Nat Commun. 2025 Aug 5;16(1):7178. doi: 10.1038/s41467-025-62593-1.

ABSTRACT

Spatial transcriptomics links gene expression with tissue morphology, however, current tools often prioritize genomic analysis, lacking integrated image interpretation. To address this, we present Thor, a comprehensive platform for cell-level analysis of spatial transcriptomics and histological images. Thor employs an anti-shrinking Markov diffusion method to infer single-cell spatial transcriptome from spot-level data, effectively combining gene expression and cell morphology. The platform includes 10 modular tools for genomic and image-based analysis, and is paired with Mjolnir, a web-based interface for interactive exploration of gigapixel images. Thor is validated on simulated data and multiple spatial platforms (ISH, MERFISH, Xenium, Stereo-seq). Thor characterizes regenerative signatures in heart failure, screens breast cancer hallmarks, resolves fine layers in mouse olfactory bulb, and annotates fibrotic heart tissue. In high-resolution Visium HD data, it enhances spatial gene patterns aligned with histology. By bridging transcriptomic and histological analysis, Thor enables holistic tissue interpretation in spatial biology.

PMID:40764306 | PMC:PMC12325965 | DOI:10.1038/s41467-025-62593-1

Oncologist. 2025 Jun 24:oyaf191. doi: 10.1093/oncolo/oyaf191. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a major cause of cancer death. Traditional histopathological classification overlooks molecular heterogeneity, limiting personalized treatment. This study used multiomic data to define LUAD subtypes, assess prognostic significance, and analyze immune features, aiming to improve targeted therapy and clinical outcomes.

METHODS: This study used Consensus Clustering and Gap Statistics to analyze LUAD multiomic data, including mRNA, lncRNA, miRNA, DNA methylation, and mutations. Clustering was validated by silhouette plots and heatmaps. Molecular characterization involved regulon activity, immune and metabolic profiling. Functional assays (qPCR, WB, CCK-8, flow cytometry) assessed NDNF's role in LUAD.

RESULTS: Two molecular LUAD subtypes showed distinct clustering and survival outcomes. One subtype had worse prognosis and unique immune features, including checkpoint expression and microenvironment differences. Gene signatures and metabolism varied by subtype. NDNF was downregulated in tumors; its overexpression suppressed LUAD cell viability and promoted apoptosis, suggesting tumor-suppressive function.

CONCLUSION: This study identifies two LUAD subtypes with distinct molecular and immune features linked to prognosis and therapy response. NDNF downregulation and its tumor-suppressive effects highlight its therapeutic potential. These findings support improved LUAD stratification and personalized treatment strategies.

IMPLICATIONS FOR PRACTICE: This study reveals two LUAD molecular subtypes with distinct prognoses and immune features, offering a basis for more precise treatment strategies. The identification of NDNF as a potential tumor suppressor suggests its value as both a biomarker and therapeutic target. These findings support improved LUAD stratification and personalized therapy.

PMID:40554675 | DOI:10.1093/oncolo/oyaf191

Front Pharmacol. 2025 May 15;16:1534974. doi: 10.3389/fphar.2025.1534974. eCollection 2025.

ABSTRACT

BACKGROUND: Frizzled class receptor 2 (FZD2), is a critical protein in the Wnt signaling pathway, which plays significant roles in various cancers. However, its role in cancer progression, prognosis, and diagnosis remains largely unexplored. This study investigates the correlation between FZD2 expression and clinical outcomes, as well as its underlying molecular mechanisms in pan-cancer.

METHODS: A comprehensive bioinformatic analysis was performed using pan-cancer data from The Cancer Genome Atlas (TCGA), which included 33 cancer types. Gene set enrichment analysis (GSEA) was conducted to explore functional pathways, while a protein-protein interaction (PPI) network was constructed to further elucidate the role of FZD2 in tumor biology. The relationship between FZD2 expression and immune cell infiltration across 22 categories was assessed using CIBERSORT. Additionally, single-cell analysis was employed to examine FZD2 expression levels across different cell types. To investigate the functional impact of FZD2, loss-of-function experiments were carried out in gastric cancer cell lines using siRNA-mediated knockdown. Subsequent assays, including Polymerase Chain Reaction (PCR), Western blotting (WB), Cell Counting Kit-8 (CCK8), Flow Cytometry, wound healing, and transwell migration and invasion assays, were performed to assess cellular responses. A subcutaneous gastric cancer xenograft model was established in nude mice to investigate the effect of FZD2 knockdown on tumor growth in vivo.

RESULTS: Our analysis revealed significant upregulation of FZD2 in multiple malignancies, including stomach adenocarcinoma (STAD), bladder cancer (BLCA), and cholangiocarcinoma (CHOL). FZD2 expression was correlated with various cancer characteristics, including stemness score, matrix score, immune score, tumor mutational burden (TMB), microsatellite instability (MSI), RNA modification genes, and drug sensitivity. Notably, FZD2 was associated with altered sensitivity to several anticancer agents, suggesting its role in modulating treatment responses. FZD2 knockdown was demonstrated by both in vitro and in vivo experiments to suppress tumor cell proliferation, migration, and invasion in gastric cancer cell lines, indicating its critical role in tumor progression. Furthermore, FZD2 exhibited significant correlations with other Wnt pathway genes (e.g., Wnt2, Wnt4, Wnt5B), indicating a complex interaction network contributing to tumorigenesis.

CONCLUSION: FZD2 is widely upregulated in various tumor types, with its expression closely associated with key clinical outcomes, including overall survival, disease-specific survival, disease-free interval, as well as tumor mutations, drug sensitivity, immune cell infiltration, and immunotherapy-related biomarkers such as TMB and MSI. These findings highlight the pivotal role of FZD2 in cancer prognosis and treatment, offering potential for novel therapeutic approaches and the development of personalized medicine strategies in oncology.

PMID:40444048 | PMC:PMC12120476 | DOI:10.3389/fphar.2025.1534974

World J Hepatol. 2025 Feb 27;17(2):101201. doi: 10.4254/wjh.v17.i2.101201.

ABSTRACT

Liver cancer, particularly hepatocellular carcinoma (HCC), remains a significant global health challenge due to its high mortality rate and late-stage diagnosis. The discovery of reliable biomarkers is crucial for improving early detection and patient outcomes. This review provides a comprehensive overview of current and emerging biomarkers for HCC, including alpha-fetoprotein, des-gamma-carboxy prothrombin, glypican-3, Golgi protein 73, osteopontin, and microRNAs. Despite advancements, the diagnostic limitations of existing biomarkers underscore the urgent need for novel markers that can detect HCC in its early stages. The review emphasizes the importance of integrating multi-omics approaches, combining genomics, proteomics, and metabolomics, to develop more robust biomarker panels. Such integrative methods have the potential to capture the complex molecular landscape of HCC, offering insights into disease mechanisms and identifying targets for personalized therapies. The significance of large-scale validation studies, collaboration between research institutions and clinical settings, and consideration of regulatory pathways for clinical implementation is also discussed. In conclusion, while substantial progress has been made in biomarker discovery, continued research and innovation are essential to address the remaining challenges. The successful translation of these discoveries into clinical practice will require rigorous validation, standardization of protocols, and cross-disciplinary collaboration. By advancing the development and application of novel biomarkers, we can improve the early detection and management of HCC, ultimately enhancing patient survival and quality of life.

PMID:40027561 | PMC:PMC11866143 | DOI:10.4254/wjh.v17.i2.101201

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