Discovery of PANoptosis-related signatures correlates with immune cell infiltration in psoriasis.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 17 05 2024
accepted: 25 08 2024
medline: 1 11 2024
pubmed: 1 11 2024
entrez: 31 10 2024
Statut: epublish

Résumé

Psoriasis is an inflammatory skin disease that relapses frequently. Keratinocyte apoptosis dysregulation plays a crucial role in the pathological mechanisms of psoriasis. PANoptosis is a process with intermolecular interaction among pyroptosis, apoptosis, and necroptosis. The mechanism of PANoptosis in the occurrence and development of psoriasis is still unclear. Here we present a novel approach by identifying PANoptosis-related signatures (PANoptosis-sig) from skin tissue of psoriasis patients and healthy controls on transcriptional and protein levels. Five PANoptosis-sig (TYMP, S100A8, S100A9, NAMPT, LCN2) were identified. Enrichment analysis showed they were mainly enriched in response to leukocyte aggregation, leukocyte migration, chronic inflammatory response and IL-17 signaling pathway. Single cell transcriptome analysis showed TYMP and NAMPT were expressed in almost all cell populations, while LCN2, S100A8 and S100A9 were significantly highly expressed in keratinocyte. We then constructed predictive and diagnostic models with the PANoptosis-sig and evaluated their performance. Finally, unsupervised consensus clustering analysis was conducted to ascertain psoriasis molecular subtypes by the PANoptosis-sig. The psoriasis cohort was divided into two distinct subtypes. Immune landscape showed that the stromal score of cluster 1 was significantly higher than cluster 2, while the immune and estimate scores of cluster 2 were expressively higher than cluster 1. Cluster 1 exhibited high expression of Plasma cells, Tregs and Mast cells resting, while cluster 2 showed high expression of T cells, Macrophages M1, Dendritic cells activated, and Neutrophils in immune infiltration analysis. And cluster 2 was more sensitive to immune checkpoints. In conclusion, our findings revealed potential biomarkers and therapeutic targets for the prevention, diagnosis, and treatment of psoriasis, enhancing our understanding of the molecular mechanisms underlying PANoptosis.

Identifiants

pubmed: 39480805
doi: 10.1371/journal.pone.0310362
pii: PONE-D-24-19929
doi:

Substances chimiques

Lipocalin-2 0
Calgranulin B 0
LCN2 protein, human 0
Calgranulin A 0
Cytokines 0
S100A9 protein, human 0
S100A8 protein, human 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0310362

Informations de copyright

Copyright: © 2024 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Li Wu (L)

Department of Anesthesiology, Shanxi Provincial People's Hospital, Taiyuan, China.
School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.

Xin-Long Jiao (XL)

Academy of Medical Sciences, Shanxi Medical University, Taiyuan, China.
Department of Social Medicine, School of Public Health, Shanxi Medical University, Taiyuan, China.

Ming Jing (M)

Jinan Dermatosis Prevention and Control Hospital, Jinan, China.

Sheng-Xiao Zhang (SX)

Department of Rheumatology and Immunology, The Second Hospital of Shanxi Medical University, Taiyuan, China.

Yang Wang (Y)

School of Management, Shanxi Medical University, Taiyuan, China.
Shanxi Key Laboratory of Big Data for Clinical Decision Research, Shanxi Medical University, Taiyuan, China.

Chen-Long Li (CL)

Department of Anesthesiology, Shanxi Provincial People's Hospital, Taiyuan, China.

Gao-Xiang Shi (GX)

Department of Anesthesiology, Shanxi Provincial People's Hospital, Taiyuan, China.
Department of Anaesthesia, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China.

Zhuo-Yang Li (ZY)

School of Management, Shanxi Medical University, Taiyuan, China.

Ge-Liang Liu (GL)

School of Management, Shanxi Medical University, Taiyuan, China.

Kai Yan (K)

School of Management, Shanxi Medical University, Taiyuan, China.
Shanxi Key Laboratory of Big Data for Clinical Decision Research, Shanxi Medical University, Taiyuan, China.
Department of Information Technology, Digital Health Guidance Center of Shanxi Province, Taiyuan, China.

Li-Xuan Yan (LX)

School of Management, Shanxi Medical University, Taiyuan, China.
Department of Anesthesiology, Second Hospital of Shanxi Medical University, Taiyuan, China.

Qi Wang (Q)

Department of Anesthesiology, Shanxi Provincial People's Hospital, Taiyuan, China.
School of Management, Shanxi Medical University, Taiyuan, China.
Shanxi Key Laboratory of Big Data for Clinical Decision Research, Shanxi Medical University, Taiyuan, China.

Pei-Feng He (PF)

Shanxi Key Laboratory of Big Data for Clinical Decision Research, Shanxi Medical University, Taiyuan, China.

Qi Yu (Q)

School of Management, Shanxi Medical University, Taiyuan, China.
Shanxi Key Laboratory of Big Data for Clinical Decision Research, Shanxi Medical University, Taiyuan, China.

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Classifications MeSH