Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36466549", "kind": "science-record", "revision": 4, "content_hash": "sha256:6ab4a6026f6f2acaa4294dcb050c450952f1a2077cf9245f0a28d70d7722d23c", "created_at": "2026-11-01T19:00:56Z", "updated_at": "2026-11-01T22:50:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying the Effect of COVID-19 Infection in Multiple Myeloma and Diffuse Large B-Cell Lymphoma Patients Using Bioinformatics and System Biology.", "doi": "10.1155/2022/7017317", "pmid": "36466549", "authors": ["Chengcheng Li", "Ying Zhang", "Yingying Xiao", "Yun Luo"], "journal": "Computational and mathematical methods in medicine", "year": 2022, "abstract": "The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also referred to as COVID-19, has spread to several countries and caused a serious threat to human health worldwide. Patients with confirmed COVID-19 infection spread the disease rapidly throughout the region. Multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL) are risk factors for COVID-19, although the molecular mechanisms underlying the relationship among MM, DLBCL, and COVID-19 have not been elucidated so far. In this context, transcriptome analysis was performed in the present study to identify the shared pathways and molecular indicators of MM, DLBCL, and COVID-19, which benefited the overall understanding of the effect of COVID-19 in patients with MM and DLBCL. Three datasets (GSE16558, GSE56315, and GSE152418) were downloaded from the Gene Expression Omnibus (GEO) and searched for the shared differentially expressed genes (DEGs) in patients with MM and DLBCL who were infected with SARS-CoV-2. The objective was to detect similar pathways and prospective medicines. A total of 29 DEGs that were common across these three datasets were selected. A protein-protein interaction (PPI) network was constructed using data from the STRING database followed by the identification of hub genes. In addition, the association of MM and DLBCL with COVID-19 infection was analyzed through functional analysis using ontologies terms and pathway analysis. Three relationships were observed in the evaluated datasets: transcription factor-gene interactions, protein-drug interactions, and an integrated regulatory network of DEGs and miRNAs with mutual DEGs. The findings of the present study revealed potential pharmaceuticals that could be beneficial in the treatment of COVID-19.", "claims": [{"id": "claim-method", "statement": "The Linear Models for Microarray Data (LIMMA) package were employed to identify the significant DEGs from the datasets using the criteria of P value < 0.05 and |logFC| ≥ 1.0.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36466549", "locator": "sections[4] \"2.2. Identification of the DEGs and Shared DEGs among MM, DLBCL, and COVID-19\" paragraph 2", "quote": "The Linear Models for Microarray Data (LIMMA) package were employed to identify the significant DEGs from the datasets using the criteria of P value < 0.05 and |logFC| ≥ 1.0."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. These data can be found here in National Center for Biotechnology Information (NCBI), Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih.gov/geo/, GSE152418, GSE16558, and GSE56315.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36466549", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. These data can be found here in National Center for Biotechnology Information (NCBI), Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih.gov/geo/, GSE152418, GSE16558, and GSE56315."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 29 DEGs that were common across these three datasets were selected.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36466549", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A total of 29 DEGs that were common across these three datasets were selected."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 29 DEGs that were common across these three datasets were selected.", "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:36466549"], "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": {}, "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:36466549", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/7017317", "sha256": "sha256:82cd4cbffcbb716c47a6a680a0bfea883f0134f8542be425185b0ca59e032277", "bytes": 35140, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/7017317", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9711963", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE152418", "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=GSE152418", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE152418", "version": null, "note": "Publicly available datasets were analyzed in this study. 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