Identification of markers of acute lung injury based on bioinformatics and machine learning_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

XU Lang ^1,2,3 , SU Jianfen ^1,2,4 , ZHANG Canhua ^1,2,3 , WANG Bingna ^2,4 , FU Xihua ² ,  PENG Xinsheng ¹

1. School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong 523808, P. R. China;
2. The Affiliated Panyu Central Hospital of Guangzhou Medical University, Guangzhou, Guangdong 511400, P. R. China;
3. Affiliated Qingyuan Hospital, Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, Guangdong 511518, P. R. China;
4. School of Pharmaceutical Science, Guangzhou Medical University, Guangzhou, Guangdong 511436, P. R. China;

Corresponding author：

PENG Xinsheng, Email: xspeng@gdmu.edu.cn

Keywords：

Acute lung injury; machine learning; immunoinfiltration; ferroptosis

DOI：

10.7507/1671-6205.202404045

Video：

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Objective To identify genes of lipopolysaccharide (LPS) -induced acute lung injury (ALI) in mice base on bioinformatics and machine learning. Methods The acute lung injury dataset (GSE2411, GSE111241 and GSE18341) were download from the Gene Expression Database (GEO). Differential gene expression analysis was conducted. Gene ontology (GO) analysis, KEGG pathway analysis, GSEA enrichment analysis and protein-protein interaction analysis (PPI) network analysis were performed. LASSO-COX regression analysis and Support Vector Machine Expression Elimination (SVM-RFE) was utilized to identify key biomarkers. Receiver operator characteristic curve was used to evaluate the diagnostic ability. Validation was performed in GSE18341. Finally, CIBERSORT was used to analyze the composition of immune cells, and immunocorrelation analysis of biomarkers was performed. Results A total of 29 intersection DEGs were obtained after the intersection of GSE2411 and GSE111241 differentially expressed genes. Enrichment analysis showed that differential genes were mainly involved in interleukin-17, cytokine - cytokine receptor interaction, tumor necrosis factor and NOD-like receptor signaling pathways. Machine learning combined with PPI identified Gpx2 and Ifi44 were key biomarkers. Gpx2 is a marker of ferroptosis and Ifi44 is an type I interferon-induced protein, both of which are involved in immune regulation. Immunocorrelation analysis showed that Gpx2 and Ifi44 were highly correlated with Neutrophils, TH17 and M1 macrophage cells. Conclusion Gpx2 and Ifi44 have potential immunomodulatory abilities, and may be potential biomarkers for predicting and treating ALI in mince.

Citation： XU Lang, SU Jianfen, ZHANG Canhua, WANG Bingna, FU Xihua, PENG Xinsheng. Identification of markers of acute lung injury based on bioinformatics and machine learning. Chinese Journal of Respiratory and Critical Care Medicine, 2024, 23(11): 784-790. doi: 10.7507/1671-6205.202404045 Copy

