Trends Mol Med. 2025 Jun 23:S1471-4914(25)00118-2. doi: 10.1016/j.molmed.2025.05.007. Online ahead of print.

ABSTRACT

Gastric cancer (GC) remains a deadly disease because of late detection and limited treatment options at advanced stages. Treatment of patients with metastatic disease is based on chemotherapy, complemented by antibodies targeting HER2, VEGFR2, and more recently PD-1 or claudin 18.2. Further targets, such as FGFR2b, as well as novel drug classes including antibody-drug conjugates (ADCs) and bispecific antibodies, are promising developments in GC treatment. Despite the failure of several targeted agents, the landscape of GC therapy is evolving rapidly, facilitated by umbrella or platform precision medicine trials. The integration of next-generation sequencing and other omics techniques into molecular tumor boards, as well as functional drug testing on patient-derived models, might bring us closer to personalized oncology and ultimately improve patient survival.

PMID:40555635 | DOI:10.1016/j.molmed.2025.05.007

Front Immunol. 2025 Jun 6;16:1606125. doi: 10.3389/fimmu.2025.1606125. eCollection 2025.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is characterized by metabolic and immune heterogeneity, driving tumor progression and therapy resistance. While G protein-coupled receptors (GPCR) signaling is known to regulate metabolism and immunity in cancers, its role in LUAD remains poorly defined. This study explores the influence of GPCR signaling on LUAD metabolism and immune landscape.

METHODS: We performed non-negative matrix factorization (NMF) clustering of GPCR signaling genes in TCGA-LUAD cohort to identify distinct molecular subgroups. A prognostic model was developed based on GPCR signaling genes using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression. Differentially expressed genes were analyzed for metabolic pathway enrichment and immune infiltration. In addition, key genes within GPCR signaling were identified and validated through functional assays.

RESULTS: NMF clustering based on GPCR signaling identified three subgroups in LUAD, with cluster 3 exhibiting poorer overall survival and significant enrichment in multiple prognostic associated metabolism pathways including purine, pyrimidine, glyoxylate and dicarboxylate metabolism. Then, we developed a GPCRscore prognostic model and validated across multiple cohorts, which effectively stratified LUAD patients into distinct risk groups. High-risk LUAD patients had an immunosuppressive microenvironment and activated metabolic reprogramming. ADM was identified as a key gene in the high-risk group, correlating with tumor stage, immune suppression, and resistance to immunotherapy. Clinically, ADM was highly expressed in tumor tissues and shows elevated concentrations in the peripheral blood of patients with advanced-stage LUAD. Subsequently, we demonstrated that knock-down of ADM in LUAD cells impaired their proliferation, migration, and invasion, while also reducing the angiogenic potential of endothelial cells in vitro. Adrenomedullin promoted LUAD progression in a murine metastasis model. Further, adrenomedullin inhibited CD8⁺ T cells proliferation, induced exhaustion, and impaired cytotoxic function. Finally, drug sensitivity and cell viability analysis showed LUAD patients with high levels of ADM exhibited sensitivity to the treatment of Staurosporine and Dasatinib.

CONCLUSIONS: In summary, this study reveals the pivotal role of GPCR signaling particularly mediated by ADM in orchestrating metabolic reprogramming and immune modulation in LUAD. ADM emerges as a potential predictive biomarker and therapeutic target, offering valuable implications for optimizing strategies.

PMID:40547013 | PMC:PMC12179119 | DOI:10.3389/fimmu.2025.1606125

iScience. 2025 May 15;28(6):112681. doi: 10.1016/j.isci.2025.112681. eCollection 2025 Jun 20.

ABSTRACT

Metabolic reprogramming is a defining feature of pancreatic cancer, influencing tumor progression and the tumor microenvironment. By integrating single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics, this study visualized the spatial co-localization of metabolites and gene expression within tumor samples, uncovering metabolic heterogeneity and intercellular interactions. Spatial transcriptomics identified distinct pathological regions, which were further characterized using single-cell transcriptomic data and pathologist annotations. Pseudotime trajectory analysis revealed metabolic shifts along the malignant progression, while single-cell Metabolism (scMetabolism) delineated metabolic differences between pathological regions, classifying them as hypermetabolic or hypometabolic. Notably, aberrant cell communication between cancer cells, macrophages, and fibroblasts was observed, with key receptor-ligand pairs significantly co-expressed in malignant regions and correlated with poor prognosis. Spatial metabolomics imaging identified signature metabolites, highlighting metabolic alterations in amino acid metabolism, polyamine metabolism, fatty acid synthesis, and phospholipid metabolism. This integrated analysis provides critical insights into pancreatic cancer metabolism, offering potential avenues for targeted therapeutic interventions.

