Identification of Differentially Expressed Genes and Enriched Pathways in SARS-CoV-2/ COVID-19 using Bioinformatics Analysis

Alshabi, Ali Mohamed; Shaikh, Ibrahim Ahmed; Vastrad, Basavaraj; Vastrad, Chanabasayya

doi:10.21203/rs.3.rs-122015/v1

Cited by 3 publications

(3 citation statements)

References 97 publications

(97 reference statements)

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“…SORT1 has been shown to be upregulated in almost all lung infections due to its specific relationships with neutrophil recruitment in lung tissues/surrounding vascular against pathogens, especially for bacterial infections [60][61][62]. By contrast, specifically, in COVID-19, a network based analyses has shown that such gene is associated with the infection of SARS-CoV-2 with a relatively low expression, corresponding with our predictions [63]. Similar decreased expression levels of RPL21P28, SIDT2, and TKT have also been validated in the transcriptomic analyses of COVID-19 host cells [64,65].…”

Section: Quantitative Rules Associated With Disease-specificsupporting

confidence: 86%

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Lei

Zeng

et al. 2021

BioMed Research International

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COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.

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Section: Quantitative Rules Associated With Disease-specificsupporting

confidence: 86%

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Lei

Zeng

et al. 2021

BioMed Research International

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“…Gasparello et al [ 26 ] discovered that hsa-miR-450a-5p may bind to the IL-8 gene, which is involved in cytokine storms, and that this biomarker is one of the predictors of patient survival when they are hospitalized. Another example is how miRNA hsa-miR-192–3p binds to NR1H4 and contributes to SARS-COV-2 progression [ 27 ]. According to Alshabi et al [ 19 ], miRNA hsa-miR-6809–5p binds to the S-region or spike gene from the SARS-COV-2 genome; however, we discovered that it could also bind to the 5′UTR region.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the fact that the genome of the SARS-COV-2 virus differs from that of influenza cases, some miRNAs are present in them as well, which are also prone to bind to the 5 UTR region of the SARS-COV-2 mRNA sequence. These interactions were discovered with the hsa-miR-6873–5p [ 28 ] and the hsa-miR-4276 [ 27 ]. Other types of miRNAs, such as the hsa-miR-7111–5p, which binds the HOXC8 gene and up-regulates it, are also found in diseases that could result in co-morbidity in SARS-COV virus scenarios [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

Prediction of binding miRNAs involved with immune genes to the SARS-CoV-2 by using sequence features extraction and One-class SVM

Gutiérrez-Cárdenas

Wang

2022

Informatics in Medicine Unlocked

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Identification of genes related to immune enhancement caused by heterologous ChAdOx1–BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods

Huang²,

Ma³

et al. 2023

Front. Immunol.

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The widely used ChAdOx1 nCoV-19 (ChAd) vector and BNT162b2 (BNT) mRNA vaccines have been shown to induce robust immune responses. Recent studies demonstrated that the immune responses of people who received one dose of ChAdOx1 and one dose of BNT were better than those of people who received vaccines with two homologous ChAdOx1 or two BNT doses. However, how heterologous vaccines function has not been extensively investigated. In this study, single-cell RNA sequencing data from three classes of samples: volunteers vaccinated with heterologous ChAdOx1–BNT and volunteers vaccinated with homologous ChAd–ChAd and BNT–BNT vaccinations after 7 days were divided into three types of immune cells (3654 B, 8212 CD4+ T, and 5608 CD8+ T cells). To identify differences in gene expression in various cell types induced by vaccines administered through different vaccination strategies, multiple advanced feature selection methods (max-relevance and min-redundancy, Monte Carlo feature selection, least absolute shrinkage and selection operator, light gradient boosting machine, and permutation feature importance) and classification algorithms (decision tree and random forest) were integrated into a computational framework. Feature selection methods were in charge of analyzing the importance of gene features, yielding multiple gene lists. These lists were fed into incremental feature selection, incorporating decision tree and random forest, to extract essential genes, classification rules and build efficient classifiers. Highly ranked genes include PLCG2, whose differential expression is important to the B cell immune pathway and is positively correlated with immune cells, such as CD8+ T cells, and B2M, which is associated with thymic T cell differentiation. This study gave an important contribution to the mechanistic explanation of results showing the stronger immune response of a heterologous ChAdOx1–BNT vaccination schedule than two doses of either BNT or ChAdOx1, offering a theoretical foundation for vaccine modification.

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Identification of Differentially Expressed Genes and Enriched Pathways in SARS-CoV-2/ COVID-19 using Bioinformatics Analysis

Cited by 3 publications

References 97 publications

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods

Prediction of binding miRNAs involved with immune genes to the SARS-CoV-2 by using sequence features extraction and One-class SVM

Identification of genes related to immune enhancement caused by heterologous ChAdOx1–BNT162b2 vaccines in lymphocytes at single-cell resolution with machine learning methods

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