Transl Oncol. 2026 Apr 3;67:102748. doi: 10.1016/j.tranon.2026.102748. Online ahead of print.

ABSTRACT

BACKGROUND: Epithelial ovarian cancer (EOC) encompasses five major histological subtypes with marked genetic, immunological, and clinical heterogeneity. While genome-wide association studies (GWAS) have identified subtype-specific risk loci, a critical gap remains in understanding how plasma proteins influence immune-cell traits and contribute to EOC pathogenesis.

METHODS: We integrated subtype-stratified GWAS data from two EOC cohorts with plasma proteomics and immune-cell traits to construct protein-immune-EOC regulatory landscapes using a three-stage Mendelian randomization framework. Single-cell RNA-seq and multiplex immunofluorescence were employed to delineate the cellular distribution and spatial context of causal proteins. Subsequent analyses characterized immune infiltration, macrophage polarization, and clinicopathological associations. Drug-gene correlations were used to identify potential therapeutic targets, and transcriptomic analyses were applied to delineate the underlying transcriptional landscape.

RESULTS: We identified 20 subtype-specific protein-immune-EOC regulatory axes, with FCGR2B emerging as a causal plasma protein in immune regulation and high-grade serous ovarian cancer (HGSOC) progression. FCGR2B was highly expressed in tumor-associated macrophages and was associated with an M2-like polarization phenotype. Functional characterization revealed that FCGR2B was associated with shorter progression-free survival and an immunosuppressive tumor microenvironment. Transcriptomic analyses revealed altered NF-κB signaling upon FCGR2B knockdown, and drug-response data suggested a potential association between high FCGR2B expression and sensitivity to NF-κB inhibitors.

CONCLUSIONS: These findings delineate subtype-specific genetically informed protein-immune regulatory landscapes in EOC and identify FCGR2B as a key immunoregulatory and prognostic biomarker in HGSOC, suggesting FCGR2B as a potential therapeutic vulnerability that warrants further investigation.

PMID:41934917 | DOI:10.1016/j.tranon.2026.102748

Clin Chim Acta. 2026 Mar 31;588:120992. doi: 10.1016/j.cca.2026.120992. Online ahead of print.

ABSTRACT

Bladder cancer diagnosis and surveillance remain anchored in cystoscopy and urine cytology, despite their invasiveness, operator dependence, and limited sensitivity for low-grade or flat lesions. These constraints have accelerated interest in liquid biopsy approaches that provide noninvasive, real-time molecular insights into tumor biology. This narrative review examines emerging evidence on three key classes of circulating biomarkers, namely, circulating tumor DNA (ctDNA), exosomes, and circulating tumor cells (CTCs), and their expanding roles in diagnosis, prognosis, and treatment guidance. ctDNA reflects tumor-specific genomic alterations and shows particular strength in detecting minimal residual disease, identifying early molecular relapse, and monitoring therapeutic response, although its diagnostic sensitivity remains moderate. Urinary exosomes demonstrate some of the highest diagnostic accuracies among liquid biopsy platforms, with multimarker RNA panels achieving sensitivities and specificities above 90%, while their diverse cargo of miRNAs, mRNAs, and lncRNAs provides robust prognostic information linked to recurrence and survival. CTCs, although technically challenging to isolate owing to their phenotypic heterogeneity, offer valuable insights into tumor aggressiveness, metastatic potential, and treatment responsiveness, especially in muscle-invasive disease. Advances in ultrasensitive sequencing, microfluidic CTC capture, and multi-omics integration are accelerating clinical translation. Collectively, circulating biomarkers are poised to complement and, in selected contexts, transform bladder cancer management by enabling earlier detection, individualized risk stratification, and more precise therapeutic decision-making.

PMID:41933678 | DOI:10.1016/j.cca.2026.120992

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

Am J Hum Genet. 2026 Apr 2;113(4):828-841. doi: 10.1016/j.ajhg.2026.03.008.

