Blood inflammatory biomarkers in participants with idiopathic epiretinal membrane: A retrospective case series study.

The objective was to evaluate the levels of monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in patients with idiopathic epiretinal membrane (iERM). This retrospective case series study comprised of participants with iERM and participants with cataract. The values of MLR, NLR, PLR and from participants' peripheral blood were assessed among groups. The best cutoff value of MLR, NLR, and PLR in iERM was found by performing a receiver operating characteristic curve analysis and determining the optimum cutoff value for each variable. In total, 95 participants with iERM were included in the study group, and 61 participants with senile cataract were included as controls. The lymphocyte count in the iERM group was significantly lower than the control group (1.69 ± 0.63 vs. 1.95 ± 0.53, P = .003). The monocyte count in the iERM group was significantly higher than the control group (0.39 ± 0.11 vs. 0.31 ± 0.10, P < .001). The area under the curve of MLR, NLR, and PLR in differentiating patients with IERM and controls was 0.782, 0.645, and 0.657, respectively, according to receiver operating characteristic. The best cutoff value of MLR was > 0.18, with sensitivity and specificity of 74.7% and 75.4%, respectively. The NLR was > 2.06, with a sensitivity and specificity of 50.5% and 83.6%, respectively. The PLR was > 95.89, with a sensitivity and specificity of 86.3% and 41.0%, respectively. The findings of this study suggest that systemic inflammation may be associated with iERM. IERM patients may be prone to have high MLR, NLR, and PLR values.

IFI44L and C1QTNF5 as promising biomarkers of proliferative diabetic retinopathy.

Proliferative diabetic retinopathy (PDR) is a world-wide leading cause of blindness among adults and may be associated with the influence of genetic factors. It is significant to search for genetic biomarkers of PDR. In our study, we collected genomic data about PDR from gene expression omnibus (GEO) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were carried out. The gene module with the highest gene significance (GS) was defined as the key module. Hub genes were identified by Venn diagram. Then we verified the expression of hub genes in validation data sets and built a diagnostic model by least absolute shrinkage and selection operator (LASSO) regression. Enrichment analysis, including gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA) and construction of a protein-protein interaction (PPI) network were conducted. In GSE60436, we identified 466 DEGs. WGCNA established 14 gene modules, and the blue module (GS = 0.64), was the key module. Interferon (IFN)-induced protein 44-like (IFI44L) and complement C1q tumor necrosis factor-related protein 5 (C1QTNF5) were identified as hub genes. The expression of hub genes in GEO datasets was verified and a diagnostic model was constructed by LASSO as follows: index = IFI44L * 0.0432 + C1QTNF5 * 0.11246. IFI44L and C1QTNF5 might affect the disease progression of PDR by regulating metabolism-related and inflammatory pathways. IFI44L and C1QTNF5 may play important roles in the disease process of PDR, and a LASSO regression model suggested that the 2 genes could serve as promising biomarkers of PDR.