Meierparsons4102

The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P < 0·05).

MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT.

The funding bodies that contributed to this study are listed in the Acknowledgements section.

The funding bodies that contributed to this study are listed in the Acknowledgements section.

COVID-19 has been associated with Interstitial Lung Disease features. The immune transcriptomic overlap between Idiopathic Pulmonary Fibrosis (IPF) and COVID-19 has not been investigated.

we analyzed blood transcript levels of 50 genes known to predict IPF mortality in three COVID-19 and two IPF cohorts. The Scoring Algorithm of Molecular Subphenotypes (SAMS) was applied to distinguish high versus low-risk profiles in all cohorts. SAMS cutoffs derived from the COVID-19 Discovery cohort were used to predict intensive care unit (ICU) status, need for mechanical ventilation, and in-hospital mortality in the COVID-19 Validation cohort. A COVID-19 Single-cell RNA-sequencing cohort was used to identify the cellular sources of the 50-gene risk profiles. The same COVID-19 SAMS cutoffs were used to predict mortality in the IPF cohorts.

50-gene risk profiles discriminated severe from mild COVID-19 in the Discovery cohort (P=0·015) and predicted ICU admission, need for mechanical ventilation, and in-hospital morta1-HL-130704 (AJ); The University of South Florida (USF) Academic Support Fund and the USF Foundation, Ubben Fibrosis Fund (JHM).

This work was supported in part by National Institute for Health Research Clinician Scientist Fellowship NIHR CS-2013-13-017 (TMM); Action for Pulmonary Fibrosis Mike Bray fellowship (PLM); The National Heart, Lung, and Blood Institute (NHLBI) through award K01-HL-130704 (AJ); The University of South Florida (USF) Academic Support Fund and the USF Foundation, Ubben Fibrosis Fund (JHM).

Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms.

We developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs).

We characterized and validated DAGM framework at multiple levels. Based on an input of germline rare coding mutations, we obtained the corresponding APSP spectrum to calculate the APSP risk score, which was capable of distinguish HER2-negative from HER2-positive cases. These findings were validated using breast cancer data from TCGA (AUC=0.7). DAGM revealed that HER2 signalling pathway was up-regulated in germline of HER2-negative patients, Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW and S2013010012048 to MY); Hefei National Laboratory for Physical Sciences at the Microscale (grant no. KF2020009 to GN); and RGC General Research Fund (grant no. 17114519 to YQS).

Due to the molecular mechanism complexity and heterogeneity of gastric cancer (GC), mechanistically interpretable biomarkers were required for predicting prognosis and discovering therapeutic targets for GC patients.

Based on a total of 824 GC-specific fitness genes from the Project Score database, LASSOCox regression was performed in TCGA-STAD cohort to construct a GC Prognostic (GCP) model which was then evaluated on 7 independent GC datasets. Targets prioritization was performed in GC organoids. ARGLU1 was selected to further explore the biological function and molecular mechanism. We evaluated the potential of ARGLU1 serving as a promising therapeutic target for GC using patients derived xenograft (PDX) model.

The 9-gene GCP model showed a statistically significant prognostic performance for GC patients in 7 validation cohorts. Perturbation of SSX4, DDX24, ARGLU1 and TTF2 inhibited GC organoids tumor growth. The results of tissue microarray indicated lower expression of ARGLU1 was correlated with advanced TNM stage and worse overall survival. Over-expression ARGLU1 significantly inhibited GC cells viability in vitro and in vivo. ARGLU1 could enhance the transcriptional level of mismatch repair genes including MLH3, MSH2, MSH3 and MSH6 by potentiating the recruitment of SP1 and YY1 on their promoters. Moreover, inducing ARGLU1 by LNP-formulated saRNA significantly inhibited tumor growth in PDX model.

Based on genome-wide functional screening data, we constructed a 9-gene GCP model with satisfactory predictive accuracy and mechanistic interpretability. Out of nine prognostic genes, ARGLU1 was verified to be a potential therapeutic target for GC.

National Natural Science Foundation of China.

National Natural Science Foundation of China.Diffuse midline glioma (DMG) is an incurable malignancy with the highest mortality rate among pediatric brain tumors. While radiotherapy and chemotherapy are the most common treatments, these modalities have limited promise. Due to their diffuse nature in critical areas of the brain, the prognosis of DMG remains dismal. read more DMGs are characterized by unique phenotypic heterogeneity and histological features. Mutations of H3K27M, TP53, and ACVR1 drive DMG tumorigenesis. Histological artifacts include pseudopalisading necrosis and vascular endothelial proliferation. Mouse models that recapitulate human DMG have been used to study key driver mutations and the tumor microenvironment. DMG consists of a largely immunologically cold tumor microenvironment that lacks immune cell infiltration, immunosuppressive factors, and immune surveillance. While tumor-associated macrophages are the most abundant immune cell population, there is reduced T lymphocyte infiltration. Immunotherapies can stimulate the immune system to find, attack, and eliminate cancer cells.