USP31 serves as a potential biomarker for predicting prognosis and immune responses for clear cell renal cell carcinoma via single-cell and bulk RNA-sequencing

USP31 Activates the Wnt/β-catenin Signaling Pathway and Promotes Gastric Cancer Cell Proliferation, Invasion and Migration

BACKGROUND

Gastric cancer (GC) has a poor prognosis because it is highly aggressive, yet there are currently few effective therapies available. Although protein ubiquitination has been shown to play a complex role in the development of gastric cancer, to date, no efficient ubiquitinating enzymes have been identified as treatment targets for GC.

METHODS

The TCGA database was used for bioinformatic investigation of ubiquitin-specific protease 31 (USP31) expression in GC, and experimental techniques, including Western blotting, qRT-PCR, and immunohistochemistry, were used to confirm the findings. We also analyzed the relationship between USP31 expression and clinical prognosis in patients with GC. We further investigated the effects of USP31 on the proliferation, invasion, migration, and glycolysis of GC cells in vitro and in vivo by using colony formation, CCK-8 assays, Transwell chamber assays, cell scratch assays, and cell-derived xenograft. Furthermore, we examined the molecular processes by which USP31 influences the biological development of GC.

RESULTS

Patients with high USP31 expression have a poor prognosis because USP31 is abundantly expressed in GC. Therefore, USP31 reduces the level of ubiquitination of the Wnt/β-catenin pathway by binding to β-catenin, thereby activating glycolysis, which ultimately promotes GC proliferation and aggressive metastasis.

CONCLUSION

USP31 inhibits ubiquitination of β-catenin by binding to it, stimulates the Wnt/β-- catenin pathway, activates glycolysis, and accelerates the biology of GCs, which are all demonstrated in this work.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Low-Salt Diet Regulates the Metabolic and Signal Transduction Genomic Fabrics, and Remodels the Cardiac Normal and Chronic Pathological Pathways

Low-salt diet (LSD) is a constant recommendation to hypertensive patients, but the genomic mechanisms through which it improves cardiac pathophysiology are still not fully understood. Our publicly accessible transcriptomic dataset of the left ventricle myocardium of adult male mice subjected to prolonged LSD or normal diet was analyzed from the perspective of the Genomic Fabric Paradigm. We found that LSD shifted the metabolic priorities by increasing the transcription control for fatty acids biosynthesis while decreasing it for steroid hormone biosynthesis. Moreover, LSD remodeled pathways responsible for cardiac muscle contraction (CMC), chronic Chagas (CHA), diabetic (DIA), dilated (DIL), and hypertrophic (HCM) cardiomyopathies, and their interplays with the glycolysis/glucogenesis (GLY), oxidative phosphorylation (OXP), and adrenergic signaling in cardiomyocytes (ASC). For instance, the statistically (p < 0.05) significant coupling between GLY and ASC was reduced by LSD from 13.82% to 2.91% (i.e., -4.75×), and that of ASC with HCM from 10.50% to 2.83% (-3.71×). The substantial up-regulation of the CMC, ASC, and OXP genes, and the significant weakening of the synchronization of the expression of the HCM, CHA, DIA, and DIL genes within their respective fabrics justify the benefits of the LSD recommendation.