Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and immunotherapy response in bladder cancer.

Background: T cells have been proven to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in bladder cancer (BLCA).

Methods: Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the gene expression omnibus (GEO) database to screen T-cell marker genes. Bulk RNA-sequencing data and clinical information from BLCA patients were downloaded from the cancer genome atlas (TCGA) database to develop a prognosis signature.

Identification of exosomes-related lncRNAs in clear cell renal cell carcinoma based on Bayesian spike-and-slab lasso approach.

Exosomes-related long non-coding RNAs (lncRNAs) have been reported to play significant roles in clear cell renal cell carcinoma (ccRCC). However, there is little known about the relationship between exosomes-related lncRNAs and ccRCC. This study aimed to select optimal prognostic model based on exosomes-related lncRNAs to provide a methodological reference for high-dimensional data.

Prognostic Model for Clear-cell Renal Cell Carcinoma Based on Natural Killer Cell-related Genes.

Background: Natural killer (NK) cells are a key factor affecting progression and immune surveillance of clear-cell renal cell carcinoma (ccRCC). This study sought to construct a natural killer cell-related prognostic signature (NKRPS) to predict the outcome of ccRCC patients and to furnish guidance for finding appropriate treatment strategies.

Methods: From the TCGA and ArrayExpress databases, transcriptomic profiles and relevant clinical information of ccRCC patients were downloaded for the TCGA cohort (n = 515) and the E-MTAB-1980 cohort (n = 101).

Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures.

Background: Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.

Methods: The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs).

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on T-cell marker genes to predict prognosis and therapeutic response in lung squamous cell carcinoma.

Cancer immunotherapy is an increasingly successful strategy for treating patients with advanced or conventionally drug-resistant cancers. T cells have been proved to play important roles in anti-tumor and tumor microenvironment shaping, while these roles have not been explained in lung squamous cell carcinoma (LUSC). In this study, we first performed a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from the gene expression omnibus (GEO) database to identify 72 T-cell marker genes.

Prognostic characteristics of immune subtypes associated with acute myeloid leukemia and their identification in cell subsets based on single-cell sequencing analysis.

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration.

Identifying pleiotropic genes for major psychiatric disorders with GWAS summary statistics using multivariate adaptive association tests.

Background: Genome wide association studies (GWAS) have discovered a few of single nucleotide polymorphisms (SNPs) related to major psychiatric disorders. However, it is not completely clear which genes play a pleiotropic role in multiple disorders. The study aimed to identify the pleiotropic genes across five psychiatric disorders using multivariate adaptive association tests.

Identification of a Prognostic Model Based on Immune Cell Signatures in Clear Cell Renal Cell Carcinoma.

Background: Accumulating evidence substantiated that the immune cells were intricately intertwined with the prognosis and therapy of clear cell renal cell carcinoma (ccRCC). We aimed to construct an immune cell signatures (ICS) score model to predict the prognosis of ccRCC patients and furnish guidance for finding appropriate treatment strategies.

Methods: Based on The Cancer Genome Atlas (TCGA) database, the normalized enrichment score (NES) of 184 ICSf was calculated using single-sample gene set enrichment analysis (ssGSEA).

Comprehensive Analysis of N6-Methyladenosine-Related Long Noncoding RNA Prognosis of Acute Myeloid Leukemia and Immune Cell Infiltration.

N6-Methyladenosine-related long noncoding RNAs play an essential role in many cancers' development. However, the relationship between m6A-related lncRNAs and acute myelogenous leukemia (AML) prognosis remains unclear. We systematically analyzed the association of m6A-related lncRNAs with the prognosis and tumor immune microenvironment (TME) features using the therapeutically applicable research to generate effective treatment (TARGET) database.

Rational Design of Graphene-Supported Single-Atom Catalysts for Electroreduction of Nitrogen.

Critically, the central metal atoms along with their coordination environment play a significant role in the catalytic performance of single-atom catalysts (SACs). Herein, 12 single Fe, Mo, and Ru atoms supported on defective graphene are theoretically deigned for investigation of their structural and electronic properties and catalytic nitrogen reduction reaction (NRR) performance using first-principles calculations. Our results reveal that graphene with vacancies can be an ideal anchoring site for stabilizing isolated metal atoms owing to the strong metal-support interaction, forming stable TMC or TMN active centers ( = 3 or 4).