Pathogenetic profiling of COVID-19 and SARS-like viruses

Using a targeted metabolomics approach to explore differences in ARDS associated with COVID-19 compared to ARDS caused by H1N1 influenza and bacterial pneumonia

RATIONALE

Acute respiratory distress syndrome (ARDS) is a life-threatening critical care syndrome commonly associated with infections such as COVID-19, influenza, and bacterial pneumonia. Ongoing research aims to improve our understanding of ARDS, including its molecular mechanisms, individualized treatment options, and potential interventions to reduce inflammation and promote lung repair.

OBJECTIVE

To map and compare metabolic phenotypes of different infectious causes of ARDS to better understand the metabolic pathways involved in the underlying pathogenesis.

METHODS

We analyzed metabolic phenotypes of 3 ARDS cohorts caused by COVID-19, H1N1 influenza, and bacterial pneumonia compared to non-ARDS COVID-19-infected patients and ICU-ventilated controls. Targeted metabolomics was performed on plasma samples from a total of 150 patients using quantitative LC-MS/MS and DI-MS/MS analytical platforms.

RESULTS

Distinct metabolic phenotypes were detected between different infectious causes of ARDS. There were metabolomics differences between ARDSs associated with COVID-19 and H1N1, which include metabolic pathways involving taurine and hypotaurine, pyruvate, TCA cycle metabolites, lysine, and glycerophospholipids. ARDSs associated with bacterial pneumonia and COVID-19 differed in the metabolism of D-glutamine and D-glutamate, arginine, proline, histidine, and pyruvate. The metabolic profile of COVID-19 ARDS (C19/A) patients admitted to the ICU differed from COVID-19 pneumonia (C19/P) patients who were not admitted to the ICU in metabolisms of phenylalanine, tryptophan, lysine, and tyrosine. Metabolomics analysis revealed significant differences between C19/A, H1N1/A, and PNA/A vs ICU-ventilated controls, reflecting potentially different disease mechanisms.

CONCLUSION

Different metabolic phenotypes characterize ARDS associated with different viral and bacterial infections.

Generic model to unravel the deeper insights of viral infections: an empirical application of evolutionary graph coloring in computational network biology

PURPOSE

Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein-protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations.

METHODS

To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins.

RESULTS

We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences.

CONCLUSION

Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

SARS-CoV-2 primed platelets-derived microRNAs enhance NETs formation by extracellular vesicle transmission and TLR7/8 activation

BACKGROUND

Hyperactive neutrophil extracellular traps (NETs) formation plays a key role in the pathogenesis of severe COVID-19. Extracellular vesicles (EVs) are vehicles which carry cellular components for intercellular communication. The association between COVID-19 patients-derived EVs and NETs formation remains elusive.

METHODS

We explored the roles of EVs in NETs formation from 40 COVID-19 patients with different disease severities as well as 30 healthy subjects. The EVs-carried microRNAs profile was analyzed using next generation sequencing approach which was validated by quantitative reverse transcription PCR. The regulatory mechanism of EVs on NETs formation was investigated by using an in vitro cell-based assay, including immunofluorescence assay, flow cytometry, and immunoblotting.

RESULTS

COVID-19 patient-derived EVs induced NETs formation by endocytosis uptake. SARS-CoV-2 spike protein-triggered NETs formation was significantly enhanced in the presence of platelet-derived EVs (pEVs) and this effect was Toll-like receptor (TLR) 7/8- and NADPH oxidase-dependent. Increased levels of miR-21/let-7b were revealed in EVs from COVID-19 patients and were associated with disease severity. We demonstrated that the spike protein activated platelets directly, followed by the subsequent intracellular miR-21/let-7b upregulation and then were loaded into pEVs. The pEVs-carried miR-21 interacted with TLR7/8 to prime p47phox phosphorylation in neutrophils, resulting in NADPH oxidase activation to promote ROS production and NETs enhancement. In addition, miR-21 modulates NF-κB activation and IL-1β/TNFα/IL-8 upregulation in neutrophils upon TLR7/8 engagement. The miR-21 inhibitor and TLR8 antagonist could suppress efficiently spike protein-induced NETs formation and pEVs primed NETs enhancement.

CONCLUSIONS

We identified SARS-CoV-2 triggered platelets-derived GU-enriched miRNAs (e.g., miR-21/let-7b) as a TLR7/8 ligand that could activate neutrophils through EVs transmission. The miR-21-TLR8 axis could be used as a potential predisposing factor or therapeutic target for severe COVID-19.

COVID-19 of differing severity: from bulk to single-cell expression data analysis

Coronavirus disease 2019 (COVID-19) is raging worldwide and causes an immense disease burden. Despite this, the biomarkers and targeting drugs of COVID-19 of differing severity remain largely unknown. Based on the GSE164805 dataset, we identified modules most critical for mild COVID-19 (mCOVID-19) and severe COVID-19 (sCOVID-19) through WGCNA, respectively. We subsequently constructed a protein-protein interaction network, and detected 16 hub genes for mCOVID-19 and 10 hub genes for sCOVID-19, followed by the prediction of upstream transcription factors (TFs) and kinases. The enrichment analysis then showed downregulation of TNFA signaling via NFKB for mCOVID-19, as well as downregulation of MYC targets V1 for sCOVID-19. Infiltration degrees of many immune cells, such as macrophages, were also sharply different between mCOVID-19 and sCOVID-19 samples. Predicted protein targeting drugs with the highest scores nearly all belong to naturally derived or synthetic glucocorticoids. For the two single-cell RNA-seq datasets, we explored the expression distribution of hub genes for mCOVID-19/sCOVID-19 in each cell type. The expression levels of PRKCA, MCM5, TYMS, RBBP4, BCL6, FLOT1, KDM6B, and TLR2 were found to be cell-type-specific. Furthermore, the expression levels of 10 hub genes for mCOVID-19 were significantly upregulated in PBMCs between eight healthy controls and eight mCOVID-19 patients at our institution. Collectively, we detected critical modules, pathways, TFs, kinases, immune cells, targeting drugs, hub genes, and their expression distributions in different cell types that may involve the pathogenesis of COVID-19 of differing severity, which may propel earlier diagnosis and more effective treatment of this intractable disease in the future.

