerosol infection with Mycobacterium tuberculosis.J. Leukoc. Biol.20088441011101810.1189/jlb.1207852 18591414
     [Google Scholar]
  49. PuteraI. SchrijverB. KolijnP.M. van StigtA.C. ten BergeJ.C.E.M. IJspeertH. NagtzaamN.M.A. SwagemakersS.M.A. van LaarJ.A.M. AgrawalR. RombachS.M. van HagenP.M. La Distia NoraR. DikW.A. A serum B-lymphocyte activation signature is a key distinguishing feature of the immune response in sarcoidosis compared to tuberculosis.Commun. Biol.202471111410.1038/s42003‑024‑06822‑1 39256610
     [Google Scholar]
  50. ShimakataT. FujitaY. KusakaT. Involvement of one of two enoyl-CoA hydratases and enoyl-CoA reductase in the acetyl-CoA-dependent elongation of medium chain fatty acids by Mycobacterium smegmatis.J. Biochem.19808841051105810.1093/oxfordjournals.jbchem.a133056 7451405
     [Google Scholar]
  51. CroweA.M. CasabonI. BrownK.L. LiuJ. LianJ. RogalskiJ.C. HurstT.E. SnieckusV. FosterL.J. EltisL.D. Catabolism of the last two steroid rings in Mycobacterium tuberculosis and other bacteria.MBio201782e00321e1710.1128/mBio.00321‑17 28377529
     [Google Scholar]
  52. YuanY. ZhuY. CraneD.D. BarryC.E. The effect of oxygenated mycolic acid composition on cell wall function and macrophage growth in Mycobacterium tuberculosis.Mol. Microbiol.19982961449145810.1046/j.1365‑2958.1998.01026.x 9781881
     [Google Scholar]
  53. BondsA.C. YuanT. WermanJ.M. JangJ. LuR. NesbittN.M. Garcia-DiazM. SampsonN.S. Post-translational succinylation of Mycobacterium tuberculosis enoyl-CoA hydratase EchA19 slows catalytic hydration of cholesterol catabolite 3-Oxo-chol-4,22-diene-24-oyl-CoA.ACS Infect. Dis.2020682214222410.1021/acsinfecdis.0c00329 32649175
     [Google Scholar]
  54. YangM. GujaK.E. ThomasS.T. Garcia-DiazM. SampsonN.S. A distinct MaoC-like enoyl-CoA hydratase architecture mediates cholesterol catabolism in Mycobacterium tuberculosis.ACS Chem. Biol.20149112632264510.1021/cb500232h 25203216
     [Google Scholar]
  55. MaragozidisP. PapanastasiE. ScutelnicD. TotomiA. KokkoriI. ZarogiannisS.G. KerenidiT. GourgoulianisK.I. BalatsosN.A.A. Poly(A)-specific ribonuclease and Nocturnin in squamous cell lung cancer: Prognostic value and impact on gene expression.Mol. Cancer201514118710.1186/s12943‑015‑0457‑3 26541675
     [Google Scholar]
  56. StuartB.D. ChoiJ. ZaidiS. XingC. HolohanB. ChenR. ChoiM. DharwadkarP. TorresF. GirodC.E. WeisslerJ. FitzgeraldJ. KershawC. Klesney-TaitJ. MagetoY. ShayJ.W. JiW. BilguvarK. ManeS. LiftonR.P. GarciaC.K. Exome sequencing links mutations in PARN and RTEL1 with familial pulmonary fibrosis and telomere shortening.Nat. Genet.201547551251710.1038/ng.3278 25848748
     [Google Scholar]
  57. ShuklaS. SchmidtJ.C. GoldfarbK.C. CechT.R. ParkerR. Inhibition of telomerase RNA decay rescues telomerase deficiency caused by dyskerin or PARN defects.Nat. Struct. Mol. Biol.201623428629210.1038/nsmb.3184 26950371
     [Google Scholar]
  58. GhoshS.G. ScalaM. BeetzC. HelmanG. StanleyV. YangX. BreussM.W. MazaheriN. SelimL. HadipourF. PaisL. StutterdC.A. KarageorgouV. BegtrupA. CrunkA. JuusolaJ. WillaertR. FloreL.A. KennellyK. SpencerC. BrownM. TrapaneP. HurstA.C.E. Lane RutledgeS. GoodloeD.H. McDonaldM.T. ShashiV. SchochK. TomoumH. ZaitounR. HadipourZ. GalehdariH. PagnamentaA.T. MojarradM. SedaghatA. DiasP. QuintasS. EslahiA. ShariatiG. BauerP. SimonsC. HouldenH. IssaM.Y. ZakiM.S. MaroofianR. GleesonJ.G. A relatively common homozygous TRAPPC4 splicing variant is associated with an early-infantile neurodegenerative syndrome.Eur. J. Hum. Genet.202129227127910.1038/s41431‑020‑00717‑5 32901138
