Biomedicines. 2025 Sep 11;13(9):2238. doi: 10.3390/biomedicines13092238.

ABSTRACT

Background:HTR1F (5-Hydroxytryptamine Receptor 1F) encodes a G protein-coupled receptor involved in serotonin signaling. Although dysregulated HTR1F expression has been implicated in certain malignancies, its biological functions and clinical significance across cancer types remain largely unexplored. Methods: We performed an integrative pan-cancer analysis of transcriptomic and pharmacogenomic datasets covering 34 cancer types (PAN-CAN cohort, N = 19,131; normal tissues, G = 60,499). Drug sensitivity and molecular docking analyses were conducted using the GSCALite database. The protein-protein interaction (PPI) network of HTR1F was constructed via the STRING database. Additionally, we evaluated the effects of HTR1F overexpression on proliferation and invasion in human lung squamous cell carcinoma (LUSC) cell lines NCI-H520 and NCI-H226. Results:HTR1F expression was significantly upregulated in 17 cancer types and was associated with poor prognosis, with LUSC showing an AUC of 0.912 for 1-year survival prediction. In LUSC, 695 genes were upregulated and 67 downregulated in response to HTR1F overexpression. HTR1F expression correlated with immune-related genes, immune checkpoints, tumor-infiltrating immune cells, tumor mutation burden (TMB), microsatellite instability (MSI), and drug responses. Genomic alterations, including amplification and deletion, were positively associated with HTR1F expression. Drug sensitivity analysis identified compounds such as sotrastaurin (-10.2 kcal/mol), austocystin D (-9.7 kcal/mol), and tivozanib (-9.3 kcal/mol) as potentially effective inhibitors based on predicted binding affinity. Functional enrichment analyses (GO, KEGG) and GSEA revealed that HTR1F is primarily involved in cell cycle regulation, DNA replication, cellular senescence, and immune-related pathways. Functional validation showed that HTR1F overexpression promotes proliferation of LUSC cells via the MAPK signaling pathway. Conclusions: Our integrative analysis highlights HTR1F as a potential biomarker associated with prognosis, immune modulation, and drug sensitivity across multiple cancer types. These findings provide a foundation for future experimental and clinical studies to explore HTR1F-targeted therapies.

PMID:41007799 | PMC:PMC12467612 | DOI:10.3390/biomedicines13092238

Nat Commun. 2025 Sep 25;16(1):8388. doi: 10.1038/s41467-025-63050-9.

ABSTRACT

Spatial technologies examining the cell and tissue microenvironment at near single-cell resolution are revealing important molecular insights. However, few tools enable integrated, interactive analysis of spatial-omics with tissue morphology in the same functional tissue unit. Here, we present FUSION (Functional Unit State Identification in Whole Slide Images), a web-based platform for visualizing and analyzing spatial-omics data with high-resolution histology. FUSION provides workflows for assessing cell compositions, quantitative morphometrics, and comparative tissue analyses. We demonstrate applicability across spatial assays, including 10x Visium, Visium HD, 10x Xenium, Cell DIVE, and PhenoCycler, applied to healthy and diseased tissues from kidney, small intestine, lung, and skin in the Human BioMolecular Atlas Program. FUSION is cloud-based, open-source, and accessible at https://fusion.hubmapconsortium.org/ , hosting over 50 paired datasets and tutorials. In a series of use cases, we show its capacity to distinguish renal glomeruli injury states, quantify morphometric changes, and characterize fibrosis with immune infiltration.

PMID:40998789 | PMC:PMC12462499 | DOI:10.1038/s41467-025-63050-9

Am J Clin Exp Immunol. 2025 Aug 15;14(4):237-240. doi: 10.62347/EBRY4326. eCollection 2025.

