Analysis and identification of potential type II helper T cell (Th2)-Related key genes and therapeutic agents for COVID-19.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36201886", "kind": "science-record", "revision": 4, "content_hash": "sha256:b831e9c6a07baa6bd41a66f9c28bf7c3340c9b017b080e52bb565e747b3d084b", "created_at": "2026-11-01T18:15:31Z", "updated_at": "2026-11-01T22:07:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis and identification of potential type II helper T cell (Th2)-Related key genes and therapeutic agents for COVID-19.", "doi": "10.1016/j.compbiomed.2022.106134", "pmid": "36201886", "authors": ["Qiying Jin", "Wanxi Li", "Wendi Yu", "Maosen Zeng", "Jinyuan Liu", "Peiping Xu"], "journal": "Computers in biology and medicine", "year": 2022, "abstract": "COVID-19 pandemic poses a severe threat to public health. However, so far, there are no effective drugs for COVID-19. Transcriptomic changes and key genes related to Th2 cells in COVID-19 have not been reported. These genes play an important role in host interactions with SARS-COV-2 and may be used as promising target. We analyzed five COVID-19-associated GEO datasets (GSE157103, GSE152641, GSE171110, GSE152418, and GSE179627) using the xCell algorithm and weighted gene co-expression network analysis (WGCNA). Results showed that 5 closely correlated modular genes to COVID-19 and Th2 cell enrichment levels, including purple, blue, pink, tan and turquoise, were intersected with differentially expressed genes (DEGs) and 648 shared genes were obtained. GO and KEGG pathway enrichment analyses revealed that they were enriched in cell proliferation, differentiation, and immune responses after virus infection. The most significantly enriched pathway involved the regulation of viral life cycle. Three key genes, namely CCNB1, BUB1, and UBE2C, may clarify the pathogenesis of COVID-19 associated with Th2 cells. 11 drug candidates were identified that could down-regulate three key genes using the cMAP database and demonstrated strong drugs binding energies aganist the three keygenes using molecular docking methods. BUB1, CCNB1 and UBE2C were identified key genes for COVID-19 and could be promising therapeutic targets.", "claims": [{"id": "claim-method", "statement": "We used the WGCNA to obtain the COVID-19 and Th2 cell associated modules.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36201886", "locator": "sections[6] \"Establishment and identification of Co-expression networks\" paragraph 2", "quote": "We used the WGCNA to obtain the COVID-19 and Th2 cell associated modules."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GSE157103, GSE152641, GSE171110, GSE152418, and GSE179627 microarray data were downloaded from the gene expression omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36201886", "locator": "sections[4] \"COVID-19 microarray datasets\" paragraph 2", "quote": "GSE157103, GSE152641, GSE171110, GSE152418, and GSE179627 microarray data were downloaded from the gene expression omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "BUB1, CCNB1 and UBE2C were identified key genes for COVID-19 and could be promising therapeutic targets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36201886", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "BUB1, CCNB1 and UBE2C were identified key genes for COVID-19 and could be promising therapeutic targets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "BUB1, CCNB1 and UBE2C were identified key genes for COVID-19 and could be promising therapeutic targets.", "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:36201886"], "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:GSE152418"], "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": {"sample_size": "expression among COVID-19 patients with different disease"}, "inputs": ["data:geo:GSE152418"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36201886", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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