Helicobacter pylori infection is one of the most common gastric pathogens; however, the molecular mechanisms driving its progression to gastric cancer remain poorly understood. This study aimed to identify the key transcriptomic drivers and therapeutic targets of H. pylori-associated gastric cancer through an integrative transcriptomic analysis. This analysis integrates microarray and RNA-seq datasets to identify significant differentially expressed genes (DEGs) involved in the progression of H. pylori-associated gastric cancer. In addition to independent analyses, data were integrated using ComBat to detect consistent expression patterns of hub genes. This approach revealed distinct clustering patterns and stage-specific transcriptional changes in common DEGs across disease progression, including H. pylori infection, gastritis, atrophy, and gastric cancer. Genes such as TPX2, MKI67, EXO1, and CTHRC1 exhibited progressive upregulation from infection to cancer, highlighting involvement in cell cycle regulation, DNA repair, and extracellular matrix remodeling. These findings provide insights into molecular shifts linking inflammation-driven infection to malignancy. Furthermore, network analysis identified hub genes, including CXCL1, CCL20, IL12B, and STAT4, which are enriched in immune pathways such as chemotaxis, leukocyte migration, and cytokine signaling. This emphasizes their role in immune dysregulation and tumor development. Expression profiling demonstrated the upregulation of hub genes in gastric cancer and stage-specific changes correlating with disease progression. Finally, drug-gene interaction analysis identified therapeutic opportunities, with hub genes interacting with approved drugs like abatacept and zoledronic acid, as well as developmental drugs such as adjuvant and relapladib. These findings highlight the key role of these hub genes as biomarkers and therapeutic targets, providing a foundation for advancing precision medicine in H. pylori-associated gastric cancer. Overall, this study paves the way for advancing precision medicine in H. pylori-associated gastric cancer by providing insights into the development of early detection biomarkers, risk stratification, and targeted therapies. This supports the clinical translation of precision medicine strategies in H. pylori-associated gastric cancer.

SUMMARY: TFmiR2 is a freely available web server for constructing and analyzing integrated transcription factor (TF) and microRNA (miRNA) co-regulatory networks for human and mouse. TFmiR2 generates tissue- and biological process-specific networks for the set of deregulated genes and miRNAs provided by the user. Furthermore, the service can now identify key driver genes and miRNAs in the constructed networks by utilizing the graph theoretical concept of a minimum connected dominating set. These putative key players as well as the newly implemented four-node TF-miRNA motifs yield novel insights that may assist in developing new therapeutic approaches. AVAILABILITY AND IMPLEMENTATION: The TFmiR2 web server is available at http://service.bioinformatik.uni-saarland.de/tfmir2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: Mutations in genomic key elements can influence gene expression and function in various ways, and hence greatly contribute to the phenotype. We developed MutaNET to score the impact of individual mutations on gene regulation and function of a given genome. MutaNET performs statistical analyses of mutations in different genomic regions. The tool also incorporates the mutations in a provided gene regulatory network to estimate their global impact. The integration of a next-generation sequencing pipeline enables calling mutations prior to the analyses. As application example, we used MutaNET to analyze the impact of mutations in antibiotic resistance (AR) genes and their potential effect on AR of bacterial strains. Availability and implementation: MutaNET is freely available at https://sourceforge.net/projects/mutanet/. It is implemented in Python and supported on Mac OS X, Linux and MS Windows. Step-by-step instructions are available at http://service.bioinformatik.uni-saarland.de/mutanet/. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.