OMICS. 2025 Aug 19. doi: 10.1177/15578100251366980. Online ahead of print.

ABSTRACT

Ferroptosis, an iron-dependent form of oxidative cell death, plays a critical role in cancer progression and immune regulation. However, the functional connections of ferroptosis with specific immune cell types remain poorly defined, limiting the future possibilities to harness ferroptosis for cancer biology, diagnosis, and treatment. To address this knowledge gap, we conducted an integrated transcriptomic analysis to investigate ferroptosis-related immune dynamics in gastric cancer (GC). We utilized GC datasets from The Cancer Genome Atlas-stomach adenocarcinoma (n = 412) and the GSE66229 (n = 300) that were clustered into three GC immune subtypes based on single-sample Gene Set Enrichment Analysis scores of 29 immune gene sets. Bulk RNA-seq analysis revealed that the immune-inflamed subtype (HIS) of tumor samples in both GC datasets exhibited the highest ferroptosis enrichment and showed a positive correlation with activated mast cells and neutrophils. Given the regulatory role of mast cells in the tumor microenvironment (TME), particularly in recruiting neutrophils, we further examined their link to ferroptosis. In a fibroblast-mast cell coculture RNA-seq data (GSE223179), fibroblasts exhibited increased ferroptosis enrichment, supporting a mast cell-mediated influence. Single-cell RNA-seq data confirmed stronger interactions between mast cells and fibroblasts in GC compared to normal tissues. Specifically, they revealed a positive correlation between mast cell activity and ferroptosis enrichment in tumor-associated fibroblasts. In conclusion, these findings suggest that mast cells may promote ferroptosis in the TME through paracrine signaling, possibly via annexin and cyclophilin A. By uncovering this novel pathophysiological axis, our study reveals a previously unrecognized role of mast cells in regulating ferroptosis within the TME. The findings call for translational and experimental medical research and have potential implications for innovation toward GC diagnostics and therapeutics.

PMID:40831393 | DOI:10.1177/15578100251366980

Sci Prog. 2025 Jul-Sep;108(3):368504251362350. doi: 10.1177/00368504251362350. Epub 2025 Aug 19.

ABSTRACT

Breast cancer recurrence remains a major cause of mortality, with up to 30% of early-stage patients relapsing as incurable metastatic disease. Conventional surveillance with imaging and serum markers (CA15-3, CEA) lacks the sensitivity and specificity to detect minimal residual disease. This narrative review examines non-invasive biomarkers such as circulating tumor DNA (ctDNA), circulating tumor cells (CTCs) and exosomes and the technologies enhancing their performance. Droplet digital PCR and next-generation sequencing detect ctDNA at allele frequencies below 0.1%, identifying molecular relapse a median of 10-12 months before radiologic progression. Microfluidic and affinity-based platforms isolate CTCs with over 75% sensitivity in metastatic settings. Nanoengineered sensors and standardized workflows improve exosome isolation, revealing miRNA and protein signatures predictive of recurrence. Proteomic and metabolomic profiling identify dysregulated metabolic pathways and protein networks, offering functional insights that complement molecular assays. Integrative multi-omics approaches merge genomic, transcriptomic, proteomic and metabolomic data; machine-learning frameworks detect subtle patterns and correlations, enabling dynamic, personalized surveillance. By detecting molecular and functional biomarkers early, clinicians can tailor therapy, monitor treatment response and intervene promptly. Challenges include low analyte abundance, assay variability, high costs and lack of standardized protocols, limiting clinical adoption. Prospective validation in large cohorts is critical. We highlight ongoing clinical trials such as ctDNA-guided adjuvant therapy and CTC-driven stratification studies that aim to establish clinical utility. Non-invasive biomarker platforms could shift breast cancer follow-up from reactive detection to proactive intervention, ultimately improving survival and quality of life through personalized, real-time monitoring.

PMID:40831186 | PMC:PMC12368403 | DOI:10.1177/00368504251362350

J Clin Microbiol. 2025 Aug 19:e0058525. doi: 10.1128/jcm.00585-25. Online ahead of print.

