J Clin Invest. 2025 Jul 15;135(14):e191937. doi: 10.1172/JCI191937. eCollection 2025 Jul 15.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is known to progress from one of two main precursor lesions: pancreatic intraepithelial neoplasia (PanIN) or intraductal papillary mucinous neoplasm (IPMN). The poor survival rates for patients with PDAC, even those diagnosed with localized disease, highlight the need for pancreatic cancer interception at the precursor stage. Although their basic biological drivers are well characterized, practical strategies for PanIN and IPMN interception remain elusive due to difficulties with detection, risk stratification, and low-morbidity intervention. Recently, advances in liquid biopsy, spatial multiomics analysis, and machine learning technology have provided deeper understanding of the molecular landscapes underlying pancreatic precursor development and progression. In this Review, we outline the different histologic phenotypes, clinical characteristics, and neoplastic cell-intrinsic and -extrinsic drivers of PanINs and IPMNs, with particular focus on current and potential future opportunities for pancreatic precancer interception.

PMID:40662372 | PMC:PMC12259249 | DOI:10.1172/JCI191937

Int J Med Sci. 2025 Jun 12;22(12):2906-2918. doi: 10.7150/ijms.113736. eCollection 2025.

ABSTRACT

ACTL6A, a core subunit of the SWI/SNF chromatin remodeling complex, has emerged as a critical oncogenic driver across multiple malignancies. Recent studies reveal that aberrant ACTL6A overexpression promotes tumor initiation, progression, and metastasis by orchestrating chromatin remodeling, transcriptional reprogramming, and crosstalk with key signaling pathways (e.g., Hippo/YAP, Notch, and PI3K/AKT). This review systematically synthesizes evidence from in vitro, in vivo, and clinical studies spanning hepatocellular carcinoma, breast cancer, glioblastoma, and 10 other cancer types, highlighting ACTL6A's dual role as a chromatin remodeler and an independent oncogenic effector. Key mechanisms include sustaining cancer stemness, suppressing apoptosis, enhancing DNA repair, and driving metabolic reprogramming. Clinically, ACTL6A overexpression correlates with advanced tumor stage, therapy resistance, and poor prognosis, positioning it as a promising prognostic biomarker and therapeutic target. We further discuss emerging strategies to inhibit ACTL6A (e.g., siRNA, small-molecule inhibitors) and propose combinatorial approaches to overcome drug resistance. By integrating multi-omics data and preclinical models, this review not only clarifies ACTL6A's context-dependent oncogenic networks but also bridges mechanistic insights to translational challenges, offering a roadmap for future research and therapeutic development.

PMID:40657395 | PMC:PMC12243864 | DOI:10.7150/ijms.113736

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

Curr Opin Oncol. 2025 Sep 1;37(5):408-413. doi: 10.1097/CCO.0000000000001178. Epub 2025 Jul 2.

ABSTRACT

PURPOSE OF REVIEW: This review evaluates the importance of circulating tumor DNA (ctDNA) as a minimally invasive tool in lymphoma management.

RECENT FINDINGS: Current literature demonstrates ctDNA's ability to alleviate the shortcomings of standard biopsy and imaging, providing real-time insights into tumor burden, clonal evolution, and treatment resistance. In Hodgkin lymphoma, ctDNA allows for comprehensive genomic profiling and treatment monitoring. In diffuse large B-cell lymphoma (DLBCL), ctDNA correlates with disease burden and is valuable for tracking resistance, especially in CAR T-cell therapy. In rare subtypes like primary central nervous system lymphoma (PCNSL) and intravascular large B-cell lymphoma (IVLBCL), ctDNA enhances diagnostic precision and enables early relapse detection. Even in indolent lymphomas, ctDNA could prove useful in relapse monitoring and risk assessment.

SUMMARY: CtDNA analysis could become a key element in personalized lymphoma management, enabling earlier interventions and tailored treatment strategies. However, future efforts should focus on harmonizing methodologies and validating findings in large-scale trials to allow these techniques to be adopted in routine practice.

PMID:40658005 | DOI:10.1097/CCO.0000000000001178

Brief Bioinform. 2025 Jul 2;26(4):bbaf312. doi: 10.1093/bib/bbaf312.

ABSTRACT

Advancements in high-throughput sequencing technologies and artificial intelligence (AI) offer unprecedented opportunities for groundbreaking discoveries in bioinformatics research. However, the challenges of exponential growth of omics data and the rapid development of AI technologies require automated big biological data analysis capability and interdisciplinary knowledge-driven scientific insight. Here, we propose a data-intelligence-intensive bioinformatics copilot (Bio-Copilot) system that synergizes AI capabilities with human researchers to facilitate hypothesis-free exploratory research and inspire novel scientific insights in large-scale omics studies. Bio-Copilot forms high-quality intensive intelligence through close collaboration between multiple agents, driven by large language models (LLMs), and human researchers. To augment the capabilities of Bio-Copilot, this study devises an agent group management strategy, an effective human-agent interaction mechanism, a shared interdisciplinary knowledge database, and continuous learning strategies for the agents. We comprehensively compare Bio-Copilot against GPT-4o and several leading AI agents across diverse bioinformatics tasks, using a broad range of evaluation metrics. Bio-Copilot achieves overall state-of-the-art performance across all tasks, while showcasing exceptional task completeness. Furthermore, on application to constructing a large-scale human lung cell atlas, Bio-Copilot not only reproduces the intricate data integration process detailed in a seminal study but also introduces a recursive, multilevel annotation strategy to capture the continuous nature of cellular states and uncovers the characteristics of rare cell types, highlighting its potential to unravel hidden complexities in biological systems. Beyond the technical achievements, this study also underscores the profound implications of integrating AI capabilities with expert knowledge in accelerating impactful biological discoveries and exploring uncharted territories.

