Objectives Bladder cancer is a highly heterogeneous malignancy characterized by complex immune interactions that influence disease progression and patient outcomes. Identifying immune-related molecular determinants with prognostic and therapeutic relevance is therefore critical. This study aimed to systematically investigate immune-related genes involved in bladder cancer and to identify key predictive molecular prognostic biomarkers, with particular emphasis on cell-cycle–associated immune mechanisms. Methods RNA expression profiles from bladder tumor and corresponding normal tissues were analyzed across five independent cohorts (GSE13507, GSE19915, GSE40355, GSE42089, and TCGA-Bladder Cancer). Common differentially expressed immune-related genes (DEIRGs) were identified using GEO2R and TCGA analyses. Functional enrichment and pathway analyses were performed using Metascape and DAVID, while hub genes were identified through protein–protein interaction networks using MCODE, CytoHubba, and ClueGO. Expression patterns, prognostic relevance, and immune associations were validated using UALCAN, the Human Protein Atlas, TIMER2.0, and PrognoScan databases. Results A total of 300 common DEIRGs were identified across datasets. Network analysis revealed 21 significant immune-related hub genes, several of which were consistently enriched in the G2M checkpoint pathway. Eleven hub genes were significantly associated with reduced overall survival in bladder cancer patients. Notably, six G2M checkpoint–related genes ( TOP2A, CCNA2, CDK1, BIRC5, PLK1, and E2F1 ) demonstrated strong prognostic relevance across multiple cohorts. Conclusions This comprehensive in silico analysis highlights the critical role of G2M checkpoint–associated immune-related genes in bladder cancer progression. These genes represent predictive molecular prognostic immune biomarkers and potential therapeutic targets, providing a foundation for the development of personalized immunotherapeutic strategies in bladder cancer.
Objective In randomized clinical trials, prediction models can be used to explore the relationships between patients’ variables and treatment effect magnitude. Our aim was to evaluate flexible machine learning models that incorporate interactions and nonlinear effects from high-dimensional data to estimate individualized treatment recommendations in trials with time-to-event outcomes. Methods We compared neural networks (CoxCC and CoxTime) and random survival forests, including Interaction Forests (IF) and Causal Survival Forest (CSF) against a Cox proportional hazards model with an adaptive LASSO (ALASSO) penalty as a benchmark. For individualized treatment recommendations, we adapted metrics originally designed for binary outcomes to accommodate time-to-event data with censoring, using both survival-probability and restricted mean survival time estimates of treatment benefit. Metrics included the C-for-Benefit, E50-for-Benefit, Integrated Calibration Index for benefit, and root-mean-squared error for treatment benefit. We also evaluated decision-making utility using the Area Under the Targeting Operator Characteristic and Population Benefit (PB). An extensive simulation study used two data generation processes incorporating nonlinearity and interactions. The models were applied to three cancer clinical trial datasets. Results In the first data generation process, neural networks outperformed ALASSO in calibration while IF showed superior C-for-benefit performance. In the second one, machine learning methods outperformed the benchmark linear ALASSO method across discrimination, calibration, and RMSE. In the cancer trial datasets, the machine learning methods often performed better than ALASSO: neural networks consistently achieved the best calibration, IF achieved the highest C-for-Benefit, and CSF showed strong calibration and the highest PB in the trastuzumab dataset. Conclusion Machine learning-based survival models can efficiently estimate conditional average treatment effects from randomized trials when strong nonlinear and interaction effects are expected. These flexible approaches offer valuable alternatives to traditional regression methods for developing treatment recommendation rules from clinical trials with time-to-event outcomes and medium to high-dimensional data set sizes.
The interleukin-13 receptor α2 (IL-13Rα2) is overexpressed in a variety of solid tumors with restricted expression in normal tissues, making it a promising tumor target. However, there’s been limited progress in IL-13Rα2 targeting tumor therapy. Here, we designed 313TE, a novel IL-13E13Y-based T cell engager targeting IL-13Rα2 and CD3 and evaluated its function in vitro and in vivo. The results demonstrated that 313TE binds strongly to IL-13Rα2-positive cells and T cells, bridging T cells and tumor cells. It effectively promoted T cell-mediated IL-13Rα2-dependent cytotoxicity against tumor cells and elicited the production of immune-stimulatory cytokines. In the xenograft tumor model, 313TE treatment significantly delayed tumor growth and reduced tumor weight, with efficacy strictly dependent on IL-13Rα2 expression. Our findings establish 313TE as a novel T cell engager capable of bridging T cells and IL-13Rα2-positive tumors, promoting anti-tumor function both in vitro and in vivo, offering a promising immunotherapeutic strategy for IL-13Rα2-positive solid malignancies.
GFPT1, the rate-limiting enzyme of the hexosamine biosynthesis pathway, promotes various cancers but its role in esophageal cancer is unclear. GFPT1 expression was assessed by public datasets, western blotting, and IHC. GFPT1-silenced cells were established via siRNA. Proliferation, migration, invasion, apoptosis, ER stress, and autophagy markers were evaluated by CCK-8, colony formation, Transwell, wound healing, flow cytometry, western blotting, and LC3 immunofluorescence. In addition, rescue experiments were performed using the ER stress inhibitor 4-PBA in GFPT1-knockdown cells, and O-GlcNAcylation levels were examined by western blot. GFPT1 was highly expressed in esophageal cancer tissues. Knockdown of GFPT1 suppressed proliferation, invasion, and migration, while enhancing apoptosis. GFPT1 depletion upregulated ER stress markers (GRP78, GRP94, p-PERK, p-eIF2α) and autophagy-related changes (increased LC3-II/LC3-I ratio, ATG5, ATG7, and reduced p62), and reduced overall O-GlcNAcylation. Importantly, treatment with 4-PBA significantly reversed the GFPT1 knockdown-induced suppression of proliferation, migration, and invasion, and rescued the alterations in ER stress markers, indicating that the phenotypic effects are causally mediated, at least in part, through the ER stress pathway. GFPT1 knockdown impedes esophageal cancer progression by reducing aggressive phenotypes and promoting apoptosis, potentially via ER stress and autophagy activation.
Colorectal cancer (CRC) remains a major global health burden, with incidence and mortality rates continuing to rise worldwide. By 2040, CRC cases are projected to reach 3.2 million, driven by complex interactions between genetic, environmental, and lifestyle factors. While the molecular mechanisms of CRC have been extensively studied, increasing evidence highlights the critical role of non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) in regulating tumor progression, immune evasion, and therapeutic resistance. Recent research reveals that ncRNAs not only influence tumor cell behavior but also modulate the tumor microenvironment (TME), particularly by regulating tumor-associated macrophage (TAM) polarization. The balance between pro-inflammatory M1 and pro-tumorigenic M2 macrophages profoundly affects CRC progression and treatment response. Dysregulated ncRNAs, such as miR-934, lncRNA RPPH1, and circPLCE1, promote M2 polarization and metastasis via PI3K/AKT, NF-κB, and STAT3 signaling pathways, while others like miR-195-5p and lncRNA GAS5 favor M1 polarization and inhibit tumor growth. These molecules act through diverse mechanisms, including exosomal transfer, ceRNA interactions, and transcriptional regulation, forming intricate communication networks between CRC cells and immune components. Understanding these ncRNA-mediated pathways provides novel opportunities for therapeutic intervention, enhancing immunotherapy efficacy, and improving diagnostic precision through circulating exosomal ncRNA biomarkers. Collectively, targeting ncRNA-regulated macrophage polarization holds promise for developing next-generation precision therapies and advancing immunomodulatory strategies in CRC.
