Identification and Analysis of PANoptosis-Related Genes in Sepsis-Induced Lung Injury by Bioinformatics and Experimental Verification

Abstract

Zhen Yang,1,* Xingyu Kao,1,* Na Huang,1 Kang Yuan,2 Jingli Chen,2 Mingfeng He2 1The Eighth School of Clinical Medicine, Guangzhou University of Chinese Medicine, Foshan, Guangdong Province, People’s Republic of China; 2Foshan Hospital of Traditional Chinese Medicine, Foshan, Guangdong Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jingli Chen; Mingfeng He, Email [email protected]; [email protected]: Sepsis-induced lung injury (SLI) is a serious complication of sepsis. PANoptosis, a novel form of inflammatory programmed cell death that is not yet to be fully investigated in SLI. Our research aims to screen and validate the signature genes of PANoptosis in SLI by bioinformatics and in vivo experiment.Methods: SLI-related datasets were downloaded from NCBI Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) of SLI were identified and intersected with the PANoptosis gene set to obtain DEGs related to PANoptosis (SPAN_DEGs). Then, Protein–Protein Interaction (PPI) network and functional enrichment analysis were conducted based on SPAN_DEGs. SVM-REF, LASSO and RandomForest three algorithms were combined to identify the signature genes. The Nomogram and ROC curves were performed to predict diagnostic value. Immune infiltration analysis, correlation analysis and differential expression analysis were used to explore the immunological characterization, correlation and expression levels of the signature genes. Finally, H&E staining and qRT-PCR were conducted for further verification in vivo experiment.Results: Twenty-four SPAN_DEGs were identified by intersecting 675 DEGs with the 277 PANoptosis genes. Four signature genes (CD14, GSDMD, IL1β, and FAS) were identified by three machine learning algorithms, which were highly expressed in the SLI group, and had high diagnostic value in the diagnostic model. Moreover, immune infiltration analysis showed that most immune cells and immune-related functions were higher in the SLI group than those in the control group and were closely associated with the signature genes. Finally, it was confirmed that the cecum ligation and puncture (CLP) group mice showed significant pathological damage in lung tissues, and the mRNA expression levels of CD14, IL1β, and FAS were significantly higher than the sham group.Conclusion: CD14, FAS, and IL1β may be the signature genes in PANoptosis to drive the progression of SLI and involved in regulating immune processes.Keywords: sepsis, lung injury, PANoptosis, machine learning, immune infiltration analysis

Keywords

• sepsis
• lung injury
• panoptosis
• machine learning
• immune infiltration analysis