CDH1 overexpression predicts bladder cancer from early stage and inversely correlates with immune infiltration

Bladder cancer biomarkers

Bladder cancer (BCa) is among the most frequently diagnosed urinary tract cancers, characterized by a high recurrence rate and significant clinical heterogeneity. Effective diagnosis and treatment of BCa demand continuous advancements in medical technologies, particularly given the limitations of classical methods such as cystoscopy and urine cytology. A comprehensive search of PubMed and Web of Science was conducted using relevant keywords to structure this narrative review. Additionally, specialist journals were reviewed. Only articles in English were included, with selection based on titles, abstracts, and availability of full texts. In recent years, biomarkers have emerged as crucial tools complementing traditional techniques, providing more precise, sensitive, and non-invasive methods for early detection, prognosis, and monitoring treatment response in BCa. Molecular, genetic, and protein biomarkers enable a deeper understanding of BCa biology, creating opportunities for personalized therapy tailored to individual patient needs. However, despite their potential, certain challenges remain, including standardization, validation, and integration into routine clinical practice. This review highlights recent advancements in BCa biomarkers and their transformative potential in oncological care. It underscores the importance of incorporating these innovations to refine diagnostic and therapeutic approaches, ultimately improving patient outcomes. Modern diagnostic and prognostic tools for BCa can enhance treatment outcomes by enabling early disease detection and reducing recurrence risks. This progress promises to improve patients' quality of life by minimizing disease burden and fostering effective, tailored care strategies.

Programmed Cell Death-Related Gene Signature Associated with Prognosis and Immune Infiltration and the Roles of HMOX1 in the Proliferation and Apoptosis were Investigated in Uveal Melanoma

BACKGROUND

Uveal melanoma (UVM) is the most common primary ocular malignancy, with a wide range of symptoms and outcomes. The programmed cell death (PCD) plays an important role in tumor development, diagnosis, and prognosis. There is still no research on the relationship between PCD-related genes and UVM. A novel PCD-associated prognostic model is urgently needed to improve treatment strategies.

OBJECTIVE

We aim to screen PCD-related prognostic signature and investigate its proliferation ability and apoptosis in UVM cells.

METHODS

The clinical information and RNA-seq data of the UVM patients were collected from the TCGA cohort. All the patients were classified using consensus clustering by the selected PCD-related genes. After univariate Cox regression and PPI network analysis, the prognostic PCD-related genes were then submitted to the LASSO regression analysis to build a prognostic model. The level of immune infiltration of 8-PCD signature in high- and low-risk patients was analyzed using xCell. The prediction on chemotherapy and immunotherapy response in UVM patients was assessed by GDSC and TIDE algorithm. CCK-8, western blot and Annexin V-FITC/PI staining were used to explore the roles of HMOX1 in UVM cells.

RESULTS

A total of 8-PCD signature was constructed and the risk score of the PCD signature was negatively correlated with the overall survival, indicating strong predictive ability and independent prognostic value. The risk score was positively correlated with CD8 Tcm, CD8 Tem and Th2 cells. Immune cells in high-risk group had poorer overall survival. The drug sensitivity demonstrated that cisplatin might impact the progression of UVM and better immunotherapy responsiveness in the high-risk group. Finally, Overespression HMOX1 (OE-HMOX1) decreased the cell viability and induced apoptosis in UVM cells. Recuse experiment results showed that ferrostatin-1 (fer-1) protected MP65 cells from apoptosis and necrosis caused by OE-HMOX1.

CONCLUSION

The PCD signature may have a significant role in the tumor microenvironment, clinicopathological characteristics, prognosis and drug sensitivity. More importantly, HMOX1 depletion greatly induced tumor cell growth and inhibited cell apoptosis and fer-1 protected UVM cells from apoptosis and necrosis induced by OE-HMOX1. This work provides a foundation for effective therapeutic strategy in tumour treatment.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.

CRISPR du-HITI an attractive approach to targeting Long Noncoding RNA HCP5 as inhibitory factor for proliferation of ovarian cancer cell

This research provides a glimmer of hope that the knockout of HCP5 leads to a therapy response to considerably prolong the life of patients with OC. RT-PCR evaluated the expression of lncRNA HCP5 in the ovarian cancer OVCAR-3 cell line. CRISPR knockout cell lines validated by western blot. Small genomic deletions at the targeted locus were induced. CCK-8 colony formation assays were used to analyze the effect of HCP5 knockout on the proliferation capacity of OVCAR-3 cells. Transwell migration and invasion assayed. Furthermore, the Sphere-formation assay isolated the most aggressive population of cancer stem cells. Bioinformatic analysis showed a significant correlation between lncRNA HCP5 up-regulation and OVCAR-3 cell proliferation. The ChIP technique assesses specific sites of interaction between transcription factors and DNA. Real-time PCR assays explored the relationship between HCP5, Hsa-miR-9-5p, CXCR4, CDH1, caspase-3, p53, bcl2 and survivin. PCR carried out amplification of the 448-bp band for sgRNA1 and sgRNA2 after the use of particular primers for HCP5. the number of breast cancer cells that moved to the bottom chamber reduced considerably after transfection with PX461-sgRNA1/2 vectors compared to the Blank control groups (P < 0.05). MTT assay designated growth curves that showed the rate of OVCAR-3 growth was significantly repressed (***P < 0.001) when compared with control OVCAR-3 cells after HCP5 knockdown. Also, the survival results of W.T cells in 24, 48 and 72 h showed 92%, 87% and 85%, respectively. This is while the cells of the CRISPR/Cas9 group in which LncRNA HCP5 was knocked out had 42% (*P < 0.05), 23%(**P < 0.01) and 14% (**P < 0.01) survival, respectively. The expression levels of caspase-3, Hsa-miR-9-5p, P53 genes in the HCP5 deletion of CRISPR/Cas9 group significantly increased than the W.T. control group; the deletion group showed a considerable reduction in HCP5 expression compared to the blank control group (3.6-fold, p < 0.01). Whereas BCL2, SURVIVIN, CXCR4, CDH1 genes expression markedly increased than in HCP5 knockout cells (5.8-fold, p < 0.05). These results indicate that CRISPR/Cas9-mediated HCP5 disruption on OVCAR-3 cell lines promotes anti-tumor biomarkers, suppressing ovarian cancer progression. Consistent with these results, HCP5 is one of the most critical lnc for the efficient proliferation and migration of OVCAR-3 cell lines.

