Research Paper Volume 15, Issue 21 pp 12570—12587

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

Peipei Zhou1, *, , Jie Shen1, *, , Xiao Ge1, *, , Haien Cheng1, , Yanli Sun1, , Meng Li1, , Heng Li1,2, , Zhengjun Yi1,2, , Zhenpeng Li1,2, ,

  • 1 School of Medical Laboratory, Weifang Medical University, Weifang, Shandong 261053, People’s Republic of China
  • 2 Engineering Research Institute of Precision Medicine Innovation and Transformation of Infections Diseases, Weifang Medical University, Weifang, Shandong 261053,
* Equal contribution and share first authorship

Received: July 4, 2023       Accepted: October 7, 2023       Published: November 9, 2023
How to Cite

Copyright: © 2023 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Background: Mycobacterium tuberculosis (Mtb) is the bacterial pathogen responsible for causing tuberculosis (TB), a severe public health concern that results in numerous deaths worldwide. Ubiquitination (Ub) is an essential physiological process that aids in maintaining homeostasis and contributes to the development of TB. Therefore, the main objective of our study was to investigate the potential role of Ub-related genes in TB.

Methods: Our 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).

Results: In 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.

Conclusion: The 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.


TB: Tuberculosis; HC: Healthy control; Ub: Ubiquitination; ssGSEA: Single sample gene set enrichment analysis; GEO: Gene Expression Omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSVA: Gene set variation analysis; LASSO: Least Absolute Shrinkage and Selection Operator; SVM: Support vector machine; RF: Random forest; Xgboost: eXtreme Gradient Boosting; ROC: Receiver operating characteristic; BP: Biological process; CC: Cellular component; MF: Molecular function; RA: Rheumatoid arthritis; COPD: Chronic obstructive pulmonary disease; LA: Lung adenocarcinoma.