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2.	Rubenfeld G D, Caldwell E, Peabody E, et al. Incidence and outcomes of acute lung injury. N Engl J Med, 2005, 353(16): 1685-1693.
3.	急性肺损伤/急性呼吸窘迫综合征诊断与治疗指南(2006). 中华内科杂志, 2007, 46(5): 430-435.
4.	Ma A, Feng Z, Li Y, et al. Ferroptosis-related signature and immune infiltration characterization in acute lung injury/acute respiratory distress syndrome. Respir Res, 2023, 24(1): 154.
5.	Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med, 1997, 16(4): 385-95.
6.	Lin X, Li C, Zhang Y, et al. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics. Molecules, 2017, 23(1): 52.
7.	Ritchie M E, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015, 43(7): e47.
8.	Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5): 284-287.
9.	Chen Z, Huang A, Sun J, et al. Inference of immune cell composition on the expression profiles of mouse tissue. Sci Rep, 2017, 7: 40508.
10.	Meyer NJ, Gattinoni L, Calfee CS. Acute respiratory distress syndrome. Lancet, 2021, 398(10300): 622-637.
11.	Thompson BT, Chambers RC, Liu KD. Acute respiratory distress syndrome. N Engl J Med, 2017, 377(19): 1904-1905.
12.	Abeel T, Helleputte T, Van De Peer Y, et al. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics, 2010, 26(3): 392-398.
13.	Chen D, Liu J, Zang L, et al. Integrated machine learning and bioinformatic analyses constructed a novel stemness-related classifier to predict prognosis and immunotherapy responses for hepatocellular carcinoma patients. Int J Biol Sci, 2022, 18(1): 360-373.
14.	兰垚, 陈浏阳, 宋文慧. 椎间盘退变伴氧化应激关键生物标志物: 生物信息学和机器学习算法的识别. 中国组织工程研究, 2024, 28(35): 5591-5596.
15.	侯甜甜, 董斐雅, 董佳琪, 等. 基于生物信息学和机器学习筛选类风湿关节炎关键基因. 内蒙古大学学报(自然科学版), 2024, 55(1): 38-45.
16.	Yang C, Delcher C, Shenkman E, et al. Machine learning approaches for predicting high cost high need patient expenditures in health care. Biomed Eng Online, 2018, 17(Suppl 1): 131.
17.	Mundra PA, Rajapakse JC. Gene and sample selection using T-score with sample selection. J Biomed Inform, 2016, 59: 31-41.
18.	Ahmed KM, Veeramachaneni R, Deng D, et al. Glutathione peroxidase 2 is a metabolic driver of the tumor immune microenvironment and immune checkpoint inhibitor response. J Immunother Cancer, 2022, 10(8): e004752.
19.	Tian Q, Zhou Y, Zhu L, et al. Development and validation of a ferroptosis-related gene signature for overall survival prediction in lung adenocarcinoma. Front Cell Dev Biol, 2021, 9: 684259.
20.	Liu J, Kang R, Tang D. Signaling pathways and defense mechanisms of ferroptosis. FEBS J, 2022, 289(22): 7038-7050.
21.	Qu M, Zhang H, Chen Z, et al. The Role of ferroptosis in acute respiratory distress syndrome. Front Med (Lausanne), 2021, 8: 651552.
22.	Sun Y, Chen P, Zhai B, et al. The emerging role of ferroptosis in inflammation. Biomed Pharmacother, 2020, 127: 110108.
23.	Dediego ML, Nogales A, Martinez-Sobrido L, et al. Interferon-induced protein 44 interacts with cellular FK506-binding protein 5, negatively regulates host antiviral responses, and supports virus replication. mBio, 2019, 10(4): e01839-19.
24.	Zheng Q, Wang D, Lin R, et al. IFI44 is an immune evasion biomarker for SARS-CoV-2 and Staphylococcus aureus infection in patients with RA. Front Immunol, 2022, 13: 1013322.
25.	Ivashkiv LB, Donlin LT. Regulation of type I interferon responses. Nat Rev Immunol, 2014, 14(1): 36-49.
26.	Snell LM, Mcgaha TL, Brooks D G. Type I interferon in chronic virus infection and cancer. Trends Immunol, 2017, 38(8): 542-557.
27.	Huang X, Xiu H, Zhang S, et al. The role of macrophages in the pathogenesis of ALI/ARDS. Mediators Inflamm, 2018, 2018: 1264913.
28.	Liu S, Su X, Pan P, et al. Neutrophil extracellular traps are indirectly triggered by lipopolysaccharide and contribute to acute lung injury. Sci Rep, 2016, 6: 37252.
29.	Scozzi D, Liao F, Krupnick AS, et al. The role of neutrophil extracellular traps in acute lung injury. Front Immunol, 2022, 13: 953195.
30.	Tan W, Zhang C, Liu J, et al. Regulatory T-cells promote pulmonary repair by modulating T helper cell immune responses in lipopolysaccharide-induced acute respiratory distress syndrome. Immunology, 2019, 157(2): 151-162.
31.	Sakaguchi R, Chikuma S, Shichita T, et al. Innate-like function of memory Th17 cells for enhancing endotoxin-induced acute lung inflammation through IL-22. Int Immunol, 2016, 28(5): 233-243.
32.	Butt Y, Kurdowska A, Allen TC. Acute lung injury: a clinical and molecular review. Arch Pathol Lab Med, 2016, 140(4): 345-350.
33.	Luh SP, Chiang CH. Acute lung injury/acute respiratory distress syndrome (ALI/ARDS): the mechanism, present strategies and future perspectives of therapies. J Zhejiang Univ Sci B, 2007, 8(1): 60-69.
34.	Zhu W, Zhang Y, Wang Y. Immunotherapy strategies and prospects for acute lung injury: focus on immune cells and cytokines. Front Pharmacol, 2022, 13: 1103309.
35.	Jiang Z, Zhou Q, Gu C, et al. Depletion of circulating monocytes suppresses IL-17 and HMGB1 expression in mice with LPS-induced acute lung injury. Am J Physiol Lung Cell Mol Physiol, 2017, 312(2): L231-L242.

