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

Ann Surg Oncol. 2025 Jul 3. doi: 10.1245/s10434-025-17738-3. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to identify the most effective drug therapeutics for patients with the mesenchymal subtype of advanced gastric cancer (AGC). Extensive research employing diverse omics methodologies has unveiled a varied landscape of AGC. Recent progress in next-generation sequencing and other genomic technologies has facilitated a more intricate exploration of AGC at the molecular level. Nonetheless, the optimal treatment for patients with the mesenchymal subtype of gastric cancer remains elusive. Lei's molecular classification of AGC is based on gene expression profiles named "mesenchymal," "immunogenic," "classical," and "metabolic."

PATIENTS AND METHODS: Based on RNA-seq transcriptome, 234 patients were divided into four molecular subtypes: mesenchymal (n = 96), immunogenic (n = 37), metabolic (n = 61), and classic (n = 40).

RESULTS: Among those with mesenchymal-subtype AGC, compared with non-Apatinib group, the Apatinib treatment group demonstrated a significant increase in objective response rate (ORR 89.3% versus 69.3%, p = 0.038; odds ratio (OR) 0.269, 95% confidence interval (CI) (0.073-0.989)); overall survival (OS) 89.3% versus 60.2%, p = 0.010; hazard ratio (HR) 0.241, 95% CI (0.073-0.796)) and disease-free survival (DFS 78.6% versus 52.9%, p = 0.031; HR 0.400, 95% CI (0.167-0.956)). Furthermore, Apatinib significantly reduced the risk of death and recurrence in patients with mesenchymal subtype (OS: HR 0.129, 95% CI (0.030-0.563), p = 0.006; DFS: HR 0.340, 95% CI (0.138-0.833), p = 0.018). However, no significant differences were observed in the ORR, OS, or DFS between patients with metabolic and classical subtypes who underwent combination chemotherapy with additional Apatinib or camrelizumab.

CONCLUSIONS: Our analysis has revealed that, for neoadjuvant therapy in AGC, the mesenchymal subtype stands out as the ideal patient population benefiting from Apatinib.

PMID:40608168 | DOI:10.1245/s10434-025-17738-3

Sci Rep. 2025 Jul 1;15(1):21173. doi: 10.1038/s41598-025-08986-0.

ABSTRACT

Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and whole transcriptome sequencing on plasma and leukocytes with advanced machine learning techniques to satisfy all three clinical testing needs on one platform. Caris Assure for therapy selection was CLIA validated using 1,910 samples. 376,197 tissue profiles along with 7,061 paired blood and tissue profiles were used to engineer features for three machine learning models. The MCED model was trained on 1,013 patients and validated on an independent set of 2,675 patients. The tissue of origin for MCED model was trained on 1,166 samples and validated using 5-fold cross validation. The MRD & Monitoring model was trained on 3,439 patients and validated on two independent sets of 86 patients for MRD and 101 patients for monitoring. For early detection, sensitivities for stages I-IV cancers (n = 284, 129, 90, 23 respectively) were 83.1%, 86.0%, 84.4%, and 95.7%, all at 99.6% specificity (n = 2149). The diagnostic first-line procedure for tissue of origin was determined for 8 categories with a top-3 accuracy of 85% for stage I and II cancers. Detection of driver mutations for therapy selection from blood collected within 30 days of matched tumor tissue, demonstrated high concordance (PPA of 93.8%, PPV of 96.8%) using CHIP subtraction. For MRD and recurrence monitoring, the disease-free survival of patients whose cancers were predicted to have an event was significantly shorter than those predicted not to have an event using a tumor naïve approach (HR = 33.4, p < 0.005, HR = 4.39, p = 0.008, respectively). The data presented here demonstrate a unified liquid biopsy platform that uses blood-based whole-exome and transcriptome sequencing coupled with artificial intelligence to address the important clinical needs in multi-cancer early detection, monitoring of MRD and recurrent cancers, and precision selection of molecularly targeted therapies.

PMID:40596693 | PMC:PMC12214926 | DOI:10.1038/s41598-025-08986-0

J Am Soc Mass Spectrom. 2025 Aug 6;36(8):1598-1608. doi: 10.1021/jasms.4c00505. Epub 2025 Jul 2.

ABSTRACT

Gastric signet ring cell carcinoma (GSRC) is an aggressive subtype of gastric cancer (GC) with a poor prognosis. The lack of a systematic molecular and metabolic heterogeneity overview has led to slow progress in clinical practice. This study used mass spectrometry imaging (MSI) to investigate the metabolic landscape of GSRC in GC tissue with various differentiation grades. Our comprehensive spatial profiling of metabolites and lipids unveiled distinct metabolic signatures across different tissue subregions. A substantial number of lipidomic biomarkers associated with GSRC were identified, including phosphatidylethanolamine N-methyl (PE-NMe), phosphatidylethanolamine (PE), sphingomyelin (SM), diacylglycerol (DG), phosphatidic acid (PA), and phosphatidylcholine (PC), which may provide insights into its pathogenesis and potential therapeutic targets. Furthermore, multi-omics network analysis revealed intricate metabolic pathways involved in GSRC progression. Our findings highlight the importance of understanding the metabolic heterogeneity of GSRC and pave the way for future studies exploring its clinical implications and therapeutic strategies.