ABSTRACT

Cell-free DNA (cfDNA) fragmentomics has emerged as a powerful and noninvasive approach for cancer detection, characterization, and monitoring. By analyzing genome-wide fragmentation patterns - including fragment length distributions, end motifs, nucleosome footprints, and copy number variations - cfDNA fragmentomics provides high-resolution insights into tumor-specific biological signals even at low tumor burden. This technology offers advantages over conventional mutation-based assays by capturing aggregate structural and epigenomic alterations without requiring prior knowledge of driver mutations. In non-small cell lung cancer (NSCLC), cfDNA fragmentomics enables early detection, discrimination of malignant pulmonary nodules, and post-surgical monitoring of minimal residual disease. Recent studies have demonstrated that fragmentomic risk scores can accurately stratify recurrence risk and improve prognostic sensitivity beyond traditional genomic assays. In hepatocellular carcinoma (HCC), integration of fragment size selection, CNV profiling, and end-motif analysis has led to high-performing models for early diagnosis, particularly in high-risk populations. Moreover, cfDNA fragmentomics has proven effective in detecting malignant transformation in patients with neurofibromatosis-associated peripheral nerve sheath tumors, distinguishing benign from premalignant or malignant lesions with high precision. Expanding beyond these major cancers, fragmentomic approaches have demonstrated diagnostic potential in gastric, urological, hematologic, and pediatric malignancies. Notably, the DELFI-TF (DNA Evaluation of Fragments for early Interception-Tumor Fraction) framework has shown prognostic relevance by correlating pre-treatment cfDNA features with survival outcomes in colorectal and lung cancer patients, outperforming conventional imaging. All of these results highlight the translational importance of cfDNA fragmentomics as a cutting-edge precision oncology tool. Its continued integration into clinical workflows may redefine early cancer detection, facilitate subtype-specific interventions, and enable real-time, individualized treatment monitoring.

PMID:40977920 | PMC:PMC12444407 | DOI:10.62347/EBRY4326

Stem Cell Rev Rep. 2025 Sep 11. doi: 10.1007/s12015-025-10973-x. Online ahead of print.

ABSTRACT

The emergence of organoid models has significantly bridged the gap between traditional cell cultures/animal models and authentic human disease states, particularly for genetic disorders, where their inherent genetic fidelity enables more biologically relevant research directions and enhances translational validity. This review systematically analyzes established organoid models of genetic diseases across organs (e.g., brain, eye, kidney, lung, and heart), highlighting their pivotal roles in identifying novel pathogenic genes, elucidating disease mechanisms, and advancing therapeutic strategies such as drug screening platforms, gene-editing therapies, and organ transplantation strategies. Furthermore, we critically address current limitations-including challenges in recapitulating complex pathologies and scaling production-while underscoring their potential for personalized medicine through multi-omics integration and bioengineering innovations. Although the scope of "genetic diseases" is broad, this synthesis focuses on disorders with well-defined inheritance patterns, such as monogenic disorders, copy number variations (CNVs), and aneuploidies. Despite covering only a subset of these conditions, this review aims to provide researchers with a comprehensive overview of the field, emphasizing how organoid-based approaches could accelerate both mechanistic discoveries and clinical translation in genetic disease research.

PMID:40931310 | DOI:10.1007/s12015-025-10973-x

Front Biosci (Landmark Ed). 2025 Aug 30;30(8):44091. doi: 10.31083/FBL44091.

ABSTRACT

Zhang et al.'s recent article utilizes comprehensive single-cell data to identify differences in tumor cell populations, highlighting the CKS1B+ malignant cell subcluster as a potential target for immunotherapy. It develops a prognostic and immunotherapeutic signature (PIS) based on this subcluster, demonstrating good performance in predicting lung adenocarcinoma (LUAD) prognosis. The study also validates the role of PSMB7 in LUAD progression. However, there are areas for improvement. There is a lack of clarity regarding the relationship between the CKS1B+ malignant cell subcluster and the PIS, particularly in terms of why PSMB7 was selected for functional studies. The sequencing data are retrospectively obtained from public databases and lack prospective clinical validation. It is suggested to collect LUAD patient tissues for RT-qPCR and RNA-seq analysis and seek external multi-center validations. Additionally, integrating emerging multi-omics methods is recommended to further validate the findings. Despite these limitations, the study represents progress in understanding LUAD and treatment strategies, and continuous evaluation and refinement of multi-omics and machine learning methods are expected for future research and clinical practice.

