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Identification and validation of a pyroptosis-related signature in identifying active tuberculosis via a deep
Yuchen Liu1,2,3,4, Lifan Zhang1,2,3, Fengying Wu1,2,3
1Division of Infectious Diseases, Department of Internal Medicine, State Key Laboratory of Complex Severe and Rare Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Introduction:
Active tuberculosis (ATB), instigated by Mycobacterium tuberculosis (M.tb), rises as a primary instigator of morbidity and mortality within the realm of infectious illnesses. A significant portion of M.tb infections maintain an asymptomatic nature, recognizably termed as latent tuberculosis infections (LTBI). The complexities inherent to its diagnosis significantly hamper the initiatives aimed at its control and eventual eradication.
Methodology:
Utilizing the Gene Expression Omnibus (GEO), we procured two dedicated microarray datasets, labeled GSE39940 and GSE37250. The technique of weighted correlation network analysis was employed to discern the co-expression modules from the differentially expressed genes derived from the first dataset, GSE39940. Consequently, a pyroptosis-related module was garnered, facilitating the identification of a pyroptosis-related signature (PRS) diagnostic model through the application of a neural network algorithm. With the aid of Single Sample Gene Set Enrichment Analysis (ssGSEA), we further examined the immune cells engaged in the pyroptosis process in the context of active ATB. Lastly, dataset GSE37250 played a crucial role as a validating cohort, aimed at evaluating the diagnostic prowess of our model.
Results:
In executing the Weighted Gene Co-expression Network Analysis (WGCNA), a total of nine discrete co-expression modules were lucidly elucidated. Module 1 demonstrated a potent correlation with pyroptosis. A predictive diagnostic paradigm comprising three pyroptosis-related signatures, specifically AIM2, CASP8, and NAIP, was devised accordingly. The established PRS model exhibited outstanding accuracy across both cohorts, with the area under the curve (AUC) being respectively articulated as 0.946 and 0.787.
Conclusion:
The present research succeeded in identifying the pyroptosis-related signature within the pathogenetic framework of ATB. Furthermore, we developed a diagnostic model which exuded a remarkable potential for efficient and accurate diagnosis.
Insights
This study identifies a pyroptosis-related signature for diagnosing active tuberculosis (ATB). A novel diagnostic model using AIM2, CASP8, and NAIP shows high accuracy, aiding in ATB control.
Area of Science:
- Immunology
- Genomics
- Infectious Diseases
Background:
- Active tuberculosis (ATB) caused by Mycobacterium tuberculosis (M.tb) is a major global health threat.
- Latent tuberculosis infections (LTBI) are common but difficult to diagnose, hindering eradication efforts.
Purpose of the Study:
- To identify pyroptosis-related molecular signatures for active tuberculosis (ATB) diagnosis.
- To develop and validate a diagnostic model for ATB based on these signatures.
Main Methods:
- Weighted Gene Co-expression Network Analysis (WGCNA) on GEO datasets (GSE39940, GSE37250).
- Identification of pyroptosis-related modules and signatures using neural network algorithms.
- Validation of the diagnostic model using a separate cohort.
Main Results:
- WGCNA identified nine co-expression modules, with Module 1 strongly correlated with pyroptosis.
- A pyroptosis-related signature (PRS) model was developed using AIM2, CASP8, and NAIP.
- The PRS model demonstrated high diagnostic accuracy (AUCs of 0.946 and 0.787 in two cohorts).
Conclusions:
- Pyroptosis-related signatures are implicated in the pathogenesis of ATB.
- A novel PRS diagnostic model shows significant potential for accurate and efficient ATB diagnosis.
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