1. Devaney J, Contreras M, Laffey J G. Clinical review: gene-based therapies for ALI/ARDS: where are we now?. Crit Care, 2011, 15(3): 224.
2. Rubenfeld G D, Caldwell E, Peabody E, et al. Incidence and outcomes of acute lung injury. N Engl J Med, 2005, 353(16): 1685-1693.
3. 急性肺损伤/急性呼吸窘迫综合征诊断与治疗指南(2006). 中华内科杂志, 2007, 46(5): 430-435.
4. Ma A, Feng Z, Li Y, et al. Ferroptosis-related signature and immune infiltration characterization in acute lung injury/acute respiratory distress syndrome. Respir Res, 2023, 24(1): 154.
5. Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med, 1997, 16(4): 385-95.
6. Lin X, Li C, Zhang Y, et al. Selecting Feature Subsets Based on SVM-RFE and the Overlapping Ratio with Applications in Bioinformatics. Molecules, 2017, 23(1): 52.
7. Ritchie M E, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015, 43(7): e47.
8. Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5): 284-287.
9. Chen Z, Huang A, Sun J, et al. Inference of immune cell composition on the expression profiles of mouse tissue. Sci Rep, 2017, 7: 40508.
10. Meyer NJ, Gattinoni L, Calfee CS. Acute respiratory distress syndrome. Lancet, 2021, 398(10300): 622-637.
11. Thompson BT, Chambers RC, Liu KD. Acute respiratory distress syndrome. N Engl J Med, 2017, 377(19): 1904-1905.
12. Abeel T, Helleputte T, Van De Peer Y, et al. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics, 2010, 26(3): 392-398.
13. Chen D, Liu J, Zang L, et al. Integrated machine learning and bioinformatic analyses constructed a novel stemness-related classifier to predict prognosis and immunotherapy responses for hepatocellular carcinoma patients. Int J Biol Sci, 2022, 18(1): 360-373.
14. 兰垚, 陈浏阳, 宋文慧. 椎间盘退变伴氧化应激关键生物标志物: 生物信息学和机器学习算法的识别. 中国组织工程研究, 2024, 28(35): 5591-5596.
15. 侯甜甜, 董斐雅, 董佳琪, 等. 基于生物信息学和机器学习筛选类风湿关节炎关键基因. 内蒙古大学学报(自然科学版), 2024, 55(1): 38-45.
16. Yang C, Delcher C, Shenkman E, et al. Machine learning approaches for predicting high cost high need patient expenditures in health care. Biomed Eng Online, 2018, 17(Suppl 1): 131.
17. Mundra PA, Rajapakse JC. Gene and sample selection using T-score with sample selection. J Biomed Inform, 2016, 59: 31-41.
18. Ahmed KM, Veeramachaneni R, Deng D, et al. Glutathione peroxidase 2 is a metabolic driver of the tumor immune microenvironment and immune checkpoint inhibitor response. J Immunother Cancer, 2022, 10(8): e004752.
19. Tian Q, Zhou Y, Zhu L, et al. Development and validation of a ferroptosis-related gene signature for overall survival prediction in lung adenocarcinoma. Front Cell Dev Biol, 2021, 9: 684259.
20. Liu J, Kang R, Tang D. Signaling pathways and defense mechanisms of ferroptosis. FEBS J, 2022, 289(22): 7038-7050.
