Identification and validation of programmed cell death related biomarkers for the treatment and prevention COVID-19

TL;DR

This study identifies six biomarkers related to programmed cell death that could help treat and prevent COVID-19.

Contribution

The study discovers and validates six novel PCD-related biomarkers for potential use in treating and preventing COVID-19.

Findings

Six PCD-related biomarkers (CCNB1, CDK1, IRF4, LTA, MMP9, OSM) were identified as having diagnostic potential for COVID-19.

The biomarkers showed strong correlations with clinical features like age, sex, and ICU admission.

Biomarkers were associated with immune cell infiltration and could classify patients into three distinct clusters.

Abstract

Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 (COVID-19). 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.

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).

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.

The six biomarkers (CCNB1, CDK1, IRF4, LTA, MMP9 and OSM) can be served as the biomarkers for the treatment and prevention of COVID-19.

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