Bioinformatics-based investigation on the genetic influence between SARS-CoV-2 infections and idiopathic pulmonary fibrosis (IPF) diseases, and drug repurposing

Some recent studies showed that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and idiopathic pulmonary fibrosis (IPF) disease might stimulate each other through the shared genes. Therefore, in this study, an attempt was made to explore common genomic biomarkers for SARS-CoV-2 infections and IPF disease highlighting their functions, pathways, regulators and associated drug molecules. At first, we identified 32 statistically significant common differentially expressed genes (cDEGs) between disease (SARS-CoV-2 and IPF) and control samples of RNA-Seq profiles by using a statistical r-package (edgeR). Then we detected 10 cDEGs (CXCR4, TNFAIP3, VCAM1, NLRP3, TNFAIP6, SELE, MX2, IRF4, UBD and CH25H) out of 32 as the common hub genes (cHubGs) by the protein-protein interaction (PPI) network analysis. The cHubGs regulatory network analysis detected few key TFs-proteins and miRNAs as the transcriptional and post-transcriptional regulators of cHubGs. The cDEGs-set enrichment analysis identified some crucial SARS-CoV-2 and IPF causing common molecular mechanisms including biological processes, molecular functions, cellular components and signaling pathways. Then, we suggested the cHubGs-guided top-ranked 10 candidate drug molecules (Tegobuvir, Nilotinib, Digoxin, Proscillaridin, Simeprevir, Sorafenib, Torin 2, Rapamycin, Vancomycin and Hesperidin) for the treatment against SARS-CoV-2 infections with IFP diseases as comorbidity. Finally, we investigated the resistance performance of our proposed drug molecules compare to the already published molecules, against the state-of-the-art alternatives publicly available top-ranked independent receptors by molecular docking analysis. Molecular docking results suggested that our proposed drug molecules would be more effective compare to the already published drug molecules. Thus, the findings of this study might be played a vital role for diagnosis and therapies of SARS-CoV-2 infections with IPF disease as comorbidity risk.

Pathways of Coagulopathy and Inflammatory Response in SARS-CoV-2 Infection among Type 2 Diabetic Patients

Chronic inflammation and endothelium dysfunction are present in diabetic patients. COVID-19 has a high mortality rate in association with diabetes, partially due to the development of thromboembolic events in the context of coronavirus infection. The purpose of this review is to present the most important underlying pathomechanisms in the development of COVID-19-related coagulopathy in diabetic patients. The methodology consisted of data collection and synthesis from the recent scientific literature by accessing different databases (Cochrane, PubMed, Embase). The main results are the comprehensive and detailed presentation of the very complex interrelations between different factors and pathways involved in the development of arteriopathy and thrombosis in COVID-19-infected diabetic patients. Several genetic and metabolic factors influence the course of COVID-19 within the background of diabetes mellitus. Extensive knowledge of the underlying pathomechanisms of SARS-CoV-2-related vasculopathy and coagulopathy in diabetic subjects contributes to a better understanding of the manifestations in this highly vulnerable group of patients; thus, they can benefit from a modern, more efficient approach regarding diagnostic and therapeutic management.

Iso-seco-tanapartholide from Artemisia argyi inhibits the PFKFB3-mediated glycolytic pathway to attenuate airway inflammation in lipopolysaccharide-induced acute lung injury mice

ETHNOPHARMACOLOGICAL RELEVANCE

In traditional Chinese folk medicine, Artemisia argyi H.Lév. & Vaniot (A. argyi) has been used for thousands of years, and it is clinically used to treat bronchitis and asthma. However, the mechanism of action of A. argyi on respiratory tract inflammation is not clear. Accumulating evidence that phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) is actively expressed in inflammation. Here, we found that iso-seco-tanapartholide (IST), a sesquiterpene isolated from A. argyi, exhibited potent anti-inflammatory activity and significant inhibition of PFKFB3 expression. Therefore, we evaluated the effect of IST on airway inflammation and revealed its possible mechanisms.

AIM OF THE STUDY

This study aimed to investigate the protective effect and possible mechanism of IST in lipopolysaccharide (LPS)-induced acute lung injury in mice.

MATERIALS AND METHODS

In vitro, RAW264.7 cells and BMDMs were stimulated with LPS, and the level of NO and inflammatory factors TNF-α, IL-1β, and IL-6 were detected by Griess reagent and ELISA, respectively. The effect of IST on the levels of PFKFB3 and its downstream proteins (p-STAT3, p-p65) in cells was assayed by western blotting. Lactate and glycolytic phenotypes were detected by lactate kit and Seahorse assay. In vivo, a mouse model of acute lung injury was induced by LPS, and the levels of inflammatory factors were measured by ELISA. Expression of PFKFB3 and its downstream proteins (p-STAT3, p-p65) in mouse alveolar macrophages by western blotting analysis. Lung permeability assessment by Evans Blue dye assay. H&E staining and Immunocytochemistry were used to observe the protection of IST against lung injury.

