Discov Oncol. 2026 Jan 27. doi: 10.1007/s12672-026-04515-1. Online ahead of print.

ABSTRACT

BACKGROUND: Pancreatic neuroendocrine tumor (pNET) is a heterogeneous tumor originating from pancreatic endocrine cells. Emerging evidence suggests that oxidative stress plays a crucial role in pNET pathogenesis, yet the precise molecular mechanisms and their interplay with the tumor microenvironment remain unclear. This study aims to systematically elucidate how oxidative stress-related pathways drive pNET progression through an integrated multi-omics approach.

METHODS: We designed a three-tier analytical strategy to address interconnected scientific questions. First, to identify which oxidative stress-related genes are dysregulated in pNET, we performed differential expression analysis and weighted gene co-expression network analysis (WGCNA) on the GSE73338 dataset (63 pNET samples, 5 controls), intersecting the. results with oxidative stress gene sets to obtain 71 candidate genes. Second, to understand the functional implications of these genes, we conducted GO/KEGG enrichment analysis and constructed protein-protein interaction (PPI) networks, from which we identified BCL2L1 and PHGDH as key hub genes using three independent algorithms. We then assessed their diagnostic value through ROC analysis and built a prognostic nomogram model. Third, to explore how these key genes influence the tumor microenvironment, we performed immune infiltration analysis using CIBERSORTx. Fourth, to reveal upstream regulatory mechanisms, we constructed ceRNA networks and predicted transcription factors. Fifth, to identify potential therapeutic interventions, we conducted drug prediction and molecular docking analyses. Finally, to validate our findings at cellular resolution and understand cellular heterogeneity, we analyzed single-cell RNA sequencing data from GSE256136 (20 samples), identifying cell types, quantifying cell-cell communications, and confirming key gene expression patterns across different cell populations.

RESULTS: Our systematic analysis revealed that oxidative stress-related genes in pNET were significantly enriched in the PI3K-Akt signaling pathway, cysteine and methionine metabolism, and HIF-1 signaling pathway. BCL2L1 and PHGDH emerged as central regulators with excellent diagnostic performance (AUC > 0.9). Immune infiltration analysis demonstrated significant alterations in activated dendritic cells, memory B cells, and resting NK cells, which correlated strongly with BCL2L1 and PHGDH expression, suggesting these genes link oxidative stress to immune dysfunction. The ceRNA network centered on KCNQ1OT1 and hsa-miR-15a-5p revealed multi-layered transcriptional and post-transcriptional regulation. Drug prediction identified sertindole and cabozantinib as promising therapeutic candidates. Single-cell analysis identified 11 cell types and confirmed that endocrine cells are the primary site of BCL2L1 and PHGDH dysregulation, with extensive crosstalk between endocrine cells and T cells potentially mediating immune evasion.

CONCLUSION: Through integrated multi-omics analysis, we established that oxidative stress pathways may drive pNET progression through a coordinated mechanism involving metabolic reprogramming (via BCL2L1 and PHGDH downregulation), immune microenvironment remodeling (through altered dendritic cell and NK cell function), and complex regulatory networks. BCL2L1 and PHGDH represent potential diagnostic biomarkers and candidate therapeutic targets that require experimental validation, providing new directions for precision medicine in pNET.

PMID:41591671 | DOI:10.1007/s12672-026-04515-1

MedComm (2020). 2025 Jul 11;6(7):e70292. doi: 10.1002/mco2.70292. eCollection 2025 Jul.

ABSTRACT

Cancer-associated fibroblasts (CAFs) are functionally diverse stromal regulators that orchestrate tumor progression, metastasis, and therapy resistance through dynamic crosstalk within the tumor microenvironment (TME). Recent advances in single-cell multiomics and spatial transcriptomics have identified conserved CAF subtypes with distinct molecular signatures, spatial distributions, and context-dependent roles, highlighting their dual capacity to promote immunosuppression or restrain tumor growth. However, therapeutic strategies struggle to reconcile this functional duality, hindering clinical translation. This review systematically categorizes CAF subtypes by origin, biomarkers, and TME-specific functions, focusing on their roles in chemoresistance, maintenance of stemness, and formation of immunosuppressive niches. We evaluate emerging targeting approaches, including selective depletion of tumor-promoting subsets (e.g., fibroblast activation protein+ CAFs), epigenetic reprogramming toward antitumor phenotypes, and inhibition of CXCL12/CXCR4 or transforming growth factor-beta signaling pathways. Spatial multiomics-driven combinatorial therapies, such as the synergistic use of CAFs and immune checkpoint inhibitors, are highlighted as strategies to overcome microenvironment-driven resistance. By integrating CAF biology with translational advances, this work provides a roadmap for developing subtype-specific biomarkers and precision stromal therapies, directly informing efforts to disrupt tumor-stroma coevolution. Key concepts include spatial transcriptomics, stromal reprogramming, and tumor-stroma coevolution, offering actionable insights for both mechanistic research and clinical innovation.

