Transcriptome and machine learning analysis of the impact of COVID-19 on mitochondria and multiorgan damage.

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COVID-19 may involve multiorgan damage from direct viral invasion through angiotensin-converting enzyme 2 (ACE2), through inflammatory cytokine storms, or through other secondary pathways. This study involved the analysis of publicly accessible transcriptome data from the Gene Expression Omnibus (GEO) database for identifying significant differentially expressed genes related to COVID-19 and an investigation relating to the pathways associated with mitochondrial, cardiac, hepatic, and renal toxicity in COVID-19. Significant differentially expressed genes were identified and ranked by statistical approaches, and the genes derived by biological meaning were ranked by feature importance; both were utilized as machine learning features for verification. Sample set selection for machine learning was based on the performance, sample size, imbalanced data state, and overfitting assessment. Machine learning served as a verification tool by facilitating the testing of biological hypotheses by incorporating gene list adjustment. A subsequent in-depth study for gene and pathway network analysis was conducted to explore whether COVID-19 is associated with cardiac, hepatic, and renal impairments via mitochondrial infection. The analysis showed that potential cardiac, hepatic, and renal impairments in COVID-19 are associated with ACE2, inflammatory cytokine storms, and mitochondrial pathways, suggesting potential medical interventions for COVID-19-induced multiorgan damage.", "claims": [{"id": "claim-method", "statement": "The limma approach is commonly utilized in the context of differential expression analysis because of its use of linear models, which frequently leads to improved computational efficiency when compared to alternative methods for differential expression analysis [36].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38295140", "locator": "sections[4] \"Processing of RNA sequencing data\" paragraph 7", "quote": "The limma approach is commonly utilized in the context of differential expression analysis because of its use of linear models, which frequently leads to improved computational efficiency when compared to alternative methods for differential expression analysis [36]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All RNA-seq files are downloaded from the NCBI Gene Expression Omnibus (GEO) database (accession numbers GSE152075, and GSE163151, GSE157103, GSE169241, GSE152641).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38295140", "locator": "sections[15] \"Data Availability\" paragraph 2", "quote": "All RNA-seq files are downloaded from the NCBI Gene Expression Omnibus (GEO) database (accession numbers GSE152075, and GSE163151, GSE157103, GSE169241, GSE152641)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:38295140"], "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:GSE152075"], "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:GSE152075"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38295140", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0297664", "sha256": 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