PMID:40600435 | DOI:10.1021/jasms.4c00505

Eur J Trauma Emerg Surg. 2025 Jun 27;51(1):238. doi: 10.1007/s00068-025-02922-7.

ABSTRACT

PURPOSE: Obesity is a prevalent disease, but its influence on post-injury biology remains unclear. In this study, we aimed to characterize the independent effect of obesity on the proteomic and metabolomic signatures of trauma.

METHODS: Plasma was obtained on arrival from injured patients at a Level 1 Trauma Center and analyzed with modern mass spectrometry-based proteomics and metabolomics. Samples obtained after start of transfusion were excluded. Patients were stratified by "obesity" (body mass index [BMI]≥30 kg/m²) vs. "no obesity" (BMI < 30 kg/m²). In sub-group analyses, patients were sub-stratified by Low Injury/Low Shock (ISS < 15, base excess [BE]≥-6mEq/L) and High Injury/High Shock (ISS≥15, BE<-6). Multiple regression was used to adjust the omics data for significant covariates prior to performing ome-wide analyses.

RESULTS: There were 183 patients included (48 [26%] with obesity and 135 [74%] without). After covariate-adjustment, multiple proteins and metabolites were correlated with ISS and/or BE and were significantly different from Low Injury/Low Shock to High Injury/High Shock only in patients with obesity. This obesity-specific omics response to injury was characterized by increased inflammation, hypercoagulability, altered nitrogen metabolism, and mitochondrial dysfunction. Patients with obesity also exhibited excessive injury-provoked tissue destruction and organ damage compared to patients without obesity. In injury severity-adjusted analyses, the obesity signature consistently displayed markers of hemolysis, likely reflecting a pre-injury hemolytic propensity.

CONCLUSION: Obesity is independently associated with altered post-injury biology, which likely underlies unique pathology in trauma patients with obesity. Identifying this aberrant response to injury is the first step in developing personalized therapies for this patient population.

PMID:40576654 | DOI:10.1007/s00068-025-02922-7

Biomolecules. 2025 May 31;15(6):803. doi: 10.3390/biom15060803.

ABSTRACT

Lung cancer is responsible for 2.21 million annual cancer cases and is the leading worldwide cause of cancer-related deaths. Specifically, lung adenocarcinoma (LUAD) is the most prevalent lung cancer subtype resulting from genetic causes; LUAD has a 15% patient survival rate due to it commonly being detected in its advanced stages. This study aimed to identify a novel biomarker signature of early-stage LUAD utilizing gene expression analysis of human lung tissue samples. Using 22 pairs of LUAD and matched normal lung microarrays, 229 differentially expressed genes were identified. These genes were networked for their protein-protein interactions, and 44 hub genes were determined from protein essentiality. Survival analysis of 478 LUAD patient samples identified four statistically significant candidates. These candidate genes' expression profiles were validated from GTEx and TCGA (347 normal, 483 LUAD samples); immunohistochemistry validated the subsequent protein presence. Through intensive bioinformatic identification and multiple validations of the four-biomarker gene signature, AGER, MGP, and PECAM1 were identified as downregulated in LUAD; SLC2A1 was identified as upregulated in LUAD. These four biologically significant genes are involved in tumorigenesis and poor LUAD prognosis, meriting their use as a clinical biomarker signature and therapeutic targets for early-stage LUAD.

PMID:40563443 | PMC:PMC12191159 | DOI:10.3390/biom15060803

JCI Insight. 2025 Jun 26:e191595. doi: 10.1172/jci.insight.191595. Online ahead of print.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) has a poor survival rate due to late detection. PDAC arises from precursor microscopic lesions, termed pancreatic intraepithelial neoplasia (PanIN), that develop at least a decade before overt disease--this provides an opportunity to intercept PanIN-to-PDAC progression. However, immune interception strategies require full understanding of PanIN and PDAC cellular architecture. Surgical specimens containing PanIN and PDAC lesions from a unique cohort of five treatment-naïve patients with PDAC were surveyed using spatial-omics (proteomic and transcriptomic). Findings were corroborated by spatial proteomics of PanIN and PDAC from tamoxifen-inducible KPC (tiKPC) mice. We uncovered the organization of lymphoid cells into tertiary lymphoid structures (TLSs) adjacent to PanIN lesions. These TLSs lacked CD21+CD23+ B cells compared to more mature TLSs near the PDAC border. PanINs harbored mostly CD4+ T cells with fewer Tregs and exhausted T cells than PDAC. Peri-tumoral space was enriched with naïve CD4+ and central memory T cells. These observations highlight the opportunity to modulate the immune microenvironment in PanINs before immune exclusion and immunosuppression emerge during progression into PDAC.

PMID:40569674 | DOI:10.1172/jci.insight.191595