Evidence map›Paper›PMID 40164948›Full record

ArticleInflammation research : official journal of the European Histamine Research Society ... [et al.]2025

Identification of inflammatory protein biomarkers for predicting the different subtype of adult with tuberculosis: an Olink proteomic study.

Yunlin Song, Buzukela Abuduaini, Xinting Yang, Jiyuan Zhang, Guirong Wang, Xiaobo Lu

Abstract read
In one paragraph

Article in Inflammation research : official journal of the European Histamine Research Society ... [et al.], 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Targeting Latent Tuberculosis: Immunogenic Evaluation of aInternational journal of molecular sciences · 2026
    Article
  2. Article
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

6 authors.

Yunlin Song *Department of Intensive Care Unit, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
Buzukela Abuduaini *Department of Intensive Care Unit, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, China.
Xinting YangTuberculosis Department, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China.
Jiyuan ZhangFirst Clinical Institute of Xinjiang Medical University, Urumqi, 830054, Xinjiang, China.
Guirong WangDepartment of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing, 101149, China. wangguirong1230@ccmu.edu.cn.
Xiaobo LuCenter of Infection, The First Affiliated Hospital of Xinjiang Medical University, 393 South Li Yu Shan Road, Urumqi, 830054, Xinjiang, China. xjykdluxiaobo@126.com.

Funding

the National Natural Science Foundation of China 82360381Tianshan Outstanding Medical and Health High-level Talent Training Project TSYC202301B013Treatment of Central Asian High Incidence Diseases Fund of China SKL-HIDCA-2021-JH13
6 · The paper itself

Abstract

objectiveThis study aimed to identify the potential inflammatory molecular biomarkers that could be utilized for the early prediction of different subtypes of tuberculosis (TB) in adults.

methodsPlasma samples were obtained from a cohort of adults diagnosed with 48 cases of active TB, including drug-susceptible TB (S-TB, n = 28), multidrug-resistant TB (R-TB, n = 20), latent TB infection (LTBI, n = 20), as well as a control group of healthy individuals without any infection (HC, n = 20). The expression level of 92 inflammatory-related proteins was detected by using the high-throughput Olink proteomics platform.

resultsThere were 47 inflammatory proteins showing a significant difference (p < 0.05) among TB, LTBI, and HC groups, and 7 of them differed significantly between HC and LTBI groups, 43 proteins differed considerably between LTBI and TB groups, and overall, CXCL10 and TGF-alpha proteins differed substantially among the three groups which could be used as potential diagnostic biomarkers. Furthermore, SCF demonstrates remarkable discriminatory power in distinguishing TB from LTBI, with an area under the curve (AUC) score of 0.920. SLAMF1 has emerged as the top predictor for distinguishing Sputum Culture-Negative from positive tuberculosis cases, with an AUC of 0.779. The Correlation analyses showed various relationships among co-differentiated proteins. In LTBI versus HC, TGF-alpha and CXCL10 had a strong positive correlation. In non-severe versus severe TB, CXCL10 and CXCL9, as well as TNF and CCL3, were strongly positively correlated, while IL-6 and SCF had a negative correlation. These co-differentiated proteins were found to be enriched in various biological processes and molecular functions related to immune regulation and signaling pathways, such as the p53 signaling pathway, the TNF signaling pathway, and the NF-kappa B signaling pathway, highlighting the complex interplay of these proteins in the immune response to TB infection.

conclusionInflammation-related proteins exhibited distinct expression profiles in various conditions of TB. These proteins are intercorrelated and involve the pathogenesis of tuberculosis by activating diverse immune cells and promoting the secretion of pro-inflammatory cytokines. Their functions influence cellular phenotypes, which play a crucial regulatory role in the interaction between the host and Mycobacterium tuberculosis. These findings suggest that these proteins are potential disease prevention and treatment targets.

Indexed as

TuberculosisAdultBiomarkersChemokine CXCL10FemaleHumansLatent TuberculosisMaleMiddle AgedProteomicsTransforming Growth Factor alphaYoung AdultBiomarkersChemokine CXCL10Transforming Growth Factor alphaBiomarkerInflammationOlink proteomicsTuberculosis

Identifiers

PMID40164948
PMCPMC11958430

What Socratic holds

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LicenceCC BY
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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 Socratic graph.