PLoS One. 2026 Apr 3;21(4):e0346173. doi: 10.1371/journal.pone.0346173. eCollection 2026.

ABSTRACT

Cancer remains a leading cause of mortality worldwide and a significant barrier to improving quality of life across all populations. The protein kinase D family, including PRKD3, has been demonstrated to play a crucial role in cancer development through its involvement in regulating key cellular processes. Although growing evidence highlights the role of PRKD3 in the tumorigenesis of certain cancers, a comprehensive pan-cancer analysis of PRKD3 remains unavailable. To address this, we performed an integrative pan-cancer analysis of PRKD3 using multi-omics datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and cBioPortal. We examined PRKD3 expression, copy number variation, mutation, and DNA methylation, and evaluated their associations with clinicopathological features, patient survival, and diagnostic potential across 33 cancer types. Immune relevance was further assessed through correlations with immune infiltration, checkpoint gene expression, and immunotherapy response-related genomic biomarkers. Our results revealed that PRKD3 expression was highly heterogeneous, showing significant upregulation in liver cancer, gastric cancer, and adrenocortical carcinoma, and downregulation in others. Elevated expression was consistently associated with poor prognosis and increased stromal, neutrophil, and cancer-associated fibroblast infiltration in adrenocortical carcinoma, liver cancer, and stomach cancer, whereas paradoxical associations with favorable outcomes were observed in kidney clear cell carcinoma. PRKD3 expression also correlated with immune checkpoint molecules including PD-1, PD-L1, and CTLA-4, supporting an immunosuppressive role, while context-dependent associations with TMB and MSI highlighted its potential influence on tumor immunogenicity and responsiveness to immune checkpoint blockade. Collectively, these findings identify PRKD3 as a potential context-dependent modulator of tumor biology, prognosis, and immune interactions, underscoring its potential as a biomarker of diagnostic, prognostic, and therapeutic relevance in precision oncology.

PMID:41931575 | PMC:PMC13048501 | DOI:10.1371/journal.pone.0346173

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

Arterioscler Thromb Vasc Biol. 2026 Apr 2. doi: 10.1161/ATVBAHA.125.324171. Online ahead of print.

ABSTRACT

BACKGROUND: Joint use of multiple molecular layers can be useful to prioritize targets for mechanistic studies. Application of coronary disease in large populations is an emerging field.

METHODS: We used reported circulating proteomic data (Somascan aptamer-based) from ≈3000 individuals in the CARDIA study (Coronary Artery Risk Development in Young Adults), measuring association with prevalent and 10-year incident coronary artery calcium (CAC) score. We used a multiparametric approach to prioritize circulating protein-CAC associations via genomics of circulating protein levels and coronary artery transcription.

RESULTS: Proteins linked to prevalent/incident CAC in CARDIA implicated pathogenic mechanisms of vascular disease, including fibrosis and inflammation (GDF-15 [growth/differentiation factor 15], CDCP1 [CUB domain-containing protein 1], GSN [gelsolin], THBS2 [thrombospondin-2], chemokines, RNAS6), oxidative lipid metabolism (CILP2), extracellular matrix remodeling and signaling (MMPs [matrix metalloproteinases], TIMP-1, integrins), calcification (Notch 1, ARHGAP36 [Rho GTPase-activating protein 36]), and metabolism (GIP [gastric inhibitory polypeptide]), as well as new proteins not previously reported. Using protein-wide association study genetic approaches, several targets with nominal evidence in CAC proteomics were associated with atherosclerosis or myocardial infarction in over 300K individuals, including PCSK9 (proprotein convertase subtilisin/kexin type 9) and APO C₁. Finally, the coronary artery-specific transcriptome-wide association study of CAC yielded genes with previously implicated mechanistic roles in vascular homeostasis, inflammation, and metabolism, as well as genes without previously described function in CAC. Overlap across CAC proteomics and transcriptome-wide association study highlighted genes involved in vascular inflammation (S100A9), cardiac development (HES1), vessel wall structure (SPARCL1), and vascular dysfunction or plaque (NOTCH3, TNFSF12, S100A12).

CONCLUSIONS: These results report population-level multiomics in human coronary calcification, presenting a method to identify disease-relevant targets through integration of human genetic approaches with multiomics.

PMID:41924874 | DOI:10.1161/ATVBAHA.125.324171

Cancer Treat Rev. 2026 Apr;145:103124. doi: 10.1016/j.ctrv.2026.103124. Epub 2026 Mar 26.

ABSTRACT

Gastric and gastroesophageal junction adenocarcinoma (GC) is a biologically challenging malignancy associated with suboptimal clinical outcomes due to limited effective treatment options. The recent incorporation of immune checkpoint inhibitors (ICIs) into therapeutic algorithms has improved the clinical prospects of subsets of GC patients. However, responses to anti-PD-1/PD-L1 agents remain highly heterogeneous, with only some patients deriving long-term benefits. This variability highlights the importance of identifying optimal biomarkers to enhance patient selection, thereby enabling tailored immunotherapy strategies. Whereas microsatellite instability has demonstrated a potent capacity for predicting immunotherapy benefits in GC, other predictive biomarkers, such as PD-L1 expression, remain suboptimal. Advances in gene expression and epigenetic profiling, liquid biopsy approaches, gut microbiome characterization, and artificial intelligence-driven multimodal algorithms applied to multi-omics or digital pathology are key drivers for the comprehensive characterization of the GC tumour microenvironment (TME), which could be used for better treatment selection. Similarly, elucidating the complex tumour-immune interplay with these technologies will be crucial for the success of novel immunotherapeutic approaches under clinical development, by evaluating alternative immune pathways alone or in combination with current actionable targets of GC. The current review aims to give an overview of the current immunotherapeutic landscape in GC, evaluate standard-of-care and emerging biomarkers of immunotherapy response, and discuss the translational potential of incorporating multi-omic and AI-derived biomarkers into biomarker-enriched clinical decision-making frameworks.

PMID:41921305 | DOI:10.1016/j.ctrv.2026.103124