Evidence map›Paper›PMID 40289505›Full record

ArticleJournal of cellular and molecular medicine2025

Integrated Bioinformatic Analyses Constructed a Novel Immune Escape-Related Signature and Classifier to Predict Tuberculosis.

Zhenpeng Li, Yixin Xu, Huizi Zhou, Wentao Wang, Haien Cheng, Meng Li, Aili Chen, Chao Zhao

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Zhenpeng LiSchool of Medical Laboratory Shandong Second Medical University, Weifang, Shandong, China.
Yixin XuSchool of Medical Laboratory Shandong Second Medical University, Weifang, Shandong, China.
Huizi ZhouSchool of Medical Laboratory Shandong Second Medical University, Weifang, Shandong, China.
Wentao WangSchool of Medical Laboratory Shandong Second Medical University, Weifang, Shandong, China.
Haien ChengSchool of Medical Laboratory Shandong Second Medical University, Weifang, Shandong, China.
Meng LiSchool of Medical Laboratory Shandong Second Medical University, Weifang, Shandong, China.
Aili ChenSchool of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, China.
Chao ZhaoOffice of Academic Affairs, Shandong Second Medical University, Weifang, Shandong, China.ORCID 0009-0005-7382-7740

Funding

Health Science and Technology Development Program of Shandong Province 202101060342Health Science and Technology Development Program of Shandong Province 202302041353National Natural Science Foundation of China 32200512Youth Innovation Team of Colleges and Universities in Shandong Province 2023KJ248
6 · The paper itself

Abstract

Despite its high preventability and curability, tuberculosis (TB) remains a leading cause of morbidity and mortality worldwide. One factor that contributes to the susceptibility and progression of various diseases is immune escape. Therefore, the primary aim of our study was to explore the involvement of immune escape-related genes in the pathogenesis of TB. Two TB datasets retrieved from the gene expression omnibus database were used to identify differentially expressed genes (DEGs). Machine learning was used to identify the hub immune escape-related genes (HIERGs). Weighted gene co-expression network analysis supported and further validated these findings. Subsequently, we scrutinised two distinct subgroups that were determined through the identification of hub immune escape-related genes, and evaluated the distinct function of the subgroups. Our study identified a total of 11 genes related to immune escape in TB. Additionally, six HIERGs were identified through the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Diagnostic models constructed using HIERGs exhibited high accuracy. Two immune escape-related subclusters were identified in TB samples, which delineated differences in immune infiltration cells with the distinct TB subgroups. The heightened expression of six HIERGs serves as a significant risk factor for TB. The six HIERGs also contribute towards the development of TB-related diseases. Our findings demonstrate a significant enrichment of immune escape-related gene expression in individuals with TB, suggesting a close relationship between immune escape activity and immune cell abundance. These results underscore the putative role of immune escape in the advancement of TB by disrupting or perturbing the immune response.

Indexed as

Computational BiologyImmune EvasionTuberculosisDatabases, GeneticGene Expression ProfilingGene Expression RegulationGene Regulatory NetworksHumansMachine LearningSupport Vector MachineTranscriptomebiomarkerimmune escapemachine learningsubgroupstuberculosis

Identifiers

PMID40289505
PMCPMC12034850

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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.