Evidence map›Paper›PMID 29636444›Full record

ArticleEmerging microbes & infections2018

Identification of potential urine proteins and microRNA biomarkers for the diagnosis of pulmonary tuberculosis patients.

Jieru Wang, Xiaojie Zhu, Xuekai Xiong, Pan Ge, Han Liu, Ningning Ren, Farhan Anwar Khan, Xia Zhou, Li Zhang, Xu Yuan and 6 more

Abstract read
In one paragraph

Article in Emerging microbes & infections, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
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  8. The role ofFrontiers in microbiology · 2024
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  13. A systematic review of potential screening biomarkers for active TB disease.Journal of clinical tuberculosis and other mycobacterial diseases · 2021
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Jieru WangThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Xiaojie ZhuThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Xuekai XiongThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Pan GeThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Han LiuThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Ningning RenThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Farhan Anwar KhanThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Xia ZhouTuberculosis Department, Wuhan Medical Treatment Center, Wuhan, 430023, China.
Li ZhangTuberculosis Department, Wuhan Medical Treatment Center, Wuhan, 430023, China.
Xu YuanThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Xi ChenThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Yingyu ChenThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China. chenyingyu@mail.hzau.edu.cn.
Changmin HuCollege of Veterinary Medicine, Huazhong Agricultural University, Wuhan, 430070, China.
Ian D RobertsonThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Huanchun ChenThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China.
Aizhen GuoThe State Key Laboratory of Agricultural Microbiology, Wuhan, 430070, China. aizhen@mail.hzau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study identified urinary biomarkers for tuberculosis (TB) diagnosis. The urine proteomic profiles of 45 pulmonary tuberculosis patients prior to anti-TB treatment and 45 healthy controls were analyzed and compared using two-dimensional electrophoresis with matrix-assisted laser desorption/ionization time of flight mass spectrometry. Nineteen differentially expressed proteins were identified preliminarily, and western blotting and qRT-PCR were performed to confirm these changes at the translational and transcriptional levels, respectively, using samples from 122 additional pulmonary tuberculosis patients and 73 additional healthy controls. Two proteins, mannose-binding lectin 2 and a 35-kDa fragment of inter-α-trypsin inhibitor H4, exhibited the highest differential expression. We constructed a protein-microRNA interaction network that primarily involved complement and inflammatory responses. Eleven microRNAs from microRNA-target protein interactions were screened and validated using qRT-PCR with some of the above samples, including 97 pulmonary tuberculosis patients and 48 healthy controls. Only miR-625-3p exhibited significant differential expression (p < 0.05). miR-625-3p was increased to a greater extent in samples of smear-positive than smear-negative patients. miR-625-3p was predicted to target mannose-binding lectin 2 protein. A binary logistic regression model based on miR-625-3p, mannose-binding lectin 2, and inter-α-trypsin inhibitor H4 was further established. This three-biomarker combination exhibited better performance for tuberculosis diagnosis than individual biomarkers or any two-biomarker combination and generated a diagnostic sensitivity of 85.87% and a specificity of 87.50%. These novel urine biomarkers may significantly improve tuberculosis diagnosis.

Indexed as

BiomarkersBlood ProteinsElectrophoresis, Gel, Two-DimensionalFemaleGlycoproteinsHumansMaleMannose-Binding LectinMicroRNAsMiddle AgedProteinase Inhibitory Proteins, SecretoryProteomicsSensitivity and SpecificitySpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationTuberculosis, PulmonaryBiomarkersBlood ProteinsGlycoproteinsITIH4 protein, humanMannose-Binding LectinMicroRNAsMIRN625 microRNA, humanProteinase Inhibitory Proteins, Secretory

Identifiers

PMID29636444
PMCPMC5893550

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.