Uveal melanoma (UM), a prevalent intraocular malignancy with a high rate of metastasis, particularly to the liver, presents a significant therapeutic challenge due to the absence of effective treatments. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are under scrutiny for their roles in cancer, with lncRNA-NEAT1 identified as a key contributor to tumor growth. Our study delves into the aberrant expression of NEAT1, miR-506-3p, and STAT3 in UM cells compared with retinal pigment epithelial cells, revealing their impact on UM cell proliferation, migration, and invasion. Interventions targeting NEAT1 inhibition or miR-506-3p overexpression restrict UM cell viability, migration, and invasion. Conversely, increasing NEAT1 expression or suppressing miR-506-3p enhances these biological behaviors. Bioinformatic tools and dual-luciferase assays validated the specific binding of miR-506-3p to NEAT1 and its regulatory effect on STAT3. Rescue experiments further confirmed these interactions, contributing to a comprehensive understanding of the NEAT1/miR-506-3p/STAT3 axis in UM. The NEAT1/miR-506-3p/STAT3 axis has emerged as a promising diagnostic and therapeutic target for UM, providing a novel perspective on the pathogenesis of this challenging malignancy.

Palmitoylation, a key post-translational modification, plays a crucial role in tumor progression, yet its landscape in clear cell renal cell carcinoma (ccRCC) remains poorly characterized. This study aims to systematically identify and validate key palmitoylation-modifying enzymes in ccRCC and explore their clinical significance. We integrated multi-omics data from TCGA-KIRC and GEO datasets to evaluate palmitoylation levels using the PalmScore system. Machine learning algorithms were applied to identify diagnostic and prognostic key genes. Functional roles of ZDHHC11 were validated in vitro using siRNA-mediated knockdown in ccRCC cell lines. Single-cell RNA sequencing data further confirmed expression patterns. PalmScore effectively stratified ccRCC patients into high- and low-risk groups, with the high PalmScore group showing enriched immune infiltration and poorer survival outcomes. Machine learning identified ZDHHC2 and ABHD17C as diagnostic markers, while ZDHHC11 emerged as a prognostic key gene. In vitro experiments demonstrated that ZDHHC11 knockdown significantly suppressed proliferation, migration, and invasion in ccRCC cells. Single-cell analysis validated the expression patterns of these key genes across different cell types. Our study unveils the critical roles of palmitoylation-modifying enzymes in ccRCC progression and immune regulation. The identified key genes hold promise as biomarkers for diagnosis and prognosis, offering potential targets for future therapeutic strategies.

Treg infiltration and programmed cell death are important factors influencing cancer progression, and they interact with each other. However, the significance of Treg-related programmed cell death (PCD) characteristics in clear cell renal cell carcinoma remains unclear. Through Mendelian randomization, we identified PCD genes and Treg markers that are highly associated with ccRCC outcomes. Subsequently, based on Treg-related PCD genes, we constructed a diagnostic model utilizing a multi-layer perceptron (MLP) and integrated 10 machine learning algorithms to construct a prognostic model, which was then explained by the SHAP method. After exploring functional differences and chemotherapy sensitivity differences between high- and low-risk groups in the prognostic model, we validated the core gene of the model through in vitro cell experiments. Finally, we screened molecular drugs targeting the core genes using the DSigDB database and performed molecular docking and molecular dynamics validation. Utilizing Mendelian randomization (MR), we first established causal links between specific Treg subtypes and PCD gene CASP9 with renal cancer outcomes. Leveraging shared Treg-PCD molecular features, we developed a MLP-based diagnostic model achieving an AUC of 0.987 in external validation. Further, a robust prognostic index Treg-Programmed Cell Death Score (TPCDS) was constructed using 101 machine learning combinations, demonstrating superior stratification across multi-cohort data. High TPCDS correlated with immunosuppressive microenvironments including increased Tregs, T-cell exhaustion, HLA downregulation and poor immunotherapy response, while guiding chemotherapy sensitivity. Functional assays confirmed the core gene SLC11A1 as an oncogenic driver promoting proliferation, migration, and invasion. Molecular docking and dynamics simulations identified Atovaquone as a high-affinity inhibitor of SLC11A1. We explored the significance of Treg and programmed cell death characteristics in the ccRCC tumor microenvironment and established clinically translatable tools for ccRCC diagnosis, prognosis, and personalized therapy selection, thus promoted the application of explainable machine learning models in precision oncology. Furthermore, We have identified SLC11A1 as a highly promising therapeutic target for ccRCC.

