Future Oncol. 2025 Aug 5:1-19. doi: 10.1080/14796694.2025.2542051. Online ahead of print.

ABSTRACT

Lung Cancer (LC) remains the leading cause of cancer-related mortality. While Tissue Biopsy (TB) remains the gold standard for molecular profiling, its invasiveness and inability to provide real-time monitoring have led to the adoption of Liquid Biopsy (LB) as a minimally invasive alternative. By analyzing different circulating analytes such as cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), Circulating Tumor Cells (CTCs), Extracellular Vesicles (EVs), and Tumor-Educated Platelets (TEPs), LB offers a dynamic approach to assessing tumor heterogeneity, Minimal Residual Disease (MRD), and treatment resistance. Recent clinical trials have underscored their role in guiding therapy decisions and monitoring treatment response. In early-stage disease, several Randomized Clinical Trials (RCTs) have shown that ctDNA clearance predicts survival benefits in patients receiving neoadjuvant or perioperative Immune Checkpoint Inhibitors (ICIs). Additionally, adjuvant RCTs have confirmed the ctDNA prognostic role in post-surgical relapse risk assessment. Despite its transformative potential, challenges such as assay standardization, sensitivity limitations in early-stage disease, and regulatory barriers remain. As ongoing research continues to validate its clinical utility, LB is poised to become an indispensable tool in the precision management of LC.

PMID:40762271 | DOI:10.1080/14796694.2025.2542051

J Proteome Res. 2025 Aug 2. doi: 10.1021/acs.jproteome.5c00385. Online ahead of print.

ABSTRACT

Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors have transformed lung adenocarcinoma (LUAD) treatment in EGFR-mutant (MT) patients, but strategies targeting wild-type (WT) EGFR tumors remain necessary. This study analyzed a diverse LUAD patient cohort with EGFR mutation statuses and wild-type profiles for ALK and KRAS to identify stage-specific biomarkers. Using quantitative proteomics and multiomics, we discovered 21 dysregulated proteins in early-stage EGFR-WT LUAD, identifying myeloid-derived growth factor (MYDGF) as a key candidate biomarker. Elevated MYDGF levels in tissue (n = 117) and serum (n = 196) correlated significantly with cancer stage in EGFR-WT patients but not EGFR-MT cases. Notably, a higher tumor-to-normal MYDGF ratio predicted a favorable prognosis in early-stage EGFR-WT LUAD. Functional studies demonstrated that MYDGF exerts distinct roles in cell viability and migration depending on its cellular localization and the invasive potential of cancer cells. Specifically, secreted MYDGF promoted a protumorigenic phenotype, whereas excess intracellular MYDGF appeared to suppress the oncogenic capacity of aggressive cancer cells. MYDGF knockdown and subsequent proteomic analysis provided further insights into these context-dependent functions. These findings highlight EGFR status- and stage-specific proteomic profiles in LUAD, emphasizing the importance of context-dependent biomarker assessment for personalized treatment strategies.

PMID:40752010 | DOI:10.1021/acs.jproteome.5c00385

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

World J Clin Cases. 2025 Jul 26;13(21):105066. doi: 10.12998/wjcc.v13.i21.105066.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA)-based liquid biopsy has been found to be effective for the detection of minimal residual disease and the evaluation of prognostic risk in various solid tumors, with good sensitivity and specificity for identifying patients at high risk of recurrence. However, use of its results as a biomarker for guiding the treatment and predicting the prognosis of nasopharyngeal carcinoma (NPC) has not been reported.

CASE SUMMARY: In this case study of a patient with stage IVb NPC, we utilized ctDNA as an independent biomarker to guide treatment. Chemotherapy was administered in the early stages of the disease, and local intensity-modulated radiation therapy was added when the patient tested positive for ctDNA, while radiation therapy was stopped and the patient was observed when the ctDNA test was negative. During the follow-up period, ctDNA signals became positive before tumor progression and became negative again at the end of treatment. We also explored the potential of ctDNA in combination with Epstein-Barr virus (EBV) DNA status to predict the prognosis of NPC patients, as well as the criteria for selecting genetic mutations and the testing cycle for ctDNA analysis.

CONCLUSION: The results of ctDNA-based liquid biopsy can serve as an independent biomarker, either independently or in conjunction with EBV DNA status, to guide the treatment and predict the prognosis of NPC.

PMID:40726932 | PMC:PMC12068179 | DOI:10.12998/wjcc.v13.i21.105066

J Hepatocell Carcinoma. 2025 Jul 17;12:1453-1469. doi: 10.2147/JHC.S493907. eCollection 2025.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) necessitates novel immunotherapeutic targets. PBK, a cancer/testis antigen (CTA), was identified as a pivotal hub gene influencing prognosis, tumor mutation burden (TMB), and immune microenvironment remodeling.

METHODS: PBK was prioritized using weighted gene co-expression network analysis (WGCNA) and differential expression screening in the TCGA-LIHC cohort, intersected with curated CTAs. Analyses assessed correlations with clinicopathological features (TNM stage, survival), genomic characterization (mutation frequencies), and functional validation via siRNA-mediated PBK knockdown in Huh7 cells (migration assay). Single-cell RNA sequencing (scRNA-seq) profiled of the tumor immune microenvironment.

