Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and sarcopenia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38978778", "kind": "science-record", "revision": 4, "content_hash": "sha256:3e23696cea5c0cf76414792f084053829eb97486d8b7d0888643bf1056223535", "created_at": "2026-11-01T20:29:03Z", "updated_at": "2026-11-01T22:54:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and sarcopenia.", "doi": "10.3389/fmed.2024.1378846", "pmid": "38978778", "authors": ["Jun Zhong", "Hui Yuan", "Jinghong Yang", "Yimin Du", "Zheng Li", "Xu Liu", "Haibo Yang", "Zhaojun Wang", "Zi Wang", "Lujun Jiang", "Zhiqiang Ren", "Hongliang Li", "Zhong Li", "Yanshi Liu"], "journal": "Frontiers in medicine", "year": 2024, "abstract": "Sarcopenia is a condition characterized by age-related loss of muscle mass and strength. Increasing evidence suggests that patients with sarcopenia have higher rates of coronavirus 2019 (COVID-19) infection and poorer post-infection outcomes. However, the exact mechanism and connections between the two is unknown. In this study, we used high-throughput data from the GEO database for sarcopenia (GSE111016) and COVID-19 (GSE171110) to identify common differentially expressed genes (DEGs). We conducted GO and KEGG pathway analyses, as well as PPI network analysis on these DEGs. Using seven algorithms from the Cytoscape plug-in cytoHubba, we identified 15 common hub genes. Further analyses included enrichment, PPI interaction, TF-gene and miRNA-gene regulatory networks, gene-disease associations, and drug prediction. Additionally, we evaluated immune cell infiltration with CIBERSORT and assessed the diagnostic accuracy of hub genes for sarcopenia and COVID-19 using ROC curves. In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19. GO and KEGG analyses revealed functions and pathways between the two diseases. TF-genes and TF-miRNA regulatory network suggest that FOXOC1 and hsa-mir-155-5p may be identified as key regulators, while gene-disease analysis showed strong correlations with hub genes in schizophrenia and bipolar disorder. Immune infiltration showed a correlation between the degree of immune infiltration and the level of infiltration of different immune cell subpopulations of hub genes in different datasets. The ROC curves for ALDH1L2 and KLF5 genes demonstrated their potential as diagnostic markers for both sarcopenia and COVID-19. This study suggests that sarcopenia and COVID-19 may share pathogenic pathways, and these pathways and hub genes offer new targets and strategies for early diagnosis, effective treatment, and tailored therapies for sarcopenia patients with COVID-19.", "claims": [{"id": "claim-method", "statement": "The “limma” package (version 4.3.1) of the R software (version 4.3.1) was used to select DEGs between COVID-19 and non-COVID-19 and between sarcopenia and non-sarcopenia.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38978778", "locator": "sections[4] \"Identification of common DEGs between sarcopenia and COVID-19\" paragraph 2", "quote": "The “limma” package (version 4.3.1) of the R software (version 4.3.1) was used to select DEGs between COVID-19 and non-COVID-19 and between sarcopenia and non-sarcopenia."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found at: https://www.ncbi.nlm.nih.gov/geo/, GSE111016 and GSE171110.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38978778", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found at: https://www.ncbi.nlm.nih.gov/geo/, GSE111016 and GSE171110."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38978778", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19.", "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:38978778"], "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:GSE111016"], "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:GSE111016"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38978778", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fmed.2024.1378846", "sha256": "sha256:e1920a801c637adefdb378e898428cccc5c5c26509ba99a7f921a37ca7297f8f", "bytes": 41394, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fmed.2024.1378846", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11228343", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE111016", "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=GSE111016", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE111016", "version": null, "note": "Publicly available datasets were analyzed in this study. 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