Identification of transcriptomics biomarker for the early prediction of critically ill COVID-19 patients

doi:10.21203/rs.3.rs-1838856/v1

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Research Article

Identification of transcriptomics biomarker for the early prediction of critically ill COVID-19 patients

https://doi.org/10.21203/rs.3.rs-1838856/v1

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Objective

Identifying the biological subsets of severe COVID-19 could provide a basis for finding biomarkers for the early prediction of the prognosis of severe COVID-19 and poor prognosis, and may facilitate specific treatment for COVID-19.

Methods

In this study we downloaded microarray dataset GSE172114 from the Gene Expression Omnibus (GEO) database in NCBI, and screened differentially-expressed genes (DEGs) by using the limma package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted, and the results were presented by volcano, Venn, heat, and enrichment pathway bubble maps in the R language package. Gene set enrichment analysis (GSEA) was used to explore and demonstrate the signal pathways related to severe COVID-19. Protein-Protein Interaction (PPI) Network analysis and visualization were performed by using STRING and Cytoscape. Seven key protein expression molecules were screened by the MOCDE plug-in. Then, the cytoHubba plug-in was used to screen 10 candidate genes with maximal clique centrality (MCC) algorithm as the standard, and the intersection with the Venn diagram was used to obtain seven Hub genes. Receiver operating characteristic (ROC) curves were drawn to determine the area under the curve (AUC), and the predictive value of the key genes was evaluated.

Results

A total of 210 DEGs were identified, including 186 upregulated genes as well as downregulated ones. GO enrichment and KEGG pathway analysis were used, and the results were presented by volcano, Venn, heat, and enrichment pathway bubble maps in the R language package. Gene set enrichment analysis (GSEA) was used to explore and demonstrate the signal pathways related to severe COVID-19. Protein interaction network (PPI) analysis and visualization were performed by using STRING and Cytoscape. Seven key protein expression molecules were screened by the MOCDE plug-in. Then, the cytoHubba plug-in was used to screen 10 candidate genes with maximal clique centrality (MCC) algorithm as the standard, and the intersection with the Venn diagram was used to obtain seven Hub genes. Receiver operating characteristic (ROC) curves were drawn to determine the area under the curve (AUC), and the predictive value of the key genes was evaluated. The AUC of the PLSCR1 gene was 0.879, which was the most significantly upregulated key gene in critically ill COVID-19 patients.

Conclusions

Based on bioinformatics analysis, we found that the screened candidate gene, PLSCR1, may be closely related to the occurrence of severe COVID-19, and can thus be used for the early prediction of patients with severe COVID-19, and may provide meaningful research direction for their treatment.

PLSCR1

biomarker

prediction

prognosis

COVID-19

The 2019 novel coronavirus disease (COVID-19) pandemic is considered as one of the largest global public health crises since the 1918 influenza pandemic^{[1, 2]}. COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), a single-stranded RNA virus belonging to the coronavirus family^[3]. The clinical symptoms of patients infected with SARS-COV-2 vary from asymptomatic-to-severe, and most patients develop dyspnea or hypoxemia during 1 week after onset. Severe cases can rapidly progress to acute respiratory distress syndrome (ARDS), septic shock, difficult-to-correct metabolic acidosis, coagulopathy, and multiple organ dysfunction syndrome (MODS) ^{[4, 5]}.

Various genes and signaling pathways are now known to be involved in the development of COVID-19. Identifying the biological subsets of COVID-19 can provide a basis for the early prediction of the prognosis of critically ill patients with COVID-19 and specific targeted therapies. Therefore, there is a need to study the molecular mechanism underlying the pathogenesis of COVID-19 infections.

We hypothesized that key genes may be involved in the initiation of in the early stage of critical illness in Covid-19, mediating molecular changes that further influence disease prognosis. Therefore, changes in gene expression in the early stage of COVID-19 critically ill patients might be detected by gene chips. By linking these changes to the prognosis of the disease, it would be possible to identify biomarker that is early predictors of critical illness and poor prognosis for COVID-19.

