Proteomic Profiling of Dilated Cardiomyopathy Plasma Samples ─ Searching for Biomarkers with Potential to Predict the Outcome of Therapy.

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Using in-depth proteome discovery analysis, we compared 49 plasma samples from patients suffering from dilated cardiomyopathy with plasma samples from their healthy counterparts. In total, we identified 97 proteins exhibiting statistically significant dysregulation in diseased plasma samples. The functional enrichment analysis of differentially expressed proteins uncovered dysregulation in biological processes like inflammatory response, wound healing, complement cascade, blood coagulation, and lipid metabolism in dilated cardiomyopathy patients. The same proteome approach was employed in order to find protein markers whose expression differs between the patients well-responding to therapy and nonresponders. In this case, 45 plasma proteins revealed statistically significant different expression between these two groups. Of them, fructose-1,6-bisphosphate aldolase seems to be a promising biomarker candidate because it accumulates in plasma samples obtained from patients with insufficient treatment response and with worse or fatal outcome. Data are available via ProteomeXchange with the identifier PXD046288.", "claims": [{"id": "claim-method", "statement": "A predictive statistical approach of logistic regression with elastic net optimization was applied for the proteins obtained by the evaluation of LC-MS/MS data in MaxQuant and Perseus software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38363107", "locator": "sections[13] \"Logistic Regression with Elastic Net Optimization\" paragraph 2", "quote": "A predictive statistical approach of logistic regression with elastic net optimization was applied for the proteins obtained by the evaluation of LC-MS/MS data in MaxQuant and Perseus software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available via ProteomeXchange with the identifier PXD046288.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38363107", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Data are available via ProteomeXchange with the identifier PXD046288."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A logistic regression with elastic net optimization using significantly deregulated proteins identified from label-free quantification provided 96% average agreement with the experimental data for distinguishing DCM patients from healthy controls.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38363107", "locator": "sections[16] \"Proteomic Comparison of DCM vs Healthy Control\" paragraph 2", "quote": "A logistic regression with elastic net optimization for random selection of training sample group with the significantly deregulated proteins provided 96% (min. 83%, max. 100%) average agreement of the model with the experimental data (see Supplementary Figure 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A logistic regression with elastic net optimization using significantly deregulated proteins identified from label-free quantification provided 96% average agreement with the experimental data for distinguishing DCM patients from healthy controls.", "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:38363107"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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