ABSTRACT

Human papillomavirus (HPV) is comprised of >200 genotypes and has an ~8 kb, circular, double-stranded DNA genome. Transmission of HPV occurs through skin-to-skin contact and infection of squamous epithelial cells of cutaneous and mucosal surfaces. HPV genotypes are categorized as low- or high-risk (hrHPV) based on oncogenic potential. There are approximately 14 types of hrHPV that can cause several types of cancer, including HPV-associated oropharyngeal squamous cell carcinoma (HPV(+)OPSCC). Detection of HPV(+)OPSCC is traditionally accomplished using p16 immunohistochemistry (IHC) and HPV-specific testing, either DNA or RNA in situ hybridization (ISH) staining or DNA-based PCR of suspected tumor biopsy tissue. More recently, platelet-poor plasma (PPP) samples from patients with HPV(+)OPSCC have proven useful for detection and quantitation of fragments of HPV circulating tumor DNA (ctDNA). ctDNA has been shown to be useful in determining treatment response and monitoring for disease recurrence. In this study, a novel droplet digital PCR assay (ddPCR) was developed and validated for the detection and quantitation of ctDNA from 5 hrHPV genotypes in PPP. Analytical sensitivity ranged from 7.71 to 19.45 fragments of HPV ctDNA per milliliter of PPP across five hrHPV genotypes. In patients with confirmed primary or recurrent HPV(+)OPSCC or HPV(-)OPSCC, testing of corresponding PPP samples (n = 32) by ddPCR demonstrated 90.63% (29/32) overall agreement with p16/HPV-ISH biopsy results. Compared with reference ddPCR assays performed at outside laboratories, our ddPCR assay yielded 90% (9/10) overall agreement. This assay may provide clinicians with a tool for monitoring HPV ctDNA prior to, during, and after treatment of an HPV-associated cancer.

IMPORTANCE: At least 14 genotypes of human papillomavirus (HPV) have been identified to have high oncogenic potential. While molecular diagnostic testing for HPV is widely available for liquid cytologic cervical samples, testing is limited for other sample types, including liquid biopsy samples, such as platelet-poor plasma (PPP). With the rising incidence of HPV-associated oropharyngeal squamous cell carcinoma (HPV(+)OPSCC), laboratory testing is an essential part of patient diagnosis, management, and surveillance. Here, we summarize the development and analytical performance validation of a multiplexed, droplet digital PCR (ddPCR) assay for the detection and quantitation of HPV circulating tumor DNA (ctDNA) in PPP. This assay may provide clinicians with a tool to address minimal residual disease for patients with an HPV-associated cancer.

PMID:40827899 | DOI:10.1128/jcm.00585-25

Expert Rev Anticancer Ther. 2025 Aug 18. doi: 10.1080/14737140.2025.2549538. Online ahead of print.

ABSTRACT

INTRODUCTION: In the era of precision medicine, molecular biomarker testing is increasingly becoming standard of care for Non-Small Cell Lung Cancer (NSCLC) patients. Tissue and liquid biopsy-based Next-Generation Sequencing (NGS) is now highly recommended.

AREAS COVERED: Different NGS platforms emerged as a cost-effective strategy to perform a massive and parallel sequencing performing higher technical sensitivity than old generation technologies in detecting low abundant alterations in challenging diagnostic samples. NGS systems can detect single nucleotide variants (SNV), small insertions and deletions (indels), copy number alterations (CNAs) and structural variants (SVs) or gene fusions across selected druggable genes optimizing clinical administration of NSCLC patients. The diagnostic implementation of the most adequate NGS panel depending on several factors that could impact on the clinical utility of testing assay.

EXPERT OPINION: Promising advanced technologies are emerging as potentially integrative tools in personalized medicine. In this context, multi-omic evaluation including genomic, transcriptomic, fragmentomic and epigenomic signatures are under investigation to significantly modify clinical algorithm of NSCLC patients. On this basis, sequencing strategies may play a pivotal role in the implementation of a new predictive model for cancer diagnosis and prognosis.

PMID:40823981 | DOI:10.1080/14737140.2025.2549538

Curr Pharm Des. 2025 Aug 13. doi: 10.2174/0113816128413220250728182852. Online ahead of print.