PMID:40656546 | PMC:PMC12246558 | DOI:10.1002/mco2.70292

Cancer Cell Int. 2025 Apr 24;25(1):161. doi: 10.1186/s12935-025-03792-0.

ABSTRACT

BACKGROUND: For patients with nodules detected in imaging that are indeterminate for malignancy, achieving accurate, early, and non-invasive diagnosis of Lung Squamous Cell Carcinoma (LUSC) remains a significant challenge. Therefore, we aimed to establish diagnostic and prognostic models by identifying plasma extracellular vesicles (EVs) associated protein biomarkers specific to LUSC.

METHODS: This study employed a novel nanomaterial, NaY, for the enrichment of EVs from plasma. Validation was conducted through transmission electron microscopy, nanoparticle tracking analyses, and Western blotting. Machine learning algorithms were utilized to compute protein biomarkers associated with LUSC and establish a diagnostic model. Additionally, a prognostic prediction model for LUSC was developed using a combination of 101 machine learning algorithms. Risk scoring of patients was performed to explore the underlying reasons for prognostic differences between high and low-risk groups.

RESULTS: The results of three experiments demonstrate that the new nanomaterial NaY effectively enriches EVs from plasma. Analysis of the enriched profile reveals pathways related to glycolysis/gluconeogenesis and carbon metabolism enriched in plasma EVs of LUSC patients. Thirty-eight LSCC-related EV biomarkers were identified, from which five proteins (TUBB3, RPS7, RPLP1, KRT2, and VTN) were selected to establish a diagnostic model distinguishing between benign and LUSC nodules. The diagnostic efficacy of RPS7 and VTN was further validated in independent samples using ELISA experiments. Furthermore, DPYD, GALK1, CDC23, UBE2L3, RHEB, and PSME1 were determined as potential prognostic biomarkers. Subsequently, risk scores were computed for each sample, classifying all patients into high and low-risk groups. Enrichment analysis revealed that EVs from the high-risk group contained proteins promoting cell proliferation and invasion, while those from the low-risk group were enriched in immune-related protein biomarkers.

CONCLUSIONS: The novel nanomaterial NaY effectively enriches EVs from plasma. Utilizing plasma EV biomarkers, the diagnostic model demonstrates strong discriminative ability between benign and malignant pulmonary nodules in patients.

PMID:40275246 | PMC:PMC12023671 | DOI:10.1186/s12935-025-03792-0

Cell Metab. 2025 Jan 28:S1550-4131(24)00495-9. doi: 10.1016/j.cmet.2024.12.013. Online ahead of print.

ABSTRACT

Immune checkpoint blockade (ICB) therapy has revolutionized cancer treatment but remains effective in only a subset of patients. Emerging evidence suggests that the gut microbiome and its metabolites critically influence ICB efficacy. In this study, we performed a multi-omics analysis of fecal microbiomes and metabolomes from 165 patients undergoing anti-programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1) therapy, identifying microbial and metabolic entities associated with treatment response. Integration of data from four public metagenomic datasets (n = 568) uncovered cross-cohort microbial and metabolic signatures, validated in an independent cohort (n = 138). An integrated predictive model incorporating these features demonstrated robust performance. Notably, we characterized five response-associated enterotypes, each linked to specific bacterial taxa and metabolites. Among these, the metabolite phenylacetylglutamine (PAGln) was negatively correlated with response and shown to attenuate anti-PD-1 efficacy in vivo. This study sheds light on the interplay among the gut microbiome, the gut metabolome, and immunotherapy response, identifying potential biomarkers to improve treatment outcomes.

PMID:39909032 | DOI:10.1016/j.cmet.2024.12.013