ABSTRACT
OBJECTIVE: Up-regulation of miR-765 in esophageal squamous cell carcinoma (ESCC) has been reported in the previous study. The aim of the present study was to measure the levels of miR-765 expression in ESCC and evaluate its clinical significance in ESCC patients. PATIENTS AND METHODS: Quantitative Real-time PCR assays were performed to analyze the expression of miR-765 in human ESCC tissues and adjacent esophageal tissues. The relationships between miR-765 expression levels and the clinical factors were investigated by x2-test. Kaplan-Meier analysis was performed to evaluate the overall survival (OS) and disease-free survival (DFS) of ESCC patients with a different expression level of miR-765. The Cox proportional hazards regression model was used to assess the independent prognostic factors. RESULTS: The expression level of miR-765 in ESCC tissues was significantly higher than that in their corresponding normal tissues (p < 0.01). High miR-765 expression was significantly correlated with tumor stage (p = 0.001), lymph nodes metastasis (p = 0.005), clinical stage (p = 0.007). In addition, Kaplan-Meier analysis showed that patients with higher miR-765 expression had a significantly poorer OS (p = 0.0010) and DFS (p< 0.0001) than those with lower miR-765 expression. Multivariate analyses revealed that miR-765 expression served as an independent predictor for both OS (p = 0.001) and DFS (p = 0.001). CONCLUSIONS: Our findings provided the first evidence that miR-765 may serve as an indicator for prognosis of ESCC.
Subject(s)
Carcinoma, Squamous Cell/pathology , Esophageal Neoplasms/pathology , MicroRNAs/metabolism , Carcinoma, Squamous Cell/metabolism , Carcinoma, Squamous Cell/mortality , Disease-Free Survival , Esophageal Neoplasms/metabolism , Esophageal Neoplasms/mortality , Female , Humans , Kaplan-Meier Estimate , Lymphatic Metastasis , Male , Middle Aged , Neoplasm Staging , Prognosis , Proportional Hazards Models , Up-RegulationABSTRACT
Several post-translational modifications (PTM) have been discussed in literature. Among a variety of oxidative stress-induced PTM, protein carbonylation is considered a biomarker of oxidative stress. Only certain proteins can be carbonylated because only four amino acid residues, namely lysine (K), arginine (R), threonine (T) and proline (P), are susceptible to carbonylation. The yeast proteome is an excellent model to explore oxidative stress, especially protein carbonylation. Current experimental approaches in identifying carbonylation sites are expensive, time-consuming and limited in their abilities to process proteins. Furthermore, there is no bioinformational method to predict carbonylation sites in yeast proteins. Therefore, we propose a computational method to predict yeast carbonylation sites. This method has total accuracies of 86.32, 85.89, 84.80, and 86.80% in predicting the carbonylation sites of K, R, T, and P, respectively. These results were confirmed by 10-fold cross-validation. The ability to identify carbonylation sites in different kinds of features was analyzed and the position-specific composition of the modification site-flanking residues was discussed. Additionally, a software tool has been developed to help with the calculations in this method. Datasets and the software are available at https://sourceforge.net/projects/hqlstudio/ files/CarSpred.Y/.