RESULTS

IST significantly reduced LPS-induced expression of PFKFB3 and its downstream proteins (p-STAT3, p-p65). The inhibition of PFKFB3 has an impact on the glycolytic phenotype, such as a reduction in the rate of extracellular acidification (ECAR) and elevated lactate levels, and an increase in the rate of cellular oxygen consumption (OCR). Furthermore, IST inhibited LPS-induced NO release and increased the expression of pro-inflammatory factors TNF-α, IL-1β, and IL-6. In vivo, IST reduced pulmonary edema in LPS-induced acute lung injury, improved lung function, and reduced levels of inflammatory factors and lactate secretion.

CONCLUSIONS

These results suggest that IST improves the characteristics of ALI by inhibiting the expression of the PFKFB3-mediated glycolytic pathway and may be a potential anti-inflammatory agent for inflammation-related lung diseases.

A review on computer-aided chemogenomics and drug repositioning for rational COVID-19 drug discovery

Application of materials capable of energy harvesting to increase the efficiency and environmental adaptability is sometimes reflected in the ability of discovery of some traces in an environment-either experimentally or computationally-to enlarge practical application window. The emergence of computational methods, particularly computer-aided drug discovery (CADD), provides ample opportunities for the rapid discovery and development of unprecedented drugs. The expensive and time-consuming process of traditional drug discovery is no longer feasible, for nowadays the identification of potential drug candidates is much easier for therapeutic targets through elaborate in silico approaches, allowing the prediction of the toxicity of drugs, such as drug repositioning (DR) and chemical genomics (chemogenomics). Coronaviruses (CoVs) are cross-species viruses that are able to spread expeditiously from the into new host species, which in turn cause epidemic diseases. In this sense, this review furnishes an outline of computational strategies and their applications in drug discovery. A special focus is placed on chemogenomics and DR as unique and emerging system-based disciplines on CoV drug and target discovery to model protein networks against a library of compounds. Furthermore, to demonstrate the special advantages of CADD methods in rapidly finding a drug for this deadly virus, numerous examples of the recent achievements grounded on molecular docking, chemogenomics, and DR are reported, analyzed, and interpreted in detail. It is believed that the outcome of this review assists developers of energy harvesting materials and systems for detection of future unexpected kinds of CoVs or other variants.

Cognitive dysfunction associated with COVID-19: Prognostic role of circulating biomarkers and microRNAs

COVID-19 is renowned as a multi-organ disease having subacute and long-term effects with a broad spectrum of clinical manifestations. The evolving scientific and clinical evidence demonstrates that the frequency of cognitive impairment after COVID-19 is high and it is crucial to explore more clinical research and implement proper diagnostic and treatment strategies. Several central nervous system complications have been reported as comorbidities of COVID-19. The changes in cognitive function associated with neurodegenerative diseases develop slowly over time and are only diagnosed at an already advanced stage of molecular pathology. Hence, understanding the common links between COVID-19 and neurodegenerative diseases will broaden our knowledge and help in strategizing prognostic and therapeutic approaches. The present review focuses on the diverse neurodegenerative changes associated with COVID-19 and will highlight the importance of major circulating biomarkers and microRNAs (miRNAs) associated with the disease progression and severity. The literature analysis showed that major proteins associated with central nervous system function, such as Glial fibrillary acidic protein, neurofilament light chain, p-tau 181, Ubiquitin C-terminal hydrolase L1, S100 calcium-binding protein B, Neuron-specific enolase and various inflammatory cytokines, were significantly altered in COVID-19 patients. Furthermore, among various miRNAs that are having pivotal roles in various neurodegenerative diseases, miR-146a, miR-155, Let-7b, miR-31, miR-16 and miR-21 have shown significant dysregulation in COVID-19 patients. Thus the review consolidates the important findings from the numerous studies to unravel the underlying mechanism of neurological sequelae in COVID-19 and the possible association of circulatory biomarkers, which may serve as prognostic predictors and therapeutic targets in future research.

A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy

Recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide. While COVID-19 generally affects the lungs, it also damages other organs, including those of the cardiovascular system. Hypertrophic cardiomyopathy (HCM) is a common genetic cardiovascular disorder. Studies have shown that HCM patients with COVID-19 have a higher mortality rate; however, the reason for this phenomenon is not yet elucidated. Herein, we conducted transcriptomic analyses to identify shared biomarkers between HCM and COVID-19 to bridge this knowledge gap. Differentially expressed genes (DEGs) were obtained using the Gene Expression Omnibus ribonucleic acid (RNA) sequencing datasets, GSE147507 and GSE89714, to identify shared pathways and potential drug candidates. We discovered 30 DEGs that were common between these two datasets. Using a combination of statistical and biological tools, protein-protein interactions were constructed in response to these findings to support hub genes and modules. We discovered that HCM is linked to COVID-19 progression based on a functional analysis under ontology terms. Based on the DEGs identified from the datasets, a coregulatory network of transcription factors, genes, proteins, and microRNAs was also discovered. Lastly, our research suggests that the potential drugs we identified might be helpful for COVID-19 therapy.

Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease

Background

Cardiovascular disease (CVD) is the combination of coronary heart disease, myocardial infarction, rheumatic heart disease, and peripheral vascular disease of the heart and blood vessels. It is one of the leading deadly diseases that causes one-third of the deaths yearly in the globe. Additionally, the risk factors associated with it make the situation more complex for cardiovascular patients, which lead them towards mortality, but the genetic association between CVD and its risk factors is not clearly explored in the global literature. We addressed this issue and explored the linkage between CVD and its risk factors.