Data collection

The data were obtained from the Gene Expression Omnibus (GEO) database in NCBI, a public functional genomics data repository. The expression dataset GSE172114 (https://www.ncbi.nlm.nih.gov/geo/; GSE172114) was obtained from the GPL24676 Illumina NovaSeq 6000 (Homo sapiens). Severe forms of COVID-19 by RNA-seq were performed on 69 whole blood RNA samples corresponding to 46 critical and 23 non-critical patients during hospitalization. Preparing, handling, and analyzing data were executed by using R software (version 3.6.3).

Identification of differentially-expressed genes

Differentially-expressed genes (DEGs) were identified with the "limma" R language package in R software^[6] with a threshold log2 fold change (FC) > 1.5 and P < 0.05. Then, up- and down-regulated DEGs were screened out from the identified DEGs.

DEGs analyze via GO and KEGG

The Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were conducted to pick out the biological function of DEGs^[7,8]. The up- and down-regulated DEGs analyzed by GO terms and KEGG pathways with a p-value of less than 0.05 were statistically considered to be sigificantly enriched .

GSEA identification of signaling pathways

Gene Set Enrichment analysis (GSEA) software was used to determine the statistically significant gene sets in the comparison between different subgroups. GSEA was obtained from the Broad Institute (http://software.broadinstitute.org/gsea/index.jsp) ^[9]. The GSE172114 dataset was input with the annotation file“hallmark gene sets”. The cut-off values were predefined as p < 0.05 and FDR < 0.25.

PPI network analysis, MCODE cluster modules, and Hub gene identification

The Protein-Protein Interaction (PPI) network was analyzed by using the Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org)^[10]. Functional interactions between proteins were analyzed and combined scores were performed to determine the mechanisms underlying the early onset of severe COVID-19 in patients. Cytoscape (version 3.8.2) is an open source bioinformatics software platform for visualizing molecular interaction networks for analysis using Cytoscape Plug-in Molecular Complex Detection (MCODE). Our selection criteria were as follows: MCODE parameters: degree cut-off = 2; node score cut-off = 0.2; maximum depth = 100; and K-score = 2. Significant modules in the PPI network were screened (parameter was set as the default)^[11].Genes with a high degree of connectivity within the functional modules were regarded as hub genes. The top 10 genes which were closely related to severe COVID-19 in patients were enriched and analyzed.

Selection and analysis of hub genes

The top 10 genes were obtained by using the MCC algorithm with Cytoscape's plug-in cytoHubba. Then, the intersection of related core genes were analyzed with MCODE and represented by a Venn diagram. Finally, the GeneMANIA online database (https://genemania.org/) was used to analyze the hub genes^[12].

ROC analysis of the hub genes

Hub genes were considered as candidate genes to predict the outcome. Receiver operating characteristic (ROC) curves were drawn. Their areas under the curve (AUCs) were determined to evaluate the predicted value of the key genes by using Xian tao (https://www.xiantao.love/).

Identification of DEGs

There were 15,927 genes in the GSE172114 dataset. Finally, a total of 210 DEGs were identified, including 186 up- and 24 down-regulated DEGs. The heat map distribution of all DEGs is shown in Fig. 1A. The volcano map of all DEGs is shown in Fig. 1B. In Fig. 1C, principal component analysis (PCA) is presented by clustering with a scatter plot, in which each point represents a sample.

GO enrichment and KEGG pathway of DEGs

Up- and down-regulation genes were evaluated by GO enrichment and KEGG pathway.The results of the GO analysis pathway showed that up-regulated DEGs were significantly enriched at the cellular and molecular function level by regulating the activity of the protease binding site, immunoglobulin binding protein, RAGE receptor activity, IgG protein activity, influencing the signal transmission of secreted particles to the nucleus, regulating the release of specific particles, and regulating the recruitment, activation, and degranulation of neutrophils, shown in Fig. 2A and B. The results of the KEGG pathway enrichment analysis showed that up-regulated DEGs were significantly enriched in the nod-like receptor-mediated signaling pathway, osteoclast differentiation, differentiation of specific hematopoietic lineages, and the complement system, as shown in Fig. 2C and D. The results of the GO enrichment analysis pathway showed that down-regulated DEGs were enriched in immunoglobulin receptor binding, polypeptide antigen presentation, immunoglobulin complex regulation of immunity, T cell receptor complex regulation of antigen recognition, humoral immune response, and complement activation, as shown in Fig. 3A and B. The results of the KEGG pathway enrichment analysis showed that down-regulated DEGs were significantly enriched in the hematopoietic cell signaling pathway, antigen presentation, cell adhesion molecule signal transduction, and allogeneic transplantation, as shown in Fig. 3C and D.

