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Background: Esophageal cancer (EC) causes more than 400 thousand deaths per year, and half of them occur in China. There are discrepancies regarding the survival of EC patients between population-based surveillance studies and hospital-based studies. Objectives: We aimed to synthesize the survival data from hospital-based EC studies in the Chinese population from 2000 to 2018 and to compare the survival rates between EC patients with different clinical classifications. Methods: The protocol of this systematic review was registered in PROSPERO (CRD-42019121559). We searched Embase, PubMed, CNKI, and Wanfang databases for studies published between January 1, 2000 and December 31, 2018. We calculated the pooled survival rates and 95% confidence intervals (CIs) by Stata software (V14.0). Results: Our literature search identified 933 studies, of which 331 studies with 79,777 EC patients met the inclusion criteria and were included in meta-analyses. The pooled survival rates were 74.1% (95% CI: 72.6–75.7%) for 1-year survival, 49.0% (95% CI: 44.2–53.8%) for 2-years survival, 46.0% (95% CI: 42.6–49.5%) for 3-years survival, and 40.1% (95% CI: 33.7–46.4%) for 5-years survival. An increased tendency toward EC survival was verified from 2000 to 2018. In addition, discrepancies were observed between EC patients with different clinical classifications (e.g., stages, histologic types, and cancer sites). Conclusions: Our findings showed a higher survival rate in hospital-based studies than population-based surveillance studies. Although this hospital-based study is subject to potential representability and publication bias, it offers insight into the prognosis of patients with EC in China.

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Protein-DNA bindings are critical to many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the residues shape (peak, flat, or valley) and the surrounding environment of double-stranded DNA-binding proteins (DSBs) and single-stranded DNA-binding proteins (SSBs) in protein-DNA interfaces. In the results, we found that the interface shapes, hydrogen bonds, and the surrounding environment present significant differences between the two kinds of proteins. Built on the investigation results, we constructed a random forest (RF) classifier to distinguish DSBs and SSBs with satisfying performance. In conclusion, we present a novel methodology to characterize protein interfaces, which will deepen our understanding of the specificity of proteins binding to ssDNA (single-stranded DNA) or dsDNA (double-stranded DNA). Proteins 2016; 84:979-989. © 2016 Wiley Periodicals, Inc.