World J Oncol. 2026 Sep 4;17(5):683-704. doi: 10.14740/wjon2815. eCollection 2026 Oct.

ABSTRACT

BACKGROUND: Lung cancer (LC) and venous thromboembolism (VTE) are closely associated, with VTE contributing to morbidity and mortality among patients with LC. We aimed to identify and characterize a mitochondrial-related transcriptomic signature shared between LC and VTE and to explore its association with immune microenvironment features.

METHODS: We applied a multiomics approach focused on mitochondrial-related signaling pathways. Publicly available transcriptomic datasets were analyzed using differential expression profiling and weighted gene co-expression network analysis to identify key regulatory genes. These genes were intersected with a mitochondrial gene set and subjected to functional enrichment analysis. Least absolute shrinkage and selection operator (LASSO) regression was used to identify candidate diagnostic genes validation. Immune cell infiltration was quantified, and associated regulatory mechanisms were explored.

RESULTS: Thirty-nine shared crosstalk genes were identified and were primarily enriched in mitochondrial metabolic processes. LASSO regression identified a five-gene candidate signature (ACAA1, HSD17B10, MTIF2, THOP1, and PDE2A). The model exhibited promising discriminatory performance (area under the curve > 0.9 in LC dataset and 0.7-0.9 in VTE dataset). These genes were significantly dysregulated and were associated with altered immune cell infiltration, particularly in dendritic cell and T cell subsets.

CONCLUSION: We identified a mitochondrial-related gene signature reflecting shared transcriptomic correlates between LC and VTE. The signature showed variable performance across disease contexts and correlative associations with immune features, supporting its role as a candidate biomarker for further investigation. Prospective validation in independent clinical cohorts is required before any translational application.

PMID:42730163 | PMC:PMC13568737 | DOI:10.14740/wjon2815

Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279

Gan To Kagaku Ryoho. 2026 Aug;53(8):483-488.

ABSTRACT

Spatial transcriptomics technologies enable omics analysis while preserving the spatial and histopathological context of cells within tissue sections, and have rapidly become widespread across various research fields, including cancer research. In cancer studies, these technologies are widely employed to elucidate changes in cancer cells and the surrounding tumor microenvironment associated with tumor progression and the emergence of therapeutic resistance. Through the application of spatial transcriptomics, numerous novel insights have been obtained regarding the identification of therapeutic targets and strategies to overcome treatment resistance. Sequencing-based platforms such as Visium capture transcriptome-wide information with spatial coordinates and have been applied to characterize the microenvironmental dynamics during lung adenocarcinoma progression and to identify microenvironmental states contributing to chemotherapy resistance in ovarian clear cell carcinoma. Imaging-based platforms such as Xenium and CosMx enable single-cell-resolution profiling of gene expression across hundreds of thousands of cells within tissue sections without requiring cell dissociation, and can be combined with multiplexed immunofluorescence staining to simultaneously obtain gene expression and protein expression data from the same section. These approaches have facilitated the discovery of novel cell subsets associated with patient prognosis and the detection of micrometastatic cancer cells in lymph nodes. In addition to the expanding range of applications, the technologies themselves have undergone remarkable development. Emerging techniques include spatial full-length transcriptome sequencing for splice isoform and immune receptor repertoire analysis, RNA-based mutation detection, spatial epigenomic profiling of chromatin accessibility and DNA methylation, and 3-dimensional spatial transcriptomics for volumetric tissue analysis. This review provides an overview of the fundamental technologies underlying spatial transcriptomics, presents representative examples of their application in cancer research, and introduces next-generation measurement techniques that are expected to further advance the field.

PMID:42723233

PLoS Comput Biol. 2026 Sep 10;22(9):e1014735. doi: 10.1371/journal.pcbi.1014735. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC), with a 12% 5-year survival rate, is the most aggressive type of cancer. Early diagnosis for this pathology is rare, and conventional treatments such as surgery, radio- or chemotherapy, have little to no effect on reducing mortality. Machine learning (ML) approaches could be used to identify biomarkers that help clinicians stratify patients and improve treatment outcomes. However, most ML techniques perform poorly with incomplete data, which is usually the case in real-world settings, often forcing researchers to discard valuable information. In this study, unsupervised ML algorithms capable of dealing with missing modalities were applied to incomplete multi-omics data from PDAC patients to identify clinically meaningful patient subgroups. Through a large-scale clustering benchmark including six omics layers, we discovered two novel subgroups with statistically significant differences in survival and recurrence after surgery, particularly within the first two years, when most patient deaths occur, as well as distinct tumor mutational burden. Comprehensive multi-omics analyses revealed substantial molecular differences between patients in both groups, identified three methylation biomarkers to stratify patients, and highlighted dysregulation in key oncogenic pathways. Importantly, the identified groups are different from previous PDAC classifications, both in their patient composition, prognosis, and in the oncogenic gene pathway profiles exhibited. Using an independent cohort, we further demonstrated that both the prognostic value of these subtypes and their underlying biological characteristics are reproducible. These results could lead to better stratified treatment regimens to improve the prognosis of PDAC patients.

PMID:42721202 | DOI:10.1371/journal.pcbi.1014735

Front Oncol. 2026 Aug 27;16:1919279. doi: 10.3389/fonc.2026.1919279. eCollection 2026.

ABSTRACT

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

PMID:42724581 | PMC:PMC13559918 | DOI:10.3389/fonc.2026.1919279