Gene set enrichment analysis (GSEA)

The GSEA showed that the signaling pathways, such as interleukin-18, stroma and adhesives, interleukin signaling, neutrophil degranulation, and innate immune system was significantly increased in the critically ill patients with COVID-19, as shown in Fig. 4.

PPI network analysis, MCODE cluster modules, and selection and analysis of hub genes

A PPI network map was obtained by STRING and imported into Cytoscape (version 3.8.2). PPI network map was beautified according to degree and log2FC size in Fig. 7A.The CytoHubba plug-in was used to screen out the top 10 genes in key positions according to MCC algorithm as the standard calculation method. The hub genes with detailed information are shown in Table 1. The most important genes in the PPI network may play an important role in the pathogenesis of COVID-19 in patients with early-onset critical disease. Then, through analysis of the MCODE plug-in, it was shown that the MCODE plug-in was used to identify gene cluster modules. In this network, we identified four modules, which are shown in Fig. 5A-D, according to the filter criteria. Cluster 1 had the highest cluster score (score: 7 [7 nodes and 21 edges]), followed by cluster 2 (score: 4 [9 nodes and 16 edges]), cluster 3 (score: 3.5 [5 nodes and 7 edges]), and cluster 4 (score: 3.333 [4 nodes and 5 edges]). A Venn diagram was used to analyze the intersection of 10 core genes and 7 key module genes. Seven core candidate genes which included RSAD2, IFI44, IFI44L, IFI6, IFIT3, IFIT1, and PLSCR1 were obtained in Fig. 7B. A PPI network diagram was obtained through STRING, as shown in Fig. 6. One down-regulated hub gene showed a complex PPI network with a co-expression rate of 76.03%, a co-location rate of 3.10%, a predictive interaction rate of 0.76%, a pathway interaction rate of 2.34%, and a physical interaction rate of 17.77% in Fig. 7C. In addition,A total of six upregulated hub genes showed a complex PPI network with a co-expression rate of 67.85%, a co-location rate of 1.32%, a predictive interaction rate of 1.14%, a pathway interaction rate of 3.88%, and a physical interaction rate of 25.80%. The main functions of these genes include calcium signaling, inducing an anti-proliferative state in cells, stabilizing mitochondrial function, and activating IFN signaling in Fig. 7D. These genes are associated with negative regulation of innate immune responses.

Table 1

Seven hub genes and their functions
Gene symbol	Description	Function
RSAD2	Radical S-adenosyl methionine domain containing 2	Directly inhibits viral replication
IFI44	Interferon-induced protein 44	Affects virus production and negatively modulates innate immune responses induced after viral infections
IFI44L	Interferon-induced protein 44 like	Antiviral and antiproliferative
IFI6	Interferon alpha inducible protein 6	Regulation of apoptosis
IFIT3	Interferon-induced protein with tetratricopeptide repeats 3	Interferon gamma signaling and innate immune system
IFIT1	Interferon-induced protein with tetratricopeptide repeats 1	Inhibit viral replication and translational initiation
PLSCR1	Phospholipid scramblase 1	Contributes to cell proliferation, differentiation, and apoptosis

Identification of the key genes associated with outcomes

ROC curve analysis of all key genes indicated that the AUC of key genes (RSAD2 0.766, IFI44 0.772, IFI44L 0.743, IFI6 0.787, IFIT3 0.769, and IFIT1 0.762) were > 0.7, which indicated prediction efficiency. Among the upregulated genes, PLSCR1 has the biggest the AUC value, indicating highest predictive efficiency, as shown in Fig. 8. IFI44L ,the downregulated key gene ,and PLSCR1 might be the most significantly key genes in critically ill COVID-19 patients.

