SEARCHOTHERinfectious disease, immunology

other·infectious disease, immunology, biomedical research, preclinical research, research methods·PMC10075178

A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures

Scientific Reports · 5 authors, 4 centres

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TL;DR

This computational study reanalyzed publicly available transcriptomic datasets from COVID-19 and influenza patient samples to identify candidate blood-based gene biomarker signatures using pathway analysis and machine learning. The analysis identified a three-gene signature (IGKC, IGLV3-16, SRP9) specific to COVID-19 validated at 93.09% accuracy and a four-gene signature (FMNL2, IGHV3-23, IGLV2-11, RPL31) for differential diagnosis from influenza validated at 87.2% accuracy on external datasets.

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This study used a computational bioinformatic approach to reanalyze eight transcriptomic profiles of COVID-19-infected versus control samples from peripheral blood, lung tissue, nasopharyngeal swab, and BALF, along with nine influenza datasets covering H1N1, H3N2, and B strains. For differential diagnosis, 87 DifBDs were identified from pathways enriched by SpeBDs but not influenza DEGs; Naive Bayes selected FMNL2, IGHV3-23, IGLV2-11, and RPL31, validated at 87.2% accuracy. The authors note that reliance on manually curated pathway aggregates is a limitation, and all findings require further practical experimental validation before clinical application.

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other

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infectious disease

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AI pipeline

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ORIGINAL PAPER

PMC10075178

A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures

Scientific Reports

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ABSTRACT

COVID-19 is a newly recognized illness with a predominantly respiratory presentation. Although initial analyses have identified groups of candidate gene biomarkers for the diagnosis of COVID-19, they have yet to identify clinically applicable biomarkers, so we need disease-specific diagnostic biomarkers in biofluid and differential diagnosis in comparison with other infectious diseases. This can further increase knowledge of pathogenesis and help guide treatment. Eight transcriptomic profiles of COVID-19 infected versus control samples from peripheral blood (PB), lung tissue, nasopharyngeal swab and bronchoalveolar lavage fluid (BALF) were considered. In order to find COVID-19 potential Specific Blood Differentially expressed genes (SpeBDs), we implemented a strategy based on finding shared pathways of peripheral blood and the most involved tissues in COVID-19 patients. This step was performed to filter blood DEGs with a role in the shared pathways. Furthermore, nine datasets of the three types of Influenza (H1N1, H3N2, and B) were used for the second step. Potential Differential Blood DEGs of COVID-19 versus Influenza (DifBDs) were found by extracting DEGs involved in only enriched pathways by SpeBDs and not by Influenza DEGs. Then in the third step, a machine learning method (a wrapper feature selection approach supervised by four classifiers of k-NN, Random Forest, SVM, Naïve Bayes) was utilized to narrow down the number of SpeBDs and DifBDs and find the most predictive combination of them to select COVID-19 potential Specific Blood Biomarker Signatures (SpeBBSs) and COVID-19 versus influenza Differential Blood Biomarker Signatures (DifBBSs), respectively. After that, models based on SpeBBSs and DifBBSs and the corresponding algorithms were built to assess their performance on an external dataset. Among all the extracted DEGs from the PB dataset (from common PB pathways with BALF, Lung and Swab), 108 unique SpeBD were obtained. Feature selection using Random Forest outperformed its counterparts and selected IGKC, IGLV3-16 and SRP9 among SpeBDs as SpeBBSs. Validation of the constructed model based on these genes and Random Forest on an external dataset resulted in 93.09% Accuracy. Eighty-three pathways enriched by SpeBDs and not by any of the influenza strains were identified, including 87 DifBDs. Using feature selection by Naive Bayes classifier on DifBDs, FMNL2, IGHV3-23, IGLV2-11 and RPL31 were selected as the most predictable DifBBSs. The constructed model based on these genes and Naive Bayes on an external dataset was validated with 87.2% accuracy. Our study identified several candidate blood biomarkers for a potential specific and differential diagnosis of COVID-19. The proposed biomarkers could be valuable targets for practical investigations to validate their potential.

CITATION EXPORT

Maryam Momeni, Maryam Rashidifar, Farinaz Hosseini Balam et al.. A comprehensive analysis of gene expression profiling data in COVID-19 patients for discovery of specific and differential blood biomarker signatures. Scientific Reports. (2023). https://doi.org/10.1038/s41598-023-32268-2

AUTHORS

5 · 4 CENTRES

Maryam Momeni, Maryam Rashidifar, Farinaz Hosseini Balam, Amir Roointan, Alieh Gholaminejad

1grid.411750.60000 0001 0454 365XDepartment of Biotechnology, Faculty of Biological Science and Technology, The University of Isfahan, Isfahan, Iran
2grid.412502.00000 0001 0686 4748Department of Plant Sciences and Biotechnology, Faculty of Life Sciences and Biotechnology, Shahid Beheshti University, Tehran, Iran
3grid.411600.2Department of Cellular and Molecular Nutrition, Faculty of Nutrition and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran
4grid.411036.10000 0001 1498 685XRegenerative Medicine Research Center, Faculty of Medicine, Isfahan Univerity of Medical Sciences, Hezar Jarib St, Isfahan, 81746-73461 Iran