Breast Cancer (Dove Med Press). 2025 Sep 25;17:851-861. doi: 10.2147/BCTT.S542704. eCollection 2025.

ABSTRACT

Despite substantial progress in the diagnosis and treatment of breast cancer, current therapeutic regimens exhibit limitations, necessitating the identification of more robust biomarkers to optimize personalized strategies. Circulating tumor DNA (ctDNA), as a non-invasive liquid biopsy modality, overcomes the inherent constraints of biopsies in capturing tumor heterogeneity. Accumulating evidence from prospective cohort studies demonstrates the clinical utility of ctDNA in risk stratification, guidance of therapeutic decision-making, recurrence surveillance and other clinical applications. Furthermore, ctDNA profiling enhances real-time pharmacodynamic monitoring and accelerates drug development by identifying molecular responders. The methodical requirements and challenges inherent in implementing liquid biopsy assessments in the clinic are examined. These encompass critical pre-analytical variables, the need for highly sensitive and specific analytical techniques, standardization of assays and bioinformatics pipelines across laboratories and the complexities of interpreting results. This review synthesizes current evidence supporting ctDNA integration into breast cancer management frameworks and systematically addresses its methodological challenges and clinical limitations.

PMID:41036092 | PMC:PMC12479222 | DOI:10.2147/BCTT.S542704

World J Clin Oncol. 2025 Jul 24;16(7):106981. doi: 10.5306/wjco.v16.i7.106981.

ABSTRACT

Cancer remains a major global health concern, with escalating incidence and mortality rates underscoring the urgent need for novel diagnostic and therapeutic strategies. Increasing evidence has identified the oral microbiota as a critical contributor to tumorigenesis, thereby expanding the understanding of cancer pathogenesis beyond conventional risk factors such as tobacco use and genetic predisposition. This review summarizes recent progress in elucidating the complex relationship between the oral microbiota and various malignancies, particularly oral squamous cell carcinoma, esophageal adenocarcinoma, and pancreatic ductal adenocarcinoma. Pathogenic bacteria, including Porphyromonas gingivalis and Fusobacterium nucleatum, have been implicated in promoting tumor progression through mechanisms involving chronic inflammation, the production of metabolic toxins, and immune evasion. The dysbiosis of the oral microbiota, often driven by lifestyle factors such as poor diet, tobacco use, and alcohol consumption, further exacerbates these carcinogenic processes. Emerging therapeutic approaches including probiotics, oral microbiota transplantation, and CRISPR-based bacterial editing are under investigation for their potential to restore microbial homeostasis and suppress pathogenic species. Additionally, saliva-based microbial biomarkers have shown promise for non-invasive cancer screening. The integration of multi-omics technologies and artificial intelligence-driven platforms is further advancing the development of precision oncology. This review aims to consolidate fragmented findings concerning the oral microbiota-cancer axis and address existing gaps in mechanistic understanding. The review's significance lies in the translational potential of microbial research to clinical applications, offering opportunities to reduce the global cancer burden through early detection and microbiota-targeted therapies.

PMID:40741186 | PMC:PMC12304933 | DOI:10.5306/wjco.v16.i7.106981

Hereditas. 2025 Jul 7;162(1):121. doi: 10.1186/s41065-025-00487-4.

ABSTRACT

Accurate classification of cancer subtypes plays a pivotal role in advancing precision medicine. In this study, we introduce WMRCA + , a novel clustering approach based on a weighted majority rule that integrates multi-omics data and incorporates metabolic gene sets to robustly determine the optimal number of clusters for tumor subtype identification. WMRCA + evaluates clustering performance using ten internal metrics and offers comprehensive functionalities for data preprocessing and visualization. When applied to The Cancer Genome Atlas (TCGA) lung cancer dataset using lipid metabolism-related gene sets, WMRCA + outperformed widely used clustering algorithms-including iCluster, SNF, NMF, CC, and CNMF-achieving an AUC of 0.947. WMRCA + provides robust, interpretable, and biologically meaningful clustering results, offering a valuable tool for improving the accuracy of cancer subtype prediction. The WMRCA + R package is freely available at https://github.com/guojunliu7/WMRCA .

PMID:40624602 | PMC:PMC12235908 | DOI:10.1186/s41065-025-00487-4

J Transl Med. 2025 May 2;23(1):504. doi: 10.1186/s12967-025-06517-z.