Tumorigenesis is a complex and dynamic process in which the tumor microenvironment (TME) plays a central role. In solid tumors, the TME contributes to key mechanisms of tumor progression, including metastasis, immune evasion, and resistance to therapies. One major challenge in preclinical cancer research is the development of reliable three-dimensional (3D) in vitro models, which more accurately replicate the in vivo tumor architecture and microenvironmental conditions, such as hypoxia and extracellular matrix (ECM) organization. However, reproducing functional vascular networks and neo-angiogenesis within these models remains a key challenge. In this study, an advanced 3D tumor model, referred to as angiotumoroids, was developed by co-culturing primary murine breast tumor cells (PTCs) with species-specific adipose-derived microvascular fragments (MVFs). Angiotumoroids were characterized using scanning electron microscopy and immunostaining, and angiogenesis was evaluated through collagen gel sprouting assays. High-resolution proteomic profiling was conducted, focusing on signatures associated with angiogenesis, extracellular matrix (ECM) composition, and tissue remodeling. Additionally, the response and internalization to anticancer drug treatments were evaluated. MVFs are successfully integrated in angiotumoroids, resulting in the formation of vasculature-like structures and demonstrating robust structural organization with dynamic modulation of matrix metalloproteinase 9. Formation of neovasculature was visualized through sprouting and branching, driven by both direct PTC-MVF interactions and PTC-conditioned media, highlighting the roles of juxtacrine and paracrine signaling. Proteomic profiling revealed distinct expression patterns associated with angiogenesis, ECM components (including collagen types I and IV), and active ECM remodeling with elevated MMP expression. Additionally, angiotumoroids showed increased expression of ATP-binding cassette (ABC) transporters, particularly ABCB1 (P-glycoprotein), suggesting potential mechanisms of drug efflux. Functionally, angiotumoroids demonstrated reduced sensitivity to doxorubicin compared to PTC spheroids, maintaining structural integrity and higher cell viability post-treatment. Time-course analysis revealed preferential doxorubicin accumulation in MVF-enriched regions, as confirmed by colocalization with CD31, indicating a spatially regulated distribution of the drug mediated by the vascular compartment. Collectively, these findings establish angiotumoroids as a robust and physiologically relevant in vitro model for studying tumor vascularization, ECM dynamics, and therapeutic response. This platform holds significant promise for predictive cancer research and preclinical drug screening, bridging the gap between traditional in vitro systems and in vivo models.

Approximately 90% of bladder cancer deaths are due to distant metastases rather than local tumor effects. The current first-line systemic treatment for metastatic bladder cancer (mBC) is chemoimmunotherapy or immunotherapy with pembrolizumab plus enfortumab vedotin (EV). However, most mBC patients treated with chemoimmunotherapy or pembrolizumab/EV do not respond or eventually relapse, highlighting the critical need for robust immunocompetent animal models to elucidate the mechanisms of primary and acquired resistance. We previously generated a syngeneic murine cell line CMV-TRP (triple knockout of Trp53, Rb1, Pten) via ex vivo transduction with adenovirus (Ad5CMVCre). To establish an mBC model, the TKO cells were effectively labeled with a lentiviral luciferase and GFP double-expressing reporter and injected into tail veins of C57 BL/6J mice. Tail vein injection of TKO cells reliably established distant metastases with lung and bone lesions. In immunotherapy experiments, mice injected with TKO-labeled cell lines were randomly treated with an anti-PD-1 or control IgG2a antibody. All mice developed lung and/or bone (hind limb or sacrum) metastases. There was no difference in tumor bioluminescence between the control group and anti-PD-1 group (median proton/second 6.94 × 108 vs. 4.32 × 108, p = 0.85). Kaplan-Meier analysis showed no difference in median survival between the control group and anti-PD-1 group (19 days vs. 20 days, p = 0.47). Histology and immunohistochemical profile of lung and bone metastases revealed high-grade basal-like urothelial carcinoma, closely resembling the profile observed in subcutaneous tumor. No significant changes in immune cell infiltrations (CD4+, CD8+, or F4/80+) between groups may explain anti-PD-1 immunotherapy resistance. Therefore, the novel TKO metastatic model represents a useful and reproducible tool for studying tumor-cell dissemination, bone/lung metastasis, and the underlying mechanisms of anti-PD-1 immunotherapy resistance.