Methods

We developed an analytical approach to reveal the risk factors and their linkages with CVD. We used GEO microarray datasets for the CVD and other risk factors in this study. We performed several analyses including gene expression analysis, diseasome analysis, protein-protein interaction (PPI) analysis, and pathway analysis for discovering the relationship between CVD and its risk factors. We also examined the validation of our study using gold benchmark databases OMIM, dbGAP, and DisGeNET.

Results

We observed that the number of 32, 17, 53, 70, and 89 differentially expressed genes (DEGs) is overlapped between CVD and its risk factors of hypertension (HTN), type 2 diabetes (T2D), hypercholesterolemia (HCL), obesity, and aging, respectively. We identified 10 major hub proteins (FPR2, TNF, CXCL8, CXCL1, IL1B, VEGFA, CYBB, PTGS2, ITGAX, and CCR5), 12 significant functional pathways, and 11 gene ontological pathways that are associated with CVD. We also found the connection of CVD with its risk factors in the gold benchmark databases. Our experimental outcomes indicate a strong association of CVD with its risk factors of HTN, T2D, HCL, obesity, and aging.

Conclusions

Our computational approach explored the genetic association of CVD with its risk factors by identifying the significant DEGs, hub proteins, and signaling and ontological pathways. The outcomes of this study may be further used in the lab-based analysis for developing the effective treatment strategies of CVD.

To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also known as COVID-19, is currently prevalent worldwide and poses a significant threat to human health. Individuals with cancer may have an elevated risk for SARS-CoV-2 infections and adverse outcomes. Therefore, it is necessary to explore the internal relationship between these two diseases. In this study, transcriptome analyses were performed to detect mutual pathways and molecular biomarkers in three types of common cancers of the breast, liver, colon, and COVID-19. Such analyses could offer a valuable understanding of the association between COVID-19 and cancer patients. In an analysis of RNA sequencing datasets for three types of cancers and COVID-19, we identified a sum of 38 common differentially expressed genes (DEGs). A variety of combinational statistical approaches and bioinformatics techniques were utilized to generate the protein-protein interaction (PPI) network. Subsequently, hub genes and critical modules were found using this network. In addition, a functional analysis was conducted using ontologies keywords, and pathway analysis was also performed. Some common associations between cancer and the risk and prognosis of COVID-19 were discovered. The datasets also revealed transcriptional factors-gene interplay, protein-drug interaction, and a DEGs-miRNAs coregulatory network with common DEGs. The potential medications discovered in this investigation could be useful in treating cancer and COVID-19.

Meta-Data Analysis to Explore the Hub of the Hub-Genes That Influence SARS-CoV-2 Infections Highlighting Their Pathogenetic Processes and Drugs Repurposing

The pandemic of SARS-CoV-2 infections is a severe threat to human life and the world economic condition. Although vaccination has reduced the outspread, but still the situation is not under control because of the instability of RNA sequence patterns of SARS-CoV-2, which requires effective drugs. Several studies have suggested that the SARS-CoV-2 infection causing hub differentially expressed genes (Hub-DEGs). However, we observed that there was not any common hub gene (Hub-DEGs) in our analyses. Therefore, it may be difficult to take a common treatment plan against SARS-CoV-2 infections globally. The goal of this study was to examine if more representative Hub-DEGs from published studies by means of hub of Hub-DEGs (hHub-DEGs) and associated potential candidate drugs. In this study, we reviewed 41 articles on transcriptomic data analysis of SARS-CoV-2 and found 370 unique hub genes or studied genes in total. Then, we selected 14 more representative Hub-DEGs (AKT1, APP, CXCL8, EGFR, IL6, INS, JUN, MAPK1, STAT3, TNF, TP53, UBA52, UBC, VEGFA) as hHub-DEGs by their protein-protein interaction analysis. Their associated biological functional processes, transcriptional, and post-transcriptional regulatory factors. Then we detected hHub-DEGs guided top-ranked nine candidate drug agents (Digoxin, Avermectin, Simeprevir, Nelfinavir Mesylate, Proscillaridin, Linifanib, Withaferin, Amuvatinib, Atazanavir) by molecular docking and cross-validation for treatment of SARS-CoV-2 infections. Therefore, the findings of this study could be useful in formulating a common treatment plan against SARS-CoV-2 infections globally.

Transcriptome Analysis Reveals Hub Genes Regulating Autophagy in Patients With Severe COVID-19

Background: The COVID-19 pandemic has currently developed into a worldwide threat to humankind. Importantly, patients with severe COVID-19 are believed to have a higher mortality risk than those with mild conditions. However, despite the urgent need to develop novel therapeutic strategies, the biological features and pathogenic mechanisms of severe COVID-19 are poorly understood.

Methods: Here, peripheral blood mononuclear cells (PBMCs) from four patients with severe COVID-19, four patients with mild COVID-19, and four healthy controls were examined by RNA sequencing (RNA-Seq). We conducted gene expression analysis and Venn diagrams to detect specific differentially expressed genes (DEGs) in patients with severe disease compared with those with mild conditions. Gene Ontology (GO) enrichment analysis was performed to identify the significant biological processes, and protein–protein interaction networks were constructed to extract hub genes. These hub genes were then subjected to regulatory signatures and protein–chemical interaction analysis for certain regulatory checkpoints and identification of potent chemical agents. Finally, to demonstrate the cell type-specific expression of these genes, we performed single-cell RNA-Seq analyses using an online platform.

