Esophageal squamous cell carcinoma (ESCC) has one of the highest mortality rates worldwide. AU-rich element RNA-binding factor 1 (AUF1) is an established RNA-binding protein. AUF1 influences the process of development, apoptosis and tumorigenesis via interacting with adenylate-uridylate rich elements (AREs) bearing mRNAs. However, the clinical relevance of AUF1 and its biological function in ESCC progression have not been reported. In the present study, we first investigated the expression of AUF1 in the ESCC tissue samles and normal samples. We found a significantly higher expression of AUF1 in ESCC tissues than that in normal tissues and tumor adjacent tissues. The expression of AUF1 correlated with ESCC stage (P=0.011) and marginally correlated with lymph node metastasis (P=0.055) of ESCC patients. Silencing of AUF1 by an siRNA inhibited the proliferation and enhanced the apoptosis of ESCC cells. mRNA profiling by microarray analysis revealed that AUF1 knockdown affected 285 genes (fold change ≥2) that function in multiple pathways. GTP cyclohydrolase I (GCH1), the rate limiting enzyme for BH4 synthesis, was found to be downregulated. One of the AU-rich elements in the 3'UTR of GCH1 was found to be responsive to AUF1 expression by luciferase assay. Knockdown of GCH1 suppressed cell proliferation and colony formation of ESCC cells. The expression of AUF1 significantly correlated with that of GCH1 in ESCC tissues. Taken together, we demonstrated the overexpression of AUF1 in esophageal carcinoma and identified GCH1 as AUF1's effector for the proliferation of ESCC cells.

Background Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. Methods Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. Results Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values < 0.01 for both cohorts). Multivariable logistic regression model showed the best performance (AUC = 0.92 ± 0.04 and 0.87 ± 0.02, accuracy = 0.87 and 0.86, and F-1score = 0.93 and 0.86 in training and validation cohorts, respectively) in comparison with any other model (P-value < 0.001). Good calibration was observed for multivariable logistic regression model. Conclusion CT radiomic models could help predict resectability of oesophageal SCC, and multivariable logistic regression model is the most predictive model.

No abstract

No abstract

Unsupervised domain adaptation aims at learning a shared model for two related, but not identical, domains by leveraging supervision from a source domain to an unsupervised target domain. A number of effective domain adaptation approaches rely on the ability to extract discriminative, yet domain-invariant, latent factors which are common to both domains. Extracting latent commonality is also useful for disentanglement analysis, enabling separation between the common and the domain-specific features of both domains. In this paper, we present a method for boosting domain adaptation performance by leveraging disentanglement analysis. The key idea is that by learning to separately extract both the common and the domain-specific features, one can synthesize more target domain data with supervision, thereby boosting the domain adaptation performance. Better common feature extraction, in turn, helps further improve the disentanglement analysis and disentangled synthesis. We show that iterating between domain adaptation and disentanglement analysis can consistently improve each other on several unsupervised domain adaptation tasks, for various domain adaptation backbone models. IntroductionMany machine learning solutions are supervised, requiring well-annotated training datasets. Such annotations can be overly expensive to attain for the amount of data required for plausible performance by today's deep neural networks. Domain adaptation approaches attempt to compensate for lack of annotated data in a target domain, by adapting information from a source domain, for which annotated data is easier to obtain. For example, in many cases, it is easy to generate synthetic data (source domain) with inherent annotations, and use this data for the supervised training of a network that is ultimately intended for carrying out a task over real data (the target domain). While the source data may closely resemble the real target data, there are typically inevitable differences between the two domains, and domain adaptation faces the challenge of overcoming such domain shifts.In this work, we target the unsupervised domain adaptation scenario, where the source domain data, either synthetic or real, is fully annotated, while the target domain data has no annotations whatsoever. Existing approaches to domain adaptation, which are reviewed in more detail in the next section, can be broadly classified into two categories: methods based on domain transfer (e.g., by translating data from one domain to another), and those based on embedding both domains in a common feature space. The goal of the current work is to propose a framework which improves the performance of methods in the latter category.