ABSTRACT
ObjectiveTo analyze the expression of molecular marker affecting the prognosis of acute myeloid leukemia （AML） patients from bioinformatics database， thus providing an experimental basis for further exploration of a novel molecular marker for the prognosis of AML. MethodsThe prognostic data of 179 AML patients from The Cancer Genome Atlas （TCGA） database were examined for differential gene analysis and survival analysis. The bone marrow samples of 74 healthy individuals （HI） and 542 de novo AML patients in the dataset GSE13159 downloaded from the Gene Expression Omnibus （GEO） database were analyzed to detect the difference in the expression levels of differential target genes. Peripheral blood and bone marrow samples were collected from 18 de novo AML patients and 20 age- and gender-matched healthy controls， and real-time fluorescent quantitative PCR was used to validate the expression levels of the differential genes in the AML patients. ResultsBioinformatics data analysis showed that the optimal cut-off value of Homo sapiens NK2 homeobox 3 （NKX2-3） calculated by R language was 0.051. Survival analysis revealed a statistically poorer overall survival in de novo AML patients with high NKX2-3 expression than in those with low NKX2-3 expression （P = 0.0036）. NKX2-3 was highly expressed in patients with de novo AML than in HI and the difference was statistically significant （P < 0.001）. Real-time fluorescence quantitative PCR verified the expression levels of the NKX2-3 gene in AML patients and confirmed that compared with those in HI， in the de novo AML patients， NKX2-3-1 and NKX2-3-2 were highly expressed and were significantly correlated （P = 0.000， P = 0.000）. ConclusionNKX2-3 is highly expressed in de novo AML patients， and the AML patients with high NKX2-3 expression have poor overal survival. NKX2-3 may be closely related to the clinical outcome and prognosis of AML.
ABSTRACT
ObjectiveTo identify the relationship between tumor tissue interleukins （ILs） and non-small cell lung cancer （NSCLC） patients with poor response to immune checkpoint blockade （ICB） therapy， and to investigate the differential expression of ILs in tumor of NSCLC patients as well as its effect on ICB response and prognosis. MethodsA total of 61 patients diagnosed with NSCLC and treated with ICB were retrospectively collected from the data of a previous study. We obtained transcriptome sequencing data from tumor tissues and survival data of the patients before ICB treatment. Using bioinformatics methods， we screened for ILs that significantly affected the efficacy and prognosis of ICB treatment. We evaluated the efficacy of ICB treatment using progressive-free survival （PFS） and assessed the prognosis using overall survival （OS）. The Kaplan-Meier survival curve and ROC curve were used to analyze the predictive effect and efficacy of ILs on the efficacy and prognosis of ICB in NSCLC patients. ResultsThe results of the univariate Cox regression analysis in our study showed that nine ILs were found to be associated with OS of NSCLC patients treated with ICB at a significance level of P < 0.1. Further multivariate analysis revealed that high expression of IL-11， IL-17D， and IL-36A was significantly associated with poor prognosis in these patients （P < 0.05）. The results from the Kaplan-Meier survival curve analysis revealed a significant negative correlation between the high expression of IL-17D and both PFS and OS in NSCLC patients. Specifically， patients with IL-17D high expression had a median PFS of 3.1 months compared with 6.5 months in low expression patients ［95% confidence interval （CI） （1.178， 3.655）， P = 0.009］. Similarly， the median OS was 9.8 months in the high expression group versus 21.8 months in the low expression group ［95%CI （1.116， 4.392）， P = 0.018］. ROC curve showed that the prediction performance was favorable ［AUCPFS = 0.702，95%CI （0.562， 0.842）， P = 0.027； AUCOS = 0.684， 95%CI （0.550， 0.818）， P = 0.014］. Although IL-11 and IL-36A alone were not significant predictors of PFS and OS in NSCLC patients， the median PFS and OS were notably shortened to 2.2 months （P = 0.003） and 3.0 months （P < 0.001）， respectively， when high expression of IL-11 and IL-36A was combined with high expression of IL-17D. The ROC curve analysis demonstrated an improvement in prediction efficiency for both PFS and OS in NSCLC patients ［AUCPFS = 0.748， 95%CI （0.615， 0.880）， P = 0.007； AUCOS = 0.703， 95%CI （0.573， 0.833）， P = 0.007］. ConclusionThe results suggest that high expression of IL-11， IL-17D， and IL-36A is associated with a higher risk of disease progression which correlates to poor PFS and OS in NSCLC patients.
ABSTRACT
Effective medical image enhancement method can not only highlight the interested target and region, but also suppress the background and noise, thus improving the quality of the image and reducing the noise while keeping the original geometric structure, which contributes to easier diagnosis in disease based on the image enhanced. This article carries out research on strengthening methods of subtle structure in medical image nowadays, including images sharpening enhancement, rough sets and fuzzy sets, multi-scale geometrical analysis and differential operator. Finally, some commonly used quantitative evaluation criteria of image detail enhancement are given, and further research directions of fine structure enhancement of medical images are discussed.