The COVID-19 outbreak, which began in late 2019 and is still spreading globally, is highly infectious and pathogenic. With the continuous spread of the epidemic, China initiated a level I response and the World Health Organization listed COVID-19 as a public health emergency of international concern^[13]. Among COVID-19 patients, the proportion with severe and critical illnesses is as high as 5%-10% due to the rapid progression of the disease^[14]. In addition, according to a retrospective study, the 28-d fatality rate of critically ill patients hospitalized in intensive care units is as high as 61.5%^[15]. Therefore, early and effective identification and management of severe/critically ill COVID-19 patients is essential to reducing the mortality rate among infected people. The early clinical symptoms of critically ill COVID-19 patients are often atypical and easily overlooked. In addition, early critically ill COVID-19 patients are difficult to diagnose due to lack of effective biomarker. Finding new and effective biomarker is critical for early diagnosis and treatment of critically ill patients with COVID-19.

We performed an in-depth analysis of the RNA-SEQ dataset, GSE172114, using R software and bioinformatics to compare gene expression in critically ill and non-critically ill patient samples. Two hundred ten DEGs were identified, of which 186 were upregulated and 24 were downregulated.

We conducted enrichment analysis of GO and KEGG to explore the interaction between DEGs. Upregulated DEGs were mainly enriched in immunoglobulin activity regulation, ligand binding, nod-like receptor (NLR) activation, neutrophil activation, and complement activation, while downregulated DEGs were mainly enriched in cellular immunity, humoral immunity, and cell adhesion molecule signal transduction in immune responses.

Research involving COVID-19 has shown that the immune system plays a key role in determining the severity of the disease. SARS-COV-2 disrupts the normal immune response, leading to compromised immune systems and runaway inflammatory responses in severe and critically ill COVID-19 patients. These patients present with lymphocytopenia, lymphocyte activation and dysfunction, abnormal granulocytes and monocytes, high cytokine levels, and increased immunoglobulin G (IgG) and total antibodies^[16].

Previous studies have shown that SARS-COV-2 infection activates multiple immune responses, which is consistent with the GO and KEGG enrichment analysis in this study. We speculate that neutrophil upregulation in COVID-19 patients is closely related to lymphocytopenia. Lymphocyte damage in COVID-19 patients easily leads to microbial infections, in which pathogenic microbial infections can directly induce neutrophils to accumulate in tissue sites. The activation and recruitment of neutrophils is promoted in the blood of patients, thus magnifying the inflammatory response^[17]. Studies have shown that neutrophils and neutrophil-to-lymphocyte ratios are usually important indicators of severe and adverse clinical outcomes, and the neutrophil-to-lymphocyte ratios in severe patients are significantly higher than non-severe patients^[18]. In contrast, it has also been reported that SARS-COV-2 directly activates NLRP3 inflammasomes, resulting in endogenous adjuvant activity that produces an appropriate adaptive immune response to the virus^[19]. Damage-associated molecular patterns (DAMPs) released after activation of NLRP3 inflammasomes have a dual function: induce the necessary co-stimulatory activation of antigen-presenting cells (APCs) in a normal immune response; and decomposition and tissue regeneration. Excessive activation of NLRP3 inflammatory bodies promotes high concentration release of DAMPs and apoptosis, release of high mobility group 1 (HMGB1), activation of macrophages, neutrophil infiltration, cytokine overproduction (e.g., IL-1 β, IL-2, IL-6, IL-17, TNF -α, G-CSF, GM-CSF, IFN-γ, CXCL10, CCL2, and CCL3), and causes inflammation through various mechanisms^{[20, 21]}. Activation of HMGB1 by IL-1β released by inflammasome signaling is a key late marker of sepsis and infection. HMGB1 leads to epithelial barrier failure, organ dysfunction, vascular leakage, and even death^{[22, 23]}. Elevated HMGB1 is the central agent of excessive inflammatory responses and pathologic severity during viral infection^{[24, 25]}. The level of NLRP3 activation is correlated with the severity of COVID-19 disease^[26].

