Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes

Feiping Xia; Hui Chen; Yigao Liu; Lili Huang; Shanshan Meng; Jingyuan Xu; Jianfeng Xie; Guozheng Wang; Fengmei Guo

doi:10.3389/fimmu.2023.1209959

Development of genomic phenotype and immunophenotype of acute respiratory distress syndrome using autophagy and metabolism-related genes

Front Immunol. 2023 Oct 23:14:1209959. doi: 10.3389/fimmu.2023.1209959. eCollection 2023.

Authors

Feiping Xia¹, Hui Chen^{1

2}, Yigao Liu¹, Lili Huang¹, Shanshan Meng¹, Jingyuan Xu¹, Jianfeng Xie¹, Guozheng Wang³, Fengmei Guo¹

Affiliations

¹ Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
² Department of Critical Care Medicine, The First Affiliated Hospital of Soochow University, Soochow University, Suzhou, China.
³ Department of Clinical Infection Microbiology and Immunology, University of Liverpool, Liverpool, United Kingdom.

Abstract

Background: Distinguishing ARDS phenotypes is of great importance for its precise treatment. In the study, we attempted to ascertain its phenotypes based on metabolic and autophagy-related genes and infiltrated immune cells.

Methods: Transcription datasets of ARDS patients were obtained from Gene expression omnibus (GEO), autophagy and metabolic-related genes were from the Human Autophagy Database and the GeneCards Database, respectively. Autophagy and metabolism-related differentially expressed genes (AMRDEGs) were further identified by machine learning and processed for constructing the nomogram and the risk prediction model. Functional enrichment analyses of differentially expressed genes were performed between high- and low-risk groups. According to the protein-protein interaction network, these hub genes closely linked to increased risk of ARDS were identified with CytoHubba. ssGSEA and CIBERSORT was applied to analyze the infiltration pattern of immune cells in ARDS. Afterwards, immunologically characterized and molecular phenotypes were constructed according to infiltrated immune cells and hub genes.

Results: A total of 26 AMRDEGs were obtained, and CTSB and EEF2 were identified as crucial AMRDEGs. The predictive capability of the risk score, calculated based on the expression levels of CTSB and EEF2, was robust for ARDS in both the discovery cohort (AUC = 1) and the validation cohort (AUC = 0.826). The mean risk score was determined to be 2.231332, and based on this score, patients were classified into high-risk and low-risk groups. 371 differential genes in high- and low-risk groups were analyzed. ITGAM, TYROBP, ITGB2, SPI1, PLEK, FGR, MPO, S100A12, HCK, and MYC were identified as hub genes. A total of 12 infiltrated immune cells were differentially expressed and have correlations with hub genes. According to hub genes and implanted immune cells, ARDS patients were divided into two different molecular phenotypes (Group 1: n = 38; Group 2: n = 19) and two immune phenotypes (Cluster1: n = 22; Cluster2: n = 35), respectively.

Conclusion: This study picked up hub genes of ARDS related to autophagy and metabolism and clustered ARDS patients into different molecular phenotypes and immunophenotypes, providing insights into the precision medicine of treating patients with ARDS.

Keywords: ARDS; autophagy; genomic phenotype; immunophenotype; metabolism.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Autophagy / genetics
CD18 Antigens
Genomics*
Humans
Phenotype
Respiratory Distress Syndrome* / genetics

Substances

CD18 Antigens

Grants and funding

This research was supported by grants from the National Natural Science Foundation of China (grant numbers: 81871602 and 82102300), and a Special fund project for health science and technology development of Nanjing Municipal Health Commission (2022ZXNS07).