Front Cell Dev Biol. 2026 Sep 14;14:1878092. doi: 10.3389/fcell.2026.1878092. eCollection 2026.

ABSTRACT

Liquid biopsy has emerged as a powerful non-invasive tool in precision oncology, providing real-time insights into tumor evolution, host immune responses, and dynamic changes in the tumor immune microenvironment. By enabling minimally invasive sampling, it can overcome several limitations of conventional tissue biopsy. This review summarizes the major biological sources and components of liquid biopsy, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and circulating immune cells, and discusses their value as dynamic indicators of interactions during cancer immunotherapy. Particular attention is given to immune-related biomarkers associated with immune checkpoints, immunosuppressive mechanisms, and immune escape, including circulating immune cell populations, and inflammatory cytokine profiles. We further examine their potential applications in predicting treatment response, monitoring immune-related adverse events, assessing minimal residual disease, and detecting acquired resistance. In addition, recent technological advances that are accelerating the clinical translation of liquid biopsy are highlighted, including multi-omics integration, microfluidic platforms. These approaches have improved the sensitivity, accuracy, and multidimensional characterization of tumor- and immune-derived biomarkers. Nevertheless, biological heterogeneity, limited assay standardization, and the lack of large-scale prospective validation studies continue to restrict widespread clinical implementation. Overall, immune-related biomarkers detected through liquid biopsy offer considerable potential for the longitudinal monitoring of the tumor immune microenvironment and may improve non-invasive cancer diagnosis, therapeutic monitoring, and personalized immunotherapy in the era of precision oncology.

PMID:42807637 | PMC:PMC13617286 | DOI:10.3389/fcell.2026.1878092

Nat Commun. 2026 Aug 29;17(1):10311. doi: 10.1038/s41467-026-77187-8.

ABSTRACT

Type 2 diabetes (T2D) subgroups defined by clinical variables differ in disease progression and treatment response. To uncover potential molecular drivers of this heterogeneity, we performed a multi-omics analysis of 826 drug-naïve T2D patients from two phase 3 trials of the insulin sensitizer chiglitazar. Here we show that severe insulin-resistant diabetes (SIRD) is characterized by distinct miRNA profiles (e.g., miR-122-5p) correlated with liver injury, and metabolic shifts in amino acids and primary bile acids. Mild obesity-related diabetes (MOD) showed the lowest level of phenylacetylglutamine, a metabolite known to promote cardiovascular disease. Severe insulin-deficient diabetes (SIDD) exhibited high pancreas-specific miR-7-5p, while mild age-related diabetes (MARD) presented the mildest abnormalities. Finally, integrating these multi-omics signatures into machine learning models enhanced prediction of insulin sensitizer efficacy over clinical data alone. Our findings define the distinct molecular signatures of T2D subgroups, facilitating the prediction of heterogeneous treatment responses and supporting personalized clinical management.

PMID:42805981 | PMC:PMC13620142 | DOI:10.1038/s41467-026-77187-8

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8⁺ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8⁺ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A₂-arachidonate 5-lipoxygenase (cPLA₂-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8⁺ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8⁺ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8⁺ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A₂-arachidonate 5-lipoxygenase (cPLA₂-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8⁺ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

Transl Oncol. 2026 Sep 9;73:103020. doi: 10.1016/j.tranon.2026.103020. Online ahead of print.

ABSTRACT

BACKGROUND: The prognostic relevance of inflammatory bowel disease (IBD)-colorectal cancer (CRC) shared host-microbe signatures in non-intestinal epithelial malignancies remains unclear. This study aimed to evaluate the prognostic and biological significance of an IBD-CRC shared host-microbe interactome signature in bladder urothelial carcinoma (BLCA).

METHODS: Gene set variation analysis (GSVA) was used to assess the activity of the IBD-CRC shared signature across The Cancer Genome Atlas (TCGA) pan-cancer solid tumor cohorts, including lung, liver, colorectal, and urinary system tumors. In BLCA, weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO)-Cox regression were applied to construct a prognostic risk model, which was validated in independent transcriptomic cohorts. An attention-based multiple instance learning (MIL) model was developed to predict the LASSO-derived high- or low-risk group from H&E whole-slide images (WSIs), using TCGA cases for training and internal validation and an independent institutional cohort of 39 BLCA patients for external validation. Molecular subtype, immune infiltration, immunohistochemistry (IHC), machine learning, single nucleotide variation/copy number variation (SNV/CNV), single-cell/spatial transcriptomics, and WSI-based deep learning analyses were integrated to characterize the biological relevance of the signature.

