Front Immunol. 2026 Feb 13;17:1743187. doi: 10.3389/fimmu.2026.1743187. eCollection 2026.

ABSTRACT

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC), an aggressive cancer with poor prognosis, poses major challenges owing to late diagnosis and limited response to current therapies. However, the identification of candidate drugs through multi-omics analyses and therapeutic peptides targeting key molecular pathways may provide improved outcomes. Although lactate metabolism is a critical factor in tumor progression, affecting cell proliferation, metastasis, and immune evasion, its role in PDAC-particularly within the tumor microenvironment, remains underexplored.

OBJECTIVES: This study investigated lactate metabolism in PDAC using high-throughput transcriptomic sequencing and single-cell transcriptomic analysis.

METHODS: Lactate metabolism-related gene expression was analyzed in tumor cells and their microenvironment, and correlations with patient prognosis were determined. Additionally, a machine learning-based prognostic model was established to identify lactate metabolism biomarkers for early diagnosis and personalized therapy.

RESULTS: Lactate metabolism significantly impacted the survival of patients with PDAC (n = 92; log-rank test, p < 0.05). Single-cell RNA and spatial transcriptomics analyses of 50, 795 cells from 8 PDAC samples revealed that 521 malignant cells exhibited hyperactive lactate metabolism (AUCell score comparison, p < 0.001). A prognostic model constructed from lactate metabolism-related genes using ensemble machine learning (StepCox + Enet, α = 0.5) effectively stratified patients into high- and low-risk groups across multiple cohorts (ICGC: n = 92; GSE28735: n = 45; GSE62452: n = 69; GSE183795: n = 139; all log-rank p < 0.05). Key prognostic genes identified included lysozyme (LYZ) and polymeric immunoglobulin receptor, which were significantly associated with patient survival (univariate Cox regression, p < 0.05). These genes may serve as clinical biomarkers of PDAC.

CONCLUSIONS: This study provides insights into PDAC metabolic features and highlights lactate metabolism as a potential therapeutic target. The identified biomarkers could facilitate early diagnosis and improve treatment strategies, ultimately enhancing patient outcomes.

PMID:41766855 | PMC:PMC12946077 | DOI:10.3389/fimmu.2026.1743187

Aging Dis. 2025 Dec 15. doi: 10.14336/AD.2025.1444. Online ahead of print.

ABSTRACT

Chronic gastritis (CG) is a highly prevalent, age-associated inflammatory disorder of gastric mucosa and a key precursor of gastric cancer in older adults. Beyond Helicobacter pylori infection and environmental insults, accumulating evidence indicates that chronic, low-grade inflammation coupled with aging biology, "gastric inflammaging", plays a central role in driving mucosal degeneration, atrophy, and malignant transformation. Here, we synthesize current mechanistic and multi-omics evidence to conceptualize CG as a tractable model of organ-specific inflammaging. We first summarize how hallmarks of aging-including cellular senescence and the senescence-associated secretory phenotype (SASP), mitochondrial dysfunction, impaired autophagy, immune exhaustion, and microbiome dysbiosis-converge to create a self-perpetuating inflammatory microenvironment in the stomach. We then review emerging single-cell and spatial multi-omics studies that delineate senescence-inflammation niches and reveal how these molecular neighborhoods relate to disease stage and cancer risk. Finally, we discuss therapeutic implications, highlighting geroscience-guided interventions such as senolytics/senomorphics, inflammasome and cGAS-STING pathway modulators, microbiota- and metabolite-targeted strategies, lifestyle interventions, and natural products, and propose a precision framework linking inflammaging biomarkers to patient stratification and clinical endpoints. Reframing CG as a gastric inflammaging model may provide a prototype for organ-specific healthy aging strategies and near-term gerotherapeutic trials aimed at extending healthspan.

PMID:41400573 | DOI:10.14336/AD.2025.1444

Int J Surg. 2025 Jul 22. doi: 10.1097/JS9.0000000000002999. Online ahead of print.

ABSTRACT

BACKGROUND: Breast cancer remains the most prevalent malignancy among women, and patients presenting with both breast and lung cancer pose significant challenges in clinical diagnosis and treatment. Currently, comprehensive multi-omics analyses for such multiple malignancies are lacking.

METHODS: An integrated multi-omics analysis was performed, incorporating quantitative proteomics and radiomics data from patients with single primary breast cancer as well as those with multiple primary tumors (breast and lung cancer).

RESULTS: Quantitative proteomics analysis revealed four distinct molecular signatures (Types I-IV). Patients with single breast cancer exhibited driving pathways primarily linked to cell proliferation (e.g., HER2), whereas those with multiple breast cancers showed enrichment in ER-related and proliferative pathways. In contrast, patients with multiple lung cancers displayed pathways associated with immune response and immune escape. Additionally, immune subtyping identified three distinct immune landscapes (Types I-III). Radiomic analysis demonstrated strong correlations between these molecular/immune subtypes and imaging findings. Patients with high imaging information scores exhibited pronounced tumor heterogeneity and reduced immune infiltration.

CONCLUSIONS: This study provides new insights into the molecular pathogenesis of multiple primary malignancies, particularly breast and lung cancer.

PMID:40694032 | DOI:10.1097/JS9.0000000000002999

Cell Death Dis. 2025 May 15;16(1):382. doi: 10.1038/s41419-025-07720-y.

