Evidence map›Paper›PMID 37950733›Full record

ArticleAging2023

Identification and validation of ubiquitination-related signature and subgroups in immune microenvironment of tuberculosis.

Peipei Zhou, Jie Shen, Xiao Ge, Haien Cheng, Yanli Sun, Meng Li, Heng Li, Zhengjun Yi, Zhenpeng Li

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Aging, 2023. 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
0.5field-weighted citation impact, top 30% of its field
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, 3 citations in OpenAlex.

  1. Review
  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

9 authors at 1 institution in 1 country.

Peipei ZhouSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Jie ShenSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Xiao GeSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Haien ChengSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Yanli SunSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Meng LiSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Heng LiSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Zhengjun YiSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Zhenpeng LiSchool of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China.
Weifang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background

methodsOur research entailed utilizing single sample gene set enrichment analysis (ssGSEA) in combination with several machine learning techniques to discern the Ub-related signature of TB and identify potential diagnostic markers that distinguish TB from healthy controls (HC).

resultsIn summary, we used the ssGSEA algorithm to determine the score of Ub families (E1, E2, E3, DUB, UBD, and ULD). Notably, the score of E1, E3, and UBD were lower in TB patients than in HC individuals, and we identified 96 Ub-related differentially expressed genes (UbDEGs). Employing machine learning algorithms, we identified 11 Ub-related hub genes and defined two distinct Ub-related subclusters. Notably, through GSVA and functional analysis, it was determined that these subclusters were implicated in numerous immune-related processes. We further investigated these Ub-related hub genes in four TB-related diseases and found that TRIM68 exhibited higher correlations with various immune cells in different conditions, indicating that it may play a crucial role in the immune process of these diseases.

conclusionThe observed enrichment of Ub-related gene expression in TB patients emphasizes the potential involvement of ubiquitination in the progression of TB. These significant findings establish a basis for future investigations to elucidate the molecular mechanisms associated with TB, select suitable diagnostic biomarkers, and design innovative therapeutic interventions for combating this fatal infectious disease.

Indexed as

Mycobacterium tuberculosisTuberculosisAlgorithmsAutoantigensHumansTripartite Motif ProteinsUbiquitinationUbiquitin-Protein LigasesAutoantigensTRIM68 protein, humanTripartite Motif ProteinsUbiquitin-Protein Ligasesbiomarkerimmune cell infiltrationmachine learningtuberculosisubiquitination

Identifiers

PMID37950733
PMCPMC10683621
OpenAlexW4388520517

What OpenQuestion holds

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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.