PMID:40538442 | PMC:PMC12177182 | DOI:10.1016/j.isci.2025.112681

Cancer Treat Rev. 2025 Jun 14;139:102979. doi: 10.1016/j.ctrv.2025.102979. Online ahead of print.

ABSTRACT

Breast cancer is characterized by significant molecular heterogeneity; therefore, there are distinct clinical features, treatment modalities, and prognostic outcomes across its various molecular subtypes. In the era of precision medicine, liquid biopsy has emerged as a convenient and minimally invasive technique capable of dynamically representing the comprehensive tumor gene spectrum. This review systematically elaborates the clinical value of liquid biopsy as a breakthrough tool for precision diagnosis and treatment in breast cancer through dynamic detection of key biomarkers, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and non-coding RNA (ncRNA). Specific genetic mutations and methylation signatures in ctDNA can be applied to early breast cancer screening, minimal residual disease monitoring, and tracking drug resistance mechanisms. CTCs enumeration (≥1/7.5 mL in early-stage cancer or ≥ 5/7.5 mL in metastatic cancer) and PD-L1 expression levels demonstrate direct correlations with prognostic stratification and the efficacy of immunotherapy. As the specificity and sensitivity of liquid biopsy continue to improve, personalized treatment strategies, informed by biomarker analysis and targeted precision therapies, have unveiled new avenues of hope for patients with breast cancer. However, several challenges persist in the practical application of liquid biopsy. Despite persistent challenges, such as insufficient standardization and difficulties in resolving low-abundance variants, future advancements should focus on multi-omics integration and AI-driven technological breakthroughs to overcome bottlenecks in clinical translation. This review summarizes cutting-edge liquid biopsy technologies for identifying clinically significant molecular biomarkers, focusing on discussing critical challenges in the strategies to advance precision oncology applications for optimized treatment guidance and disease surveillance in breast cancer.

PMID:40540857 | DOI:10.1016/j.ctrv.2025.102979

Cytokine Growth Factor Rev. 2025 Jun 6:S1359-6101(25)00071-1. doi: 10.1016/j.cytogfr.2025.06.001. Online ahead of print.

ABSTRACT

Lung cancer is a leading cause of cancer-related mortality worldwide, characterized by the uncontrolled growth and spread of abnormal cells. Despite constant progress in diagnosis and treatment of lung cancer remains highly challenging. Present review explores the molecular underpinnings of lung cancer, emphasizing genetic mutations, altered metabolism, and the role of biomarkers in diagnosis and treatment. Advances in diagnostic methods, particularly liquid biopsies, have enabled non-invasive detection and monitoring of lung cancer. Therapeutic approaches have evolved significantly, with molecular-targeted therapies focusing on pathways like EGFR, ALK, KRAS, and MET. Caner Immunotherapy, including immune checkpoint inhibitors, artificial intelligence, and multi-omics analysis, including genomics and image data to predict treatment response, has further enhanced precision medicine by enabling earlier detection, better prognostic models, and efficient drug discovery. AI has a significant impact on various aspects of oncology, including improving the earlier diagnosis of lung cancer. Despite these advancements, challenges such as drug resistance, tumor heterogeneity, and limited efficacy of certain therapies persist, necessitating continued research and innovation.

PMID:40544104 | DOI:10.1016/j.cytogfr.2025.06.001

Cell Signal. 2025 Jun 18;134:111950. doi: 10.1016/j.cellsig.2025.111950. Online ahead of print.