PMID:40639418 | PMC:PMC12245162 | DOI:10.1093/bib/bbaf312

Hereditas. 2025 Jul 7;162(1):121. doi: 10.1186/s41065-025-00487-4.

ABSTRACT

Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism-related gene sets, WMRCA + outperformed widely used clustering algorithms-including iCluster, SNF, NMF, CC, and CNMF-achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at https://github.com/guojunliu7/WMRCA .

PMID:40624602 | PMC:PMC12235908 | DOI:10.1186/s41065-025-00487-4

J Hazard Mater. 2025 Jun 23;495:139049. doi: 10.1016/j.jhazmat.2025.139049. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic arsenic exposure is a global health concern linked to pulmonary diseases like fibrosis. However, its precise molecular mechanisms remain unclear. This study explored the effects of chronic arsenic exposure on a murine model (via diet) and BEAS-2B cells, focusing on oxidative stress, lipid peroxidation, mitochondrial dysfunction, and ferroptosis-mediated cell death.

METHODS: BEAS-2B cells were exposed to 1 μmol/L NaAsO₂ for 30 passages. Oxidative stress was assessed via ROS quantification, GSH depletion, and T-SOD activity. Lipid peroxidation was measured using BODIPY fluorescence and MDA levels. Mitochondrial dysfunction was determined by mtROS imaging and JC-1 staining. Ferroptosis was analyzed through GPX4 expression and TEM-based mitochondrial integrity. A 14-month murine model evaluated histopathology, metabolomic dysregulation, and gut-lung axis crosstalk.

RESULTS: Arsenic exposure significantly increased ROS, depleted GSH, and reduced T-SOD activity. Lipid peroxidation and mitochondrial dysfunction were evident, with more than 60 % decline in GPX4. Murine lung histology showed alveolar thickening, inflammatory infiltration, and elevated IL-6, TNF-α, and VEGF. Metabolomic analysis revealed disrupted lipid metabolism, correlating with ferroptosis markers (Acetyl-carnitine, L-Acetylcarnitine).

CONCLUSIONS: This was the first study to demonstrate ferroptosis as a key mechanism in arsenic-induced lung epithelial damage using a 14-month murine model and a 30-passage cellular model. We further demonstrated that ferroptosis induced by chronic exposure becomes functionally irreversible, as ferroptosis inhibition by Ferrostatin-1 failed to rescue GPX4 expression, unlike prior acute exposure models.

PMID:40614423 | DOI:10.1016/j.jhazmat.2025.139049

Ann Surg Oncol. 2025 Jul 3. doi: 10.1245/s10434-025-17738-3. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to identify the most effective drug therapeutics for patients with the mesenchymal subtype of advanced gastric cancer (AGC). Extensive research employing diverse omics methodologies has unveiled a varied landscape of AGC. Recent progress in next-generation sequencing and other genomic technologies has facilitated a more intricate exploration of AGC at the molecular level. Nonetheless, the optimal treatment for patients with the mesenchymal subtype of gastric cancer remains elusive. Lei's molecular classification of AGC is based on gene expression profiles named "mesenchymal," "immunogenic," "classical," and "metabolic."

PATIENTS AND METHODS: Based on RNA-seq transcriptome, 234 patients were divided into four molecular subtypes: mesenchymal (n = 96), immunogenic (n = 37), metabolic (n = 61), and classic (n = 40).

RESULTS: Among those with mesenchymal-subtype AGC, compared with non-Apatinib group, the Apatinib treatment group demonstrated a significant increase in objective response rate (ORR 89.3% versus 69.3%, p = 0.038; odds ratio (OR) 0.269, 95% confidence interval (CI) (0.073-0.989)); overall survival (OS) 89.3% versus 60.2%, p = 0.010; hazard ratio (HR) 0.241, 95% CI (0.073-0.796)) and disease-free survival (DFS 78.6% versus 52.9%, p = 0.031; HR 0.400, 95% CI (0.167-0.956)). Furthermore, Apatinib significantly reduced the risk of death and recurrence in patients with mesenchymal subtype (OS: HR 0.129, 95% CI (0.030-0.563), p = 0.006; DFS: HR 0.340, 95% CI (0.138-0.833), p = 0.018). However, no significant differences were observed in the ORR, OS, or DFS between patients with metabolic and classical subtypes who underwent combination chemotherapy with additional Apatinib or camrelizumab.

CONCLUSIONS: Our analysis has revealed that, for neoadjuvant therapy in AGC, the mesenchymal subtype stands out as the ideal patient population benefiting from Apatinib.

PMID:40608168 | DOI:10.1245/s10434-025-17738-3