ABSTRACT

M2-polarized tumor-associated macrophages (TAMs) are a key factor contributing to the poor prognosis of pancreatic ductal adenocarcinoma (PDAC). While various factors within the tumor microenvironment (TME) drive their formation, the role of PDAC-derived exosomes in this process remains unclear. We aim to clarify the regulatory impacts of tumor-derived exosomes to TAMs. After the intratumoral injection to subcutaneous tumor of C57BL/6 mice, we demonstrated PDAC-derived exosomes exacerbate PDAC progression, accompanied with upregulated M2 phenotype of TAMs and unaffected proliferation signatures. Through intratumoral injection model and multi-Omics analyses, we identified CCT6A as a novel tumor-derived exosomal protein, bridging TAMs M2 polarization and PDAC prognosis. Co-culture with exosomes derived from CCT6A^high PDAC leads to greater M2 phenotype of TAMs via PI3K-AKT signaling. According to proteomics data, chemokines' abundance reduces over tenfold once exosomal CCT6A absence, including CXCL1, CXCL3, CCL20 and CCL5, whose interaction with CCT6A in PDAC cells was confirmed by interactomics data. Moreover, we found silencing CCT6A abrogated the antagonism effects of CD47 antibody immunotherapy. Our findings implied that the subunit of the T-complex protein Ring Complex (TRiC) CCT6A serves as a matchmaker during exosome-mediated chemokines transfer from PDAC to TAMs. Silencing CCT6A effectively sensitized PDAC to CD47 antibody immunotherapy in vivo.

PMID:40374617 | PMC:PMC12081750 | DOI:10.1038/s41419-025-07720-y

Ann Med. 2025 Dec;57(1):2494671. doi: 10.1080/07853890.2025.2494671. Epub 2025 May 3.

ABSTRACT

BACKGROUND: The combination of traditional Chinese medicine (TCM) with chemotherapy has been widely applied in the treatment of gastric cancer (GC). However, previous clinical studies have been constrained by small sample sizes and a lack of investigation into the molecular mechanisms of TCM. This study aims to assess the efficacy of TCM in treating GC by leveraging the strengths of meta-analysis and multi-omics approaches while also summarizing the underlying pharmacological mechanisms.

METHODS: A systematic literature review and meta-analysis were conducted using online databases to collect data before May 2024. This was to investigate the association between TCM combined with chemotherapy and the prognosis in GC. The molecular targets between the high-frequency TCMs and GC were identified through network pharmacology. The underlying mechanisms were investigated using multi-omics.

RESULTS: 9 studies with 2,158 patients were included. The meta-analysis results demonstrated that the combination of TCM and chemotherapy significantly improved the overall survival (OS) of GC patients (OR = 2.91; 95% CI: 2.70-3.12, p < 0.00001) and enhanced their quality of life (OR = 4.00; 95% CI: 1.99-8.03, p < 0.0001). Network pharmacology analysis identified 13 potential molecular targets of TCM in GC; additionally, multi-omics analysis highlighted the significant roles of MK, MIF, GALECTIN, and CypA signaling pathways in GC.

CONCLUSION: The combination of TCM with chemotherapy significantly improves the prognosis of GC; future research can focus on these key molecular targets and signaling pathways. This supports the application of precision medicine in cancer treatment and suggests the rational use of TCM in managing GC.

PMID:40317214 | PMC:PMC12051567 | DOI:10.1080/07853890.2025.2494671

Cancer Cell Int. 2025 Apr 24;25(1):161. doi: 10.1186/s12935-025-03792-0.

ABSTRACT

BACKGROUND: For patients with nodules detected in imaging that are indeterminate for malignancy, achieving accurate, early, and non-invasive diagnosis of Lung Squamous Cell Carcinoma (LUSC) remains a significant challenge. Therefore, we aimed to establish diagnostic and prognostic models by identifying plasma extracellular vesicles (EVs) associated protein biomarkers specific to LUSC.

METHODS: This study employed a novel nanomaterial, NaY, for the enrichment of EVs from plasma. Validation was conducted through transmission electron microscopy, nanoparticle tracking analyses, and Western blotting. Machine learning algorithms were utilized to compute protein biomarkers associated with LUSC and establish a diagnostic model. Additionally, a prognostic prediction model for LUSC was developed using a combination of 101 machine learning algorithms. Risk scoring of patients was performed to explore the underlying reasons for prognostic differences between high and low-risk groups.

RESULTS: The results of three experiments demonstrate that the new nanomaterial NaY effectively enriches EVs from plasma. Analysis of the enriched profile reveals pathways related to glycolysis/gluconeogenesis and carbon metabolism enriched in plasma EVs of LUSC patients. Thirty-eight LSCC-related EV biomarkers were identified, from which five proteins (TUBB3, RPS7, RPLP1, KRT2, and VTN) were selected to establish a diagnostic model distinguishing between benign and LUSC nodules. The diagnostic efficacy of RPS7 and VTN was further validated in independent samples using ELISA experiments. Furthermore, DPYD, GALK1, CDC23, UBE2L3, RHEB, and PSME1 were determined as potential prognostic biomarkers. Subsequently, risk scores were computed for each sample, classifying all patients into high and low-risk groups. Enrichment analysis revealed that EVs from the high-risk group contained proteins promoting cell proliferation and invasion, while those from the low-risk group were enriched in immune-related protein biomarkers.

CONCLUSIONS: The novel nanomaterial NaY effectively enriches EVs from plasma. Utilizing plasma EV biomarkers, the diagnostic model demonstrates strong discriminative ability between benign and malignant pulmonary nodules in patients.

PMID:40275246 | PMC:PMC12023671 | DOI:10.1186/s12935-025-03792-0

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