N6-Methyladenosine Regulator-Mediated Methylation Modification Patterns with Distinct Prognosis, Oxidative Stress, and Tumor Microenvironment in Renal Cell Carcinoma.

OBJECTIVE: Emerging evidence suggests the biological implications of N6-methyladenosine (m6A) in carcinogenesis. Herein, we systematically analyzed the role of m6A modification in renal cell carcinoma (RCC) progression. METHODS: Based on 23 m6A regulators, unsupervised clustering analyses were conducted to determine m6A modification subtypes across 893 RCC specimens in the Cancer Genome Atlas (TCGA) cohort. By performing principal component analysis (PCA) analysis, m6A scoring system was developed for evaluating m6A modification patterns of individual RCC patients. The activity of signaling pathways was assessed by gene-set variation analysis (GSVA) algorithm. The single-sample gene set enrichment analysis (ssGSEA) algorithm was applied for quantifying the infiltration levels of immune cells and the activity of cancer immunity cycle. Drug responses were estimated by genomics of drug sensitivity in cancer (GDSC), the Cancer Therapeutics Response Portal (CTRP) and Preservice Research Institute for Science and Mathematics (PRISM) database. RESULTS: Five m6A modification subtypes were characterized by different survival outcomes, oxidative stress, cancer stemness, infiltrations of immune cells, activity of cancer immunity cycle, programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) expression and microsatellite instability (MSI) levels. According to m6A score, RCC patients were categorized into high and low m6A score groups. Patients with high m6A score displayed a prominent survival advantage, and the prognostic value of m6A score was confirmed in two anti-PD-1/PD-L1 immunotherapy cohorts. m6A score was significantly linked to oxidative stress-related genes, and high m6A score indicated the higher sensitivity to axitinib, pazopanib and sorafenib and the lower sensitivity to sunitinib. CONCLUSION: This study analyzed the extensive regulatory mechanisms of m6A modification on oxidative stress, the tumor microenvironment, and immunity. Quantifying m6A scores may enhance immunotherapeutic effects and assist in developing more effective agents.

Efficient and scalable prediction of stochastic reaction-diffusion processes using graph neural networks.

The dynamics of locally interacting particles that are distributed in space give rise to a multitude of complex behaviours. However the simulation of reaction-diffusion processes which model such systems is highly computationally expensive, the cost increasing rapidly with the size of space. Here, we devise a graph neural network based approach that uses cheap Monte Carlo simulations of reaction-diffusion processes in a small space to cast predictions of the dynamics of the same processes in a much larger and complex space, including spaces modelled by networks with heterogeneous topology. By applying the method to two biological examples, we show that it leads to accurate results in a small fraction of the computation time of standard stochastic simulation methods. The scalability and accuracy of the method suggest it is a promising approach for studying reaction-diffusion processes in complex spatial domains such as those modelling biochemical reactions, population evolution and epidemic spreading.