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.

MOTIVATION: Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells. RESULTS: We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies. AVAILABILITY AND IMPLEMENTATION: Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Transcription factors (TFs) and microRNAs (miRNAs) are two kinds of important regulators for transcriptional and post-transcriptional regulations. Understanding cross-talks between the two regulators and their targets is critical to reveal complex molecular regulatory mechanisms. Here, we developed FFLtool, a web server for detecting potential feed forward loop (FFL) of TF-miRNA-target regulation in human. In FFLtool, we integrated comprehensive regulations of TF-target and miRNA-target, and developed two functional modules: (i) The 'FFL Analysis' module can detect potential FFLs and internal regulatory networks in a user-defined gene set. FFLtool also provides three levels of evidence to illustrate the reliability for each FFL and enrichment functions for co-target genes of the same TF and miRNA; (ii) The 'Browse FFLs' module displays FFLs comprised of differentially or specifically expressed TFs and miRNAs and their target genes in cancers. FFLtool is a valuable resource for investigating gene expression regulation and mechanism study in biological processes and diseases. AVAILABILITY AND IMPLEMENTATION: FFLtool is available on http://bioinfo.life.hust.edu.cn/FFLtool/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Human specifically expressed genes (SEGs) usually serve as potential biomarkers for disease diagnosis and treatment. However, the regulation underlying their specific expression remains to be revealed. In this study, we constructed SEG regulation database (SEGreg; available at http://bioinfo.life.hust.edu.cn/SEGreg) for showing SEGs and their transcription factors (TFs) and microRNA (miRNA) regulations under different physiological conditions, which include normal tissue, cancer tissue and cell line. In total, SEGreg collected 6387, 1451, 4506 and 5320 SEGs from expression profiles of 34 cancer types and 55 tissues of The Cancer Genome Atlas, Cancer Cell Line Encyclopedia, Human Body Map and Genotype-Tissue Expression databases/projects, respectively. The cancer or tissue corresponding expressed miRNAs and TFs were identified from miRNA and gene expression profiles, and their targets were collected from several public resources. Then the regulatory networks of all SEGs were constructed and integrated into SEGreg. Through a user-friendly interface, users can browse and search SEGreg by gene name, data source, tissue, cancer type and regulators. In summary, SEGreg is a specialized resource to explore SEGs and their regulations, which provides clues to reveal the mechanisms of carcinogenesis and biological processes.