Results: A total of 144 DEGs were specifically expressed in severe COVID-19, and GO enrichment analysis revealed a significant association of these specific DEGs with autophagy. Hub genes such as MVB12A, CHMP6, STAM, and VPS37B were then found to be most significantly involved in the biological processes of autophagy at the transcriptome level. In addition, six transcription factors, including SRF, YY1, CREB1, PPARG, NFIC, and GATA2, as well as miRNAs, namely, hsa-mir-1-3p, and potent chemical agents such as copper sulfate and cobalt chloride, may cooperate in regulating the autophagy hub genes. Furthermore, classical monocytes may play a central role in severe COVID-19.

Conclusion: We suggest that autophagy plays a crucial role in severe COVID-19. This study might facilitate a more profound knowledge of the biological characteristics and progression of COVID-19 and the development of novel therapeutic approaches to achieve a breakthrough in the current COVID-19 pandemic.

Resveratrol alleviates enterotoxigenic Escherichia coli K88-induced damage by regulating SIRT-1 signaling in intestinal porcine epithelial cells

This study found that resveratrol pretreatment attenuated porcine intestinal epithelial cell damage caused by enterotoxigenic Escherichia coli (ETEC) K88 in vitro and the protective effects of resveratrol were associated with SIRT-1 signaling. ETEC K88 is a main intestinal pathogen for post-weaning diarrhea (PWD) in piglets. With the strict ban on antibiotics in animal feed, people are seeking effective antibiotic substitutes to protect the intestinal system against harmful pathogenic bacteria. This study was conducted to evaluate the effects of resveratrol, a natural plant polyphenol, on ETEC K88-induced cellular damage in porcine enterocytes and underlying mechanisms. Intestinal porcine epithelial cell line 1 (IPEC-1) cells, pretreated with or without resveratrol (30 μM, 4 h), were challenged with ETEC K88 (MOI = 1 : 10) for 3 h. The results showed that ETEC K88 infection induced severe damage and dysfunction in IPEC-1 cells, as evidenced by a reduced cell viability, decreased tight junctions, mitochondrial dysfunction, and autophagy. It is noteworthy that IPEC-1 cells pre-treated with resveratrol improved their capacity for resistance to most of these abnormal phenotypes caused by ETEC K88 infection. Furthermore, we found that the activation of SIRT-1 signaling was associated with the benefits of resveratrol, as demonstrated by EX-527, an inhibitor of SIRT-1, which reversed most of the protective effects of resveratrol. In conclusion, these results indicated that resveratrol could protect intestinal epithelial cells against ETEC K88 infection by activating SIRT-1 signaling. These findings provide new insights into the role of resveratrol in maintaining intestinal physiological functions.

The potential biological effects of quercetin based on pharmacokinetics and multi-targeted mechanism in vivo

Quercetin is a plant-derived polyphenol flavonoid that has been proven to be effective for many diseases. However, the mechanism and in vivo metabolism of quercetin remains to be clarified. It achieves a wide range of biological effects through various metabolites, gut microbiota and its metabolites, systemic mediators produced by inflammation and oxidation, as well as by multiple mechanisms. The all-round disease treatment of quercetin is achieved through the organic combination of multiple channels. Therefore, this article clarifies the metabolic process of quercetin in the body, and explores the new pattern of action of quercetin in the treatment of diseases.

Viral and Host Genetic and Epigenetic Biomarkers Related to SARS-CoV-2 Cell Entry, Infection Rate, and Disease Severity

The rapid spread of COVID-19 outbreak lead to a global pandemic declared in March 2020. The common features of corona virus family helped to resolve structural characteristics and entry mechanism of SARS-CoV-2. However, rapid mutagenesis leads to the emergence of new strains that may have different reproduction rates or infectivity and may impact the course and severity of the disease. Host related factors may also play a role in the susceptibility for infection as well as the severity and outcomes of the COVID-19. We have performed a literature and database search to summarize potential viral and host-related genomic and epigenomic biomarkers, such as genetic variability, miRNA, and DNA methylation in the molecular pathway of SARS-CoV-2 entry into the host cell, that may be related to COVID-19 susceptibility and severity. Bioinformatics tools may help to predict the effect of mutations in the spike protein on the binding to the ACE2 receptor and the infectivity of the strain. SARS-CoV-2 may also target several transcription factors and tumour suppressor genes, thus influencing the expression of different host genes and affecting cell signalling. In addition, the virus may interfere with RNA expression in host cells by exploiting endogenous miRNA and its viral RNA. Our analysis showed that numerous human miRNA may form duplexes with different coding and non-coding regions of viral RNA. Polymorphisms in human genes responsible for viral entry and replication, as well as in molecular damage response and inflammatory pathways may also contribute to disease prognosis and outcome. Gene ontology analysis shows that proteins encoded by such polymorphic genes are highly interconnected in regulation of defense response. Thus, virus and host related genetic and epigenetic biomarkers may help to predict the course of the disease and the response to treatment.

Differential Co-Expression Network Analysis Reveals Key Hub-High Traffic Genes as Potential Therapeutic Targets for COVID-19 Pandemic

Background

The recent emergence of COVID-19, rapid worldwide spread, and incomplete knowledge of molecular mechanisms underlying SARS-CoV-2 infection have limited development of therapeutic strategies. Our objective was to systematically investigate molecular regulatory mechanisms of COVID-19, using a combination of high throughput RNA-sequencing-based transcriptomics and systems biology approaches.

Methods

RNA-Seq data from peripheral blood mononuclear cells (PBMCs) of healthy persons, mild and severe 17 COVID-19 patients were analyzed to generate a gene expression matrix. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules in healthy samples as a reference set. For differential co-expression network analysis, module preservation and module-trait relationships approaches were used to identify key modules. Then, protein-protein interaction (PPI) networks, based on co-expressed hub genes, were constructed to identify hub genes/TFs with the highest information transfer (hub-high traffic genes) within candidate modules.