PMID:40917070 | DOI:10.31083/FBL44091

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

bioRxiv [Preprint]. 2025 Aug 25:2025.08.21.671519. doi: 10.1101/2025.08.21.671519.

ABSTRACT

Lung cancer is the most common cause of cancer-related death worldwide. Recent advancements in targeted therapies and immunotherapies have achieved remarkable success. However, patient responses to treatments with lung cancer vary substantially. The mutation status of driver genes can direct personalized treatment, but their prognostic value and treatment efficacy are limited. In this study, we developed a statistical framework named Genomic Aberration-Derived Signature for Patient Stratification (GASPS) to characterize the transcriptomic deregulation of driver genomic aberrations and stratify patients. By applying GASPS to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data, we developed gene signatures for 38 driver genomic aberrations, including gene mutations, amplifications, and deletions. These signatures were applied to independent lung cancer transcriptomic datasets containing a total of 2,226 patient samples. Our results indicated that these driver gene signatures are much more prognostic than their corresponding genomic mutations. Interestingly, the two EGFR-related signatures characterizing EGFR mutation and amplification, respectively, exhibited contrasting associations with prognosis, treatment response, and immune infiltration in the tumor microenvironment. Moreover, the STK11 mutation signature, rather than the mutation status, was found to be predictive of the response and long-term benefit of patients treated with immune checkpoint blockade therapy in lung cancer. This framework is readily applicable to most cancer types using existing data to improve prognostic risk assessment and treatment efficacy by guiding personalized therapies.

PMID:40909579 | PMC:PMC12407784 | DOI:10.1101/2025.08.21.671519

Diagn Cytopathol. 2025 Nov;53(11):580-591. doi: 10.1002/dc.70009. Epub 2025 Sep 4.

ABSTRACT

Colorectal cancer (CRC) is a major global health burden, ranking among the leading causes of cancer-related deaths. Despite improvements in screening and treatment, challenges such as late-stage diagnosis, high recurrence rates, and therapy resistance continue to impede optimal outcomes. Liquid biopsy, a minimally invasive technique that analyzes tumor-derived components in bodily fluids-including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and extracellular vesicles (EVs)-is emerging as a powerful tool to transform CRC management across the disease continuum. This review provides a comprehensive overview of liquid biopsy's current and emerging applications in CRC. We examine its role in early detection, where sensitive ctDNA-based assays and epigenetic biomarkers have demonstrated the ability to identify CRC at asymptomatic or early stages, potentially improving screening uptake and compliance. Furthermore, we explore how liquid biopsy enables dynamic monitoring of treatment response and clonal evolution, facilitating the timely identification of resistance mutations and supporting personalized therapy adjustments. Innovations in multi-omics integration, artificial intelligence, and ultra-sensitive sequencing technologies are also discussed as pivotal advancements that enhance the clinical utility of liquid biopsy. Despite significant progress, the widespread adoption of liquid biopsy faces several hurdles, including assay standardization, sensitivity for low-shedding tumors, regulatory approval, and cost-effectiveness. Continued research, validation in large prospective trials, and harmonization of testing protocols are essential to overcome these challenges. Ultimately, liquid biopsy holds the potential to become a cornerstone of precision oncology in CRC, enabling earlier intervention, more tailored treatment strategies, and improved patient outcomes.

PMID:40905096 | DOI:10.1002/dc.70009

Cell. 2025 Aug 22:S0092-8674(25)00922-5. doi: 10.1016/j.cell.2025.08.008. Online ahead of print.

