The availability of cancer genomic data makes it possible to analyze genes related to cancer. Cancer is usually the result of a set of genes and the signal of a single gene could be covered by background noise. Here, we present a web server named Gene Set Cancer Analysis (GSCALite) to analyze a set of genes in cancers with the following functional modules. (i) Differential expression in tumor versus normal, and the survival analysis; (ii) Genomic variations and their survival analysis; (iii) Gene expression associated cancer pathway activity; (iv) miRNA regulatory network for genes; (v) Drug sensitivity for genes; (vi) Normal tissue expression and eQTL for genes. GSCALite is a user-friendly web server for dynamic analysis and visualization of gene set in cancer and drug sensitivity correlation, which will be of broad utilities to cancer researchers. GSCALite is available on http://bioinfo.life.hust.edu.cn/web/GSCALite/. Supplementary data are available at Bioinformatics online.

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23,315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available online.

Diabetes is generally regarded as a metabolic disorder disease caused by various reasons, including pancreas islet injury and lipid metabolism disorders. The aqueous extract of Cyclocarya paliurus leaves (CPAE) was reported to be anti-diabetic. However, the possible molecular mechanisms have not been investigated. To elucidate the anti-diabetic effects of CPAE and the underlying potential mechanisms, we performed transcriptome profiling (RNA-Seq and miRNA-Seq) on the pancreas and liver from non-diabetic, diabetic and diabetic-CPAE rats. Our results demonstrated the CPAE could reduce excessive oxidative stress and inflammation in the pancreas, and maintain the balance of glucose and lipid metabolism in the liver. Transcriptome profiling and regulatory network analysis indicated that CPAE may ameliorate diabetes through improving β-cell survival and strengthening insulin secretion in the pancreas. Meanwhile, CPAE could improve impaired lipid metabolism and reduce excessive oxidative damage in the liver probably through miR-200/375-Aldh1b1/Hps5-Hes1 co-regulatory network. Taken together, our biochemical experiments combined with transcriptome profiling showed that the effects of CPAE on anti-diabetes may work through protecting pancreatic β-cell, improving dyslipidaemia and lipid metabolism disorders.

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy. Aberrant expressed genes contribute to the development and progression of T-ALL. However, the regulation underlying their aberrant expression remains elusive. Dysregulated expression of transcription factors and miRNAs played important regulatory roles in the pathogenesis of T-ALL. In this study, we analyzed the alteration of transcriptome profiling and regulatory networks between T-ALL sample and normal T cell samples at transcriptional and post-transcriptional levels. Our results demonstrated that genes related to cell cycle and cell proliferation processes were significantly upregulated in T-ALL comparing to normal samples. Meanwhile, regulatory network analyses revealed that FOXM1, MYB, SOX4 and miR-21/19b as core regulators played vital roles in the development of T-ALL. FOXM1-miR-21-5p-CDC25A and MYB/SOX4-miR-19b-3p-RBBP8 were identified as important feed-forward loops involved in the oncogenesis of T-ALL. Drug-specific analyses showed that GSK-J4 may be an effective drug, and CDC25A/CAPN2/MCM2 could serve as potential therapeutic targets for T-ALL. This study may provide novel insights for the regulatory mechanisms underlying the development of T-ALL and potential therapeutic targets.

T-cell receptors (TCRs) recognizing antigens play vital roles in T-cell immunology. Surveying TCR repertoires by characterizing complementarity-determining region 3 (CDR3) can provide valuable insights into the immune community underlying pathologic conditions, which will benefit neoantigen discovery and cancer immunotherapy. Here we present a novel tool named CATT, which can apply on TCR sequencing (TCR-Seq), RNA-Seq, and single-cell TCR(RNA)-Seq data to characterize CDR3 repertoires. CATT integrated maximum-network-flow based micro-assembly algorithm, data-driven error correction model, and Bayes classification algorithm, to self-adaptively and ultra-sensitively characterize CDR3 repertoires with high accuracy. Benchmark results of datasets from in silico and real conditions demonstrated that CATT showed superior recall and precision compared with other prevalent tools, especially for datasets with short read length and small data size. By applying CATT on a TCR-Seq dataset from aplastic anemia patients, we found the skewing of TCR repertoire was due to the oligoclonal expansion of effector memory T-cells. CATT will be a powerful tool for researchers conducting TCR and immune repertoire studies. CATT is freely available at http://bioinfo.life.hust.edu.cn/CATT .