Crit Rev Oncol Hematol. 2025 May 28;213:104776. doi: 10.1016/j.critrevonc.2025.104776. Online ahead of print.

ABSTRACT

Over the past decade, liquid biopsy (LB) has emerged as a key tool in oncology. Its utility in non-invasive sampling and real-time monitoring has made it a cornerstone in precision medicine. Since 2020, publications on LB in solid tumors have doubled, underscoring its pivotal role in advancing cancer care. Notably, 2024 marked a peak in scientific papers on this topic. Blood remained the most studied biofluid, with circulating tumor DNA (ctDNA) as the most frequently analyzed analyte, followed by circulating tumor cells, extracellular vesicles, and microRNAs. Among tumor types, gastrointestinal, lung, breast, and genitourinary cancers were the most investigated, collectively accounting for more than half of the studies. Early cancer and minimal residual disease detection are critical areas of interest, emphasizing the expanding potential of fragmentomics and methylation profiling, as well as the prognostic significance of ctDNA across various cancer types. Moreover, serial ctDNA monitoring demonstrated the ability to predict relapse and guide treatment (de)-escalation strategies. In metastatic setting, ctDNA profiling plays a crucial role in capturing tumor heterogeneity, detecting resistance mechanisms, and informing treatment selection. Non-blood biofluids gained interest for their potential to enhance the detection of clinically relevant alterations in different cancer types such as central nervous system and head and neck cancers. Other than biomarkers selection, the technological advancements and artificial intelligence significantly improved the sensitivity and specificity of LB assays. This evidence in combination with the rapid advancement of machine learning and other computational approaches, are paving the way for a new chapter of LB research.

PMID:40447209 | DOI:10.1016/j.critrevonc.2025.104776

Front Oncol. 2025 May 15;15:1520733. doi: 10.3389/fonc.2025.1520733. eCollection 2025.

ABSTRACT

Circulating tumor DNA (ctDNA), a subset of cell-free DNA (cfDNA), originates from primary tumors and metastatic lesions in cancer patients, often carrying genomic variations identical to those of the primary tumor. ctDNA analysis via liquid biopsy has proven to be a valuable biomarker for early cancer detection, minimal residual disease (MRD) assessment, monitoring tumor recurrence, and evaluating treatment efficacy. However, despite advancements in ctDNA analysis technologies, standardized protocols for its extraction and detection have yet to be established. Each step of the process-from pre-analytical variables to detection techniques-significantly impacts the accuracy and reliability of ctDNA analysis. This review examines recent developments in ctDNA detection methods, focusing on pre-analytical factors such as specimen types, collection tubes, centrifugation protocols, and storage conditions, alongside high-throughput and ultra-sensitive detection technologies. It also briefly discusses the clinical potential of liquid biopsy in nasopharyngeal carcinoma (NPC).

PMID:40444084 | PMC:PMC12119280 | DOI:10.3389/fonc.2025.1520733

BMC Cancer. 2025 May 31;25(1):972. doi: 10.1186/s12885-025-14396-2.

ABSTRACT

BACKGROUND: Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Available serum biomarkers lack specificity, making it difficult to accurately identify early non-metastatic GC cases. Reliable diagnostic biomarkers that can detect early GC are critical to improve prognosis.

METHODS: We employed serum proteomics combined with bioinformatics to identify genes differentially expressed in the serum of non-metastatic GC patients. Single-cell RNA sequencing (ScRNA-seq) and immune infiltration analysis were performed to evaluate the relationship between gene expression and immune cell function. Then we evaluated 107 machine learning models for biomarker-based early GC diagnosis and develops a nomogram validated for accuracy and clinical utility, subsequently comparing the performance of potential biomarkers with traditional tumor markers in diagnosing early gastric cancer. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) and immunohistochemical staining using the Human Protein Atlas (HPA) database were used to validate the differential expression of candidate genes in GC tissues and adjacent non-cancerous tissues.

