Potential therapeutic targets for COVID-19 complicated with pulmonary hypertension: a bioinformatics and early validation study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38653779", "kind": "science-record", "revision": 5, "content_hash": "sha256:c88bc98cc4eb1cd3c6582b93903e2a90dea27b912329efc224f820347df01eb4", "created_at": "2026-11-01T19:20:37Z", "updated_at": "2026-11-01T22:54:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Potential therapeutic targets for COVID-19 complicated with pulmonary hypertension: a bioinformatics and early validation study.", "doi": "10.1038/s41598-024-60113-7", "pmid": "38653779", "authors": ["Qingbin Hou", "Jinping Jiang", "Kun Na", "Xiaolin Zhang", "Dan Liu", "Quanmin Jing", "Chenghui Yan", "Yaling Han"], "journal": "Scientific reports", "year": 2024, "abstract": "Coronavirus disease (COVID-19) and pulmonary hypertension (PH) are closely correlated. However, the mechanism is still poorly understood. In this article, we analyzed the molecular action network driving the emergence of this event. Two datasets (GSE113439 and GSE147507) from the GEO database were used for the identification of differentially expressed genes (DEGs).Common DEGs were selected by VennDiagram and their enrichment in biological pathways was analyzed. Candidate gene biomarkers were selected using three different machine-learning algorithms (SVM-RFE, LASSO, RF).The diagnostic efficacy of these foundational genes was validated using independent datasets. Eventually, we validated molecular docking and medication prediction. We found 62 common DEGs, including several ones that could be enriched for Immune Response and Inflammation. Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms. They performed well in diagnostic tests on independent datasets. In particular, we observed an upregulation of functions associated with the adaptive immune response, the leukocyte-lymphocyte-driven immunological response, and the proinflammatory response. Moreover, by ssGSEA, natural killer T cells, activated dendritic cells, activated CD4 T cells, neutrophils, and plasmacytoid dendritic cells were correlated with COVID-19 and PH, with SELE and CCL20 showing the strongest correlation with dendritic cells. Potential therapeutic compounds like FENRETI-NIDE, AFLATOXIN B1 and 1-nitropyrene were predicted. Further molecular docking and molecular dynamics simulations showed that 1-nitropyrene had the most stable binding with SELE and CCL20.The findings indicated that SELE and CCL20 were identified as novel diagnostic biomarkers for COVID-19 complicated with PH, and the target of these two key genes, FENRETI-NIDE and 1-nitropyrene, was predicted to be a potential therapeutic target, thus providing new insights into the prediction and treatment of COVID-19 complicated with PH in clinical practice.", "claims": [{"id": "claim-method", "statement": "Three different machine-learning algorithms—support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF)72–77—were utilized for identifying potential new biomarkers for pediatric sepsis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38653779", "locator": "sections[18] \"C-DEGs-related enrichment and identification analyses\" paragraph 2", "quote": "Three different machine-learning algorithms—support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF)72–77—were utilized for identifying potential new biomarkers for pediatric sepsis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets used in the study (GSE113439, GSE147507,GSE53408 and GSE196822) can be downloaded without restriction from the public GEO database.GSE113439: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE113439; GSE147507: https://www.ncbi.nlm.nih.gov/gds/? acc = GSE147507; GSE53408: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE53408;.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE196822;.The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38653779", "locator": "sections[34] \"notes\" paragraph 1", "quote": "Datasets used in the study (GSE113439, GSE147507,GSE53408 and GSE196822) can be downloaded without restriction from the public GEO database.GSE113439: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE113439; GSE147507: https://www.ncbi.nlm.nih.gov/gds/? acc = GSE147507; GSE53408: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE53408;.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE196822;.The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38653779", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Two DEGs (SELE and CCL20) could be identified by machine-learning algorithms.", "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:38653779"], "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:GSE113439"], "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": {}, "inputs": ["data:geo:GSE113439"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38653779", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-024-60113-7", "sha256": "sha256:64dee77f565df6c3229949de0ad14ec63774132d2e51aa1c0c5b24ff6ac4fb23", "bytes": 50346, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-024-60113-7", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11039624", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE113439", "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=GSE113439", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE113439", "version": null, "note": "Datasets used in the study (GSE113439, GSE147507,GSE53408 and GSE196822) can be downloaded without restriction from the public GEO database.GSE113439: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE113439; GSE147507: https://www.ncbi.nlm.nih.gov/gds/? acc = GSE147507; GSE53408: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE53408;.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE196822;.The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Datasets used in the study (GSE113439, GSE147507,GSE53408 and GSE196822) can be downloaded without restriction from the public GEO database.GSE113439: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE113439; GSE147507: https://www.ncbi.nlm.nih.gov/gds/? acc = GSE147507; GSE53408: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE53408;.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE196822;.The original contributions presented in the study are included in the article/Supplementary Material. 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Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Datasets used in the study (GSE113439, GSE147507,GSE53408 and GSE196822) can be downloaded without restriction from the public GEO database.GSE113439: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE113439; GSE147507: https://www.ncbi.nlm.nih.gov/gds/? acc = GSE147507; GSE53408: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE53408;.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE196822;.The original contributions presented in the study are included in the article/Supplementary Material. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE113439", "GSE147507", "GSE53408", "GSE196822"], "resolved_accessions": ["GSE113439", "GSE147507", "GSE53408", "GSE196822"], "statements": ["Datasets used in the study (GSE113439, GSE147507,GSE53408 and GSE196822) can be downloaded without restriction from the public GEO database.GSE113439: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE113439; GSE147507: https://www.ncbi.nlm.nih.gov/gds/? acc = GSE147507; GSE53408: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE53408;.https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi? acc = GSE196822;.The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE113439, GSE147507, GSE53408, GSE196822 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:38653779", "data:geo:GSE113439"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE113439"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:16:45Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13395, "completion_tokens": 2162, "cached_tokens": 256}}, "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:38653779", "type": "paper", "label": "pmid:38653779"}, {"id": "source:pmid:38653779", "type": "manuscript", "label": "PMID 38653779 full text"}, {"id": "data:geo:GSE113439", "type": "dataset", "label": "GEO GSE113439"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38653779", "type": "paper", "label": "pmid:38653779"}, {"id": "source:pmid:38653779", "type": "manuscript", "label": "PMID 38653779 full text"}, {"id": "data:geo:GSE113439", "type": "dataset", "label": "GEO GSE113439"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38653779", "type": "extraction", "used": ["source:pmid:38653779"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE113439", "type": "accession_resolution", "used": ["data:geo:GSE113439"], "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:38653779", "parent_hash": "sha256:f9437d35f87eef92ec1047bdccd6a7a1ab850223ba2d79ec3c1cf637be85e1cb", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE113439 licence not read", "asset:data:geo:GSE113439 not downloaded", "asset:data:geo:GSE147507 licence not read", "asset:data:geo:GSE147507 not downloaded", "asset:data:geo:GSE196822 licence not read", "asset:data:geo:GSE196822 not downloaded", "asset:data:geo:GSE53408 licence not read", "asset:data:geo:GSE53408 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Three different machine-learning algorithms—support vector machine-recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF)72–77—were utilized for identifying potential new biomarkers for pediatric sepsis."], "order_basis": "analysis_only", "id": "assay:1"}]}