RESULTS: High GSVA scores were significantly associated with poor prognosis in BLCA. The LASSO-derived high-risk group was enriched in basal/squamous molecular features and exhibited an immune-infiltrated but immunosuppressive tumor microenvironment, characterized by increased immunosuppressive cell infiltration and elevated immune checkpoint expression. Conventional IHC markers supported distinct subtype-related protein phenotypes between risk groups. Single-cell and spatial transcriptomic analyses revealed that malignant cells with high signature activity were enriched in Wnt, Hippo, and cell adhesion pathways. The WSI-based MIL model achieved an area under the curve (AUC) of 0.852 in the internal validation cohort. Machine learning and SNV/CNV analyses further characterized key molecular features associated with the LASSO risk score, including AKR1B1, LY6E, MEST, and others. Pan-cancer characterization of AKR1B1 across multiple malignancies, including lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), and kidney renal clear cell carcinoma (KIRC), revealed cancer-type-specific associations with immunosuppressive microenvironmental features and tumor stemness.

CONCLUSION: The IBD-CRC shared host-microbe signature has significant prognostic value in BLCA and is associated with basal/squamous differentiation, immunosuppressive microenvironmental features, genomic alteration patterns, and malignant cell functional heterogeneity. The integrated multi-omics framework and externally validated pathology AI model provide potential tools for BLCA risk stratification and biological interpretation.

PMID:42715652 | DOI:10.1016/j.tranon.2026.103020

Transl Oncol. 2026 Sep 9;73:103020. doi: 10.1016/j.tranon.2026.103020. Online ahead of print.

ABSTRACT

BACKGROUND: The prognostic relevance of inflammatory bowel disease (IBD)-colorectal cancer (CRC) shared host-microbe signatures in non-intestinal epithelial malignancies remains unclear. This study aimed to evaluate the prognostic and biological significance of an IBD-CRC shared host-microbe interactome signature in bladder urothelial carcinoma (BLCA).

METHODS: Gene set variation analysis (GSVA) was used to assess the activity of the IBD-CRC shared signature across The Cancer Genome Atlas (TCGA) pan-cancer solid tumor cohorts, including lung, liver, colorectal, and urinary system tumors. In BLCA, weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO)-Cox regression were applied to construct a prognostic risk model, which was validated in independent transcriptomic cohorts. An attention-based multiple instance learning (MIL) model was developed to predict the LASSO-derived high- or low-risk group from H&E whole-slide images (WSIs), using TCGA cases for training and internal validation and an independent institutional cohort of 39 BLCA patients for external validation. Molecular subtype, immune infiltration, immunohistochemistry (IHC), machine learning, single nucleotide variation/copy number variation (SNV/CNV), single-cell/spatial transcriptomics, and WSI-based deep learning analyses were integrated to characterize the biological relevance of the signature.

RESULTS: High GSVA scores were significantly associated with poor prognosis in BLCA. The LASSO-derived high-risk group was enriched in basal/squamous molecular features and exhibited an immune-infiltrated but immunosuppressive tumor microenvironment, characterized by increased immunosuppressive cell infiltration and elevated immune checkpoint expression. Conventional IHC markers supported distinct subtype-related protein phenotypes between risk groups. Single-cell and spatial transcriptomic analyses revealed that malignant cells with high signature activity were enriched in Wnt, Hippo, and cell adhesion pathways. The WSI-based MIL model achieved an area under the curve (AUC) of 0.852 in the internal validation cohort. Machine learning and SNV/CNV analyses further characterized key molecular features associated with the LASSO risk score, including AKR1B1, LY6E, MEST, and others. Pan-cancer characterization of AKR1B1 across multiple malignancies, including lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), and kidney renal clear cell carcinoma (KIRC), revealed cancer-type-specific associations with immunosuppressive microenvironmental features and tumor stemness.

