HOW TO DEPOSIT FROM COINBASE TO GATE IOIS GATEIO EXCHANGE SAFEGATEIO FON ŞIFRESI KALDIRMA

Introduction

scICB is a pan-cancer scRNA-seq database specifically designed for studying immune checkpoint blockade (ICB) therapy in human tumors, featuring over 3 million high-quality single-cell transcriptomes across 13 cancer types. In addition to standard analyses such as dimensionality reduction, clustering, and consensus cell type annotation (Browse Module), a key feature of scICB is its detailed clinical information for each sample, including patient ID, tissue origin (tumor, PBMC, adjacent normal tissue, and tumor-draining lymph nodes), sampling timepoints relative to immunotherapy (Pre and Post ICB treatment), drug types, and efficacy assessments based on radiographic or pathological criteria (responders or non-responders). By integrating this carefully curated clinical data, users can easily filter by cancer type, project, cell type subgroup, and tissue type, then perform differential gene expression and functional enrichment analyses based on treatment timepoints (Pre vs. Post Module) or therapeutic responses (R vs. NR Module) to identify potential biomarkers predictive of ICB efficacy. This offers a comprehensive understanding of the molecular mechanisms underlying the heterogeneity of immunotherapy responses across different cancers, with significant implications for future clinical applications. Additionally, users can upload custom gene sets to analyze specific changes before and after ICB treatment, or between R and NR groups, enhancing the database’s flexibility and utility.

Features

Extensive single-cell transcriptome datasets : scICB features a comprehensive collection of over 3 million high-quality single-cell transcriptomes from various cancer types across immune cells, stroma cells and epithelial derived cells.

Detailed clinical annotations for treatment efficacy: Each dataset has detailed clinical annotations including cancer type, patient ID, cell type, tissue type, sampling timepoints (pre or post ICB treatment) and ICB efficacy assessments: Responders (R) or Nonresponders (NR).

Advanced cell analysis tools available: Users can access routine analysis results like dimensionality reduction, clustering, heatmap and cell type annotation to understand the cellular landscape of ICB related datasets.

Subgroup selection for differential gene analysis based on multiple aspects described above: The database allows for users to select the subgroups they are interested to facilitate refined differential gene expression analysis and functional enrichment analysis.

Custom gene sets analysis: Through uploading custom gene sets, users can identify the relative changes of their own gene sets that are potentially predictive biomarkers for immunotherapy efficacy.

Comprehensive insight into immunotherapy responses: The database contributes to a comprehensive understanding of the molecular mechanisms underlying immunotherapy responses across various cancer types, with implications for future clinical applications.