Search

Purpose. To investigate the choroidal vascularity index (CVI) as a prognostic factor for the visual efficacy of antivascular endothelial growth factor (VEGF) treatment in diabetic macular edema (DME). Methods. We retrospectively reviewed 92 DME eyes receiving anti-VEGF treatment, which were stratified as responders (≥5 letters gained) and nonresponders (<5 letters gained or lost). Baseline systematic features and optical coherence tomography features, including the CVI, adjusted ellipsoid zone (EZ) reflectivity, subretinal fluid (SRF), and disorganization of the retinal inner layers (DRIL), were evaluated between the two groups. Results. The baseline CVI was significantly lower in nonresponders than in responders (0.66 ± 0.05 , 0.69 ± 0.05 , and 0.72 ± 0.05 , p = 0.014). After adjusting for other factors, the baseline CVI, DRIL, SRF, and adjusted EZ reflectivity were significantly associated with visual outcomes (CVI: odds ratio OR = 0.17 , p = 0.006 ; adjusted EZ reflectivity: OR = 0.56 , p = 0.007 ; DRIL: OR = 6.71 , p = 0.001 ; and SRF: OR = 0.29 , p = 0.008). Conclusion. DME patients with a higher CVI, higher adjusted EZ reflectivity, the presence of SRF, and the absence of DRIL at baseline were more likely to gain >5 letters in visual acuity after anti-VEGF treatment. CVI may serve as a novel biomarker for visual response to anti-VEGF treatment in DME. [ABSTRACT FROM AUTHOR]

Background: Endometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients' survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers. Methods: We downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment. Results: The prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient's ESTIMATE score and the higher the infiltration of immune cells. Conclusions: We used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC. [ABSTRACT FROM AUTHOR]

Background: Endometrial cancer (EC) is one of the most common gynecological cancers. Epithelial–mesenchymal transition (EMT) is believed to be significantly associated with the malignant progression of tumors. However, there is no relevant study on the relationship between EMT-related gene (ERG) signatures and the prognosis of EC patients. Methods: We extracted the mRNA expression profiles of 543 tumor and 23 normal tissues from The Cancer Genome Atlas database. Then, we selected differentially expressed ERGs (DEERGs) among these mRNAs. Next, univariate and multivariate Cox regression analyses were performed to select the ERGs with predictive ability for the prognosis of EC patients. In addition, risk score models were constructed based on the selected genes to predict patients' overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Finally, nomograms were constructed to estimate the OS and PFS of EC patients, and pan-cancer analysis was performed to further analyze the functions of a certain gene. Results: Six OS-, ten PFS-, and five DFS-related ERGs were obtained. By constructing the prognostic risk score model, we found that the OS, PFS, and DFS of the high-risk group were notably poorer. Last, we found that AQP5 appeared in all three gene signatures, and through pan-cancer analysis, it was also found to play an important role in immunity in lower grade glioma (LGG), which may contribute to the poor prognosis of LGG patients. Conclusions: We constructed ERG signatures to predict the prognosis of EC patients using bioinformatics methods. Our findings provide a thorough understanding of the effect of EMT in patients with EC and provide new targets and ideas for individualized treatment, which has important clinical significance. [ABSTRACT FROM AUTHOR]

