Bioinformatics and system biology approach to identify the influences among COVID-19, ARDS and sepsis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37261353", "kind": "science-record", "revision": 4, "content_hash": "sha256:e3a442af739ccc8fa7bb262ef440f3dd5b9210e6c7cc0274b05b1a6bfe326481", "created_at": "2026-11-01T19:01:58Z", "updated_at": "2026-11-01T22:52:02Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics and system biology approach to identify the influences among COVID-19, ARDS and sepsis.", "doi": "10.3389/fimmu.2023.1152186", "pmid": "37261353", "authors": ["Peiyu Li", "Tao Li", "Zhiming Zhang", "Xingui Dai", "Bin Zeng", "Zhen Li", "Zhiwang Li"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Background Severe coronavirus disease 2019 (COVID -19) has led to severe pneumonia or acute respiratory distress syndrome (ARDS) worldwide. we have noted that many critically ill patients with COVID-19 present with typical sepsis-related clinical manifestations, including multiple organ dysfunction syndrome, coagulopathy, and septic shock. The molecular mechanisms that underlie COVID-19, ARDS and sepsis are not well understood. The objectives of this study were to analyze potential molecular mechanisms and identify potential drugs for the treatment of COVID-19, ARDS and sepsis using bioinformatics and a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342) from Gene Expression Omnibus (GEO) were employed to detect mutual differentially expressed genes (DEGs) for the patients with the COVID-19, ARDS and sepsis for functional enrichment, pathway analysis, and candidate drugs analysis. Results We obtained 110 common DEGs among COVID-19, ARDS and sepsis. ARG1, FCGR1A, MPO, and TLR5 are the most influential hub genes. The infection and immune-related pathways and functions are the main pathways and molecular functions of these three diseases. FOXC1, YY1, GATA2, FOXL, STAT1 and STAT3 are important TFs for COVID-19. mir-335-5p, miR-335-5p and hsa-mir-26a-5p were associated with COVID-19. Finally, the hub genes retrieved from the DSigDB database indicate multiple drug molecules and drug-targets interaction. Conclusion We performed a functional analysis under ontology terms and pathway analysis and found some common associations among COVID-19, ARDS and sepsis. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs were also identified on the datasets. We believe that the candidate drugs obtained in this study may contribute to the effective treatment of COVID-19.", "claims": [{"id": "claim-method", "statement": "The DEGs for GSE171110 were identified by using the limma package of R programming language. Data generated by microarray analysis were retrieved through DESeq2 and limma package. DEGs for GSE76293 and GSE137342 datasets were analyzed through GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool which also uses limma package for identifying DEGs. Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes which were statistically significant and limited false positives. Genes that met the cut-off criteria, adjust P-values <0.01 and |log2FC|≥1.0, were considered as DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37261353", "locator": "sections[4] \"Identification of DEGs and common DEGs among COVID-19, ARDS and sepsis\" paragraph 2", "quote": "The DEGs for GSE171110 were identified by using the limma package of R programming language. Data generated by microarray analysis were retrieved through DESeq2 and limma package. DEGs for GSE76293 and GSE137342 datasets were analyzed through GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool which also uses limma package for identifying DEGs. Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes which were statistically significant and limited false positives. Genes that met the cut-off criteria, adjust P-values <0.01 and |log2FC|≥1.0, were considered as DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The data could be downloaded from the GEO database of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/geo/), accession numbers GSE171110, GSE76293, and GSE137342.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37261353", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The data could be downloaded from the GEO database of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/geo/), accession numbers GSE171110, GSE76293, and GSE137342."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis identified 110 common differentially expressed genes among COVID-19, ARDS and sepsis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37261353", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We obtained 110 common DEGs among COVID-19, ARDS and sepsis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis identified 110 common differentially expressed genes among COVID-19, ARDS and sepsis.", "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:37261353"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE137342"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {"fdr_threshold": "with Benjamini–Hochberg false discovery rate correction"}, "inputs": ["data:geo:GSE137342"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37261353", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1152186", "sha256": "sha256:45c5b54ee260df279d2cb5017129552431655ec9386f5c3745fae9ce15555f57", "bytes": 37483, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1152186", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10227520", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE137342", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137342", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE137342", "version": null, "note": "Publicly available datasets were analyzed in this study. 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Data generated by microarray analysis were retrieved through DESeq2 and limma package. DEGs for GSE76293 and GSE137342 datasets were analyzed through GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool which also uses limma package for identifying DEGs. Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes which were statistically significant and limited false positives. Genes that met the cut-off criteria, adjust P-values <0.01 and |log2FC|≥1.0, were considered as DEGs."}}, {"id": "method-2", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: deseq2."}, "capability": {"tier": "R1", "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": {"tier": "R1", "reachable": true, "accessions": ["GSE171110", "GSE76293", "GSE137342"], "resolved_accessions": ["GSE171110", "GSE76293", "GSE137342"], "statements": ["Publicly available datasets were analyzed in this study. The data could be downloaded from the GEO database of the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/geo/), accession numbers GSE171110, GSE76293, and GSE137342."], "basis": "The paper names GSE171110, GSE76293, GSE137342 in Gene Expression Omnibus (GEO) database of the National Center for Biotechnology Information (NCBI) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:37261353", "data:geo:GSE137342"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE137342"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:42:33Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11569, "completion_tokens": 1725, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37261353", "type": "paper", "label": "pmid:37261353"}, {"id": "source:pmid:37261353", "type": "manuscript", "label": "PMID 37261353 full text"}, {"id": "data:geo:GSE137342", "type": "dataset", "label": "GEO GSE137342"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37261353", "type": "paper", "label": "pmid:37261353"}, {"id": "source:pmid:37261353", "type": "manuscript", "label": "PMID 37261353 full text"}, {"id": "data:geo:GSE137342", "type": "dataset", "label": "GEO GSE137342"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37261353", "type": "extraction", "used": ["source:pmid:37261353"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE137342", "type": "accession_resolution", "used": ["data:geo:GSE137342"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:37261353", "parent_hash": "sha256:2990547411000d74063e70f8e6490bfbaf24ee790820fc53fe92b25ff62730fe", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE137342 licence not read", "asset:data:geo:GSE137342 not downloaded", "asset:data:geo:GSE171110 licence not read", "asset:data:geo:GSE171110 not downloaded", "asset:data:geo:GSE76293 licence not read", "asset:data:geo:GSE76293 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["nd a systems biology approach. Methods Three RNA-seq datasets (GSE171110, GSE76293 and GSE137342)"], "order_basis": "paper_text_order:709", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The DEGs for GSE171110 were identified by using the limma package of R programming language. Data generated by microarray analysis were retrieved through DESeq2 and limma package. DEGs for GSE76293 and GSE137342 datasets were analyzed through GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) web tool which also uses limma package for identifying DEGs. Benjamini–Hochberg false discovery rate (FDR) method was applied to discover genes which were statistically significant and limited false positives. Genes that met the cut-off criteria, adjust P-values <0.01 and |log2FC|≥1.0, were considered as DEGs."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}