RESULTS: PBK overexpression was significantly correlated with advanced TNM stage (P < 0.05) and poor survival (log-rank P = 0.003). Genomic analysis revealed distinct mutation profiles: high-PBK tumors exhibited increased TP53 mutation frequency (39% vs 17%) but decreased CTNNB1 mutations (20% vs 31%). Patients exhibiting with combined PBK overexpression and high TMB demonstrated the poorest prognosis. Functional validation confirmed that PBK knockdown significantly inhibited Huh7 cell migration capacity (P < 0.05). scRNA-seq analysis showed PBK-enriched tumors contained elevated proportions of immunosuppressive SPP1(+) macrophages (22.33% vs 6.6%, FDR corrected P < 0.001) and CD8(+) SLC4A10(+) MAIT cells (9.82% vs 4.7%, FDR corrected P < 0.001).

CONCLUSION: PBK synergistically drives HCC progression through three synergistic mechanisms: (1) promoting oncogenic mutation accumulation (eg, TP53), (2) increasing metastatic potential, and (3) reprogramming an immune-suppressive microenvironment enriched for SPP1(+) macrophages and CD8(+)SLC4A10(+) MAIT cells. This establishes PBK as a dual-purpose biomarker for prognostic stratification and immunotherapy resistance prediction, providing a mechanistic rationale for developing PBK-targeted therapies in HCC.

PMID:40697330 | PMC:PMC12279550 | DOI:10.2147/JHC.S493907

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 Hazard Mater. 2025 Jun 23;495:139049. doi: 10.1016/j.jhazmat.2025.139049. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic arsenic exposure is a global health concern linked to pulmonary diseases like fibrosis. However, its precise molecular mechanisms remain unclear. This study explored the effects of chronic arsenic exposure on a murine model (via diet) and BEAS-2B cells, focusing on oxidative stress, lipid peroxidation, mitochondrial dysfunction, and ferroptosis-mediated cell death.

METHODS: BEAS-2B cells were exposed to 1 μmol/L NaAsO₂ for 30 passages. Oxidative stress was assessed via ROS quantification, GSH depletion, and T-SOD activity. Lipid peroxidation was measured using BODIPY fluorescence and MDA levels. Mitochondrial dysfunction was determined by mtROS imaging and JC-1 staining. Ferroptosis was analyzed through GPX4 expression and TEM-based mitochondrial integrity. A 14-month murine model evaluated histopathology, metabolomic dysregulation, and gut-lung axis crosstalk.

RESULTS: Arsenic exposure significantly increased ROS, depleted GSH, and reduced T-SOD activity. Lipid peroxidation and mitochondrial dysfunction were evident, with more than 60 % decline in GPX4. Murine lung histology showed alveolar thickening, inflammatory infiltration, and elevated IL-6, TNF-α, and VEGF. Metabolomic analysis revealed disrupted lipid metabolism, correlating with ferroptosis markers (Acetyl-carnitine, L-Acetylcarnitine).

CONCLUSIONS: This was the first study to demonstrate ferroptosis as a key mechanism in arsenic-induced lung epithelial damage using a 14-month murine model and a 30-passage cellular model. We further demonstrated that ferroptosis induced by chronic exposure becomes functionally irreversible, as ferroptosis inhibition by Ferrostatin-1 failed to rescue GPX4 expression, unlike prior acute exposure models.

PMID:40614423 | DOI:10.1016/j.jhazmat.2025.139049

Front Genet. 2025 Jun 4;16:1610284. doi: 10.3389/fgene.2025.1610284. eCollection 2025.

ABSTRACT

INTRODUCTION: Lung cancer continues to pose significant global health burdens due to its high morbidity and mortality. This study aimed to systematically integrate biomedical datasets, particularly incorporating traditional Chinese medicine (TCM)-associated multi-omics data, employing advanced deep-learning methods enhanced by graph attention mechanisms. We sought to investigate molecular mechanisms underlying stage-wise lung cancer progression and identify pivotal stage-specific biomarkers to support precise cancer staging classification.

METHODS: We developed a novel multi-omics integrative model, named the Multi-Omics Lung Cancer Graph Network (MOLUNGN), based on Graph Attention Networks (GAT). Clinical datasets of non-small cell lung cancer (NSCLC), including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), were analyzed to create omics-specific feature matrices comprising mRNA expression, miRNA mutation profiles, and DNA methylation data. MOLUNGN incorporated omics-specific GAT modules (OSGAT) combined with a Multi-Omics View Correlation Discovery Network (MOVCDN), effectively capturing intra- and inter-omics correlations. This framework enabled comprehensive classification of clinical cases into precise cancer stages, alongside the extraction of stage-specific biomarkers.

RESULTS: Evaluations utilizing publicly available datasets confirmed MOLUNGN's superior performance over existing methodologies. On the LUAD dataset, MOLUNGN achieved accuracy (ACC) of 0.84, Recall_weighted of 0.84, F1_weighted of 0.83, and F1_macro of 0.82. On the LUSC dataset, the model further improved, achieving ACC of 0.86, Recall_weighted of 0.86, F1_weighted of 0.85, and F1_macro of 0.84. Notably, critical stage-specific biomarkers with significant biological relevance to lung cancer progression were identified, facilitating robust gene-disease associations.

DISCUSSION: Our findings underscore the efficacy of MOLUNGN as an integrative framework in accurately classifying lung cancer stages and uncovering essential biomarkers. These biomarkers provide deep insights into lung cancer progression mechanisms and represent promising targets for future clinical validation. Integrating these biomarkers into the TCM-target-disease network enriches the understanding of TCM therapeutic potentials, laying a robust foundation for future precision medicine applications.

PMID:40534839 | PMC:PMC12174459 | DOI:10.3389/fgene.2025.1610284