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Objective To explore the transcriptomic changes of adenocarcinoma at the gastroesophageal junction (ACGEJ) and identify key genes in the occurrence and progression. Methods RNA sequencing data of ACGEJ tumors and adjacent normal tissues samples from 58 patients who were recruited at the Linzhou Cancer Hospital and Linzhou Esophageal Cancer Hospital. The gene expression level of paired ACGEJ and adjacent normal tissue samples were compared to identify dysregulated genes. And the enriched pathways of these genes were analyzed by using gene set variation analysis (GSVA). LASSO regression model was used to test the association between the expression of dysregulated genes and the prognosis of ACGEJ patients, and the clinical features and prognosis associated gene set were applied to establish the nomogram model. Results Through transcriptomic profiling of tumor and non-tumor ACGEJ samples, 737 significantly differentially expression genes (DEGs) in ACGEJ were identified(｜log2FC｜＞1.2, Q＜0.05). The DEGs were related to tumor proliferation, growth, metastasis and metabolism. Among these DEGs, a total of 9 genes (ASF1B,ACTN1,KNL1,SAPCD2,TP53I11,DMBT1,CNFN,ID2,DPT) were identified to associate with the prognosis of ACGEJ patients by LASSO regression model. These 9 genes were further selected to build a prognostic model. The patients could be divided into two groups by this prognostic model, and the overall survival (OS) of the low-risk group was significantly higher than that of the high-risk group (P＜0.01). A nomogram was established which included the age, clinical stage and prognosis associated gene set for eventual clinical translation. Conclusions Several genes associated with the occurrence and progression of ACGEJ are identified. These genes may be potential treatment targets and prognostic biomarkers of this disease.

Abstract Background Malaria re-emerged in the Huang-Huai Plain of central China during 2006–2008, dominated with Anopheles sinensis as a vector. However, there is no information on strategies based on multi-factor analysis to effectively control the re-emergence of malaria in these areas. Previous experience indicates some relationship between the distribution of water bodies and malaria cases, but more detailed data are not available and in-depth studies have not been conducted up to now. The objective of this study was to identify the relationship between the distribution of water bodies and presentation of malaria cases using spatial analysis tools in order to provide guidance to help formulate effective strategies for use in controlling the sources of malaria infection, based on the identification of risk areas and population. Methods The geographic information of malaria cases and their surrounding water bodies were collected from Suixi, Guoyang, Guzhen, Yingshang, Fengyang and Yongqiao County in Anhui province, Yongcheng and Tongbai County in Henan province. All malaria cases distributed in 113 villages in these 8 counties were collected from the China Information System for Disease Control and Prevention and confirmed by household investigation. Data on GIS and malaria cases were mapped and analyzed with the software of ArcGIS 9.2 to identify the spatial correlation between malaria cases and water bodies. The distance from households with malaria cases to the nearest water bodies was used to calculate the OR value by Chi-square test. The risk area was identified through the comparison of OR values in different distances. Results 357 malaria cases and their GPS data as well as surrounding water bodies were collected and analyzed. 74% of malaria cases were located within the extent of 60 m proximity to the water bodies. The risk rate of people living there and presenting with malaria was significantly higher than others (OR = 1.6,95%CI (1.042, 2.463),P Conclusions The results revealed that distribution of water bodies is an important factor influencing the occurrence and distribution of malaria cases in the An.sinensis areas, and implies that the scope and population within 60 m around water bodies are at risk and could be a targeted population for case management of malaria.