Additional studies have shown that COVID-19 is closely related to cellular and humoral responses^[27]. It has been reported that during COVID-19 infection, particularly in severe cases, the total number of T cells, and CD4 + and CD8 + T cells declines significantly, and the surviving T cells fail. Total T cell number, CD8 + T cell number, and CD4 + T cell number < 800, 300, and 400/ L, respectively, are negatively correlated with survival rate^{[28, 29]}. In addition, SARS-COV-2 inhibits T-cell-mediated immune response by downregulating MHC class I and II molecules by inhibiting antigen presentation, similar to other coronaviruses^[30]. In contrast, the humoral immune response also plays an important role in COVID-19 infections, although antibodies may not be enough to neutralize the virus because SARS-COV-2 inhibits antigen presentation by downregulating MHC class I and II molecules, thus inhibiting T-cell-mediated immune responses^[31]. These results suggest that SARS-COV-2 has strong adaptive immunosuppressive function.

To reduce bias, we first performed GSEA analysis on all detected genes. Gene enrichment analysis showed that SARS-COV-2 infection is still closely related to the activation of inflammatory responses. An effective immune response is essential to fight off viral infections; however, overactivation of the inflammatory immune response can lead to cytokine storm and subsequent immune failure. Further clinical evidence has shown that proinflammatory cytokines are elevated in severe COVID-19 patients, and the surge of these inflammatory molecules is also known as the cytokine storm in COVID-19^[32,33].

The development of cytokine storm is a potentially fatal immune state characterized by rapid proliferation and overactivation of T cells, macrophages, and natural killer cells, as well as excessive production of over 150 inflammatory cytokines and chemical mediators released by immune or non-immune cells^[34]. Abnormal release of pro-inflammatory factors leads to apoptosis of pulmonary epithelial and endothelial cells, damage to pulmonary microvascular and alveolar epithelial cell barriers, and leads to vascular leakage, alveolar edema, and hypoxia in viral infections^[35]. Clinical studies have shown that the severity of COVID-19 is positively correlated with the level of inflammatory cytokines, including IL-1β, IL-2, IL-6, IL-7, IL-10,IL-17, IL-18, and other cytokines in plasma/serum^[36]. Among patients infected with SARS coronavirus or Middle East respiratory syndrome (MERS), serum levels of cytokines, such as IL-6, IFN-α/β and CXCL10, in patients with severe infections were also significantly higher than those in patients with mild infections^[37]. An inflammatory cytokine storm is an important factor leading to severe infection or death in H7N9, SARS-COV-2, MERS-CoV, and other viral infections, and the mechanism may be that the overexpression of inflammatory cytokines and chemokines leads to acute lung injury and ARDS^[37–39].

In conclusion, cytokine storm in the pathogenesis of severe COVID-19 pneumonia deserves attention, and excessive activation of inflammatory immune response can lead to the persistence of cytokine storm. Although current therapies are based on inhibiting the onset and development of cytokine storms, the efficacy of current therapies is not ideal, and more research is needed to explore the key inflammatory pathways triggered by SARS-COV-2.