21. Qu M, Zhang H, Chen Z, et al. The Role of ferroptosis in acute respiratory distress syndrome. Front Med (Lausanne), 2021, 8: 651552.
22. Sun Y, Chen P, Zhai B, et al. The emerging role of ferroptosis in inflammation. Biomed Pharmacother, 2020, 127: 110108.
23. Dediego ML, Nogales A, Martinez-Sobrido L, et al. Interferon-induced protein 44 interacts with cellular FK506-binding protein 5, negatively regulates host antiviral responses, and supports virus replication. mBio, 2019, 10(4): e01839-19.
24. Zheng Q, Wang D, Lin R, et al. IFI44 is an immune evasion biomarker for SARS-CoV-2 and Staphylococcus aureus infection in patients with RA. Front Immunol, 2022, 13: 1013322.
25. Ivashkiv LB, Donlin LT. Regulation of type I interferon responses. Nat Rev Immunol, 2014, 14(1): 36-49.
26. Snell LM, Mcgaha TL, Brooks D G. Type I interferon in chronic virus infection and cancer. Trends Immunol, 2017, 38(8): 542-557.
27. Huang X, Xiu H, Zhang S, et al. The role of macrophages in the pathogenesis of ALI/ARDS. Mediators Inflamm, 2018, 2018: 1264913.
28. Liu S, Su X, Pan P, et al. Neutrophil extracellular traps are indirectly triggered by lipopolysaccharide and contribute to acute lung injury. Sci Rep, 2016, 6: 37252.
29. Scozzi D, Liao F, Krupnick AS, et al. The role of neutrophil extracellular traps in acute lung injury. Front Immunol, 2022, 13: 953195.
30. Tan W, Zhang C, Liu J, et al. Regulatory T-cells promote pulmonary repair by modulating T helper cell immune responses in lipopolysaccharide-induced acute respiratory distress syndrome. Immunology, 2019, 157(2): 151-162.
31. Sakaguchi R, Chikuma S, Shichita T, et al. Innate-like function of memory Th17 cells for enhancing endotoxin-induced acute lung inflammation through IL-22. Int Immunol, 2016, 28(5): 233-243.
32. Butt Y, Kurdowska A, Allen TC. Acute lung injury: a clinical and molecular review. Arch Pathol Lab Med, 2016, 140(4): 345-350.
33. Luh SP, Chiang CH. Acute lung injury/acute respiratory distress syndrome (ALI/ARDS): the mechanism, present strategies and future perspectives of therapies. J Zhejiang Univ Sci B, 2007, 8(1): 60-69.
34. Zhu W, Zhang Y, Wang Y. Immunotherapy strategies and prospects for acute lung injury: focus on immune cells and cytokines. Front Pharmacol, 2022, 13: 1103309.
35. Jiang Z, Zhou Q, Gu C, et al. Depletion of circulating monocytes suppresses IL-17 and HMGB1 expression in mice with LPS-induced acute lung injury. Am J Physiol Lung Cell Mol Physiol, 2017, 312(2): L231-L242.

Chinese Journal of Respiratory and Critical Care Medicine

Identification of markers of acute lung injury based on bioinformatics and machine learning

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