Among all fatal gynecological malignant tumors, ovarian cancer has the highest mortality rate. The purpose of this study was to develop a stable and personalized glycometabolism-related prognostic signature to predict the overall survival of ovarian cancer patients. The gene expression profiles and clinical information of ovarian cancer patients were derived from four public GEO datasets, which were divided into training and testing cohorts. Glycometabolism-related genes significantly associated with prognosis were selected. A risk score model was established and validated to evaluate its predictive value. We found 5 genes significantly related to prognosis and established a five-mRNA signature. The five-mRNA signature significantly divided patients into a low-risk group and a high-risk group in the training set and validation set. Survival analysis showed that high risk scores obtained by the model were significantly correlated with adverse survival outcomes and could be regarded as an independent predictor for patients with ovarian cancer. In addition, the five-mRNA signature can predict the overall survival of ovarian cancer patients in different subgroups. In summary, we successfully constructed a model that can predict the prognosis of patients with ovarian cancer, which provides new insights into postoperative treatment strategies, promotes individualized therapy, and provides potential new targets for immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Gastric cancer (GC) is one of the most common malignancies worldwide, exhibiting a high morbidity, and mortality. As the various treatment methods for gastric cancer are limited by disadvantages, many efforts to improve the efficacy of these treatments are being taken. Metabolic recombination is an important characteristic of cancer and has gradually caused a recent upsurge in research. However, systematic analysis of the interaction between glycolysis and GC patient prognosis and its potential associations with immune infiltration is lacking but urgently needed. Methods: We obtained the gene expression data and clinical materials of GC derived from The Cancer Genome Atlas (TCGA) dataset. Univariate and multivariate Cox proportional regression analyses were performed to select the optimal prognosis-related genes for subsequent modeling. We then validated our data in the GEO database and further verified the gene expression using the Oncomine database and PCR experiments. Besides, Gene set variation analysis (GSVA) analysis was employed to further explore the differences in activation status of biological pathways between the high and low risk groups. Furthermore, a nomogram was adopted to predict the individualized survival rate of GC patients. Finally, a violin plot and a TIMMER analysis were performed to analyse the characteristics of immune infiltration in the microenvironment. Results: A seven-gene signature, including STC1, CLDN9, EFNA3, ZBTB7A, NT5E, NUP50, and CXCR4, was established. Based on this seven-gene signature, the patients in the training set and testing sets could be divided into high-risk and low-risk groups. In addition, a nomogram based on risk and age showed good calibration and moderate discrimination. The results proved that the seven-gene signature had a strong capacity to predict the GC patient prognosis. Collectively, the violin plot and TIMMER analysis demonstrated that an immunosuppressive tumor microenvironment caused by hyperglycolysis led to poor prognosis. Conclusion: Taken together, these results established a genetic signature for gastric cancer based on glycolysis, which has reference significance for the in-depth study of the metabolic mechanism of gastric cancer and the exploration of new clinical treatment strategies. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the effects of various triglyceride (TG)-lowering therapies on hypertriglyceridemia-induced acute pancreatitis (HTGAP). A total of 132 patients with HTGAP were retrospectively divided into an insulin intensive therapy (IIT), a plasma exchange (PE) and a non-intensive insulin therapy (NIIT) group according to the TG-lowering therapies they had received. The clinical and biochemical data of the subjects were analyzed. The baseline data, including sex, age, TG, amylase, severe acute pancreatitis and systemic inflammatory response syndrome were not significantly different among the three groups (P>0.05). The 24-h TG clearance rate (χ2=7.74, P=0.021), onset to treatment time (χ2=14.50, P<0.001) and the time required to reach the target TG level (χ2=6.12, P=0.047) were different in these three groups, but no significant differences were observed between the IIT and NIIT groups (P>0.05). The incidence of therapy-associated complications in the PE group (30.23%) was higher than that in the IIT (2.17%) and NIIT (4.65%) groups. The difference in the incidence of therapy-associated complications was significant among the three groups (P<0.001), but no significant difference was present between the IIT and NIIT groups (P>0.05). In the PE group, the length of stay was increased compared with that in the IIT and NIIT groups (χ2=7.05, P<0.05), while there was no significant difference between the IIT and NIIT groups (P>0.05). The present study suggested that NIIT at presentation had a similar therapeutic efficacy to that of IIT to improve the prognosis of HTGAP, and NIIT and IIT were associated with fewer complications than PE treatment. NIIT may favorably perform in patients presenting early after symptom onset and may be considered for clinical application. [ABSTRACT FROM AUTHOR]