Results

Based on differential co-expression network analysis, connectivity patterns and network density, 72% (15 of 21) of modules identified in healthy samples were altered by SARS-CoV-2 infection. Therefore, SARS-CoV-2 caused systemic perturbations in host biological gene networks. In functional enrichment analysis, among 15 non-preserved modules and two significant highly-correlated modules (identified by MTRs), 9 modules were directly related to the host immune response and COVID-19 immunopathogenesis. Intriguingly, systemic investigation of SARS-CoV-2 infection identified signaling pathways and key genes/proteins associated with COVID-19's main hallmarks, e.g., cytokine storm, respiratory distress syndrome (ARDS), acute lung injury (ALI), lymphopenia, coagulation disorders, thrombosis, and pregnancy complications, as well as comorbidities associated with COVID-19, e.g., asthma, diabetic complications, cardiovascular diseases (CVDs), liver disorders and acute kidney injury (AKI). Topological analysis with betweenness centrality (BC) identified 290 hub-high traffic genes, central in both co-expression and PPI networks. We also identified several transcriptional regulatory factors, including NFKB1, HIF1A, AHR, and TP53, with important immunoregulatory roles in SARS-CoV-2 infection. Moreover, several hub-high traffic genes, including IL6, IL1B, IL10, TNF, SOCS1, SOCS3, ICAM1, PTEN, RHOA, GDI2, SUMO1, CASP1, IRAK3, HSPA5, ADRB2, PRF1, GZMB, OASL, CCL5, HSP90AA1, HSPD1, IFNG, MAPK1, RAB5A, and TNFRSF1A had the highest rates of information transfer in 9 candidate modules and central roles in COVID-19 immunopathogenesis.

Conclusion

This study provides comprehensive information on molecular mechanisms of SARS-CoV-2-host interactions and identifies several hub-high traffic genes as promising therapeutic targets for the COVID-19 pandemic.

Potential therapeutic effect of Shufeng Jiedu capsule and its major herbs on coronavirus disease 2019 (COVID-19): A review

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) poses a huge threat to human health and social stability. Shufeng Jiedu capsule (SFJDC), a patented herbal drug composed of eight medicinal plants, is used to treat different viral respiratory tract infectious diseases. Based on its antiviral, anti-inflammatory, and immunoregulatory activities in acute lung injury, SFJDC can be effectively used as a treatment for COVID-19 patients according to the diagnosis and treatment plan issued in China and existing clinical data. SFJDC has been recommended in 15 therapeutic regimens for COVID-19 in China. This review summarizes current data on the ingredients, chemical composition, pharmacological properties, clinical efficacy, and potential therapeutic effect of SFJDC on COVID-19, to provide a theoretical basis for its anti-viral mechanism and the clinical treatment of COVID-19.

Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies

The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.

Evaluation of potential sponge effects of SARS genomes in human

To date the coronavirus family is composed of seven different viruses which were commonly known as cold viruses until the appearance of the severe acute respiratory coronavirus (SARS-CoV) in 2002, the middle east respiratory syndrome coronavirus (MERS) in 2012 and the severe acute respiratory coronavirus 2 (SARS-CoV-2) which caused the COVID-19 global pandemic in 2019. Using bioinformatic approaches we tested the potential interactions of human miRNAs, expressed in pulmonary epithelial cells, with the available coronavirus genomes. Putative miRNA binding sites were then compared between pathogenic and non pathogenic virus groups. The pathogenic group shares 6 miRNA binding sites that can be potentially involved in the sequestration of miRNAs already known to be associated with deep vein thrombosis. We then analysed ∼100k SARS-CoV-2 variant genomes for their potential interaction with human miRNAs and this study highlighted a group of 97 miRNA binding sites which is present in all the analysed genomes. Among these, we identified 6 miRNA binding sites specific for SARS-CoV-2 and the other two pathogenic viruses whose down-regulation has been seen associated with deep vein thrombosis and cardiovascular diseases. Interestingly, one of these miRNAs, namely miR-20a-5p, whose expression decreases with advancing age, is involved in cytokine signaling, cell differentiation and/or proliferation. We hypothesize that depletion of poorly expressed miRNA could be related with disease severity.

Bioinformatics and system biology approaches to identify the diseasome and comorbidities complexities of SARS-CoV-2 infection with the digestive tract disorders

Coronavirus Disease 2019 (COVID-19), although most commonly demonstrates respiratory symptoms, but there is a growing set of evidence reporting its correlation with the digestive tract and faeces. Interestingly, recent studies have shown the association of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection with gastrointestinal symptoms in infected patients but any sign of respiratory issues. Moreover, some studies have also shown that the presence of live SARS-CoV-2 virus in the faeces of patients with COVID-19. Therefore, the pathophysiology of digestive symptoms associated with COVID-19 has raised a critical need for comprehensive investigative efforts. To address this issue we have developed a bioinformatics pipeline involving a system biological framework to identify the effects of SARS-CoV-2 messenger RNA expression on deciphering its association with digestive symptoms in COVID-19 positive patients. Using two RNA-seq datasets derived from COVID-19 positive patients with celiac (CEL), Crohn's (CRO) and ulcerative colitis (ULC) as digestive disorders, we have found a significant overlap between the sets of differentially expressed genes from SARS-CoV-2 exposed tissue and digestive tract disordered tissues, reporting 7, 22 and 13 such overlapping genes, respectively. Moreover, gene set enrichment analysis, comprehensive analyses of protein-protein interaction network, gene regulatory network, protein-chemical agent interaction network revealed some critical association between SARS-CoV-2 infection and the presence of digestive disorders. The infectome, diseasome and comorbidity analyses also discover the influences of the identified signature genes in other risk factors of SARS-CoV-2 infection to human health. We hope the findings from this pathogenetic analysis may reveal important insights in deciphering the complex interplay between COVID-19 and digestive disorders and underpins its significance in therapeutic development strategy to combat against COVID-19 pandemic.

Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection

The coronavirus disease 2019 (COVID-19) is caused by the infection of highly contagious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as the novel coronavirus. In most countries, the containment of this virus spread is not controlled, which is driving the pandemic towards a more difficult phase. In this study, we investigated the impact of the Bacille Calmette Guerin (BCG) vaccination on the severity and mortality of COVID-19 by performing transcriptomic analyses of SARS-CoV-2 infected and BCG vaccinated samples in peripheral blood mononuclear cells (PBMC). A set of common differentially expressed genes (DEGs) were identified and seeded into their functional enrichment analyses via Gene Ontology (GO)-based functional terms and pre-annotated molecular pathways databases, and their Protein-Protein Interaction (PPI) network analysis. We further analysed the regulatory elements, possible comorbidities and putative drug candidates for COVID-19 patients who have not been BCG-vaccinated. Differential expression analyses of both BCG-vaccinated and COVID-19 infected samples identified 62 shared DEGs indicating their discordant expression pattern in their respected conditions compared to control. Next, PPI analysis of those DEGs revealed 10 hub genes, namely ITGB2, CXCL8, CXCL1, CCR2, IFNG, CCL4, PTGS2, ADORA3, TLR5 and CD33. Functional enrichment analyses found significantly enriched pathways/GO terms including cytokine activities, lysosome, IL-17 signalling pathway, TNF-signalling pathways. Moreover, a set of identified TFs, miRNAs and potential drug molecules were further investigated to assess their biological involvements in COVID-19 and their therapeutic possibilities. Findings showed significant genetic interactions between BCG vaccination and SARS-CoV-2 infection, suggesting an interesting prospect of the BCG vaccine in relation to the COVID-19 pandemic. We hope it may potentially trigger further research on this critical phenomenon to combat COVID-19 spread.

Bioinformatics and system biology approaches to identify pathophysiological impact of COVID-19 to the progression and severity of neurological diseases

The Coronavirus Disease 2019 (COVID-19) still tends to propagate and increase the occurrence of COVID-19 across the globe. The clinical and epidemiological analyses indicate the link between COVID-19 and Neurological Diseases (NDs) that drive the progression and severity of NDs. Elucidating why some patients with COVID-19 influence the progression of NDs and patients with NDs who are diagnosed with COVID-19 are becoming increasingly sick, although others are not is unclear. In this research, we investigated how COVID-19 and ND interact and the impact of COVID-19 on the severity of NDs by performing transcriptomic analyses of COVID-19 and NDs samples by developing the pipeline of bioinformatics and network-based approaches. The transcriptomic study identified the contributing genes which are then filtered with cell signaling pathway, gene ontology, protein-protein interactions, transcription factor, and microRNA analysis. Identifying hub-proteins using protein-protein interactions leads to the identification of a therapeutic strategy. Additionally, the incorporation of comorbidity interactions score enhances the identification beyond simply detecting novel biological mechanisms involved in the pathophysiology of COVID-19 and its NDs comorbidities. By computing the semantic similarity between COVID-19 and each of the ND, we have found gene-based maximum semantic score between COVID-19 and Parkinson's disease, the minimum semantic score between COVID-19 and Multiple sclerosis. Similarly, we have found gene ontology-based maximum semantic score between COVID-19 and Huntington disease, minimum semantic score between COVID-19 and Epilepsy disease. Finally, we validated our findings using gold-standard databases and literature searches to determine which genes and pathways had previously been associated with COVID-19 and NDs.

Identifying molecular insight of synergistic complexities for SARS-CoV-2 infection with pre-existing type 2 diabetes

The ongoing COVID-19 outbreak, caused by SARS-CoV-2, has posed a massive threat to global public health, especially to people with underlying health conditions. Type 2 diabetes (T2D) is lethal comorbidity of COVID-19. However, its pathogenetic link remains unclear. This research aims to determine the genetic factors and processes contributing to the synergistic severity of SARS-CoV-2 infection among T2D patients through bioinformatics approaches. We analyzed two sets of transcriptomic data of SARS-CoV-2 infection obtained from lung epithelium cells and PBMCs, and two sets of T2D data from pancreatic islet cells and PBMCs to identify the associated differentially expressed genes (DEGs) followed by their functional enrichment analyses in terms of protein-protein interaction (PPI) to detect hub-proteins and associated comorbidities, transcription factors (TFs), microRNAs (miRNAs) as well as the potential drug candidates. In PPI analysis, four potential hub-proteins (i.e., BIRC3, C3, MME, and IL1B) were identified among 25 DEGs shared between the disease pair. Enrichment analyses using the mutually overlapped DEGs revealed the most prevalent GO and cell signalling pathways, including TNF signalling, cytokine-cytokine receptor interaction, and IL-17 signalling, which are related to cytokine activities. Furthermore, as significant TFs, we identified IRF1, KLF11, FOSL1, and CREB3L1 while miRNAs including miR-1-3p, 34a-5p, 16–5p, 155–5p, 20a-5p, and let-7b-5p were found to be noteworthy. The findings illustrated the significant association between COVID-19 and T2D at the molecular level. These genetic determinants can further be explored for their specific roles in disease progression and therapeutic intervention, while significant pathways can also be studied as molecular checkpoints. Finally, the identified drug candidates may be evaluated for their potency to minimize the severity of COVID-19 patients with pre-existing T2D.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein–protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors–genes interaction, protein–drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Background

Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction.