ABSTRACT

The use of CRISPR-Cas9 to engineer cancer cell lines has made it possible to precisely examine how cancer cells react to different drugs and therapies. Some of the key improvements are in the use of Mediator Complex Subunit 12 (MED12)-knockout cells to study cell resistance to BRAF inhibitors, CRISPR models of Epithelial-Mesenchymal Transition for breast cancer, and pharmacogenomic analysis in various cancer cell lines. CRISPR is used in immunotherapy to help Chimeric Antigen Receptor T (CAR-T) cells function better by disrupting the immune checkpoints like Programmed Cell Death Protein 1 (PD-1) and Cytotoxic T-lymphocyte- associated protein 4 (CTLA-4) and to adapt T cells to react with various antigens. As a result of these innovations, it is now possible to track how cancers like non-small cell lung cancer (NSCLC) and ovarian cancer evolve, change their epigenetic features, and find strategies to reverse their resistance. Moving forward, mixing AI analytics, single-cell multi-omics, patient-derived organoids, and CRISPR mechanisms will help improve precision oncology and speed up effective treatment planning.

PMID:40814875 | DOI:10.2174/0113816128413220250728182852

Transl Lung Cancer Res. 2025 Jul 31;14(7):2369-2373. doi: 10.21037/tlcr-2025-523. Epub 2025 Jul 17.

NO ABSTRACT

PMID:40799451 | PMC:PMC12337028 | DOI:10.21037/tlcr-2025-523

Biomark Res. 2025 Aug 11;13(1):101. doi: 10.1186/s40364-025-00812-z.

ABSTRACT

BACKGROUND: Early identification of high-risk individuals is crucial for optimizing cancer screening, particularly when considering expensive and invasive methods such as multi-omics technologies and endoscopic procedures. However, developing a robust, practical multi-cancer risk prediction model that integrates diverse, multi-scale data and with proper validation remains a significant challenge.

METHODS: We initialized the FuSion study by recruiting 42,666 participants from Taizhou, China, with a discovery cohort (n = 16,340) and an independent validation cohort (n = 26,308) after exclusion criteria. We integrated multi-scale data from 54 blood-derived biomarkers and 26 epidemiological exposures to develop a risk prediction model for five common cancers, including lung, esophageal, liver, gastric, and colorectal cancer. Employing five supervised machine learning approaches, we used a LASSO-based feature selection strategy to identify the most informative predictors. The model was trained and internally validated in the discovery cohort, externally applied in the validation cohort, and further evaluated through a prospective clinical follow-up to assess cancer events via clinical examinations.

RESULTS: The final model comprising four key biomarkers along with age, sex, and smoking intensity, achieving an AUROC of 0.767 (95% CI: 0.723-0.814) for five-year risk prediction. High-risk individuals (17.19% of the cohort) accounted for 50.42% of incident cancer cases, with a 15.19-fold increased risk compared to the low-risk group. During follow-up of 2,863 high-risk subjects, 9.64% were newly diagnosed with cancer or precancerous lesions. Notably, cancer detection in the high-risk group was 5.02 times higher than in the low-risk group and 1.74 times higher than in the intermediate-risk group. In particular, the incidence of esophageal cancers in the high-risk group was 16.84 times that of the low-risk group.

CONCLUSIONS: This is the first population-based prospective study in a large Chinese cohort that leverage multi-scale data including biomarkers for multi-cancer risk prediction. Our effective risk stratification model not only enhances early cancer detection but also lays the foundation for the targeted application of advanced screening methods, including but not limited to multi-omics technologies and endoscopy. These findings support precision prevention strategies and the optimal allocation of healthcare resources.

PMID:40790537 | PMC:PMC12341305 | DOI:10.1186/s40364-025-00812-z

Explor Target Antitumor Ther. 2025 Aug 8;6:1002333. doi: 10.37349/etat.2025.1002333. eCollection 2025.