ABSTRACT

Performing total RNA profiling on formalin-fixed, paraffin-embedded (FFPE) samples, the predominant sample conservation method in clinical practice, remains challenging for current spatial transcriptomics techniques. Here, we introduce Stereo-seq V2, which employs random primers to capture and sequence RNAs in situ on FFPE sections and provides single-cell resolution. The random-priming-based strategy offers unbiased transcript capturing and uniform gene body coverage, which increase the sensitivity to marker genes, the efficiency of non-polyadenylation (poly(A)) RNA profiling, and immune repertoire coverage. We demonstrated the robust performance of Stereo-seq V2 on clinical FFPE samples using triple-negative breast cancer (TNBC) sections and identified tumor-specific alternative splicing events. In a Mycobacterium tuberculosis (Mtb)-infected mouse model, we monitored gene expression dynamics of host and pathogen transcriptomes simultaneously by utilizing Stereo-seq V2. We also assembled immune repertoires and identified Mtb-specific BCR clones, which could also be observed in human tuberculous lung samples. These results highlight Stereo-seq V2's potential in biomedical research and personalized medicine.

PMID:40882628 | DOI:10.1016/j.cell.2025.08.008

J Transl Med. 2025 Aug 27;23(1):964. doi: 10.1186/s12967-025-06993-3.

ABSTRACT

BACKGROUND: Circulating tumour DNA (ctDNA) in liquid biopsies has emerged as a powerful biomarker in cancer patients. Its relative abundance in cell-free DNA serves as a proxy for the overall tumour burden. Here we present GeneBits, a method for cancer therapy monitoring and relapse detection. GeneBits employs tumour-informed enrichment panels targeting 20-100 somatic single-nucleotide variants (SNVs) in plasma-derived DNA, combined with ultra-deep sequencing and unique molecular barcoding. In conjunction with the newly developed computational method umiVar, GeneBits enables accurate detection of molecular residual disease and early relapse identification.

RESULTS: To assess the performance of GeneBits and umiVar, we conducted benchmarking experiments using three different commercial cell-free DNA reference standards. These standards were tested with targeted next-generation sequencing (NGS) workflows from both IDT and Twist, allowing us to evaluate the consistency and accuracy of our approach across different oligo-enrichment strategies. GeneBits achieved comparable depth of coverage across all target sites, demonstrating robust performance independent of the enrichment kit used. For duplex reads with ≥ 4x UMI-family size, umiVar achieved exceptionally low error rates, ranging from 7.4×10^-7 to 7.5×10^-5. Even when including mixed consensus reads (duplex & simplex), error rates remained low, between 6.1×10^-6 and 9×10^-5. Furthermore, umiVar enabled variant detection at a limit of detection as low as 0.0017%, with no false positive calls in mutation-free reference samples. In a reanalysed melanoma cohort, variant allele frequency kinetics closely mirrored imaging results, confirming the clinical relevance of our method.

CONCLUSION: GeneBits and umiVar enable highly accurate therapy and relapse monitoring in plasma as well as identification of molecular residual disease within four weeks of tumour surgery or biopsy. By leveraging small, tumour-informed sequencing panels, GeneBits provides a targeted, cost-effective, and scalable approach for ctDNA-based cancer monitoring. The benchmarking experiments using multiple commercial cell-free DNA reference standards confirmed the high sensitivity and specificity of GeneBits and umiVar, making them valuable tools for precision oncology. UmiVar is available at https://github.com/imgag/umiVar .

PMID:40866952 | PMC:PMC12382282 | DOI:10.1186/s12967-025-06993-3

FASEB J. 2025 Aug 31;39(16):e70978. doi: 10.1096/fj.202500722RR.

