Int J Pharm. 2025 Nov 9:126368. doi: 10.1016/j.ijpharm.2025.126368. Online ahead of print.

ABSTRACT

PURPOSE: Gastric adenocarcinoma is a highly aggressive malignancy characterized by a complex tumor microenvironment. Nano-curcumin liposomes hold great potential in inhibiting tumor growth and survival, as well as inducing cuproptosis and oxidative stress. Although the anticancer properties of curcumin have been demonstrated, the specific mechanisms by which curcumin inhibites gastric adenocarcinoma through cuproptosis remains unclear. This study investigated how nano-curcumin liposomes mediated the inhibition of gastric adenocarcinoma cell proliferation and survival via cuproptosis.

METHODS: This study utilized the gastric adenocarcinoma cell line AGS to establish 2D and 3D in vitro gastric adenocarcinoma models. Furthermore, we prepared nano-curcumin liposomes to investigate their effects and regulatory mechanisms on AGS gastric adenocarcinoma models. A series of in vitro assays, including flow cytometry, CCK-8, scratch assays and morphological assessments, were performed to evaluate the effects of nano-curcumin liposomes on cell apoptosis, proliferation and migration. Additionally, bioinformatics and machine learning methods were employed to identify key targets that inhibited gastric adenocarcinoma growth and survival associated with nano-curcumin liposomes, which were further validated through RT-qPCR and omics analysis. Computer simulations were also conducted to assess the stability of binding interactions between curcumin and key target proteins.

RESULTS: Cellular experiments demonstrated that nano-curcumin liposomes significantly inhibited proliferation and invasive capacity of gastric adenocarcinoma cells while promoting cellular oxidative stress. Bioinformatics and machine learning analyses identified FDX1, GPX4, SERPINE1 and SLC27A5 as key targets. RT-qPCR results confirmed that nano-curcumin liposomes significantly downregulated the expression of these targets. Molecular dynamics simulations indicated that curcumin could form stable binding interactions with key protein targets.

CONCLUSION: This study revealed that nano-curcumin liposomes inhibited growth and survival of gastric adenocarcinoma cells by interfering with the expression of FDX1, GPX4, SERPINE1 and SLC27A5, which were closely linked to copper-induced oxidative stress. Nano-curcumin liposomes downregulated the expression of FDX1 and GPX4, disrupted mitochondrial energy metabolism, and induced oxidative stress, thereby promoting tumor-associated programmed cell death linked to cuproptosis. Furthermore, by downregulating SERPINE1, nano-curcumin liposomes modulated cell adhesion and migration, inhibiting the invasive and metastatic potential of tumor cells. Finally, downregulation of SLC27A5 altered tumor metabolism and cellular homeostasis, induced oxidative stress, and disrupted intracellular environmental stability, thereby suppressing the growth of gastric adenocarcinoma.

PMID:41218732 | DOI:10.1016/j.ijpharm.2025.126368

iScience. 2025 May 15;28(6):112681. doi: 10.1016/j.isci.2025.112681. eCollection 2025 Jun 20.

ABSTRACT

Metabolic reprogramming is a defining feature of pancreatic cancer, influencing tumor progression and the tumor microenvironment. By integrating single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics, this study visualized the spatial co-localization of metabolites and gene expression within tumor samples, uncovering metabolic heterogeneity and intercellular interactions. Spatial transcriptomics identified distinct pathological regions, which were further characterized using single-cell transcriptomic data and pathologist annotations. Pseudotime trajectory analysis revealed metabolic shifts along the malignant progression, while single-cell Metabolism (scMetabolism) delineated metabolic differences between pathological regions, classifying them as hypermetabolic or hypometabolic. Notably, aberrant cell communication between cancer cells, macrophages, and fibroblasts was observed, with key receptor-ligand pairs significantly co-expressed in malignant regions and correlated with poor prognosis. Spatial metabolomics imaging identified signature metabolites, highlighting metabolic alterations in amino acid metabolism, polyamine metabolism, fatty acid synthesis, and phospholipid metabolism. This integrated analysis provides critical insights into pancreatic cancer metabolism, offering potential avenues for targeted therapeutic interventions.

PMID:40538442 | PMC:PMC12177182 | DOI:10.1016/j.isci.2025.112681

Cancers (Basel). 2023 Jun 29;15(13):3418. doi: 10.3390/cancers15133418.

ABSTRACT

The clinical management of patients with indeterminate pulmonary nodules is associated with unintended harm to patients and better methods are required to more precisely quantify lung cancer risk in this group. Here, we combine multiple noninvasive approaches to more accurately identify lung cancer in indeterminate pulmonary nodules. We analyzed 94 quantitative radiomic imaging features and 41 qualitative semantic imaging variables with molecular biomarkers from blood derived from an antibody-based microarray platform that determines protein, cancer-specific glycan, and autoantibody-antigen complex content with high sensitivity. From these datasets, we created a PSR (plasma, semantic, radiomic) risk prediction model comprising nine blood-based and imaging biomarkers with an area under the receiver operating curve (AUROC) of 0.964 that when tested in a second, independent cohort yielded an AUROC of 0.846. Incorporating known clinical risk factors (age, gender, and smoking pack years) for lung cancer into the PSR model improved the AUROC to 0.897 in the second cohort and was more accurate than a well-characterized clinical risk prediction model (AUROC = 0.802). Our findings support the use of a multi-omics approach to guide the clinical management of indeterminate pulmonary nodules.

PMID:37444527 | PMC:PMC10341085 | DOI:10.3390/cancers15133418