RESULTS: The proteomic analysis identified several genes upregulated in the serum of GC patients compared to healthy controls. Single-cell RNA sequencing analysis further revealed that these upregulated genes were associated with altered immune cell infiltration in the tumor microenvironment. The glmBoost + XGBoost model incorporating B2M, CFL1, CTSD, and HSP90AB1 demonstrated strong diagnostic performance (mean AUC = 0.792), with 101 algorithm combinations achieving an average AUC > 0.7. A nomogram integrating gene expression and clinical data was developed, validated through calibration and decision curve analyses, highlighting its potential for early GC diagnosis. Additionally, four genes—TAGLN2, HSP90AB1, SH3BGRL3, and CFL1—were found to be highly expressed in non-metastatic GC tissues and were significantly correlated with immune infiltration, including CD8 + T cells, monocytes, and myeloid-derived suppressor cells. These findings were validated by qRT-PCR and immunohistochemical analyses, confirming their elevated expression in GC tissues.

CONCLUSIONS: TAGLN2, HSP90AB1, SH3BGRL3 and CFL1 are potential diagnostic biomarkers for early-stage GC, with strong associations with immune cell infiltration. Machine learning model shows excellent diagnostic performance. These results provide a foundation for future studies to improve early diagnosis and individualized treatment strategies for GC.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-025-14396-2.

PMID:40450287 | PMC:PMC12126892 | DOI:10.1186/s12885-025-14396-2

Front Pharmacol. 2025 May 15;16:1534974. doi: 10.3389/fphar.2025.1534974. eCollection 2025.

ABSTRACT

BACKGROUND: Frizzled class receptor 2 (FZD2), is a critical protein in the Wnt signaling pathway, which plays significant roles in various cancers. However, its role in cancer progression, prognosis, and diagnosis remains largely unexplored. This study investigates the correlation between FZD2 expression and clinical outcomes, as well as its underlying molecular mechanisms in pan-cancer.

METHODS: A comprehensive bioinformatic analysis was performed using pan-cancer data from The Cancer Genome Atlas (TCGA), which included 33 cancer types. Gene set enrichment analysis (GSEA) was conducted to explore functional pathways, while a protein-protein interaction (PPI) network was constructed to further elucidate the role of FZD2 in tumor biology. The relationship between FZD2 expression and immune cell infiltration across 22 categories was assessed using CIBERSORT. Additionally, single-cell analysis was employed to examine FZD2 expression levels across different cell types. To investigate the functional impact of FZD2, loss-of-function experiments were carried out in gastric cancer cell lines using siRNA-mediated knockdown. Subsequent assays, including Polymerase Chain Reaction (PCR), Western blotting (WB), Cell Counting Kit-8 (CCK8), Flow Cytometry, wound healing, and transwell migration and invasion assays, were performed to assess cellular responses. A subcutaneous gastric cancer xenograft model was established in nude mice to investigate the effect of FZD2 knockdown on tumor growth in vivo.

RESULTS: Our analysis revealed significant upregulation of FZD2 in multiple malignancies, including stomach adenocarcinoma (STAD), bladder cancer (BLCA), and cholangiocarcinoma (CHOL). FZD2 expression was correlated with various cancer characteristics, including stemness score, matrix score, immune score, tumor mutational burden (TMB), microsatellite instability (MSI), RNA modification genes, and drug sensitivity. Notably, FZD2 was associated with altered sensitivity to several anticancer agents, suggesting its role in modulating treatment responses. FZD2 knockdown was demonstrated by both in vitro and in vivo experiments to suppress tumor cell proliferation, migration, and invasion in gastric cancer cell lines, indicating its critical role in tumor progression. Furthermore, FZD2 exhibited significant correlations with other Wnt pathway genes (e.g., Wnt2, Wnt4, Wnt5B), indicating a complex interaction network contributing to tumorigenesis.

CONCLUSION: FZD2 is widely upregulated in various tumor types, with its expression closely associated with key clinical outcomes, including overall survival, disease-specific survival, disease-free interval, as well as tumor mutations, drug sensitivity, immune cell infiltration, and immunotherapy-related biomarkers such as TMB and MSI. These findings highlight the pivotal role of FZD2 in cancer prognosis and treatment, offering potential for novel therapeutic approaches and the development of personalized medicine strategies in oncology.

PMID:40444048 | PMC:PMC12120476 | DOI:10.3389/fphar.2025.1534974

J Pers Med. 2025 Apr 24;15(5):166. doi: 10.3390/jpm15050166.

ABSTRACT

Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC's heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding of GC by integrating molecular and imaging data. In this review, we summarize the current landscape of ML-based multiomics integration for GC, highlighting its role in precision diagnosis, prognosis prediction, and biomarker discovery for achieving personalized medicine.

PMID:40423038 | PMC:PMC12113022 | DOI:10.3390/jpm15050166