| evidence-covid-hf-1 | KF1: Independent COVID-19 and HF expression analyses yielded a 12-gene shared transcriptomic signature. | Overlap was calculated across separately profiled disease datasets, not in a cohort of people with both COVID-19 and HF. | Independent COVID-19 and HF expression analyses yielded a 12-gene shared transcriptomic signature. DEG threshold: |log2FC| > 1 and adjusted P < 0.05; COVID-19 DEGs: 102; HF DEGs: 253; shared genes: 12: S100A8, ABCB1, S100A11, PTGS2 (COX2), LY96, XCL1, RORA, CCL11, CCL4, S100A12, BCL2 and AQP9. | Cross-dataset transcriptomic overlap | Overlap was calculated across separately profiled disease datasets, not in a cohort of people with both COVID-19 and HF. | Results: Identification of shared transcriptomic signature between HF and COVID-19; Figure 1B-D | KF1 |
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| evidence-covid-hf-2 | KF2: Functional enrichment highlighted inflammatory and innate-immune pathways associated with the shared expression signals. | Enrichment points to pathway associations; it does not demonstrate that these pathways causally mediate COVID-19–HF comorbidity. | Functional enrichment highlighted inflammatory and innate-immune pathways associated with the shared expression signals. cellular processes: neutrophil chemotaxis and migration, monocyte chemotaxis, and inflammatory response; molecular functions: chemokine activity, CCR chemokine receptor binding, RAGE receptor binding and Toll-like receptor binding; signaling pathways: NF-κB-related and IL-17-related signaling, plus cytokine-cytokine receptor interaction. | Computational pathway enrichment | Enrichment points to pathway associations; it does not demonstrate that these pathways causally mediate COVID-19–HF comorbidity. | Results: Analysis of functional characteristics relevant to common pathogenesis; Figures 2-3; Supplementary Tables S1-S2 | KF2 |
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| evidence-covid-hf-3 | KF3: PPI topology prioritized ten inflammatory hub genes and a four-gene core of TLR4, TLR2, IL10 and STAT3. | Hub status reflects network centrality and algorithmic prioritization, not proof that these genes cause either disease. | PPI topology prioritized ten inflammatory hub genes and a four-gene core of TLR4, TLR2, IL10 and STAT3. initial PPI network: 62 nodes and 655 edges; ten hub genes: TLR4, TLR2, CXCL8, IL10, STAT3, IL1B, TLR1, TP53, CCL20 and CXCL10; hub interaction network: 59 nodes and 649 edges; seven-algorithm core: TLR4, TLR2, IL10 and STAT3; MCODE module 1: 29 nodes, 381 edges, score 27.214; includes XCL1, CCL4 and CCL11; MCODE module 2: 23 nodes, 65 edges, score 11.818. | Computational PPI network prioritization | Hub status reflects network centrality and algorithmic prioritization, not proof that these genes cause either disease. | Results: Protein-protein interaction analysis; Figures 4-5; Supplementary Tables S3-S4 | KF3 |
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| evidence-covid-hf-4 | KF4: External public PBMC expression datasets supported prioritization of TLR4, TLR2, STAT3, IL1B and CXCL8, with tissue-context nuance for IL1B. | The PCA percentages describe variance explained by the first two components, not AUC, accuracy, sensitivity, specificity, or prospective diagnostic validation. IL1B was not uniformly upregulated across tissues. | External public PBMC expression datasets supported prioritization of TLR4, TLR2, STAT3, IL1B and CXCL8, with tissue-context nuance for IL1B. COVID-19 expression dataset: GSE164805: 10 COVID-19 and 5 healthy PBMC samples; HF expression dataset: GSE9128/GDS3115: 8 HF pooled and 3 control pooled PBMC microarray samples; final prioritized genes: TLR4, TLR2, STAT3, IL1B and CXCL8; five-gene PCA first-two-component explained variance: COVID-19 96.0%; HF 93.2%; merged 89.9%; earlier ten-hub-gene PCA explained variance: COVID-19 88.5%; HF 90.0%; IL1B lung-tissue context: GSE147507 re-analysis showed increased IL1B in COVID-19 lung tissue (P=0.006), despite non-uniform PBMC patterns. | External public PBMC expression evaluation | The PCA percentages describe variance explained by the first two components, not AUC, accuracy, sensitivity, specificity, or prospective diagnostic validation. IL1B was not uniformly upregulated across tissues. | Results: Validation of hub genes performance in two independent cohorts; Figures 6-7 | KF4 |
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| evidence-covid-hf-5 | KF5: Database-derived networks nominated TF and miRNA candidates for subsequent experimental testing. | These network edges are computational predictions, not experimentally confirmed upstream regulatory mechanisms. | Database-derived networks nominated TF and miRNA candidates for subsequent experimental testing. TF network: 47 predicted TF targets, 59 nodes and 98 edges; TF-miRNA network: 98 TFs, 137 miRNAs, 246 nodes and 310 edges; highlighted predicted TFs: FOXC1, STAT3 and NFKB1 (NF-κB1); highlighted predicted miRNAs: miR-181, miR-520 and miR-143. | Computational TF and miRNA network prediction | These network edges are computational predictions, not experimentally confirmed upstream regulatory mechanisms. | Results: Determination of regulatory signature and network; Figure 8A-B; Supplementary Figure S1 | KF5 |
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| evidence-covid-hf-6 | KF6: Drug-signature enrichment nominated muramyl dipeptide and glutathione as hypothesis-generating compounds. | DSigDB prioritization is an in-silico signal; no therapeutic efficacy was demonstrated for COVID-19, HF, or their co-occurrence. | Drug-signature enrichment nominated muramyl dipeptide and glutathione as hypothesis-generating compounds. input genes: CXCL8, STAT3, IL1B, TLR4 and TLR2; analysis resource: DSigDB drug-signature enrichment; candidate compounds: muramyl dipeptide (MDP) and glutathione (GSH); evidence level: in-silico hypothesis generation, not treatment-efficacy evidence. | In-silico drug-signature enrichment | DSigDB prioritization is an in-silico signal; no therapeutic efficacy was demonstrated for COVID-19, HF, or their co-occurrence. | Results: Identification of candidate pharmacologic agents; Figure 8C; Discussion: Promising therapeutics | KF6 |
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