Early detection of oral cancer is critical for improving treatment outcomes and reducing the risk of malignant transformation. Liquid biopsy (LB), a minimally invasive diagnostic tool, offers significant promise for early dia­gnosis and disease monitoring. However, a lack of comprehensive synthesis comparing the diagnostic performance of various LB biomarkers in oral cancer as well as the absence of standardized sampling techniques, creates variability in clinical application. This systematic review and meta-analysis critically evaluated the diagnostic performance and clinical utility of LB biomarkers, including circulating tumor cells, cell-free DNA, micro­RNAs, messenger RNA, and salivary exosomes. Studies assessing sensitivity, specificity, diagnostic accuracy, and reproducibility were included, along with evaluations of methodological inconsistencies, operational challenges, and infrastructure requirements. The findings revealed that while LB demonstrates substantial potential for early detection and monitoring, the variability in sensitivity, specificity, and reproducibility remain a significant barrier. The lack of standardized pre-analytical and analytical protocols further undermines diagnostic reliability. Implementation challenges also stem from infrastructural demands and the need for specialized training. In conclusion, LB could revolutionize oral cancer diagnosis and management. However, standardized protocols, large-scale validation studies, and infrastructural

Abstract Background Lung cancer remains a leading global cause of mortality, with lung adenocarcinoma (LUAD) as the predominant histological subtype. Current serum biomarkers like carcinoembryonic antigen (CEA) lack specificity, necessitating novel diagnostic targets. Pentraxin 3 (PTX3), a homo-multimeric protein downregulated in malignancies, was evaluated for its diagnostic and prognostic roles in LUAD. Methods PTX3 expression was analyzed using TCGA/GEO datasets and clinical serum samples (97 LUAD vs. 40 controls). Diagnostic utility was assessed via ROC curves for PTX3, CEACAM5, and their combination. Prognostic value was determined by Kaplan-Meier and Cox regression. PTX3-associated differentially expressed genes (DEGs) were explored through functional enrichment, tumor microenvironment (TME) analysis, and drug sensitivity profiling. Result The TCGA and GEO datasets revealed that PTX3 mRNA expression was significantly downregulated in LUAD, and the AUC values with PTX3 were > 0.7. Detection of CEACAM5 and PTX3 combined can improve diagnostic accuracy, and patients with high PTX3 level have shorter overall survival. Multivariate Cox analysis revealed that PTX3 is an independent predictor of overall survival. The result of ELISA further confirmed the low level of PTX3 protein. PTX3 is important in the functional analysis and TME of lung adenocarcinoma. In addition, the sensitivity of tumor cells to anti-cancer drugs is significantly correlated with the expression of PTX3.

The lack of specific symptoms at early tumor stages, together with a high biological aggressiveness of the tumor contribute to the high mortality rate for pancreatic cancer (PC), which has a five year survival rate of less than 5%. Improved screening for earlier diagnosis, through the detection of diagnostic and prognostic biomarkers provides the best hope of increasing the rate of curatively resectable carcinomas. Though many serum markers have been reported to be elevated in patients with PC, so far, most of these markers have not been implemented into clinical routine due to low sensitivity

Reviews on circulating biomarkers in breast cancer usually focus on one single biomarker or a selective group of biomarkers. An overview summarizing the discovery and evaluation of all blood-based biomarkers in metastatic breast cancer is lacking. This systematic review aims to identify the available evidence of known blood-based biomarkers in metastatic breast cancer, regarding their clinical utility and state-of-the-art position in the validation process. The initial search yielded 1078 original studies, of which 420 were assessed for eligibility. A total of 320 studies were included in the final synthesis. A Development, Evaluation and Application Chart (DEAC) of all biomarkers was developed. Most studies focus on identifying new biomarkers and search for relations between these biomarkers and traditional molecular characteristics. Biomarkers are usually investigated in only one study (68.8%). Only 9.8% of all biomarkers was investigated in more than five studies. Circulating tumor cells, gene expression within tumor cells and the concentration of secreted proteins are the most frequently investigated biomarkers in liquid biopsies. However, there is a lack of studies focusing on identifying the clinical utility of these biomarkers, by which the additional value still seems to be limited according to the investigated evidence.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the optimal opportunity for treatment. Traditional biomarkers, such as neuron-specific enolase (NSE) or pro-gastrin-releasing peptide (ProGRP), have insufficient specificity and sensitivity to meet the demands of clinical diagnosis. Exosome and its contents have become burgeoning cancer biomarkers due to their diverse molecular cargo to achieve intercellular communication. Herein, a novel machine learning strategy was reported for rapid, efficient sc

Despite several advances in targeted therapies for breast cancer, breast-cancer-associated death remains high in women. This is partially due to the lack of reliable markers predicting metastatic disease or recurrence after initial therapy. Recent research into the clinical validity of circulating cancer-specific biomarkers as a "liquid biopsy" is of growing interest. Of these, exosomal microRNAs (miRNAs) are promising candidate biomarkers for clinical use in breast cancer. In addition to their diagnostic value, exosomal miRNAs play an important role in predicting clinical outcome or treatment response. In this review, it is focused on the findings concerning exosomal miRNAs in relation to disease detection, prognostic impact and therapeutic effect in breast cancer, and discuss their clinical utility.

<h4>Background/purpose</h4>Multi-omics integration linking genomic, transcriptomic, epigenomic, proteomic, metabolomic, single-cell, and spatial data has transformed the interpretation of human genetic variation by capturing molecular processes that extend beyond DNA sequence alone. Although these approaches substantially improve biomarker discovery and disease stratification, translation into clinical practice remains uneven due to methodological heterogeneity, limited validation, regulatory uncertainty, and structural inequities in data generation. This systematic review and meta-analysis aimed to evaluate scientific performance, clinical readiness, governance frameworks, and socio-technical constraints influencing multi-omics biomarker development, and to generate a roadmap for equitable global implementation.<h4>Methods</h4>Following PRISMA 2020 guidelines, we systematically searched PubMed, EMBASE, Web of Science, Scopus, medRxiv, and bioRxiv for studies published between January 2010 and December 2025. Eligible articles integrated ≥ 2 omics modalities, applied AI/ML to biomarker development or variant interpretation, assessed clinical utility or real-world implementation, or examined governance, ethics, consent, equity, or policy issues. Data extraction captured assay type, integration strategy, model performance, validation rigor, and regulatory or socio-technical insights. Random-effects meta-analyses estimated pooled improvements in AUC, sensitivity, specificity, and hazard ratio precision, and heterogeneity was assessed using I² statistics.<h4>Results</h4>From 9846 records, 528 studies met the inclusion criteria. Multi-omics integration improved predictive performance, yielding pooled gains of +0.16 in AUC (95% CI: 0.11-0.19), +13% in sensitivity, and +9% in specificity. Models combining ≥ 3 omics layers showed the largest improvements (+0.19 AUC). Single-cell and spatial assays enhanced risk stratification by 18% but demonstrated reproducibility limitations. AI/ML approaches added +0.12 AUC over traditional models, yet 67% exhibited ancestry bias, and only 22% implemented explainability tools. Only 19% of biomarkers underwent real-world evaluation due to limited validation, reimbursement gaps, interoperability challenges, and unclear data-rights governance.<h4>Conclusion</h4>Multi-omics biomarkers offer substantial analytical advantages, but their translation requires standardized validation frameworks, accountable AI governance, interoperable infrastructure, and globally inclusive data sets to ensure equitable, trustworthy implementation.