Benign prostatic hyperplasia (BPH) is an age-related condition in men with a poorly defined etiology. Chronic inflammation is increasingly recognized as a key contributor to BPH progression; however, the underlying mechanisms remain incompletely understood. This study aimed to elucidate the role of a TNF-α-induced inflammatory microenvironment in regulating BPH progression. We demonstrated that TNF-α levels were significantly elevated in patients with BPH and positively correlated with key clinical characteristics. In vitro, TNF-α promoted the proliferation of prostatic cells. Mechanistically, TNF-α induced the overexpression of SOX4, which subsequently activated the TGF-β/Smad2/3 signaling axis, thereby enhancing cellular proliferation, promoting epithelial-mesenchymal transition (EMT), and exacerbating fibrosis. Importantly, metformin (Met) treatment reduced the expression levels of relevant inflammatory cytokines in the serum of BPH rats. Further analysis confirmed that Met inhibited the TGF-β/Smad2/3 signaling pathway by downregulating the expression of SOX4, thus suppressing cell proliferation, reversing EMT, alleviating fibrosis, and ultimately exerting anti-BPH effects. Collectively, our findings suggest that TNF-α promotes BPH progression via activation of the SOX4/TGF-β/Smad2/3 axis, while Met exerts therapeutic effects by targeting this pathway. These results highlight SOX4 as a potential therapeutic target for BPH and support the clinical potential of Met in BPH management.

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.

Pseudomonas aeruginosa (P. aeruginosa) is a multidrug-resistant opportunistic pathogen that causes both acute and chronic infections and is known for its ability to form biofilms. In the current study, we applied a hypothesis-generating framework primarily based on integrating four different datasets and applying batch correction. Weighted Gene Co-Expression Network Analysis (WGCNA) was performed in parallel with differential expression analysis using limma. Therefore, we aimed to identify potential biofilm-associated gene candidates. Significant candidate genes were subjected to functional analysis and gene ontology, followed by the construction of a protein–protein interaction network using STRING. The Pseudomonas Genome Database was used to highlight the candidate genes. A total of 271, 687, 533, and 277 significantly up-regulated differentially expressed genes (DEGs), as well as 306, 985, 472, and 312 significantly down-regulated DEGs, resulted from the exploratory analysis. Through WGCNA/limma integration, 223 common significantly up-regulated/positively correlated gene candidates were identified. Functional analysis results showed significant enrichment in virulence-related pathways, such as biofilm formation (PA0083, PA0084, hcp1, hcpC, pilH, pilI, pilJ, vfr, pqsA, pqsB, pqsC, pqsE, PA1657, and PA1658). In addition, other virulence-related pathways, such as quorum sensing, phenazine biosynthesis, the bacterial secretion system, and secondary metabolite biosynthesis, were enriched. In conclusion, our hypothesis-generating integrative analysis identifies candidate genes and potential pathways associated with biofilm formation, virulence, and other processes in P. aeruginosa. In light of this, we point out that all candidate genes presented in this study remain hypothesis-generating. Further validation is recommended, including large-scale in silico analyses and in vitro experimental studies.