NAT10 functions as a pivotal regulator in gastric cancer metastasis and tumor immunity.

Gastric cancer (GC) presents a significant global health burden, with metastasis being the leading cause of treatment failure and mortality. NAT10, a regulatory protein involved in mRNA acetylation, has been implicated in various cancers. However, its role in GC, especially concerning metastasis and immune interactions, remains unclear.

Identification and validation of a T cell marker gene-based signature to predict prognosis and immunotherapy response in gastric cancer.

Although the role of T cells in tumor immunity and modulation of the tumor microenvironment (TME) has been extensively studied, their precise involvement in gastric adenocarcinoma remains inadequately explored. In this work, we analyzed the single-cell RNA sequencing data set in GSE183904 and identified 322 T cell marker genes using the "FindAllMarkers" method of the R package "Seurat". STAD patients in the TCGA database were divided into high-risk and low-risk categories based on risk scores.

Analysis of spatial-temporal dynamic distribution and related factors of tuberculosis in China from 2008 to 2018.

Through spatial-temporal scanning statistics, the spatial-temporal dynamic distribution of pulmonary tuberculosis incidence in 31 provinces and autonomous regions of China from 2008 to 2018 is obtained, and the related factors of spatial-temporal aggregation of tuberculosis in China are analyzed to provide strong scientific basis and data support for the prevention and control of pulmonary tuberculosis. This is a retrospective study, using spatial epidemiological methods to reveal the spatial-temporal clustering distribution characteristics of China's tuberculosis epidemic from 2008 to 2018, in which cases data comes from the China Center for Disease Control and prevention. Office Excel is used for general statistical description, and the single factor correlation analysis adopts χ Test (or trend χ Inspection).

Monitoring European data with prospective space-time scan statistics: predicting and evaluating emerging clusters of COVID-19 in European countries.

Background: Coronavirus disease 2019 (COVID-19) has become a pandemic infectious disease and become a serious public health crisis. As the COVID-19 pandemic continues to spread, it is of vital importance to detect COVID-19 clusters to better distribute resources and optimizing measures. This study helps the surveillance of the COVID-19 pandemic and discovers major space-time clusters of reported cases in European countries.