ABSTRACT

Reliable reference transcriptome prediction models are key to accurate multi-ancestry transcriptome-wide association studies (TWASs). We propose three methods leveraging functionally informed variants (FIVs) for transcriptome prediction models to improve multi-ancestry TWASs. We trained models on 1,287 multi-ancestry participants from the Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). We validated models' prediction accuracy on two external independent datasets, Geuvadis and Jackson Heart Study. To test robustness of our methods for TWASs, we integrated models with three multi-ancestry GWASs from blood cell, lipid, and pulmonary function traits, respectively. Our methods presented similar prediction accuracy while using a smaller and functionally informed set of variants compared to the benchmark method, elastic net (EN). Overall, our methods achieved higher power and accuracy (with average improved accuracy of 24% over EN) for TWASs. However, no single proposed method outperformed all GWAS traits. To further improve TWAS performance, we propose an omnibus approach that aggregates TWAS summary statistics from our methods. The omnibus approach yielded the highest number of Bonferroni-significant TWAS genes for all GWAS traits, and it further improved TWAS power and accuracy for blood cell traits. Additionally, the omnibus approach detected some trait-relevant important genes that the EN missed. Our study demonstrates the value of including FIVs in multi-ancestry transcriptome prediction models for improving TWAS performance. Further, the observed TWAS improvement depends on the GWAS trait's relevance to the PBMCs used to build our transcriptome prediction models.

PMID:41932314 | DOI:10.1016/j.ajhg.2026.03.008

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

Eur J Cancer. 2026 Mar 19;239:116676. doi: 10.1016/j.ejca.2026.116676. Online ahead of print.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA) is a promising biomarker in melanoma, with higher sensitivity for tumor burden detection than conventional diagnostics. While well established in research, clinical routine implementation remains pending. Key global questions concern optimal clinical applications and barriers to adoption.

METHODS: A web-based survey of 116 members of the Melanoma World Society Study Group assessed international expert opinions on ctDNA utility across predefined clinical scenarios. The questionnaire included 18 general questions on ctDNA use and 5 clinical vignettes with de-identified patient data and retrospectively obtained ctDNA results.

RESULTS: ctDNA was rated most valuable for detecting minimal residual disease (mean score 3.63), surveillance of recurrent disease (3.85), and stage IV melanoma (3.82), with limited utility in early stages. Experts considered ctDNA superior to S100 and LDH for early relapse detection and identifying progressive disease. Most participants (80%) agreed that ctDNA correlates with radiographic response, and 82% favored its integration into routine follow-ups. In urgent high-tumor-burden settings, 82.8% would initiate BRAFi/MEKi therapy based on ctDNA if tissue analysis was pending, and 93.9% if unavailable. For central nervous system lesions, 62% did not support blood ctDNA, while 66% considered cerebrospinal fluid valuable. Pragmatic approaches with small to mid-size targeted panels and short turnaround times were preferred. Major barriers included the need for prospective trials (85%), standardized guidelines (83%), and reimbursement policies (82%).

CONCLUSION: Key opinion leaders regarded ctDNA as a valuable adjunct selected melanoma scenarios. Validation through prospective studies, guideline development, and reimbursement frameworks are essential for broader clinical implementation.

PMID:41932032 | DOI:10.1016/j.ejca.2026.116676

ESMO Real World Data Digit Oncol. 2026 Jan 14;11:100652. doi: 10.1016/j.esmorw.2025.100652. eCollection 2026 Mar.

ABSTRACT

BACKGROUND: Malignant tumors release circulating tumor DNA (ctDNA) into the bloodstream, providing insights into tumor-specific mutations and pathways driving cancer progression. ctDNA testing is currently approved as a type of liquid biopsy to monitor disease burden and detect minimal residual disease (MRD). This study aimed to evaluate the adoption of ctDNA testing in a community oncology practice and assess the overall diagnostic performance of ctDNA and its association with disease progression in stage IV colorectal cancer (CRC), as determined by imaging studies.

PATIENTS AND METHODS: This retrospective study analyzed the medical records of 88 patients with gastrointestinal cancers (80 CRC, 5 gastric, 3 esophageal) who underwent ctDNA molecular testing between January 2020 and April 2022. Electronic medical records from patients aged ≥21 years who had two or more ctDNA tests with concurrent imaging studies or a pathology-confirmed CRC, gastric cancer, or esophageal cancer diagnosis were evaluated.

RESULTS: At baseline, 47 (53.4%) patients had negative and 41 (46.6%) had positive results. Most patients had CRC (90.1%). In stage IV CRC, ctDNA was increasing before radiologic progression in all documented cases (100%), with a median lead time of 2.5 months (range 0.5-15 months). In early-stage CRC (I-III), ctDNA preceded radiologic progression in 40% of cases, with a median lead time of 6 months (range 6-10 months).

CONCLUSIONS: Using real-world data, we report the first-time results of the ctDNA testing adoption in a community oncology setting among patients with gastrointestinal cancers, predominantly CRC. Our findings suggest that integration of ctDNA testing may support disease monitoring in routine clinical practice.

PMID:41930304 | PMC:PMC13040887 | DOI:10.1016/j.esmorw.2025.100652

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699