Loss of imprinting of insulin-like growth factor 2 is associated with increased risk of lymph node metastasis and gastric corpus cancer

Abstract Background The aim of this study was to determine the clinicopathological features of gastric cancers with loss of imprinting (LOI) of LIT1. Insulin-like growth factor 2 (IGF2) and H19 in Chinese patients. Methods DNA and RNA from tumours were amplified and then digested with RsaI, ApaI and HinfI, and RsaI respectively to determine the LOI status. The demographic and clinicopathological characteristics in LOI positive and LOI negative patients were compared and tested with Statistical analysis. Results Of the 89 patients enrolled for analysis, 22, 40 and 35 were heterozygous and thus informative for LIT1, IGF2 and H19 LOI analyses respectively. The positive rate of LIT1, IGF2 and H19 LOI of gastric cancer tissues were 54.6% (12/22), 45% (18/40) and 8.6% (3/32) in Chinese patients. Gastric corpus cancer (8/10, 80%) were more likely to have LOI of IGF2 in tumours than antrum cancers (10/30, 33.3%){odds ratio (OR) = 8, 95% confidence intervals (CI) = 1.425-44.920, p = 0.018)}. LOI of IGF2 in tumours was also associated with the lymph node metastasis (LNM) (OR = 4.5, 95% CI = 1.084-18.689, p = 0.038). Conclusion IGF2 LOI is present in high frequency in Chinese gastric cancer patients, especially those with gastric corpus cancer.</p

Scalable Evaluation of Trajectory Queries over Imprecise Location Data

Trajectory queries, which retrieve nearby objects for every point of a given route, can be used to identify alerts of potential threats along a vessel route, or monitor the adjacent rescuers to a travel path. However, the locations of these objects (e.g., threats, succours) may not be precisely obtained due to hardware limitations of measuring devices, as well as complex natures of the surroundings. For such data, we consider a common model, where the possible locations of an object are bounded by a closed region, called "imprecise region". Ignoring or coarsely wrapping imprecision can render low query qualities, and cause undesirable consequences such as missing alerts of threats and poor response rescue time. Also, the query is quite time-consuming, since all points on the trajectory are considered. In this paper, we study how to efficiently evaluate trajectory queries over imprecise objects, by proposing a novel concept, u-bisector, which is an extension of bisector specified for imprecise data. Based on the u-bisector, we provide an efficient and versatile solution which supports different shapes of commonly-used imprecise regions (e.g., rectangles, circles, and line segments). Extensive experiments on real datasets show that our proposal achieves better efficiency, quality, and scalability than its competitors.published_or_final_versio