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Bladder carcinoma (BLCA) represents a common urinary tract malignancy, characterized by aggressive behavior and high recurrence rates. The biological response regulation during tumor proliferation and metastasis is intimately associated with liquid-liquid phase separation (LLPS). For the purpose of enhancing early detection and treatment, this study employed transcriptomic data to examine the prognostic implications of LLPS-associated genes and formulate a predictive model. Clinical and transcriptomic data of bladder cancer patients were sourced from the GEO and TCGA databases. This study applied a clustering algorithm using non-negative matrix factorization (NMF) to classify samples, which were systematically compared based on their liquid-liquid phase separation characteristics. Prognostic models were developed using multivariate Cox regression and the Least Absolute Shrinkage Selection Operator (LASSO) algorithm to establish risk formulas for nine genes. The gene signature's validity was tested across the entire TCGA cohort (406 cases), the TCGA testing cohort (120 cases), and the external validation dataset GSE13507. The predictive accuracy of the signature system was assessed using receiver operating characteristic (ROC) and Kaplan-Meier curves. Additionally, decision curve analysis incorporating clinicopathological parameters and the genetic signature was employed to predict individual survival. This study identified two distinct molecular subtypes, C1 and C2. Patients with the C1 subtype exhibited significantly better prognoses than those with the C2 subtype. Low-risk group patients consistently showed superior prognoses compared to high-risk groups across the entire TCGA, GEO, and TCGA training cohorts. Furthermore, the LLPS-related gene model demonstrated prognostic value independent of other clinical traits. This study identifies LLPS-associated gene clusters and establishes an independent, accurate prognostic model for BLCA. The model holds potential for clinical application in BLCA prognosis assessment. [ABSTRACT FROM AUTHOR]

Cuproptosis is the latest novel form of cell death. However, the relationship between asthma and cuproptosis is not fully understood. In this study, we screened differentially expressed cuproptosis-related genes from the Gene Expression Omnibus (GEO) database and performed immune infiltration analysis. Subsequently, patients with asthma were typed and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was performed to calculate the module-trait correlations, and the hub genes of the intersection were taken to construct machine learning (XGB, SVM, RF, GLM). Finally, we used TGF-β to establish a BEAS-2B asthma model to observe the expression levels of hub genes. Six cuproptosis-related genes were obtained. Immune-infiltration analysis shows that cuproptosis-related genes are associated with a variety of biological functions. We classified asthma patients into two subtypes based on the expression of cuproptosis-related genes and found significant Gene Ontology (GO) and immune function differences between the different subtypes. WGCNA selected 2 significant modules associated with disease features and typing. Finally, we identified TRIM25, DYSF, NCF4, ABTB1, CXCR1 as asthma biomarkers by taking the intersection of the hub genes of the 2 modules and constructing a 5-genes signature, which nomograph, decision curve analysis (DCA) and calibration curves, receiver operating characteristic curve (ROC) showed high efficiency in diagnosing the probability of survival of asthma patients. Finally, in vitro experiments have shown that DYSF and CXCR1 expression is up expressed in asthma. Our study provides further directions for studying the molecular mechanism of asthma. [ABSTRACT FROM AUTHOR]

Objective: High-risk human papillomavirus (HPV) is a carcinogenic virus associated with nearly all cases of cervical cancer, as well as an increasing number of anal and oral cancers. The two carcinogenic proteins of HPV, E6 and E7, can immortalize keratinocytes and are essential for HPV-related cellular transformation. Currently, the global regulatory effects of these oncogenic proteins on the host proteome are not fully understood, and further exploration of the functions and carcinogenic mechanisms of E6 and E7 proteins is needed., Methods: We used a previously established platform in our laboratory for constructing recombinant adenoviral plasmids expressing the HPV16 E7 gene to further construct recombinant virus particles expressing HPV16/18 E6, E7, and both E6 and E7 genes. These recombinant viruses were used to infect C33A cells to achieve sustained expression of the HPV16/18 E6/E7 genes. Subsequently, total RNA was extracted and RNA-Seq technology was employed for transcriptome sequencing to identify differentially expressed genes associated with HPV infection in cervical cancer., Results: RNA-Seq analysis revealed that overexpression of the HPV16/18 E6/E7 genes upregulated GP6, CD36, HDAC6, ESPL1, and DNMT3B among the differentially expressed genes (DEGs) associated with cervical cancer. Spearman correlation analysis revealed a statistically significant correlation between the HDAC6 and DNMT3B genes and key pathways, including DNA replication, tumor proliferation signature, G2M checkpoint, p53 pathways, and PI3K/AKT/mTOR signaling pathways. Further, qRT-PCR and Western blot analyses indicated that both HPV16/18 E7 can upregulate the expression of HDAC6 and DNMT3B, genes associated with HPV infection-related cervical cancer., Conclusion: The successful expression of HPV16/18 E6/E7 in cells indicates that the recombinant viruses retain the replication and infection capabilities of Ad4. Furthermore, the recombinant viruses expressing HPV16/18 E7 can upregulate the HDAC6 and DNMT3B genes involved in cervical cancer pathways, thereby influencing the cell cycle. Additionally, HDAC6 and DNMT3B are emerging as important therapeutic targets for cancer. This study lays the foundation for further exploration of the oncogenic mechanisms of HPV E6/E7 and may provide new directions for the treatment of HPV-related cancers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Shao, Shah, Su, Li, Huang, Wang, Wang and Wu.)

The function of tubulin polymerization promoting protein family member 3 (TPPP3) in tumor cells is complicated, and the role of TPPP3 in nasopharyngeal carcinoma (NPC) remains unclear. This study aims to explore the expression of TPPP3 in NPC and its effect on NPC cells. The expression of TPPP3 in NPC tissues and other cancers were analyzed by using the Oncomine and Gene Expression Omnibus (GEO) databases. The mRNA and protein of TPPP3 were detected in NPC tissues by quantitative real-time PCR and immunohistochemistry. Furthermore, TPPP3 was overexpressed in 5-8 F and HONE1 cell lines by lentivirus transfection, and functional analysis of TPPP3 in NPC was evaluated through in vitro experiments. The expression of TPPP3 was significantly down-regulated in NPC tissues and cells. Overexpression of TPPP3 significantly inhibited proliferation of 5-8 F and HONE1 cells in vitro. In addition, overexpression of TPPP3 significantly attenuated the invasion ability of 5-8 F, HONE1 cells in vitro, but have no significant effect on migration ability. Furthermore, TPPP3 overexpression diminished the expression of MMP-2 and MMP-9 mRNA. By analyzing dataset GSE12452, it was interesting that TPPP3 high expression group mainly functioned in B cell receptor signaling pathway, cell cycle and DNA replication. In conclusion, our results suggest that TPPP3 may be considered as an antioncogene, which plays an important role in the occurrence and progression of NPC. Abbreviations: TPPP3: tubulin polymerization promoting protein family member 3; NPC: nasopharyngeal carcinoma; GEO: Gene Expression Omnibus; qRT-PCR: quantitative real-time PCR; GFP: green fluorescence protein; MOI, transfected multiplicity of infection; CCK-8: cell counting kit-8; OD: optical density; GSEA: gene set enrichment analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; MMP-2: matrix metalloproteinase-2; MMP-9: matrix metalloproteinase-9.