     [Google Scholar]
  59. Van BergenN.J. GuoY. Al-DeriN. LipatovaZ. StangaD. ZhaoS. MurtazinaR. GyurkovskaV. PehlivanD. MitaniT. GezdiriciA. AntonyJ. CollinsF. WillisM.J.H. Coban AkdemirZ.H. LiuP. PunethaJ. HunterJ.V. JhangianiS.N. FatihJ.M. RosenfeldJ.A. PoseyJ.E. GibbsR.A. KaracaE. MasseyS. RanasingheT.G. SleimanP. TroedsonC. LupskiJ.R. SacherM. SegevN. HakonarsonH. ChristodoulouJ. Deficiencies in vesicular transport mediated by TRAPPC4 are associated with severe syndromic intellectual disability.Brain2020143111213010.1093/brain/awz374 31794024
     [Google Scholar]
  60. RenY. QianY. AiL. XieY. GaoY. ZhuangZ. ChenJ. ChenY.X. FangJ.Y. TRAPPC4 regulates the intracellular trafficking of PD-L1 and antitumor immunity.Nat. Commun.2021121540510.1038/s41467‑021‑25662‑9 34518538
     [Google Scholar]
  61. WengY.R. KongX. YuY.N. WangY.C. HongJ. ZhaoS.L. FangJ.Y. The role of ERK2 in colorectal carcinogenesis is partly regulated by TRAPPC4.Mol. Carcinog201453S1E72E84(Suppl. 1).10.1002/mc.2203123625650
     [Google Scholar]
  62. WishartM.J. TaylorG.S. SlamaJ.T. DixonJ.E. PTEN and myotubularin phosphoinositide phosphatases: bringing bioinformatics to the lab bench.Curr. Opin. Cell. Biol.200113217218110.1016/S0955‑0674(00)00195‑2 11248551
     [Google Scholar]
  63. LaporteJ. BlondeauF. Buj-BelloA. MandelJ.L. The myotubularin family: From genetic disease to phosphoinositide metabolism.Trends Genet.200117422122810.1016/S0168‑9525(01)02245‑4 11275328
     [Google Scholar]
  64. LiY. MaC. LiW. YangY. LiX. LiuJ. WangJ. LiS. LiuY. LiK. LiJ. HuangD. ChenR. LvL. LiM. LuoX.J. A missense variant in NDUFA6 confers schizophrenia risk by affecting YY1 binding and NAGA expression.Mol. Psychiatry202126116896691110.1038/s41380‑021‑01125‑x 33931730
     [Google Scholar]
  65. AlstonC.L. HeidlerJ. DibleyM.G. KremerL.S. TaylorL.S. FratterC. FrenchC.E. GlasgowR.I.C. FeichtingerR.G. DelonI. PagnamentaA.T. DollingH. LemondeH. AitonN. BjørnstadA. HennekeL. GärtnerJ. ThieleH. TauchmannovaK. QuaghebeurG. HoustekJ. SperlW. RaymondF.L. ProkischH. MayrJ.A. McFarlandR. PoultonJ. RyanM.T. WittigI. HennekeM. TaylorR.W. Bi-allelic mutations in NDUFA6 establish its role in early-onset isolated mitochondrial complex I Deficiency.Am. J. Hum. Genet.2018103459260110.1016/j.ajhg.2018.08.013 30245030
     [Google Scholar]
/content/journals/cchts/10.2174/0113862073401054250526094910
Loading
Comprehensive Bioinformatics Analysis for the Identification of Hub Genes and Critical Signaling Pathways Differentiating Latent and Active Tuberculosis
Comb Chem High Throughput Screen 28, 3440 (2025); https://doi.org/10.2174/0113862073401054250526094910
/content/journals/cchts/10.2174/0113862073401054250526094910
/content/journals/cchts/10.2174/0113862073401054250526094910
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): bioinformatics analysis; latent tuberculosis infection; RT-qPCR; transcriptomics; Tuberculosis

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test