Evidence map›Paper›PMID 38935691›Full record

ArticlePloS one2024

Computational reassessment of RNA-seq data reveals key genes in active tuberculosis.

Rakesh Arya, Hemlata Shakya, Reetika Chaurasia, Surendra Kumar, Joseph M Vinetz, Jong Joo Kim

Erratum issuedAbstract read
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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Rakesh AryaDepartment of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea.ORCID 0000-0003-2089-0891
Hemlata ShakyaDepartment of Biomedical Engineering, Shri G. S. Institute of Technology and Science, Indore, Madhya Pradesh, India.
Reetika ChaurasiaDepartment of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, United States of America.ORCID 0000-0002-7718-4720
Surendra KumarDepartment of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.
Joseph M VinetzDepartment of Internal Medicine, Section of Infectious Diseases, Yale University School of Medicine, New Haven, CT, United States of America.
Jong Joo KimDepartment of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance.

methodsTwo human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment.

resultsA total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity.

conclusionsThe study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB.

Indexed as

TuberculosisBiomarkersComputational BiologyDatabases, GeneticGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansProtein Interaction MapsRNA-SeqROC CurveBiomarkers

Identifiers

PMID38935691
PMCPMC11210783

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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.