Objective

Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes.

Methods

We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods.

Results

Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction.

Conclusions

We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.

Development of symptomatic inflammatory aneurysm treated with endovascular repair in coronavirus disease 2019-infected patient

The sequelae and complications of coronavirus disease 2019 infection have continued to emerge during this pandemic era. Although the long-term effects are continually being monitored and studied, we have been seeing more acute and subacute medical complications requiring a thorough discussion and, sometimes, adjustment of our standard of care and intervention. In the present report, we describe a case of an acute, symptomatic inflammatory or mycotic abdominal aortic aneurysm in a patient with coronavirus disease 2019 who had undergone urgent endovascular repair.

COVID-19 patient transcriptomic and genomic profiling reveals comorbidity interactions with psychiatric disorders

Psychiatric symptoms are seen in some COVID-19 patients, as direct or indirect sequelae, but it is unclear whether SARS-CoV-2 infection interacts with underlying neuronal or psychiatric susceptibilities. Such interactions might arise from COVID-19 immune responses, from infection of neurons themselves or may reflect social-psychological causes. To clarify this we sought the key gene expression pathways altered in COVID-19 also affected in bipolar disorder, post-traumatic stress disorder (PTSD) and schizophrenia, since this may identify pathways of interaction that could be treatment targets. We performed large scale comparisons of whole transcriptome data and immune factor transcript data in peripheral blood mononuclear cells (PBMC) from COVID-19 patients and patients with psychiatric disorders. We also analysed genome-wide association study (GWAS) data for symptomatic COVID-19 patients, comparing GWAS and whole-genome sequence data from patients with bipolar disorder, PTSD and schizophrenia patients. These studies revealed altered signalling and ontology pathways shared by COVID-19 patients and the three psychiatric disorders. Finally, co-expression and network analyses identified gene clusters common to the conditions. COVID-19 patients had peripheral blood immune system profiles that overlapped with those of patients with psychiatric conditions. From the pathways identified, PTSD profiles were the most highly correlated with COVID-19, perhaps consistent with stress-immune system interactions seen in PTSD. We also revealed common inflammatory pathways that may exacerbate psychiatric disorders, which may support the usage of anti-inflammatory medications in these patients. It also highlights the potential clinical application of multi-level dataset studies in difficult-to-treat psychiatric disorders in this COVID-19 pandemic.

Quercetin as a potential treatment for COVID-19-induced acute kidney injury: Based on network pharmacology and molecular docking study

Kidneys are one of the targets for SARS-CoV-2, it is reported that up to 36% of patients with SARS-CoV-2 infection would develop into acute kidney injury (AKI). AKI is associated with high mortality in the clinical setting and contributes to the transition of AKI to chronic kidney disease (CKD). Up to date, the underlying mechanisms are obscure and there is no effective and specific treatment for COVID-19-induced AKI. In the present study, we investigated the mechanisms and interactions between Quercetin and SARS-CoV-2 targets proteins by using network pharmacology and molecular docking. The renal protective effects of Quercetin on COVID-19-induced AKI may be associated with the blockade of the activation of inflammatory, cell apoptosis-related signaling pathways. Quercetin may also serve as SARS-CoV-2 inhibitor by binding with the active sites of SARS-CoV-2 main protease 3CL and ACE2, therefore suppressing the functions of the proteins to cut the viral life cycle. In conclusion, Quercetin may be a novel therapeutic agent for COVID-19-induced AKI. Inhibition of inflammatory, cell apoptosis-related signaling pathways may be the critical mechanisms by which Quercetin protects kidney from SARS-CoV-2 injury.

Identification of angiotensin-converting enzyme 2 (ACE2) protein as the potential biomarker in SARS-CoV-2 infection-related lung cancer using computational analyses

COVID-19 is a pandemic that began to spread worldwide caused by SARS-CoV-2. Lung cancer patients are more susceptible to SARS-CoV-2 infection. The SARS-CoV-2 enters into the host by the ACE2 receptor. Thus, ACE2 is the key to understand the mechanism of SARS-CoV-2 infection. However, the lack of knowledge about the biomarker of COVID-19 warrants the development of ACE2 biomarkers. The analysis of ACE2 expression in lung cancer was performed using The Cancer Genome Atlas (TCGA). Therefore, we investigated the prognosis, clinical characteristics, and mutational analysis of lung cancer. We also analyzed the shared proteins between the COVID-19 and lung cancer, protein-protein interactions, gene-miRNAs, gene-transcription factors (TFs), and the signaling pathway. Finally, we compared the mRNA expression of ACE2 and its co-expressed proteins using the TCGA. The up-regulation of ACE2 in lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) was found irrespective of gender and age. We found the low survival rate in high expression of ACE2 in lung cancer patients and 16 mutational positions. The functional assessment of targeted 12,671, 3107, and 29 positive genes were found in COVID-19 disease, LUAD, and LUSC, respectively. Then, we identified eight common genes that interact with 20 genes, 219 miRNAs, and 16 TFs. The common genes performed the mRNA expression in lung cancer, which proved the ACE2 is the best potential biomarker compared to co-expressed genes. This study uncovers the relationship between COVID-19 disease and lung cancer. We identified ACE2 and also its co-expressed proteins are the potential biomarker and therapy as the current COVID-19 disease and lung cancer.