Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma.

OBJECTIVE: Oxidative stress is associated with the occurrence and development of lung cancer. However, the specific association between lung cancer and oxidative stress is unclear. This study aimed to investigate the role of oxidative stress in the progression and prognosis of lung adenocarcinoma (LUAD). METHODS: The gene expression profiles and corresponding clinical information were collected from GEO and TCGA databases. Differentially expressed oxidative stress-related genes (OSRGs) were identified between normal and tumor samples. Consensus clustering was applied to identify oxidative stress-related molecular subgroups. Functional enrichment analysis, GSEA, and GSVA were performed to investigate the potential mechanisms. xCell was used to assess the immune status of the subgroups. A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets. RESULTS: A total of 40 differentially expressed OSRGs and two oxidative stress-associated subgroups were identified. Enrichment analysis revealed that cell cycle-, inflammation- and oxidative stress-related pathways varied significantly in the two subgroups. Furthermore, a risk model was developed and validated based on the OSRGs, and findings indicated that the risk model exhibits good prediction and diagnosis values for LUAD patients. CONCLUSION: The risk model based on the oxidative stress could act as an effective prognostic tool for LUAD patients. Our findings provided novel genetic biomarkers for prognosis prediction and personalized clinical treatment for LUAD patients.

[PyRERT: Python Research Environment for Radiation Therapy].

OBJECTIVE: The Python research environment for radiation therapy (PyRERT) is a set of business software for hospital physicists to conduct radiation therapy research. METHODS: Choose the open source Enthought Tool Suite（ETS） as the core external dependency library of PyRERT. PyRERT is divided into base layer, content layer and interaction layer, and each layer is composed of different functional modules. RESULTS: PyRERT V1.0 provide a good development environment for scientific research programming in DICOM RT file processing, batch processing of water tank scan data, digital phantom creation, 3D medical image volume visualization, virtual radiotherapy equipment driver, and film scan image analysis. CONCLUSIONS: PyRERT enables the results of the research group to be iteratively inherited in the form of software. It's reusable basic classes and functional modules greatly improve the efficiency of scientific research task programming.

[Development of a Compared Software for Automatically Generated DVH in Eclipse TPS].

Objective: This study is to automatically calculate the dose volume histogram(DVH) for the treatment plan, then to compare it with requirements of doctor's prescriptions. Methods: The scripting language Autohotkey and programming language C# were used to develop a compared software for automatically generated DVH in Eclipse TPS. This software is named Show Dose Volume Histogram (ShowDVH), which is composed of prescription documents generation, operation functions of DVH, software visualization and DVH compared report generation. Results: Ten cases in different cancers have been separately selected, in Eclipse TPS 11.0 ShowDVH could not only automatically generate DVH reports but also accurately determine whether treatment plans meet the requirements of doctor's prescriptions, then reports gave direction for setting optimization parameters of intensity modulated radiated therapy. Conclusions: The ShowDVH is an user-friendly and powerful software, and can automatically generated compared DVH reports fast in Eclipse TPS 11.0. With the help of ShowDVH, it greatly saves plan designing time and improves working efficiency of radiation therapy physicists.

[Development of a Software for Automatically Generated Contours in Eclipse TPS].

OBJECTIVE: The automatic generation of planning targets and auxiliary contours have achieved in Eclipse TPS 11.0. METHODS: The scripting language autohotkey was used to develop a software for automatically generated contours in Eclipse TPS. This software is named Contour Auto Margin (CAM), which is composed of operational functions of contours, script generated visualization and script file operations. RESULTS Ten cases in different cancers have separately selected, in Eclipse TPS 11.0 scripts generated by the software could not only automatically generate contours but also do contour post-processing. For different cancers, there was no difference between automatically generated contours and manually created contours. CONCLUSION: The CAM is a user-friendly and powerful software, and can automatically generated contours fast in Eclipse TPS 11.0. With the help of CAM, it greatly save plan preparation time and improve working efficiency of radiation therapy physicists.

Peptidoglycans promotes human leukemic THP-1 cell apoptosis and differentiation.

The innate immune system coordinates the inflammatory response to pathogens. To do so, its cells must discriminate self from non-self utilizing receptors that identify molecules synthesized exclusively by microbes. Toll- like receptors have a crucial role in the detection of microbial infection in mammals and insects. In mammals, they have evolved to recognize conserved products unique to microbial metabolism. These include lipopolysaccharide (LPS), lipotechoic acids, and peptidoglycans (PGN). We show here that TLRs, including TLR2, are expressed on the THP-1 human leukemia cell line. Activation of TLR2 signaling in THP-1 by PGN induces the synthesis of various soluble factors and proteins including interleukin-1ß, interleukin-8 and TNF-α and apoptosis of THP-1 with PGN dose and time dependence. Moreover , in this study we show that PGN induces apoptosis of THP-1 cells in a TNF-α-dependent manner. These findings indicate that TLR2 signaling results in a cascade leading to tumor apoptosis and differentiation, which may suggest new clinical prospects using TLR2 agonists as cytotoxic agents in certain cancers.