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

Abstract

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.

Fig 1. Identification of DEGs and WGCNA analysis in GSE13355.

(A) Volcano plot depicting DEGs between psoriasis patients and healthy controls. (B) Heatmap of DEGs. (C) GO enrichment analysis of DEGs. (D) KEGG enrichment analysis of DEGs. (E) Sample clustering dendrogram. (F) Optimal soft threshold power. (G) Cluster dendrogram of merged similar modules. (H) Heatmap of module-trait correlations.

Fig 2. Identification of PANoptosis-sig.

(A) Volcano plot depicting DEPs between psoriasis patients and healthy controls. (B) Heatmap of DEPs. (C) Venn diagram depicting the intersection of DEGs, PANoptosis-related genes, and crucial module genes from WGCNA. (D) Venn diagram depicting the intersection of DEGs and DEPs. (E) The expression of PANoptosis-sig between psoriasis patients and healthy controls in proteome. (F-J) ROC curves displaying the AUC values in proteome.

Fig 3. Expression of PANoptosis-sig in microarray and RNAseq datasets.

(A) The expression of PANoptosis-sig in combined microarray datasets. (B-F) ROC curves displaying the AUC values in combined microarray datasets. (G) The expression of PANoptosis-sig in combined RNAseq datasets. (H-L) ROC curves displaying the AUC values in combined RNAseq datasets.

Fig 4. Correlation analysis between PANoptosis-sig and immune cells.

(A-E) Correlation analysis between TYMP, NAMPT, LCN2, S100A8, S100A9 and immune cells.

Fig 5. Expression distribution of PANoptosis-sig in GSE151177.

(A) Cell classification of scRNA-seq data presented in the t-Distributed Stochastic Neighbor Embedding plot of psoriasis skin. (B-F) Expression distribution of TYMP, NAMPT, LCN2, S100A8, S100A9 in GSE151177.

Fig 6. Construction of the prediction model by PANoptosis-sig.

(A) The AUC value of multiple machine-learning algorithm combinations in eight cohorts. (B) GO and KEGG (C) enrichment analysis of the PANoptosis-sig.

Fig 7. Identification of psoriasis subtypes.

(A) Principal component analysis of expression matrices from 5 different datasets before batch correction. (B) Principal component analysis of expression matrices from 5 different datasets after batch correction. (C) Consensus matrix heatmap when k = 2. (D) Cumulative Distribution Function (CDF) of consensus clustering. (E) Relative changes in Delta Area under the CDF curve. (F) Principal Component Analysis (PCA). (G) The expression of PANoptosis-sig in two subtypes. (H-I) GO and KEGG enrichment analysis of DEGs between the two subtypes.

Fig 8. Immune landscape between two subtypes.

(A-C) Comparison of stromal score, immune score and ESTIMATE score between different subtypes. (D) CIBERSORT analysis of immune cell infiltration in different subtypes. (E-G) Immune checkpoints analysis(MHC, immunoinhibitor, immunostimulator) in different subtypes. * P < 0.05, ** P < 0.001, and *** P < 0.0001.