DataSheet_1_Identification of colon cancer subtypes based on multi-omics data—construction of methylation markers for immunotherapy.zip

BackgroundBeing the most widely used biomarker for immunotherapy, the microsatellite status has limitations in identifying all patients who benefit in clinical practice. It is essential to identify additional biomarkers to guide immunotherapy. Aberrant DNA methylation is consistently associated with changes in the anti-tumor immune response, which can promote tumor progression. This study aims to explore immunotherapy biomarkers for colon cancers from the perspective of DNA methylation.MethodsThe related data (RNA sequencing data and DNA methylation data) were obtained from The Cancer Genome Atlas (TCGA) and UCSC XENA database. Methylation-driven genes (MDGs) were identified through the Pearson correlation analysis. Unsupervised consensus clustering was conducted using these MDGs to identify distinct clusters of colon cancers. Subsequently, we evaluated the immune status and predicted the efficacy of immunotherapy by tumor immune dysfunction and exclusion (Tide) score. Finally, The Quantitative Differentially Methylated Regions (QDMR) software was used to identify the specific DNA methylation markers within particular clusters.ResultsA total of 282 MDGs were identified by integrating the DNA methylation and RNA-seq data. Consensus clustering using the K-means algorithm revealed that the optimal number of clusters was 4. It was revealed that the composition of the tumor immune microenvironment (TIME) in Cluster 1 was significantly different from others, and it exhibited a higher level of tumor mutation burdens (TMB) and stronger anti-tumor immune activity. Furthermore, we identified three specific hypermethylation genes that defined Cluster 1 (PCDH20, APCDD1, COCH). Receiver operating characteristic (ROC) curves demonstrated that these specific markers could effectively distinguish Cluster 1 from other clusters, with an AUC of 0.947 (95% CI 0.903-0.990). Finally, we selected clinical samples for immunohistochemical validation.ConclusionIn conclusion, through the analysis of DNA methylation, consensus clustering of colon cancer could effectively identify the cluster that benefit from immunotherapy along with specific methylation biomarkers.</p

Additional file 2 of Cross-talks between gut microbiota and tobacco smoking: a two-sample Mendelian randomization study

Additional file 2: Table S4. All results of causal links between gut microbiome and smoking phenotypes. Table S5. Instrumental variables used in our study for univariate MR and multivariable MR, respectively

Molten Salt Self-Template Synthesis Strategy of Oxygen-Rich Porous Carbon Cathodes for Zinc Ion Hybrid Capacitors

Porous carbon materials are widely used in capacitive energy storage devices because of their chemical stability, low cost, and controllable textures. Molten salt self-template methods are powerful and sustainable synthesis strategies for preparing porous carbons with tunable pore textures and surface chemistries. Herein, we propose a self-template synthesis strategy for preparing oxygen-rich porous carbons (ORC) by directly carbonizing potassium chloroacetate (ClCH2COOK) as the single carbon source. The potassium chloride salts generated in the carbonization play the roles of the template and etchant agent in the pore formation process. The as-prepared ORC samples feature abundant mesopores (average pore sizes of 1.95–2.19 nm and mesopore ratio of 36.4%), high specific surface areas (1410–1886 m2 g–1), and high oxygen doping levels (4.3–8.2 atom %). The zinc ion hybrid capacitors with an ORC cathode exhibited an ultrahigh capacitance of 308 F g–1 at 0.5 A g–1 and a high energy density of 136.5 Wh kg–1 at a power density of 570 W kg–1. Density functional theory demonstrates that oxygen-containing functional groups are conducive to the adsorption of Zn ions. Our work proposes a general synthesis methodology for the synthesis of oxygen-rich porous carbons for a variety of electrochemical energy storage devices