Biology subjects

Han, G.

Publications and source records attributed to Han, G..

3 recordsLinked to original sources

bioRxiv2018

Increased H. pylori stool shedding and EPIYA-D cagA alleles are associated with gastric cancer in an East Asian hospital

BackgroundHelicobacter pylori infection induces chronic inflammation and tissue damage in the stomach, increasing risk for gastric cancer. Paradoxically, these tissue alterations may promote loss of H. pylori infection during cancer progression. H. pyloris role in cancer progression beyond initiation is unclear. Geographic variation in gastric cancer risk has been attributed to variation in carriage and type of the H. pylori oncogene cagA.\n\nMethodsTo investigate possible differences in H. pylori load in the stomach and shedding in stool, H. pylori load and cagA genotype were assessed using droplet digital PCR assays on gastric mucosa and stool samples from 49 urea breath test-positive individuals, including 25 gastric cancer and 24 non-cancer subjects at Henan Cancer Hospital, Henan, China.\n\nResultsQuantitation of H. pylori DNA indicated similar gastric loads among cancer and non-cancer cases, but the gastric cancer group had a median H. pylori load in the stool that was six times higher than that of the non-cancer subjects. While the cagA gene was uniformly present among study subjects, only 70% had the East Asian cagA allele, which was significantly associated with gastric cancer (Fishers Exact Test, p = 0.03).\n\nConclusionH. pylori persists in a subset of gastric cancer cases and thus may contribute to cancer progression. In this East Asian population with a high prevalence of the cagA gene, the East Asian allele could still provide a marker for gastric cancer risk.\n\nImpactThis study contributes to our understanding of H. pylori dynamics in the context of pathological changes.

microbiology↗

bioRxiv2017

Prometheus: omics portals for interkingdom comparative genomic analyses

Functional analyses of genes are crucial for unveiling biological responses, for genetic engineering, and for developing new medicines. However, functional analyses have largely been restricted to model organisms, representing a major hurdle for functional studies and industrial applications. To resolve this, comparative genome analyses can be used to provide clues to gene functions as well as their evolutionary history. To this end, we present Prometheus (http://prometheus.kobic.re.kr),web-based omics portal that contains more than 17,215 sequences from prokaryotic and eukaryotic genomes. This portal supports interkingdom comparative analyses via a domain architecture-based gene identification system, Gene Search, and users can easily and rapidly identify single or entire gene sets in specific pathways. Bioinformatics tools for further analyses are provided in Prometheus or through BioExpress, a cloud-based bioinformatics analysis platform. Prometheus suggests a new paradigm for comparative analyses with large amounts of genomic information.

bioinformatics↗

bioRxiv2017

scRNASeqDB: a database for gene expression profiling in human single cell by RNA-seq

Summary: Single-cell RNA sequencing (scRNA-Seq) is quickly becoming a powerful tool for high-throughput transcriptomic analysis of cell states and dynamics. Both the number and quality of scRNA-Seq datasets have dramatically increased recently. So far, there is no database that comprehensively collects and curates scRNA-Seq data in humans. Here, we present scRNASeqDB, a database that includes almost all the currently available human single cell transcriptome datasets (n= 36) covering 71 human cell lines or types and 8910 samples. Our online web interface allows user to query and visualize expression profiles of the gene(s) of interest, search for genes that are expressed in different cell types or groups, or retrieve differentially expressed genes between cell types or groups. The scRNASeqDB is a valuable resource for single cell transcriptional studies.\n\nAvailability: The database is available at https://bioinfo.uth.edu/scrnaseqdb/.\n\nContact: zhongming.zhao@uth.tmc.edu

bioinformatics↗