Alternative Non-Homologous End Joining (Alt-NHEJ) DNA repair is considered a major player in cancer genomic instability. Here, we investigated cGAS-STING pathway as crucial node in the interplay between Alt-NHEJ repair and immune response, in the aim to discover novel therapeutic vulnerability in Multiple Myeloma (MM). METHODS: In silicoanalyses were performed by querying publicly available MM datasets (GSE66293 and CoMMpass). Anti-proliferative activity was evaluated by CellTiter-Glo, while flow cytometry analysis was used to determine the apoptotic process, cell cycle, phagocytosis, micronuclei detection, Calreticulin and T-cell activation markers. Protein expression was detected by western blot of whole or fractioned protein extracts. By interrogating public MM datasets, a significant correlation between hyperactivation of cGAS-STING mRNA signature and poor PFS and OS in MM was observed. Indeed, Gene Set Enrichment Analysis (GSEA) showed enrichment of DNA repair, TNFA signaling and oxidative phosphorylation in patients with cGAS-STING activation patients, associated to higher mRNA expression of DNA Ligase 3 (LIG3) and PARP1. On this basis, we evaluated the activity of Alt-NHEJ inhibitor Talazoparib (PARP1-inhibitor) on MM cell lines, focusing on their capability to modulate cGAS-STING pathway. We first detected a significant reduction of cell proliferation and the induction of apoptosis following Talazoparib treatment, which in turn induced DNA damage response and cell cycle blockade, and finally cGAS-STING pathway activation as result of PARP1-trapping into chromatin. Next, by performing co-culture experiments with healthy donor's peripheral blood mononuclear cells (PBMCs), we finally demonstrated the induction of immunogenic cell death, which was abrogated in cGAS-knockout cells, underscoring the pathway's functional relevance. Taken together, our findings indicate that Alt-NHEJ inhibitors are potential immune-stimulating agents for MM with hyperactivation of cGAS-STING pathway, coherently with our working hypothesis.

Long non-coding RNAs (lncRNAs) are broad-spectrum cellular transcripts that can directly act as RNA regulators and/or partly encode functional peptides (lncRNA-encoded peptides, LRPs) in cancer cells. Recently, cancer LRPs have been found to be involved in cancer cell variability and proliferation, thus gaining widespread attention for their potential in cancer diagnosis, prognosis and therapy. As structures determine functions, the structural diversities of LRPs are the sources of functional variations of LRPs in cancers. Since 6135 cancer LRPs are listed in SPENCER database and 24 SPENCER-unlisted cancer LRPs are reported in several previous studies, this article reviews recent advances of cancer LRPs, analyzes amino acid compositions of them, and undertakes in silico evaluations to assess their structural and functional attributes. These LRPs are dominated by the amino acids Glu, Leu, and Ser and are rarer in the amino acids Cys, His, and Trp, and that many of the LRPs are rich in secondary or tertiary structures. Like mRNA-encoded peptides, these structure-rich cancer LRPs have a wide range of functions, including anti-cancer, cell-penetrating, anti-inflammatory, and antibacterial activities. Relatively, two groups of anticancer values (predicted by AntiCP 2.0 and PreTP-Stack) of these LRPs commonly showed positive and negative correlations with their total charge content and metal-bind aa content, respectively. The increasing amount of data and analysis on cancer LRPs, as reported here, offers opportunities to enhance practical cancer diagnosis and treatment, and to overcome remaining research challenges for cancer LRPs.