ABSTRACT

Liquid biopsy (LB) is a complex of procedures aimed at the detection of tumor-derived fragments (nucleic acids, proteins, cells, etc.) persisting in the blood or other body fluids. It can be utilized for early cancer diagnosis, analysis of biomarkers of tumor drug sensitivity and prognosis, monitoring of minimal residual disease (MRD), etc. Circulating tumor DNA (ctDNA) is an accessible and reliable LB analyte as it may contain tumor-specific mutations and is amenable to efficient detection by next-generation sequencing (NGS) or droplet digital PCR (ddPCR). High level of ctDNA is typically associated with increased tumor burden and poor prognosis, whereas treatment-related ctDNA clearance increases the probability of a favorable disease outcome. Major efforts have been invested in enhancing the analytical performance of ctDNA detection. Stimulation of apoptosis of tumor cells by irradiation of cancer lumps has been shown to result in a transient but modest increase in ctDNA concentration. There are several sophisticated modifications of ultra-deep NGS protocols, which discriminate between "true" low-copy mutation-specific signals and sequencing artifacts. Slowing physiological ctDNA decay by interfering with liver macrophages and circulating nucleases has shown promise in animal experiments. Reproducibility of ctDNA-based LB assays remains insufficient for samples with ultra-low content of ctDNA; hence, interlaboratory harmonization of ctDNA testing procedures is of paramount importance.

PMID:40787067 | PMC:PMC12332530 | DOI:10.37349/etat.2025.1002333

J Transl Med. 2025 Aug 7;23(1):877. doi: 10.1186/s12967-025-06725-7.

ABSTRACT

BACKGROUND: Gastric cancer (GC) is characterized by significant intertumoral heterogeneity, which often leads to the development of resistance to platinum-based chemotherapy. Combining platinum drugs with other therapeutic strategies may improve treatment efficacy; however, the mechanisms underlying platinum resistance in GC remain unclear.

METHODS: Key genes related to platinum resistance in GC were selected from the platinum resistance gene database and GC resistance datasets. The Similarity Network Fusion (SNF) algorithm was employed, along with prognosis-related methylation data and somatic mutation data, to classify the molecular subtypes of GC based on GC platinum resistance genes. Gene expression profiles, prognosis, immune cell infiltration, chemotherapy sensitivity, and immunotherapy responsiveness were comprehensively evaluated for each subtype. Localization and functional evaluation were conducted at the single-cell and spatial transcriptomics levels, and predictive models were developed using machine learning techniques. These functional differences in platinum resistance gene models were further explored in GC. Moreover, experimental validation was conducted to elucidate the mechanisms of key genes involved in platinum resistance in GC.

RESULTS: Stomach adenocarcinoma (STAD) patients were classified into three subtypes using the SNF algorithm and multiomics data. Patients with subtype CS2 exhibited a significantly poorer prognosis than those with subtypes CS1 and CS3 (p < 0.05). Subtype CS1 was characterized as immune-deprived, CS2 as stroma-enriched, and CS3 as immune-enriched. Patients with subtype CS2 also exhibited the most adverse therapeutic responses to docetaxel, cisplatin, and gemcitabine. Single-cell analysis revealed high enrichment of M1 module cells with elevated expression of resistance genes, including the transcription factor KLF9. Spatial transcriptomic analysis further confirmed the independent spatial distribution of malignant cells with high expression of drug resistance genes (DRGs). Predictive models based on machine learning demonstrated excellent prognostic performance. Patients in the high DRG group also exhibited poorer responses to immunotherapy. Cellular experiments revealed that KLF9 overexpression significantly inhibited the proliferation of AGS cells (p < 0.05), reduced their resistance to platinum-based drugs, and markedly decreased the levels of inflammatory cytokines in them.

CONCLUSION: KLF9 was identified as a promising therapeutic target for overcoming platinum resistance in GC, warranting further investigation into its role and potential clinical applications.

PMID:40775648 | PMC:PMC12330134 | DOI:10.1186/s12967-025-06725-7

Future Sci OA. 2025 Dec;11(1):2527598. doi: 10.1080/20565623.2025.2527598. Epub 2025 Aug 7.

ABSTRACT

AIM: This study aims to present a comprehensive international analysis of the existing techniques used in liquid biopsies and their use in isolating tumor markers to detect, predict, and monitor the results of cancer treatment.

