Cancer Res. 2025 May 16. doi: 10.1158/0008-5472.CAN-25-2070. Online ahead of print.

ABSTRACT

Emerging spatial profiling technologies have revolutionized our understanding of how tissue architecture shapes disease progression, yet the contribution of cellular diversity remains underexplored. Here, Ding and colleagues introduce multiomics and ecological spatial analysis (MESA), an ecology-inspired framework that integrates spatial and single-cell expression data to quantify tissue diversity across multiple scales. MESA both identifies distinct cellular neighborhoods and computes a variety of diversity metrics alongside the identification of diversity "hotspots". Applied to human tonsil tissue, MESA revealed previously undetected germinal center organization, while in spleen tissue of a murine lupus model, MESA highlights increasing cellular diversity with disease progression. Importantly, diversity hotspots do not correspond to conventional compartments identified by existing methods, presenting an orthogonal metric of spatial organization. In colorectal cancer, MESA's diversity metrics outperformed established subtypes at predicting patient survival, while in hepatocellular carcinoma, multi-omic integration identified significantly more ligand-receptor interactions between immune cells compared to single-modality analysis. This work establishes cellular diversity within tissues as a critical correlate of disease progression and underscores the value of multi-omic integration in spatial biology.

PMID:40378285 | DOI:10.1158/0008-5472.CAN-25-2070

Front Digit Health. 2025 May 1;7:1583490. doi: 10.3389/fdgth.2025.1583490. eCollection 2025.

ABSTRACT

Lung transplantation (LTx) is an effective method for treating end-stage lung disease. The management of lung transplant recipients is a complex, multi-stage process that involves preoperative, intraoperative, and postoperative phases, integrating multidimensional data such as demographics, clinical data, pathology, imaging, and omics. Artificial intelligence (AI) and machine learning (ML) excel in handling such complex data and contribute to preoperative assessment and postoperative management of LTx, including the optimization of organ allocation, assessment of donor suitability, prediction of patient and graft survival, evaluation of quality of life, and early identification of complications, thereby enhancing the personalization of clinical decision-making. However, these technologies face numerous challenges in real-world clinical applications, such as the quality and reliability of datasets, model interpretability, physicians' trust in the technology, and legal and ethical issues. These problems require further research and resolution so that AI and ML can more effectively enhance the success rate of LTx and improve patients' quality of life.

PMID:40376618 | PMC:PMC12078212 | DOI:10.3389/fdgth.2025.1583490

Cell Death Dis. 2025 May 15;16(1):382. doi: 10.1038/s41419-025-07720-y.

ABSTRACT

M2-polarized tumor-associated macrophages (TAMs) are a key factor contributing to the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). While various factors within the tumor microenvironment (TME) drive their formation, the role of PDAC-derived exosomes in this process remains unclear. We aim to clarify the regulatory impacts of tumor-derived exosomes to TAMs. After the intratumoral injection to subcutaneous tumor of C57BL/6 mice, we demonstrated PDAC-derived exosomes exacerbate PDAC progression, accompanied with upregulated M2 phenotype of TAMs and unaffected proliferation signatures. Through intratumoral injection model and multi-Omics analyses, we identified CCT6A as a novel tumor-derived exosomal protein, bridging TAMs M2 polarization and PDAC prognosis. Co-culture with exosomes derived from CCT6A^high PDAC leads to greater M2 phenotype of TAMs via PI3K-AKT signaling. According to proteomics data, chemokines' abundance reduces over tenfold once exosomal CCT6A absence, including CXCL1, CXCL3, CCL20 and CCL5, whose interaction with CCT6A in PDAC cells was confirmed by interactomics data. Moreover, we found silencing CCT6A abrogated the antagonism effects of CD47 antibody immunotherapy. Our findings implied that the subunit of the T-complex protein Ring Complex (TRiC) CCT6A serves as a matchmaker during exosome-mediated chemokines transfer from PDAC to TAMs. Silencing CCT6A effectively sensitized PDAC to CD47 antibody immunotherapy in vivo.

PMID:40374617 | PMC:PMC12081750 | DOI:10.1038/s41419-025-07720-y

Dermatologie (Heidelb). 2025 Jun;76(6):361-364. doi: 10.1007/s00105-025-05506-z. Epub 2025 May 7.

ABSTRACT

BACKGROUND: Personalized treatment of melanoma is becoming increasingly more important. Biomarkers offer the possibility of controlling treatment more precisely and reducing side effects.

OBJECTIVE: The aim of this text is to provide an overview of current tissue-based, blood-based and radiological biomarkers and their clinical application in melanomas.

MATERIAL AND METHODS: A literature research and analysis of current studies on biomarkers in adjuvant and neoadjuvant treatment of melanomas were carried out and relevant congress contributions were additionally included.

