Selection of disease-specific biomarkers by integrating inflammatory mediators with clinical informatics in AECOPD patients: a preliminary study

Abstract

Systemic inflammation is a major factor influencing the outcome and quality of patient with chronic obstructive pulmonary disease (COPD) and acute exacerbations (AECOPD). Because of the inflammatory complexity, a great challenge is still confronted to optimize the identification and validation of disease-specific biomarkers. This study aimed at developing a new protocol of specific biomarker evaluation by integrating proteomic profiles of inflammatory mediators with clinical informatics in AECOPD patients, understand better their function and signal networks. Plasma samples were collected from healthy non-smokers or patients with stable COPD (sCOPD) or AECOPD on days 1 and 3 of the admission and discharging day (day 7-10). Forty chemokines were measured using a chemokine multiplex antibody array. Clinical informatics was achieved by a Digital Evaluation Score System (DESS) for assessing severity of patients. Chemokine data was compared among different groups and its correlation with DESS scores was performed by SPSS software. Of 40 chemokines, 30 showed significant difference between sCOPD patients and healthy controls, 16 between AECOPD patients and controls and 13 between AECOPD patients and both sCOPD and controls, including BTC, IL-9, IL-18Bpa, CCL22,CCL23, CCL25, CCL28, CTACK, LIGHT, MSPa, MCP-3, MCP-4 and OPN. Of them, some had significant correlation with DESS scores. There is a disease-specific profile of inflammatory mediators in COPD and AECOPD patients which may have a potential diagnostics together with clinical informatics of patients. Our preliminary study suggested that integration of proteomics with clinical informatics can be a new way to validate and optimize disease-special biomarkers.

Fig 1

Plasma levels of hemofiltrate CC-chemokine (HCC)-1 and -4, chemokine (C-C motif) ligand 21 (6Ckine), interleukin (IL)-17F and -31 and macrophage inflammatory proteins (MIP)-3b in healthy, patients with stable COPD (sCOPD) and AECOPD patients on days 1, 3 and 7–10. * and ** stand for P values less than 0.05 and 0.01, respectively, as compared with healthy control.

Fig 2

Plasma levels of interferon γ-induced protein 10 kD (IP-10), granulocyte chemotactic protein 2 (GCP-2), macrophage inflammatory proteins (MIP), macrophage migration inhibitory factor (MIF) and interleukin (IL)-28A and -29 in healthy, patients with stable COPD (sCOPD) and AECOPD patients on days 1, 3 and 7–10. * and ** stand for P values less than 0.05 and 0.01, respectively, as compared with healthy control. ^† and ^†† stand for P values less than 0.05 and 0.01, respectively, as compared with sCOPD patients.

Fig 3

Plasma levels of tyrosin-protein kinase 7 (Axl), epithelial neutrophil activating protein 78 (ENA-78), growth-regulated oncogene (GRO) and lymphotactin in healthy, patients with stable COPD (sCOPD), and AECOPD patients on days 1, 3 and 7–10. * and ** stand for P values less than 0.05 and 0.01, respectively, as compared with healthy control. ^† and ^†† stand for P values less than 0.05 and 0.01, respectively, as compared with sCOPD patients.

Fig 4

Plasma levels of interferon-inducible T cell α chemoattractant (I-TAC), leukaemia inhibitory factor (LIF), macrophage stimulating protein a (MSPa), osteopontin (OPN), stromal cell-derived factor-1a (SDF-1a) and thymus and activation-regulated chemokine (TARC) in healthy, patients with stable COPD (sCOPD) and AECOPD patients on days 1, 3 and 7–10. * and ** stand for P values less than 0.05 and 0.01, respectively, as compared with healthy control. ^† and ^†† stand for P values less than 0.05 and 0.01, respectively, as compared with sCOPD patients. ^# stands for P values less than 0.05, as compared with AECOPD patients on day 1.

Fig 5

Plasma levels of lymphotoxin-like inducible protein that competes with glycoprotein D for herpesvirus entry on T cells (LIGHT), monocyte chemoattractant protein (MCP)-2, -3 and -4, macrophage-derived cytokine (MDC), myeloid progenitor inhibitory factor-1 (MPIF-1) in healthy, patients with stable COPD (sCOPD) and AECOPD patients on days 1, 3 and 7–10. * and ** stand for P values less than 0.05 and 0.01, respectively, as compared with healthy control. ^† and ^†† stand for P values less than 0.05 and 0.01, respectively, as compared with sCOPD patients.

Fig 6

Plasma levels of betacellulin (BTC), C-C motif ligand 28 (CCL28), cutaneous T cell attracting chemokines/CCL27 (CTACK), interleukin (IL)-9 and-18Bpa, thymus expressed chemokine (TECK) in healthy, patients with stable COPD (sCOPD) and AECOPD patients on days 1, 3 and 7–10. * and ** stand for P values less than 0.05 and 0.01, respectively, as compared with healthy control. ^† and ^†† stand for P values less than 0.05 and 0.01, respectively, as compared with sCOPD patients.

Fig 7

A new protocol of biomarker evaluation by comparing systemic profiles of inflammatory mediators among different study groups, disease stages and severities, integrating clinical informatics and bioinformatics, and understanding the biological function and signal networks. It is important to clarify recruitment criteria of healthy, sCOPD and AECOPD at different stages, and collect clinical information and blood sample. Clinical informatics is generated through a new Digital Evaluation Score System, while inflammatory mediators are measured by multiplex antibody array and followed by proteomics-based bioinformatics. Disease-specific biomarkers are identified by integrating clinical informatics and functional networks through the global proteomics data set, to develop diagnostics and predictive for personalized medicine and disease prevention.

Fig 8

The integration of BTC pathway, IL-9 pathway, CCR3 pathway and OPN pathway between biological function and pathology demonstrated cell proliferation-related remodeling, intracellular signal-associated inflammatory responses and overactivation of kinases-correlated emphysia in the pathogenesis of COPD.

Fig 9

Characterized clusters of inflammatory mediators and severities of clinical informatics were mapped and distribution of scored groups was imaged. The score was calculated as the ratio ∇ (each value in disease group − correspondence value in healthy)/correspondence value in healthy. The score grades were divided into <1, <1, 1–1.5, 15–2, 2–3 and >3, from which matched groups of factors between clinical findings and inflammatory mediators were layered as various stages of the disease. Al: albumin; Ap: appetite; Bc: barrel chest; Bp: blood pressure; Ca: calcium; Cl: chloride; Co: consciousness; CO₂: PaCO₂; COPD: chronic obstructive pulmonary disease; Cp: chest pressure; Cr: C-reactive protein; Cs: cough severeness; Df: duration of fever; Di: diabetes mellitus; Ell: oedema of lower limbs; Em: emphysema; En: enlargement of lymph nodes; Fb: fasting blood glucose; Fe: fever; HDL: high-density lipid; He: haemoglobin; Hr: heart rate; Hy: hypertension; K: potassium; La: limitation of activity; Lc: lung consolidation; Na: sodium; Ne: neutrophil percentage; Nu: nutrition; O₂: SaO₂; On: orthopnoea at night; Pe: pleural effusion; pH: potential of hydrogen; Pl: platelet; Ra: rales; Rr: respiratory rate; Sb: short breathiness; Sp: sputum; Su: stool and urine; Ur: urea; WBC: white blood cells.