Immune-related biomarkers predict the prognosis and immune response of breast cancer based on bioinformatic analysis and machine learning.

Breast cancer (BC) is the malignancy with the highest mortality rate among women, identification of immune-related biomarkers facilitates precise diagnosis and improvement of the survival rate in early-stage BC patients. 38 hub genes significantly positively correlated with tumor grade were identified based on weighted gene coexpression network analysis (WGCNA) by integrating the clinical traits and transcriptome analysis. Six candidate genes were screened from 38 hub genes basing on least absolute shrinkage and selection operator (LASSO)-Cox and random forest. Four upregulated genes (CDC20, CDCA5, TTK and UBE2C) were identified as biomarkers with the log-rank p < 0.05, in which high expression levels of them showed a poor overall survival (OS) and recurrence-free survival (RFS). A risk model was finally constructed using LASSO-Cox regression coefficients and it possessed superior capability to identify high risk patients and predict OS (p < 0.0001, AUC at 1-, 3- and 5-years are 0.81, 0.73 and 0.79, respectively). Decision curve analysis demonstrated risk score was the best prognostic predictor, and low risk represented a longer survival time and lower tumor grade. Importantly, multiple immune cell types and immunotherapy targets were observed increase in expression levels in high-risk group, most of which were significantly correlated with four genes. In summary, the immune-related biomarkers could accurately predict the prognosis and character the immune responses in BC patients. In addition, the risk model is conducive to the tiered diagnosis and treatment of BC patients.

Interleukin-17 receptor C gene polymorphism reduces treatment effect and promotes poor prognosis of ischemic stroke.

OBJECTIVE: To study the relationship between Interleukin-17 receptor C (IL-17RC) gene polymorphism and ischemic stroke (IS). METHODS: Three hundred cases of IS patients and 300 cases of the healthy controls were selected. Serum of IS patients and the controls was collected. The relative mRNA levels of IL-17, IL-17RC, IL-6, IL-8, G-CSF and granulocyte-macrophage colony stimulating factor (GM-CSF) by qRT-PCR. The protein expression of IL-17 and IL-17RC was determined by Western blotting. IL-17RC genotype was identified by PCR amplification. The proportion of IL-17RC, SNP and re37511 in IS and control group was determined. The treatment effect on IS and prognosis of patients with IL-17RC, SNP and re37511 was compared. RESULTS: The relative mRNA levels of IL-17, IL-17RC, IL-6, IL-8, G-CSF and GM-CSF in IS group were significantly higher than the control group. The protein expression of IL-17 and IL-17RC in IS group was also markedly higher than the control group. The proportion of IL-17RC re37511 in IS group was much larger than control group and proportion of IL-17RC much less. The percent of poor treatment effect in re37511 was much larger than IL-17RC. The percent of death and recrudescence in patients with IL-17RC re37511 was the highest. CONCLUSION: IS up-regulates the expression of IL-17 and IL-17RC. IL-17RC re37511 indicates the patients have a poorer treatment effect and prognosis.