In addition, 10 candidate genes (IL10, MMP9, RETN, RSAD2, IFI44, IFI44L, IFI6, IFIT3, IFIT1, and PLSCR1) were screened through the PPI network. Further, a MCODE plug-in was used to identify the gene cluster module method and clusters to four modules. The highest score was obtained by intersection of the Venn diagram and 10 candidate genes identified by PPI to obtain 7 core genes (RSAD2, IFI44, IFI44L, IFI6, IFIT3, IFIT1, and PLSCR1). By analyzing the area under the ROC curve of the 7 core genotypes, it was found that the AUC area of PLSCR1 was the largest, representing the best sensitivity and specificity. Further analysis of the PLSCR1 gene revealed that human phospholipid scramblase 1 (PLSCR1) is an upregulated gene in patients with severe coronavirus infection, and further revealed that PLCSR1 is a calcium-dependent, non-specific enzyme involved in rapid phospholipid reallocation^[40,41]. Studies have shown that PLCSR1 is abnormally expressed during interferon (IFN) therapy and viral infections. Negative regulation of human cytomegalovirus (HCMV) replication by inhibiting transcription of major immediate early proteins (MIEPs) and early promoters of the virus plays an important role in the regulation of inflammatory immune responses and IFN-dependent antiviral responses^[42]. PLSCR1 is transcriptionally-regulated by IFN through pathways involved in the activation of PKC-δ, JNK, and STAT1^[43]. PLSCR1 also regulates TLR9 signaling and subsequent production of IFN in plasmacytoid dendritic cells^[44]. The analysis showed that PLSCR1 is expressed in both neutrophils and monocytes, which induced neutrophil chemotaxis and adhesion, which was consistent with the inflammatory response of COVID-19 patients in our previous GO, KEGG, and GSEA analyses. A detailed analysis of the effects of viral proteins on calcium signaling showed that elevated levels of virus-associated cytoplasmic calcium lead to viral protein expression, and calcium (Ca²⁺) acts as a cellular second messenger and regulates a variety of cellular functions^[45]. A variety of viruses control the host cell Ca²⁺ signal cascade by controlling the host cell Ca²⁺ processing mechanism. Viruses can also induce changes in the cytoplasmic Ca²⁺ concentration and activate Ca²⁺-dependent enzymes, resulting in maladjustment of the host cell signal cascade and highly affected cellular calcium dynamics, thus driving virus infection and pathogenesis^[46]. It has been reported that Ca²⁺ is required for fusion of MERS-COV, SARS-COV, and rubella virus with host cells, suggesting that Ca²⁺ plays a key role in infection of these viruses^[47–49]. Interestingly, based on structural homology, bioinformatics, and meta-analysis, Ca²⁺ may be an important regulator of SARS-CoV-2 entry into host cells^[46]. In addition, recent studies have shown that PLSCR1 negatively regulates viral replication by inhibiting multiple viral transcriptional pathways, such as PLSCR1, can form a trimer complex with NP and the imported protein α family members, which inhibits β, a key mediator of the classical nuclear import pathway, from entering the complex, thus destroying the nuclear import of NP, inhibiting viral replication, and negatively regulating viral replication^[50]. Furthermore, PLSCR1 can interact with HCV envelope proteins (E1 and E2) to promote HCV virus replication^[51].

In summary, PLSCR1 encodes calcium-binding proteins that regulate cellular Ca²⁺ levels, thereby affecting a variety of cellular biological signal transductions, and also regulates viral replication through other pathways. Therefore, early recognition may be important for the early diagnosis and prognosis of critically ill patients with COVID-19. Thus, molecular typing of critically ill patients with COVID-19 can reveal diagnostic, prognostic, and therapeutic biomarker for critically ill patients with COVID-19, leading to early diagnosis and treatment. Nevertheless, biomarker need to be improved and prognostic models developed.

Funding statement

This study was supported by the 2020 Municipal Applied Technology Tesearch and Development Fund Project (no. 20YFZJ0105).

Acknowledgements

We are grateful to the participants for providing the information in our study as arranging for data collection.

Authors’ contributions

YC and WB-Z initiated and designed this study. YL-Y and XJ-C performed the conceptualization, formal analysis, and writing-original draft.GL-J and GO supervised the study. QL and LJ wrote the manuscript. JJ-C performed data curation and resources. Yong Chen and Wenbo zhang contributed to this work equally and should be regarded as co-first authors.All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study has been approved by the Affiliated Hospital of North Sichuan Medical College Ethics Committee in accordance with the Declaration of Helsinki. All data were obtained from a public domain database.

Consent for publication

Not applicable.

Conflicts of Interest
The authors declare that they have no conflicts of interest.Data Availability
.The datasets analyzed during the current study are publicly available in the [Gene Expression Omnibus databased] repository/database, [https://www.ncbi.nlm.nih.gov/geo/; GSE172114],the Search Tool for the Retrieval of Interacting Genes[ http://string-db.org], the [GeneMANIA] online database,[ https://genemania.org/],and Xian tao,[https://www.xiantao.love/].

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No competing interests reported.

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Identification of transcriptomics biomarker for the early prediction of critically ill COVID-19 patients

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