Identification and validation of programmed cell death related biomarkers for the treatment and prevention COVID-19
Jie Yang, Yaoxi Tan, Xing Liu
Annals of Medicine, doi:10.1080/07853890.2025.2492830
Purpose: Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 . However, PCD-relevant biomarkers have not been fully discovered. The aim of this study was to explore the PCD-relevant biomarkers for the treatment and prevention of COVID-19. Methods: Bioinformatic analyses were performed to explore the clinical relevant PCD genes with differential expression (DE) in COVID-19 compared with matched controls. PPI network was used for hub genes screening and machine learning methods were employed for filtering feature genes. The biomarker genes were screened by Venn diagram. The correlations between biomarkers with clinical features and immune microenvironment were further explored. Biomarker validation was performed in clinical samples by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). Results: In total, 118 clinically relevant and PCD associated differential expressed genes (DEGs) were screened, which were mainly related with apoptosis related pathways, among which six biomarkers (Cyclin B1 (CCNB1), cyclin-dependent kinase 1 (CDK1), interferon regulatory factor 4 (IRF4), lipoteichoic acid (LTA), matrix metallopeptidase 9 (MMP9) and Oncostatin M (OSM)) were identified. The excellent or good diagnostic performance of biomarkers was determined by receiver operating characteristic (ROC) curve analysis. The biomarkers showed diverse correlations with clinical indicators, such as age, sex and Intensive Care Unit (ICU) admission. Total 14 types of immune cells exerted differential infiltration between COVID-19 and controls. Biomarkers were correlated with immune cells at varying levels. COVID-19 was classified in three clusters, which showed differential expression of biomarker genes and significant associations with clinical information, such as sex, age and ICU admission. The DEGs of biomarkers were determined in COVID-19 patients relative to controls.
Conclusion: The six biomarkers (CCNB1, CDK1, IRF4, LTA, MMP9 and OSM) can be served as the biomarkers for the treatment and prevention of COVID-19.
Author contributions statement Jie Yang: Conceptualization, Data Curation and Writing -Original Draft Preparation. YaoXi Tan: Conceptualization and Formal Analysis. Xing Liu: Conceptualization, Data Curation and Writing -Review & Editing. All authors agree to be accountable for all aspects of the work.
Database ethics statement Based on the database is publicly available, the ethics application was exempted by the Affiliated Wuxi Fifth Hospital of Jiangnan University.
Disclosure statement No potential conflict of interest was reported by the author(s).
References
Bader, Cooney, Pellegrini, Programmed cell death: the pathways to severe COVID-19?, Biochem J,
doi:10.1042/BCJ20210602Cascella, Rajnik, Aleem, Features, Evaluation, and Treatment of Coronavirus (COVID-19)
Chen, Yang, Luo, Identification of key regulatory genes in the pathogenesis of COVID-19 and sepsis: an observational study, Medicine,
doi:10.1097/md.0000000000038378Chen, Zhou, Dong, Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study, Lancet,
doi:10.1016/S0140-6736(20)30211-7Ciaccio, Agnello, Biochemical biomarkers alterations in Coronavirus Disease 2019 (COVID-19), Diagnosis (Berl),
doi:10.1515/dx-2020-0057Croft, The role of TNF superfamily members in T-cell function and diseases, Nat Rev Immunol,
doi:10.1038/nri2526Dimitriadou, Hornik, Fpackage, R Software package
Gillot, Favresse, Mullier, NETosis and the immune system in COVID-19: mechanisms and potential treatments, Front Pharmacol,
doi:10.3389/fphar.2021.708302González, Programmed cell death. Review and its impact in covid-19, Clinical Research and Trials
Hahn, Hamilton, Wangen, Development of a PROTAC-based targeting strategy provides a mechanistically unique mode of anti-cytomegalovirus activity, Int J Mol Sci,
doi:10.3390/ijms222312858Huang, Hung, SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier, The Scientific World Journal,
doi:10.1155/2014/795624Jorgensen, Rayamajhi, Miao, Programmed cell death as a defence against infection, Nat Rev Immunol,
doi:10.1038/nri.2016.147Karki, Sharma, Tuladhar, Synergism of TNF-α and IFN-γ triggers inflammatory cell death, tissue damage, and mortality in SARS-CoV-2 infection and cytokine shock syndromes, Cell,
doi:10.1016/j.cell.2020.11.025Kim, Witwit, Cubitt, Inhibitors of anti-apoptotic Bcl-2 family proteins exhibit potent and broad-spectrum anti-mammarenavirus activity via cell cycle arrest at G0/G1 phase, J Virol,
doi:10.1128/jviLuo, Xie, Zheng, Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm, Cancer Med,
doi:10.1002/cam4.2983Marone, Mozzetti, Ritis, Semiquantitative RT-PCR analysis to assess the expression levels of multiple transcripts from the same sample, Biol Proced Online,
doi:10.1251/bpo20Mceligot, Poynor, Sharma, Logistic LASSO Regression for Dietary Intakes and Breast Cancer, Nutrients,
doi:10.3390/nu12092652Merad, Martin, Pathological inflammation in patients with COVID-19: a key role for monocytes and macrophages, Nat Rev Immunol,
doi:10.1038/s41577-020-0331-4Pijls, Jolani, Atherley, Demographic risk factors for COVID-19 infection, severity, ICU admission and death: a meta-analysis of 59 studies, BMJ Open,
doi:10.1136/bmjopen-2020-044640Qin, Abulaiti, Maimaiti, Integrated machine learning survival framework develops a prognostic model based on inter-crosstalk definition of mitochondrial function and cell death patterns in a large multicenter cohort for lower-grade glioma, J Transl Med,
doi:10.1186/s12967-023-04468-xRobin, Turck, Hainard, pROC: an open-source package for R and S + to analyze and compare ROC curves, BMC Bioinf,
doi:10.1186/1471-2105-12-77Sasso, Agnello, Giglio, Longitudinal analysis of anti-SARS-CoV-2 S-RBD IgG antibodies before and after the third dose of the BNT162b2 vaccine, Sci Rep,
doi:10.1038/s41598-022-12750-zShannon, Markiel, Ozier, Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res,
doi:10.1101/gr.1239303Song, Zhang, Li, Significance of neutrophil extracellular traps-related gene in the diagnosis and classification of atherosclerosis, Apoptosis,
doi:10.1007/s10495-023-01923-4Strobl, Boulesteix, Zeileis, Bias in random forest variable importance measures: illustrations, sources and a solution, BMC Bioinf,
doi:10.1186/1471-2105-8-25Suykens, Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters
Wilkerson, Waltman, Wilkerson, Package 'ConsensusClusterPlus
Xiao, Liu, Li, Identification and verification of immune-related gene prognostic signature based on ssGSEA for osteosarcoma, Front Oncol,
doi:10.3389/fonc.2020.607622Yu, Wang, Han, clusterProfiler: an R package for comparing biological themes among gene clusters, OMICS,
doi:10.1089/omi.2011.0118Zhou, Gao, Xu, Implications of different cell death patterns for prognosis and immunity in lung adenocarcinoma, NPJ Precis Oncol,
doi:10.1038/s41698-023-00456-yDOI record:
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