ABSTRACT

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS), particularly perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), are synthetic chemicals known for their widespread use and environmental persistence. These compounds have been increasingly linked to hepatotoxicity and the development of hepatocellular carcinoma (HCC). However, the molecular mechanisms by which PFAS contribute to HCC remain underexplored.

METHODS: This study employs a multi-omics approach that combines network toxicology, integrated machine learning, single-cell RNA sequencing, spatial transcriptomics, experimental validation, and molecular docking simulations to uncover the mechanisms through which PFAS exposure drives HCC. We analyzed publicly available transcriptomic data from several HCC cohorts and used differential gene expression analysis to identify targets associated with both PFAS exposure and HCC. We constructed a protein-protein interaction (PPI) network and a survival risk model, the PFAS-related HCC signature (PFASRHSig), based on integrated machine learning to identify prognostic biomarkers, with the goal of identifying core targets of PFAS in HCC progression and prognosis. RT-qPCR and immunohistochemical (IHC) staining were used to validate the expression levels of the targets in both tumor and normal tissues. Molecular docking simulations were conducted to assess the binding affinities between PFAS compounds and selected target proteins.

RESULTS: Functional enrichment studies revealed that PFAS targets were associated with metabolic signaling pathways, which are actively involved in lipid, glucose, drug metabolism, etc. Through integrated machine learning and PPI network analysis, we identified six genes, APOA1, ESR1, IGF1, PPARGC1A, SERPINE1, and PON1, that serve as core targets of PFAS in both HCC progression and prognosis. These targets were further validated via bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics, which revealed differential expression patterns across various cell types in the HCC tumor microenvironment. The results of RT-qPCR and IHC staining were consistent with the in silico findings. Molecular docking simulations revealed strong binding affinities between PFAS compounds and these core targets, supporting their potential roles in PFAS-induced hepatocarcinogenesis.

CONCLUSIONS: Our study highlights key molecular targets and pathways involved in PFAS-induced liver carcinogenesis and proposes a robust survival risk model (PFASRHSig) for HCC. These findings provide new insights into PFAS toxicity mechanisms and offer potential therapeutic targets for mitigating the health risks associated with PFAS exposure. Collectively, our findings help in advancing clinical applications by providing insights into disease mechanisms and potential therapeutic interventions.

PMID:40317014 | PMC:PMC12049027 | DOI:10.1186/s12967-025-06517-z

Clin Transl Med. 2025 Jan;15(1):e70160. doi: 10.1002/ctm2.70160.

ABSTRACT

BACKGROUND: Plasma protein has gained prominence in the non-invasive predicting of lung cancer. We utilised Zeolite Zotero NaY-based plasma proteomics to investigate its potential for multiple event predicting, including lung cancer diagnosis (task #1), lymph node metastasis detection (task #2) and tumour‒node‒metastasis (TNM) staging (task #3).

METHODS: A total of 4703 plasma proteins were quantified from 241 participants based on a prospective cohort of 2757 participants. An additional 46 participants from external prospective cohort of 735 participants were used for validation. Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. Feature importance was interpreted using Shapley additive explanations (SHAP) algorithm.

RESULTS: For task #1, 10 proteins panel showed an AUC of .87 (.77‒.97) in the external validation. After integrating clinical factors, a significant increase diagnostic accuracy was observed with AUC of .91 (.85‒.98). For task #2, nine proteins panel achieved an AUC of .88 (.80‒.96), integration model showed an increase diagnostic accuracy with AUC of .90 (.85‒.97). For task #3, 10 proteins panel showed an AUC of .88 (.74‒.96) for stage I, .92 (.84‒.97) for stage II, .88 (.76‒.96) for stage III and .99 (.98‒.99) for stage IV in the integration model.

CONCLUSIONS: This study comprehensively profiled the NaY-based plasma proteome biomarker, laying the foundation for a high-performance blood test for predicting multiple events in lung cancer.

KEY POINTS: Our study developed an innovative nanomaterial, Zeolite NaY, which addressed the masking effect and improved the depth of the proteome. The performance of NaY-based plasma proteomics as a preclinical diagnostic tool was validated through both internal and external cohort. Furthermore, we explored the different patterns of plasma protein changes during the progression of lung cancer and used the explanations method to elucidate the roles of proteins in the multitask predictive model.

PMID:39783847 | PMC:PMC11714244 | DOI:10.1002/ctm2.70160