MATERIALS AND METHODS: We conducted a narrative review using a scoping review model based on three databases, including PubMed/Medline, Scopus, and Cochrane. The search criteria included all articles on liquid biopsy of the last five years (June 30th, 2023-Oct 30, 2024) ((liquid Biopsy) AND (("2023/06/30"[Date - Publication]: "2024/10/30"[Date - Publication]))). We also approached gray literature on this topic. We focused on review articles as an eligibility criterion for this narrative review, but we also carried out a United States registered clinical trials review targeting immunotherapy and liquid biopsy with the limitation "recruiting" and/or "not yet recruiting" (updated on March 31, 2025).

RESULTS: We screened 2645 articles from PubMed/Medline, Scopus, and Cochrane and 45 articles from the gray literature. We retrieved the full text for 325 articles. Liquid biopsies involve the extraction of tumor-derived components such as circulating tumor cells, circulating tumor DNA, and tumor extracellular vesicles from the bodily fluids of cancer patients. We found 25 United States registered governmental clinical trials targeting immunotherapy and liquid biopsy, of which 20 trials are recruiting and five trials are not yet recruiting.

DISCUSSION: Developments in medicine have led to a more comprehensive understanding of tumor features, including tumor load, tumor staging, heterogeneity, gene mutations, and clonal evolution. The utilization of liquid biopsies from cancer patients has provided novel opportunities for detection and ongoing monitoring, precision medicine-based therapy, and identification of markers for therapeutic resistance.

PMID:40772765 | PMC:PMC12333414 | DOI:10.1080/20565623.2025.2527598

Commun Biol. 2025 Aug 6;8(1):1160. doi: 10.1038/s42003-025-08533-7.

ABSTRACT

The rapid advancement of single-cell technologies has led to the development of various analysis methods, each with trade-offs between predictive power and interpretability particularly for multimodal data integration. Complex machine learning models achieve high accuracy, but they often lack transparency, while simpler models are more interpretable but less effective for prediction. In this manuscript, we introduce an innovative method for single-cell analysis using Multiple Kernel Learning (scMKL), that merges the predictive capabilities of complex models with the interpretability of linear approaches, aimed at providing actionable insights from single-cell multiomics data. scMKL excels at classifying healthy and cancerous cell populations across multiple cancer types, utilizing data from single-cell RNA sequencing, ATAC sequencing, and 10x Multiome. It outperforms existing methods while delivering interpretable results that identify key transcriptomic and epigenetic features, as well as multimodal pathways- that existing methods have failed to achieve, in breast, lymphatic, prostate, and lung cancers. Leveraging insights from one dataset to inform analysis in a new dataset, scMKL uncovers biological pathways that distinguish treatment responses in breast cancer, low-grade from high-grade prostate tumors, and subtypes in lung cancer, thereby enhancing our understanding of cancer biology and tumor progression.

PMID:40770488 | PMC:PMC12328712 | DOI:10.1038/s42003-025-08533-7

Int J Med Sci. 2025 Jul 10;22(13):3268-3276. doi: 10.7150/ijms.117603. eCollection 2025.

ABSTRACT

Hepatocellular carcinoma (HCC) is a genetically heterogeneous malignancy in which single-gene mutations serve as critical drivers of tumor initiation, progression, and therapeutic resistance. Advances in high-throughput genomics and liquid biopsy technologies have highlighted the clinical utility of mutations in genes such as TP53, CTNNB1, and TERT as diagnostic, prognostic, and predictive biomarkers. These mutations disrupt key oncogenic pathways, modulate the tumor immune microenvironment, and contribute to intratumoral heterogeneity, complicating disease management. Mutation-guided precision medicine, including telomerase inhibitors, Wnt/β-catenin pathway modulators, and immune checkpoint blockade, offers promising avenues for individualized treatment in HCC. However, challenges persist in translating these findings into clinical practice due to mutation complexity, resistance mechanisms, and limitations in biomarker standardization. Emerging strategies such as multi-omics integration, artificial intelligence, and gene editing technologies hold potential to overcome these barriers and facilitate the development of personalized therapeutic regimens. This review summarizes the molecular mechanisms, clinical applications, and translational challenges of single-gene mutations in HCC, with the aim of guiding future research and precision oncology.

PMID:40765562 | PMC:PMC12320797 | DOI:10.7150/ijms.117603

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