Predictive value of the monocyte-to-lymphocyte ratio in the diagnosis of prostate cancer.

ABSTRACT: It has been reported that inflammation and immune system are related to prostate cancer. The neutrophil-to-lymphocyte ratio (NLR), as well as the platelet-to-lymphocyte ratio (PLR), have already been proposed as new indices to help diagnose prostate cancer (PCa). However, the monocyte-to-lymphocyte ratio (MLR) with regard to PCa has rarely been mentioned.To investigate the capability of the MLR to predict PCa.Patients who were pathologically diagnosed with PCa in our hospital and healthy control subjects who conformed to the inclusion criteria were enrolled. Patient data were recorded, including age, complete blood counts, blood biochemistry, and serum prostate-specific antigen (PSA) levels. The differences in these data between the groups were analyzed and the diagnostic value of the MLR was compared with PSA.Our study included a total of 100 patients with PCa and 103 healthy control subjects. Patients with PCa presented with a significantly higher NLR, MLR, and PLR compared to control subjects. However, the hemoglobin and lymphocyte levels were lower (P < .05) in PCa patients. The area under the curve (AUC) of PSA and ratio of free/total serum prostate-specific antigen were 0.899 (95% confidence interval [CI]: 0.857-0.942) and 0.872 (95% CI: 0.818-0.926), respectively, while the AUC of the MLR was 0.852 (95% CI: 0.798-0.906), which was higher than that of the NLR, PLR, and any other blood parameters. Additionally, the optimal cut-off value of the MLR for PCa was 0.264, with a specificity of 87.4% and a sensitivity of 72.0%. An evaluation of the diagnostic value of MLR + PSA gave an AUC of 0.936 (95% CI: 0.902-0.970). However, the AUC of MLR + PSA + f/tPSA was 0.996 (95% CI: 0.991-1.000). The diagnostic value of MLR + NLR + PSA gave an AUC of 0.945 (95% CI: 0.913-0.977), and the specificity is 0.971.PSA remains the most important diagnostic indicator. MLR combined with PSA and f/tPSA has the higher predictive value than PSA. It suggests that MLR may be another good predictive indicator of PCa. It can help reduce the clinical false positive rate.

Data-Independent Acquisition-Based Quantitative Proteomics Analysis Reveals Dynamic Network Profiles during the Macrophage Inflammatory Response.

Understanding of the molecular regulatory mechanisms underlying the inflammatory response is incomplete. The present study focuses on characterizing the proteome in a model of inflammation in macrophages treated with lipopolysaccharide (LPS). A total of 3597 proteins are identified in macrophages with the data-independent acquisition (DIA) method. Bioinformatic analyses reveal discrete modules and the underlying molecular mechanisms, as well as the signaling network that modulates the development of inflammation. It is found that a total of 87 differentially expressed proteins are shared by all stages of LPS-induced inflammation in macrophages and that 18 of these proteins participate in metabolic processes by forming a tight interaction network. Data support the hypothesis that ribosome proteins play a key role in regulating the macrophage response to LPS. Interestingly, conjoint analyses of the transcriptome and proteome in macrophages treated with LPS reveal that the genes upregulated at both the mRNA and protein levels are mainly involved in inflammation and the immune response, whereas the genes downregulated are significantly enriched in metabolism-related processes. These results not only provide a more comprehensive understanding of the molecular mechanisms of inflammation mediated by bacterial infection but also provide a dynamic proteomic resource for further studies.