The Animal Transcription Factor DataBase (AnimalTFDB) is a resource aimed to provide the most comprehensive and accurate information for animal transcription factors (TFs) and cofactors. The AnimalTFDB has been maintained and updated for seven years and we will continue to improve it. Recently, we updated the AnimalTFDB to version 3.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB/) with more data and functions to improve it. AnimalTFDB contains 125,135 TF genes and 80,060 transcription cofactor genes from 97 animal genomes. Besides the expansion in data quantity, some new features and functions have been added. These new features are: (i) more accurate TF family assignment rules; (ii) classification of transcription cofactors; (iii) TF binding sites information; (iv) the GWAS phenotype related information of human TFs; (v) TF expressions in 22 animal species; (vi) a TF binding site prediction tool to identify potential binding TFs for nucleotide sequences; (vii) a separate human TF database web interface (HumanTFDB) was designed for better utilizing the human TFs. The new version of AnimalTFDB provides a comprehensive annotation and classification of TFs and cofactors, and will be a useful resource for studies of TF and transcription regulation.

Hepatocellular carcinoma (HCC) is a worldwide malignance, and the underlying mechanisms of this disease are not fully elucidated. In this study, the existence and function of achaete-scute homolog-1 (ASH1)-miR-375-YWHAZ signaling axis in HCC were determined. Our experiments and the Cancer Genome Atlas (TCGA) sequencing data analyses showed that ASH1 and miR-375 were significantly downregulated, whereas YWHAZ was significantly upregulated in HCC. Furthermore, we found that ASH1 positively regulates miR-375, and miR-375 directly downregulates its target YWHAZ. Gain- and loss-of-function study demonstrated ASH1 and miR-375 function as tumor suppressors, whereas YWHAZ acts as an oncogene in HCC. Animal experiment indicated that YWHAZ small interfering RNAs (siRNAs) (si-YWHAZ) delivered by nanoliposomes could suppress the growth of hepatoma xenografts and was well tolerant by nude mice. Further studies revealed that YWHAZ was involved in several protein networks, such as cell autophagy, epithelial-mesenchymal transition (EMT), apoptosis, cell cycle, invasion, and migration. In addition, the patient group with ASH1-high-expression-miR-375-high-expression-YWHAZ-low-expression was correlated with a better clinical prognosis compared with the opposite expression group. In conclusion, we proved the existence of ASH1-miR-375-YWHAZ signaling axis and interpreted its important role in driving HCC tumor progression.

Transcription factors (TFs) are proteins that interact with specific DNA sequences to regulate gene expression and play crucial roles in all kinds of biological processes. To keep up with new data and provide a more comprehensive resource for TF research, we updated the Animal Transcription Factor Database (AnimalTFDB) to version 4.0 (http://bioinfo.life.hust.edu.cn/AnimalTFDB4/) with up-to-date data and functions. We refined the TF family rules and prediction pipeline to predict TFs in genome-wide protein sequences from Ensembl. As a result, we predicted 274 633 TF genes and 150 726 transcription cofactor genes in AnimalTFDB 4.0 in 183 animal genomes, which are 86 more species than AnimalTFDB 3.0. Besides double data volume, we also added the following new annotations and functions to the database: (i) variations (including mutations) on TF genes in various human cancers and other diseases; (ii) predicted post-translational modification sites (including phosphorylation, acetylation, methylation and ubiquitination sites) on TFs in 8 species; (iii) TF regulation in autophagy; (iv) comprehensive TF expression annotation for 38 species; (v) exact and batch search functions allow users to search AnimalTFDB flexibly. AnimalTFDB 4.0 is a useful resource for studying TF and transcription regulation, which contains comprehensive annotation and classification of TFs and transcription cofactors.

Immune checkpoint blockade (ICB) therapy provides remarkable clinical benefits for multiple cancer types. Much work is currently being conducted to investigate the mechanisms of ICB therapy at the transcriptional level. Integrating the data produced by these studies will help us give more insight into the transcriptomic features of ICB therapy. We collected the transcriptome and clinical data of ICB-treated patient samples from the GEO, ArrayExpress, TCGA, and dbGaP databases. Based on the clinical information, all samples are initially classified into response/non-response or pre-treatment/on-treatment groups. Differential expression, pathway enrichment, and immune cell infiltration analyses are performed between the samples from different groups. We also introduce the Response Score (RS) calculated by integrating the variability degree and the frequency of the dysregulated genes in the responders to evaluate the impact of gene expression on the response. Finally, all the abovementioned contents are integrated into the ICBatlas database. ICBatlas provides the transcriptome features of ICB therapy through the analysis of 1515 ICB-treated samples from 25 studies across nine cancer types. The data in ICBatlas include clinical outcomes, treatment-related genes, biological pathways, and immune cell infiltration. Users can investigate the abovementioned transcriptome features in the Response (R vs. NR) or Treatment (Pre vs. On) modules at the dataset, cancer type, or immune checkpoint level and compare the degree of gene impact on the response in the RS module. ICBatlas is the first database to show the transcriptome features on ICB therapy in human cancers and freely available at http://bioinfo.life.hust.edu.cn/ICBatlas/.