ABSTRACT

Ribophorin 1 (RPN1), a key component of the oligosaccharyltransferase complex, is implicated in tumor progression through glycosylation-mediated pathways, yet its pan-cancer roles remain unexplored. This study presents a comprehensive multi-omics analysis of RPN1 across 33 cancers such as sarcoma (SARC), integrating genomic, transcriptomic, and proteomic data from TCGA, GTEx, and CPTAC. RPN1 was significantly overexpressed in 14 malignancies and correlated with advanced tumor stages and poor prognosis in glioblastoma (GBM), lower-grade glioma, SARC and hepatocellular carcinoma, validated in an independent glioma cohort (n = 151). Genomically, RPN1 amplification linked to homologous recombination deficiency and elevated tumor mutational burden, suggesting a role in genomic instability. Critically, multiplex immunofluorescence demonstrates RPN1 overexpression colocalizes with CD206⁺ M2 macrophages in tumor microenvironments, while in vitro coculture experiments confirm RPN1-dependent microglial recruitment and M2 polarization. RPN1 expression negatively correlates with CD8⁺ T cell infiltration and predicts resistance to chemotherapy (GBM, ovarian cancer) and immunotherapy (GBM, esophageal carcinoma), though it associates with PD-1 inhibitor sensitivity in bladder cancer. Functional validation shows RPN1 knockdown suppresses proliferation, migration, and invasion in GBM cells. Pathway enrichment connects RPN1 to endoplasmic reticulum stress, glycosylation, DNA repair, and immune checkpoint regulation. These findings position RPN1 as a multimodal oncogenic driver promoting genomic instability, immunosuppressive microenvironment remodeling, and context-dependent therapeutic vulnerabilities across cancers.

PMID:40857034 | DOI:10.1096/fj.202500722RR

Cell Rep. 2025 Aug 21;44(9):116191. doi: 10.1016/j.celrep.2025.116191. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease, and new therapeutic strategies are urgently needed. Here, we conduct an integrative, genome-scale examination of genetic dependencies and cell surface targets using CRISPR-Cas screening and multi-omic data, including single-nucleus and spatial transcriptomic data from patient tumors. We systematically identify clinically tractable and biomarker-linked PDAC dependencies, including CDS2 as a synthetic lethal target in cancer cells expressing signatures of epithelial-to-mesenchymal transition. We examine biomarkers and co-dependencies of the KRAS oncogene, defining gene expression signatures of sensitivity and resistance associated with response to pharmacological inhibition of KRAS. mRNA and protein profiling reveal cell surface protein-encoding genes with robust expression in patient tumors and minimal expression in non-malignant tissues. Furthermore, we define intratumoral and interpatient heterogeneity of target gene expression and identify orthogonal targets that suggest combinatorial strategies. Collectively, this work identifies multiple targets that may inform therapeutic strategies for patients with PDAC.

PMID:40848256 | DOI:10.1016/j.celrep.2025.116191

Nat Commun. 2025 Aug 22;16(1):7827. doi: 10.1038/s41467-025-63146-2.

ABSTRACT

While dysregulation of polyamine metabolism is frequently observed in cancer, it is unknown how polyamines alter the tumor microenvironment (TME) and contribute to therapeutic resistance. Analysis of polyamines in the plasma of pancreatic cancer patients reveals that spermine levels are significantly elevated and correlate with poor prognosis. Using a multi-omics approach, we identify Serpinb9 as a vulnerability in spermine metabolism in pancreatic cancer. Serpinb9, a serine protease inhibitor, directly interacts with spermine synthase (SMS), impeding its lysosome-mediated degradation and thereby augmenting spermine production and secretion. Mechanistically, the accumulation of spermine in the TME alters the metabolic landscape of immune cells, promoting CD8⁺ T cell dysfunction and pro-tumor polarization of macrophages, thus creating an immunosuppressive microenvironment. Small peptides that disrupt the Serpinb9-SMS interaction significantly enhance the efficacy of immune checkpoint blockade therapy. Together, our findings suggest that targeting spermine metabolism is a promising strategy to improve pancreatic cancer immunotherapy.

PMID:40846845 | PMC:PMC12373741 | DOI:10.1038/s41467-025-63146-2