CONCLUSION: The IBD-CRC shared host-microbe signature has significant prognostic value in BLCA and is associated with basal/squamous differentiation, immunosuppressive microenvironmental features, genomic alteration patterns, and malignant cell functional heterogeneity. The integrated multi-omics framework and externally validated pathology AI model provide potential tools for BLCA risk stratification and biological interpretation.

PMID:42715652 | DOI:10.1016/j.tranon.2026.103020

Transl Oncol. 2026 Sep 9;73:103020. doi: 10.1016/j.tranon.2026.103020. Online ahead of print.

ABSTRACT

BACKGROUND: The prognostic relevance of inflammatory bowel disease (IBD)-colorectal cancer (CRC) shared host-microbe signatures in non-intestinal epithelial malignancies remains unclear. This study aimed to evaluate the prognostic and biological significance of an IBD-CRC shared host-microbe interactome signature in bladder urothelial carcinoma (BLCA).

METHODS: Gene set variation analysis (GSVA) was used to assess the activity of the IBD-CRC shared signature across The Cancer Genome Atlas (TCGA) pan-cancer solid tumor cohorts, including lung, liver, colorectal, and urinary system tumors. In BLCA, weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO)-Cox regression were applied to construct a prognostic risk model, which was validated in independent transcriptomic cohorts. An attention-based multiple instance learning (MIL) model was developed to predict the LASSO-derived high- or low-risk group from H&E whole-slide images (WSIs), using TCGA cases for training and internal validation and an independent institutional cohort of 39 BLCA patients for external validation. Molecular subtype, immune infiltration, immunohistochemistry (IHC), machine learning, single nucleotide variation/copy number variation (SNV/CNV), single-cell/spatial transcriptomics, and WSI-based deep learning analyses were integrated to characterize the biological relevance of the signature.

RESULTS: High GSVA scores were significantly associated with poor prognosis in BLCA. The LASSO-derived high-risk group was enriched in basal/squamous molecular features and exhibited an immune-infiltrated but immunosuppressive tumor microenvironment, characterized by increased immunosuppressive cell infiltration and elevated immune checkpoint expression. Conventional IHC markers supported distinct subtype-related protein phenotypes between risk groups. Single-cell and spatial transcriptomic analyses revealed that malignant cells with high signature activity were enriched in Wnt, Hippo, and cell adhesion pathways. The WSI-based MIL model achieved an area under the curve (AUC) of 0.852 in the internal validation cohort. Machine learning and SNV/CNV analyses further characterized key molecular features associated with the LASSO risk score, including AKR1B1, LY6E, MEST, and others. Pan-cancer characterization of AKR1B1 across multiple malignancies, including lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), and kidney renal clear cell carcinoma (KIRC), revealed cancer-type-specific associations with immunosuppressive microenvironmental features and tumor stemness.

CONCLUSION: The IBD-CRC shared host-microbe signature has significant prognostic value in BLCA and is associated with basal/squamous differentiation, immunosuppressive microenvironmental features, genomic alteration patterns, and malignant cell functional heterogeneity. The integrated multi-omics framework and externally validated pathology AI model provide potential tools for BLCA risk stratification and biological interpretation.

PMID:42715652 | DOI:10.1016/j.tranon.2026.103020

Transl Oncol. 2026 Sep 8;73:102990. doi: 10.1016/j.tranon.2026.102990. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant and lethal subtype of non-small cell lung cancer, with a lack of reliable prognostic biomarkers to guide clinical management. Basigin (BSG) has been implicated in tumor progression across multiple cancers, yet its expression pattern, prognostic significance, and underlying mechanisms in LUAD remain incompletely elucidated.

METHODS: We integrated multi-omics data from TCGA, GTEx, CCLE, and GEO databases to analyze BSG expression profiles. Clinical correlations were assessed via Kruskal-Wallis tests. Prognostic value was determined using Kaplan-Meier survival analysis, univariate/multivariate Cox regression, and nomogram construction with calibration curves. Functional enrichment (GO/KEGG) and immune infiltration analyses were performed to explore BSG-related mechanisms, followed by immunohistochemical (IHC) validation in A549 cells and clinical LUAD tissue microarrays.

