Diagnostic biomarkers for invasive aspergillosis utilizing weighted gene co-expression network analysis

Background: Invasive aspergillosis (IA) has a significant mortality in immunocompromised patients. In recent years, with more aggressive immunosuppressed therapies, the incidence of IA was increasing. However, diagnostic biomarkers with high sensitivity and specificity remain rare. To get new diagnostic biomarkers, microarray dataset GSE78000 was analyzed. Methods: Weighted gene co-expression network analysis (WGCNA) was used to identify hub genes. Roc curves were employed for investigating diagnostic biomarkers for IA.Results: Hub genes were TLR4, TP53I3/PIG3, TMTC1, ITGAM, CYSTM1, FAR1, GAS7 and MKNK1. However, after we compared gene expression of hematological patients suffering from IA with non-IA patients, only TLR4, TP53I3/PIG3 and TMTC1 were significantly high expression in IA patients. At the optimal cut‐off value, TLR4 can diagnose patients with IA with 78.3% sensitivity and 72.7% specificity. TP53I3/PIG3 can diagnose patients with IA with 91.3% sensitivity and 54.5% specificity. TMTC1 can diagnose patients with IA with 78.3% sensitivity and 81.8% specificity. In addition, the data of hematological patients suffering from Staphylococcus aureus (S. aureus) and Escherichia coli (E.coli) infections were also analyzed. The results showed that TLR4 and TP53I3/PIG3 were also significantly high expression in S. aureus and E.coli infections, while only TP53I3/PIG3 was obviously higher expression in patients with bacterial infections compared with IA. As for TMTC1, we cannot annotate the gene from the microarray data. Conclusions: our results suggested that TLR4, TP53I3/PIG3 and TMTC1 might be used for the diagnosis of IA, and TP53I3/PIG3 can also be used to discriminate hematological aspergillosis and bacterial infections.