DriverDBv4: a multi-omics integration database for cancer driver gene research

Advancements in high-throughput technology offer researchers an extensive range of multi-omics data that provide deep insights into the complex landscape of cancer biology. However, traditional statistical models and databases are inadequate to interpret these high-dimensional data within a multi-omics framework. To address this limitation, we introduce DriverDBv4, an updated iteration of the DriverDB cancer driver gene database (http://driverdb.bioinfomics.org/). This updated version offers several significant enhancements: (i) an increase in the number of cohorts from 33 to 70, encompassing approximately 24 000 samples; (ii) inclusion of proteomics data, augmenting the existing types of omics data and thus expanding the analytical scope; (iii) implementation of multiple multi-omics algorithms for identification of cancer drivers; (iv) new visualization features designed to succinctly summarize high-context data and redesigned existing sections to accommodate the increased volume of datasets and (v) two new functions in Customized Analysis, specifically designed for multi-omics driver identification and subgroup expression analysis. DriverDBv4 facilitates comprehensive interpretation of multi-omics data across diverse cancer types, thereby enriching the understanding of cancer heterogeneity and aiding in the development of personalized clinical approaches. The database is designed to foster a more nuanced understanding of the multi-faceted nature of cancer.

Identifying circRNA-miRNA-mRNA Regulatory Networks in Chemotherapy-Induced Peripheral Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a frequent and severe side effect of first-line chemotherapeutic agents. The association between circular RNAs (circRNAs) and CIPN remains unclear. In this study, CIPN models were constructed with Taxol, while 134 differentially expressed circRNAs, 353 differentially expressed long non-coding RNAs, and 86 differentially expressed messenger RNAs (mRNAs) were identified utilizing RNA sequencing. CircRNA-targeted microRNAs (miRNAs) were predicted using miRanda, and miRNA-targeted mRNAs were predicted using TargetScan and miRDB. The intersection of sequencing and mRNA prediction results was selected to establish the circRNA-miRNA-mRNA networks, which include 15 circRNAs, 18 miRNAs, and 11 mRNAs. Functional enrichment pathway analyses and immune infiltration analyses revealed that differentially expressed mRNAs were enriched in the immune system, especially in T cells, monocytes, and macrophages. Cdh1, Satb2, Fas, P2ry2, and Zfhx2 were further identified as hub genes and validated by RT-qPCR, correlating with macrophages, plasmacytoid dendritic cells, and central memory CD4 T cells in CIPN. Additionally, we predicted the associated diseases, 36 potential transcription factors (TFs), and 30 putative drugs for hub genes using the DisGeNET, TRRUST, and DGIdb databases, respectively. Our results indicated the crucial role of circRNAs, and the immune microenvironment played in CIPN, providing novel insights for further research.

An autonomous mathematical model for the mammalian cell cycle

A mathematical model for the mammalian cell cycle is developed as a system of 13 coupled nonlinear ordinary differential equations. The variables and interactions included in the model are based on detailed consideration of available experimental data. A novel feature of the model is inclusion of cycle tasks such as origin licensing and initiation, nuclear envelope breakdown and kinetochore attachment, and their interactions with controllers (molecular complexes involved in cycle control). Other key features are that the model is autonomous, except for a dependence on external growth factors; the variables are continuous in time, without instantaneous resets at phase boundaries; mechanisms to prevent rereplication are included; and cycle progression is independent of cell size. Eight variables represent cell cycle controllers: the Cyclin D1-Cdk4/6 complex, APC^Cdh1, SCF^βTrCP, Cdc25A, MPF, NuMA, the securin-separase complex, and separase. Five variables represent task completion, with four for the status of origins and one for kinetochore attachment. The model predicts distinct behaviors corresponding to the main phases of the cell cycle, showing that the principal features of the mammalian cell cycle, including restriction point behavior, can be accounted for in a quantitative mechanistic way based on known interactions among cycle controllers and their coupling to tasks. The model is robust to parameter changes, in that cycling is maintained over at least a five-fold range of each parameter when varied individually. The model is suitable for exploring how extracellular factors affect cell cycle progression, including responses to metabolic conditions and to anti-cancer therapies.