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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, METHODS, RESULTS. Meshes: Adult, Antibodies, Biomarkers, Cell Lineage, Cell Proliferation, Cytokines, False Positive Reactions, Female, Gene Expression Profiling, Gene Expression Regulation, Graft Rejection, HLA Antigens, Humans, Inflammation, Kidney Failure, Chronic, Kidney Transplantation, Male, Middle Aged, Phenotype, Protein Interaction Mapping, Systems Biology.
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Does network analysis reveal roles of inflammatory factors in different phenotypes of kidney transplant patients? Context: Systems-level characterization of inflammation in kidney transplantation remains incomplete. By stratifying kidney transplant patients based on phenotypes, the present study sought to identify the role of inflammatory proteins in disease progress and assess potential biomarkers for allograft monitoring. Kidney transplant patients with different allograft status were enrolled in the study: stable renal function (ST), impaired renal function (UNST), acute rejection (AR), and chronic rejection (CR). We stratified the patients into 3 phenotype levels according to their symptoms and pathogenesis. Serum protein concentrations were measured by a quantitative protein array. All differentially expressed proteins were analyzed by protein-protein interaction networks (PPINs) to highlight protein interactions in patients with the above dysfunction levels. We identified level-related proteins and evaluated the classification efficiency of these biomarkers based on leave-one-out validation. The candidate proteins related to phenotype transforming were annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Based on the hypothesis that common proteins and their up- or down-regulation promote disease progress, we obtained 12 common proteins and 11 level-specific proteins from the phenotype-related PPINs. The common proteins were annotated for KEGG enrichment: (1) cytokine-cytokine receptor interaction; (2) hematopoietic cell lineage; (3) Jak-STAT signaling pathway; (4) allograft rejection; and (5) T cell receptor signaling pathway. The level-specific proteins could be potential biomarkers with diagnostic value. The classification potency of the 11 level-specific proteins (IL-1R-1, IL-16, TIMP-1, G-CSF, MIG, IL-11, BLC, TNF-β, Eotaxin-2, I-309 and IL-6 sR) was better than the performance using all 40 proteins.
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The study demonstrated the potential value of PPINs-based approach to understanding inflammation-derived mechanisms and developing diagnostic biomarkers. Independent evaluations are required to further estimate the clinical relevance of the new diagnostic biomarkers. Final Decision: yes