RESULTS: Tissue-based programmed cell death 1 ligand 1 (PD-L1) expression, interferon gamma (IFNγ) signature, gene expression profiles (GEP) and tumor mutational burden (TMB) are of prognostic and predictive relevance. Blood-based circulating tumor DNA (ctDNA) in the sense of a liquid biopsy should be emphasized as a personalized biomarker for longitudinal tracking during treatment or aftercare. Positron emission tomography computed tomography (PET-CT) and body composition enable an improved assessment of treatment efficiency. There are currently no data from prospective validation studies on these biomarkers; initial data from the NivoMela study are awaited.

CONCLUSION: The combination of tissue-based, blood-based and radiological biomarkers in terms of multiparametric approaches is promising but further prospective validation is needed for broad clinical use. These are currently not comprehensively implemented in the clinical routine in centers or in remuneration procedures.

PMID:40335648 | DOI:10.1007/s00105-025-05506-z

Clin Transl Oncol. 2025 May 10. doi: 10.1007/s12094-025-03947-5. Online ahead of print.

ABSTRACT

Pancreatic cancer (PC) remains one of the most lethal malignancies, primarily due to its poor prognosis and late diagnosis. Biomarkers are essential in enhancing diagnostic accuracy, prognostic assessments, and therapeutic strategies, thereby addressing these challenges. Conventional biomarkers, such as CA 19-9, are widely used for monitoring disease progression but have limitations in early detection and specificity, necessitating complementary markers like CEA and MUC1. Emerging genetic biomarkers, including KRAS mutations and TP53 alterations, offer critical insights into tumorigenesis and serve as valuable diagnostic, prognostic, and therapeutic targets. Epigenetic biomarkers, such as DNA methylation and histone modifications, provide additional molecular layers, with aberrant methylation patterns and dysregulated histone modifications influencing tumor aggressiveness and therapy resistance. RNA-based biomarkers, particularly microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), play pivotal roles in regulating tumor biology and offer significant diagnostic and therapeutic potential. Protein-based biomarkers, including glycoproteins and cytokines, alongside liquid biopsy components like circulating tumor DNA (ctDNA), exosomes, and circulating tumor cells (CTCs), facilitate real-time disease monitoring and early detection. Personalized therapy is increasingly guided by these biomarkers, which predict responses to chemotherapy and immunotherapy. Despite challenges in biomarker validation and clinical implementation, advancements in multi-omics, artificial intelligence, and collaborative research hold promise for improving patient outcomes and survival rates in PC.

PMID:40348906 | DOI:10.1007/s12094-025-03947-5

J Transl Med. 2025 May 2;23(1):504. doi: 10.1186/s12967-025-06517-z.

ABSTRACT

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are synthetic chemicals known for their widespread use and environmental persistence. These compounds have been increasingly linked to hepatotoxicity and the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms by which PFAS contribute to HCC remain underexplored.

METHODS: This study employs a multi-omics approach that combines network toxicology, integrated machine learning, single-cell RNA sequencing, spatial transcriptomics, experimental validation, and molecular docking simulations to uncover the mechanisms through which PFAS exposure drives HCC. We analyzed publicly available transcriptomic data from several HCC cohorts and used differential gene expression analysis to identify targets associated with both PFAS exposure and HCC. We constructed a protein-protein interaction (PPI) network and a survival risk model, the PFAS-related HCC signature (PFASRHSig), based on integrated machine learning to identify prognostic biomarkers, with the goal of identifying core targets of PFAS in HCC progression and prognosis. RT-qPCR and immunohistochemical (IHC) staining were used to validate the expression levels of the targets in both tumor and normal tissues. Molecular docking simulations were conducted to assess the binding affinities between PFAS compounds and selected target proteins.

RESULTS: Functional enrichment studies revealed that PFAS targets were associated with metabolic signaling pathways, which are actively involved in lipid, glucose, drug metabolism, etc. Through integrated machine learning and PPI network analysis, we identified six genes, APOA1, ESR1, IGF1, PPARGC1A, SERPINE1, and PON1, that serve as core targets of PFAS in both HCC progression and prognosis. These targets were further validated via bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics, which revealed differential expression patterns across various cell types in the HCC tumor microenvironment. The results of RT-qPCR and IHC staining were consistent with the in silico findings. Molecular docking simulations revealed strong binding affinities between PFAS compounds and these core targets, supporting their potential roles in PFAS-induced hepatocarcinogenesis.

CONCLUSIONS: Our study highlights key molecular targets and pathways involved in PFAS-induced liver carcinogenesis and proposes a robust survival risk model (PFASRHSig) for HCC. These findings provide new insights into PFAS toxicity mechanisms and offer potential therapeutic targets for mitigating the health risks associated with PFAS exposure. Collectively, our findings help in advancing clinical applications by providing insights into disease mechanisms and potential therapeutic interventions.

PMID:40317014 | PMC:PMC12049027 | DOI:10.1186/s12967-025-06517-z