ABSTRACT

FAAP100 plays an essential role in DNA damage repair, with dysregulation associated with elevated cancer susceptibility. Nevertheless, comprehensive pan-cancer analyses examining FAAP100 prognostic significance, immune correlations, and epigenetic regulation remains unexplored. This study systematically characterized FAAP100 across 33 cancer types utilizing multi-omics data from TCGA, UALCAN, cBioPortal, TIMER2.0, and CPTAC. Analytical assessments included expression profiles, prognostic significance, and diagnostic utility, alongside associations with DNA methylation, immune cell infiltration, immune checkpoint gene expression, tumor mutational load (TMB), microsatellite instability (MSI), and drug resistance. Findings revealed significant FAAP100 upregulation across multiple cancer types, exhibiting inverse correlations to patient survival. Genomic characterization identified associations between FAAP100 overexpression and both copy number amplification and promoter hypomethylation. Immune profiling demonstrated robust correlations with immune cell infiltration levels and checkpoint molecule activity. Functional assays utilizing PC9 and H1299 cells indicated that FAAP100 enhances cellular proliferation and migration while inhibiting apoptosis processes. In vivo studies confirmed tumor growth suppression upon FAAP100 knockdown. Collectively, this multi-omics investigation identifies FAAP100 as a pan-cancer oncogene driver, highlighting its potential as both a prognostic biomarker and therapeutic target. The integrated analysis of expression patterns, epigenetic modifications, immune characteristics, and genomic alterations elucidates the mechanistic involvement of FAAP100 in tumor progression, providing a foundation for clinical application in precision oncology approaches..

PMID:40541815 | DOI:10.1016/j.cellsig.2025.111950

J Gastrointest Cancer. 2025 Jun 19;56(1):139. doi: 10.1007/s12029-025-01249-1.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor, with rising incidence and mortality rates posing a significant threat to global public health. Accurate prediction of liver cancer occurrence and progression is essential for improving patient prognosis. This study uses bibliometric methods to analyze the current state and future trends in liver cancer prediction research.

METHODS: A search was conducted in the Web of Science (WOS) database on October 22, 2023, identifying 1092 articles on liver cancer prediction. These articles were quantitatively analyzed using CiteSpace 6.2 software, with a focus on research hotspots, authors, countries, and keywords.

RESULTS: The study involved 114 countries, 4254 institutions, and 280 journals, with 48,788 citations. China (826 papers) and the USA (96 papers) dominate the field. Leading institutions include Sun Yat-sen University, Fudan University, Zhejiang University, and Yonsei University. The most cited journals were Hepatology (2209 citations) and Journal of Hepatology (946 citations). Frontiers in Oncology had the highest H-index (14). Key authors include Kim Seung Up (23 papers) and Ahn Sang Hoon (H-index = 14). Early research focused on risk factors and staging, while recent studies emphasize DNA methylation, immune microenvironments, and tumor metastasis. Future research will focus on multi-omics data integration and AI-driven predictive model optimization.

CONCLUSION: This study provides a comprehensive overview of liver cancer prediction research, highlighting key trends and the potential of multi-omics data and machine learning to enhance predictive models and clinical outcomes.

PMID:40537718 | DOI:10.1007/s12029-025-01249-1

Support Care Cancer. 2025 Jun 19;33(7):596. doi: 10.1007/s00520-025-09670-9.

ABSTRACT

PURPOSE: Anorexia is a frequent and serious symptom in patients with lung cancer, often leading to malnutrition and cachexia, and negatively affecting quality of life and survival. This scoping review systematically synthesizes current evidence on biomarkers associated with cancer-related anorexia (CRA) in lung cancer, aiming to clarify biological mechanisms and inform targeted interventions.

METHODS: We performed a comprehensive literature search of studies evaluating the associations between CRA and various biomarkers in patients with lung cancer. Data were extracted and analyzed for pathway, genomic, transcriptomic, epigenetic, proteomic, metabolic, and composite biomarkers.

RESULTS: A total of 33 studies were included, identifying more than 100 biomarkers closely associated with CRA in lung cancer. These include inflammatory cytokines, energy metabolism markers, epigenetic and transcriptomic alterations, and disruptions in multiple cellular signaling pathways. Our analysis demonstrates that CRA is not the result of a single factor but reflects widespread dysregulation across metabolic, immune, and signaling networks. Some studies suggest that nutritional and anti-inflammatory interventions, such as n-3 fatty acid and antioxidant supplementation, can modulate biomarker profiles and potentially improve clinical outcomes.

CONCLUSION: CRA in lung cancer is a multifactorial syndrome involving complex interactions among inflammatory, metabolic, and signaling pathways. Multi-omics biomarker integration holds promise for early detection and individualized treatment, but larger, multi-center studies are needed to confirm clinical utility and optimize management strategies. Precision interventions based on biomarker profiles should be further explored in future research and practice.

PMID:40536584 | DOI:10.1007/s00520-025-09670-9