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pmid:36768914

Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis.

Changyin Fang · Yongping Ma

International journal of molecular sciences · 2023 doi:10.3390/ijms24032591

Paper

complete

Experiment

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Data

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title
Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis.
doi
10.3390/ijms24032591
pmid
36768914
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Changyin Fang
[1]
Yongping Ma
journal
International journal of molecular sciences
year
2023
abstract
Severe coronavirus disease 2019 (COVID-19) has led to a rapid increase in death rates all over the world. Sepsis is a life-threatening disease associated with a dysregulated host immune response. It has been shown that COVID-19 shares many similarities with sepsis in many aspects. However, the molecular mechanisms underlying sepsis and COVID-19 are not well understood. The aim of this study was to identify common transcriptional signatures, regulators, and pathways between COVID-19 and sepsis, which may provide a new direction for the treatment of COVID-19 and sepsis. First, COVID-19 blood gene expression profile (GSE179850) data and sepsis blood expression profile (GSE134347) data were obtained from GEO. Then, we intersected the differentially expressed genes (DEG) from these two datasets to obtain common DEGs. Finally, the common DEGs were used for functional enrichment analysis, transcription factor and miRNA prediction, pathway analysis, and candidate drug analysis. A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets. Protein-protein interactions (PPIs) were constructed using the STRING database. Subsequently, hub genes were identified based on PPI networks. In addition, we performed GO functional analysis and KEGG pathway analysis of common DEGs, and found a common association between sepsis and COVID-19. Finally, we identified transcription factor-gene interaction, DEGs-miRNA co-regulatory networks, and protein-drug interaction, respectively. Through ROC analysis, we identified 10 central hub genes as potential biomarkers. In this study, we identified SARS-CoV-2 infection as a high risk factor for sepsis. Our study may provide a potential therapeutic direction for the treatment of COVID-19 patients suffering from sepsis.
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Differential analysis was performed using the R software (version 4.0.2) “limma” package and “DESeq2” package.
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sections[11] "4.2. COVID-19 and Sepsis Differentially Expressed Genes" paragraph 2
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Differential analysis was performed using the R software (version 4.0.2) “limma” package and “DESeq2” package.
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Any data and R script in this study can be obtained from the corresponding author upon reasonable request. The final manuscript was read and approved by all authors. In this study, publicly available datasets were analyzed. These data are available at NCBI GEO (https://www.ncbi.nlm.nih.gov/).
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A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets.
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A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets.
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A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets.
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readiness object · 8 fields
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fdr_bh
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: deseq2.
capability object · 4 fields
tier
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capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
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accessions array · 2 items
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GSE179850
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resolved_accessions array · 2 items
[0]
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statements array · 1 item
[0]
Any data and R script in this study can be obtained from the corresponding author upon reasonable request. The final manuscript was read and approved by all authors. In this study, publicly available datasets were analyzed. These data are available at NCBI GEO (https://www.ncbi.nlm.nih.gov/).
basis
The paper names GSE179850, GSE134347 in National Center for Biotechnology Information (NCBI) database GEO and the accession resolves in the repository. No dataset was downloaded and no licence was read.
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fdr_bh
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data_gaps array · 1 item
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data:geo:GSE134347
gaps array · 0 items
review object · 5 fields
state
partial
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kind
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reader object · 5 fields
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MiniMax-M3.1-Flash
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minimax
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2026-11-01T18:26:56Z
independence
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source:pmid:36768914
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data:geo:GSE134347
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dataset
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GEO GSE134347
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[0] object · 4 fields
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pmid:36768914
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manuscript
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PMID 36768914 full text
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GEO GSE134347
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agent:corpus-assay-engine
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agents array · 2 items
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lineage object · 4 fields
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assays array · 1 item
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evidence array · 1 item
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Differential analysis was performed using the R software (version 4.0.2) “limma” package and “DESeq2” package.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36768914", "kind": "science-record", "revision": 4, "content_hash": "sha256:1863af16e0fa0e5c2ca4b0e14701c0c2292d84e79c9af9da28c5eae0a7ae8f13", "created_at": "2026-11-01T19:01:21Z", "updated_at": "2026-11-01T22:51:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Peripheral Blood Genes Crosstalk between COVID-19 and Sepsis.", "doi": "10.3390/ijms24032591", "pmid": "36768914", "authors": ["Changyin Fang", "Yongping Ma"], "journal": "International journal of molecular sciences", "year": 2023, "abstract": "Severe coronavirus disease 2019 (COVID-19) has led to a rapid increase in death rates all over the world. Sepsis is a life-threatening disease associated with a dysregulated host immune response. It has been shown that COVID-19 shares many similarities with sepsis in many aspects. However, the molecular mechanisms underlying sepsis and COVID-19 are not well understood. The aim of this study was to identify common transcriptional signatures, regulators, and pathways between COVID-19 and sepsis, which may provide a new direction for the treatment of COVID-19 and sepsis. First, COVID-19 blood gene expression profile (GSE179850) data and sepsis blood expression profile (GSE134347) data were obtained from GEO. Then, we intersected the differentially expressed genes (DEG) from these two datasets to obtain common DEGs. Finally, the common DEGs were used for functional enrichment analysis, transcription factor and miRNA prediction, pathway analysis, and candidate drug analysis. A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets. Protein-protein interactions (PPIs) were constructed using the STRING database. Subsequently, hub genes were identified based on PPI networks. In addition, we performed GO functional analysis and KEGG pathway analysis of common DEGs, and found a common association between sepsis and COVID-19. Finally, we identified transcription factor-gene interaction, DEGs-miRNA co-regulatory networks, and protein-drug interaction, respectively. Through ROC analysis, we identified 10 central hub genes as potential biomarkers. In this study, we identified SARS-CoV-2 infection as a high risk factor for sepsis. Our study may provide a potential therapeutic direction for the treatment of COVID-19 patients suffering from sepsis.", "claims": [{"id": "claim-method", "statement": "Differential analysis was performed using the R software (version 4.0.2) “limma” package and “DESeq2” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36768914", "locator": "sections[11] \"4.2. 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These data are available at NCBI GEO (https://www.ncbi.nlm.nih.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36768914", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 307 common DEGs were identified between the sepsis and COVID-19 datasets.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36768914"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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