The distribution and abundance of immune cells, particularly T‐cell subsets, play pivotal roles in cancer immunology and therapy. T cells have many subsets with specific function and current methods are limited in estimating them, thus, a method for predicting comprehensive T‐cell subsets is urgently needed in cancer immunology research. Here, Immune Cell Abundance Identifier (ImmuCellAI), a gene set signature‐based method, is introduced for precisely estimating the abundance of 24 immune cell types including 18 T‐cell subsets, from gene expression data. Performance evaluation on both the sequencing data with flow cytometry results and public expression data indicate that ImmuCellAI can estimate the abundance of immune cells with superior accuracy to other methods especially on many T‐cell subsets. Application of ImmuCellAI to immunotherapy datasets reveals that the abundance of dendritic cells, cytotoxic T, and gamma delta T cells is significantly higher both in comparisons of on‐treatment versus pre‐treatment and responders versus non‐responders. Meanwhile, an ImmuCellAI result‐based model is built for predicting the immunotherapy response with high accuracy (area under curve 0.80–0.91). These results demonstrate the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction.

Transcription factors (TFs) as key regulators play crucial roles in biological processes. The identification of TF–target regulatory relationships is a key step for revealing functions of TFs and their regulations on gene expression. The accumulated data of chromatin immunoprecipitation sequencing (ChIP-seq) provide great opportunities to discover the TF–target regulations across different conditions. In this study, we constructed a database named hTFtarget, which integrated huge human TF target resources (7190 ChIP-seq samples of 659 TFs and high-confidence binding sites of 699 TFs) and epigenetic modification information to predict accurate TF–target regulations. hTFtarget offers the following functions for users to explore TF–target regulations: (1) browse or search general targets of a query TF across datasets; (2) browse TF–target regulations for a query TF in a specific dataset or tissue; (3) search potential TFs for a given target gene or non-coding RNA; (4) investigate co-association between TFs in cell lines; (5) explore potential co-regulations for given target genes or TFs; (6) predict candidate TF binding sites on given DNA sequences; (7) visualize ChIP-seq peaks for different TFs and conditions in a genome browser. hTFtarget provides a comprehensive, reliable and user-friendly resource for exploring human TF–target regulations, which will be very useful for a wide range of users in the TF and gene expression regulation community. hTFtarget is available at http://bioinfo.life.hust.edu.cn/hTFtarget.

The distribution and abundance of immune cells, particularly T-cell subsets, play pivotal roles in cancer immunology and therapy. There are many T-cell subsets with specific function, however current methods are limited in estimating them, thus, a method for predicting comprehensive T-cell subsets is urgently needed in cancer immunology research. Here we introduce Immune Cell Abundance Identifier (ImmuCellAI), a novel gene set signature-based method, for precisely estimating the abundance of 24 immune cell types including 18 T-cell subsets, from gene expression data. Performance evaluation on both our sequencing data with flow cytometry results and public expression data indicated that ImmuCellAI can estimate immune cells with superior accuracy than other methods especially on many T-cell subsets. Application of ImmuCellAI to immunotherapy datasets revealed that the abundance of dendritic cells (DC), cytotoxic T, and gamma delta T cells was significantly higher both in comparisons of on-treatment vs. pre-treatment and responders vs. non-responders.Meanwhile, we built an ImmuCellAI result-based model for predicting the immunotherapy response with high accuracy (AUC 0.80~0.91). These results demonstrated the powerful and unique function of ImmuCellAI in tumor immune infiltration estimation and immunotherapy response prediction. The ImmuCellAI online server is freely available at http://bioinfo.life.hust.edu.cn/web/ImmuCellAI/.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

Extracellular vesicles (EVs) packing various molecules play vital roles in intercellular communication. Non-coding RNAs (ncRNAs) are important functional molecules and biomarkers in EVs. A comprehensive investigation of ncRNAs expression in EVs under different conditions is a fundamental step for functional discovery and application of EVs. Here, we curated 2030 small RNA-seq datasets for human EVs (1506 sEV and 524 lEV) in 24 conditions and over 40 diseases. We performed a unified reads dynamic assignment algorithm (RDAA) considering mismatch and multi-mapping reads to quantify the expression profiles of seven ncRNA types (miRNA, snoRNA, piRNA, snRNA, rRNA, tRNA and Y RNA). We constructed EVAtlas (http://bioinfo.life.hust.edu.cn/EVAtlas), a comprehensive database for ncRNA expression in EVs with four functional modules: (i) browse and compare the distribution of ncRNAs in EVs from 24 conditions and eight sources (plasma, serum, saliva, urine, sperm, breast milk, primary cell and cell line); (ii) prioritize candidate ncRNAs in condition related tissues based on their expression; (iii) explore the specifically expressed ncRNAs in EVs from 24 conditions; (iv) investigate ncRNA functions, related drugs, target genes and EVs isolation methods. EVAtlas contains the most comprehensive ncRNA expression in EVs and will be a key resource in this field.