ArticleFrontiers in cellular and infection microbiology2025
Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.
Article in Frontiers in cellular and infection microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Object: NETs constitute a pivotal mechanism in the pathogenesis and progression of TB. Despite their recognized importance, the genetic underpinnings of NETs in TB remain inadequately elucidated. Accordingly, the present study endeavors to delineate the molecular characteristics of NRGs in TB, with the objective of reliably identifying associated molecular clusters and biomarkers. Methods: Gene expression profiles were analyzed from integrated datasets retrieved from the GEO database. Differential analysis, WGCNA, and an ensemble of 113 machine learning algorithms were employed to identify the core NETs genes. Subsequently, TB patients were stratified into distinct subtypes based on the expression profiles of these core genes, and the differences in immune infiltration characteristics among the subtypes were systematically compared. Finally, RT-qPCR was utilized to validate the differential expression of the key NETs core genes. Results: Analysis of the integrated GSE83456 and GSE54992 datasets yielded 630 DEGs. WGCNA subsequently identified a module comprising 1,252 genes, from which 26 key NETs genes were extracted via intersection with known NRGs. Among the ensemble of 113 machine learning methods, the "StepgIm[both]+RF" algorithm demonstrated superior performance, ultimately identifying six core NETs genes. Consensus clustering based on the expression profiles of these core genes stratified patients into two distinct subtypes. Functional enrichment analysis further underscored the predominance of immune-related pathways in subtype B. Moreover, immune infiltration analysis revealed marked differences in immune cell composition between the subtypes, thereby confirming a close association between the core NETs genes and these immunological disparities. Conclusion: Core NETs genes are pivotal in the pathogenesis and progression of tuberculosis, and they hold significant promise as novel biomarkers for the early diagnosis and targeted treatment of TB.
Indexed as
Identifiers
What Socratic holds
Registered trials
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.