RESULTS: BSG was significantly upregulated in LUAD tissues versus normal/paired adjacent tissues, correlating with advanced T/N/pathologic stages. High BSG expression predicted worse survival outcomes in TCGA-LUAD, which was validated in GEO datasets. Multivariate Cox regression identified BSG as an independent prognostic factor, with a well-calibrated nomogram for survival prediction. Functional exploration indicated that BSG mainly participated in tumor-associated and immunological pathways. Immune infiltration analysis indicated that BSG was significantly correlated with the infiltration of various immune cells. Moreover, BSG exhibited a strong association with immune checkpoint proteins, chemokines, chemokine receptors, and MHC genes. IHC further confirmed its cytoplasmic/membranous localization and prognostic relevance.

CONCLUSION: BSG serves as an independent prognostic biomarker and potential therapeutic target in LUAD, shedding light on its regulatory roles in tumor progression and immune microenvironment remodeling.

PMID:42710246 | DOI:10.1016/j.tranon.2026.102990

Transl Oncol. 2026 Sep 8;73:102990. doi: 10.1016/j.tranon.2026.102990. Online ahead of print.

ABSTRACT

BACKGROUND: Lung adenocarcinoma (LUAD) is a predominant and lethal subtype of non-small cell lung cancer, with a lack of reliable prognostic biomarkers to guide clinical management. Basigin (BSG) has been implicated in tumor progression across multiple cancers, yet its expression pattern, prognostic significance, and underlying mechanisms in LUAD remain incompletely elucidated.

METHODS: We integrated multi-omics data from TCGA, GTEx, CCLE, and GEO databases to analyze BSG expression profiles. Clinical correlations were assessed via Kruskal-Wallis tests. Prognostic value was determined using Kaplan-Meier survival analysis, univariate/multivariate Cox regression, and nomogram construction with calibration curves. Functional enrichment (GO/KEGG) and immune infiltration analyses were performed to explore BSG-related mechanisms, followed by immunohistochemical (IHC) validation in A549 cells and clinical LUAD tissue microarrays.

RESULTS: BSG was significantly upregulated in LUAD tissues versus normal/paired adjacent tissues, correlating with advanced T/N/pathologic stages. High BSG expression predicted worse survival outcomes in TCGA-LUAD, which was validated in GEO datasets. Multivariate Cox regression identified BSG as an independent prognostic factor, with a well-calibrated nomogram for survival prediction. Functional exploration indicated that BSG mainly participated in tumor-associated and immunological pathways. Immune infiltration analysis indicated that BSG was significantly correlated with the infiltration of various immune cells. Moreover, BSG exhibited a strong association with immune checkpoint proteins, chemokines, chemokine receptors, and MHC genes. IHC further confirmed its cytoplasmic/membranous localization and prognostic relevance.

CONCLUSION: BSG serves as an independent prognostic biomarker and potential therapeutic target in LUAD, shedding light on its regulatory roles in tumor progression and immune microenvironment remodeling.

PMID:42710246 | DOI:10.1016/j.tranon.2026.102990

Cytotechnology. 2026 Jun;78(3):119. doi: 10.1007/s10616-026-00993-x. Epub 2026 May 14.

ABSTRACT

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer worldwide, with increasing incidence and mortality rates. Although several targeted therapies are currently available, the therapeutic outcomes remain unsatisfactory due to the high heterogeneity and drug resistance of HCC. Therefore, novel molecular mechanisms and therapeutic strategies urgently need to be explored. In this study, we obtained the GSE39791 dataset from the GEO database and identified 1,186 differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was conducted to obtain 776 key module genes, which were intersected with 11,671 HCC-related genes from the GeneCards database, resulting in 226 candidate genes. A protein-protein interaction (PPI) network was constructed using the STRING database, and the top 20 hub genes were identified using the MNC algorithm in Cytoscape. Among these, the five most significant hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were selected for further analysis. KEGG enrichment analysis was performed to explore their functional pathways. Potential therapeutic agents were predicted using the CMap database, and molecular docking was conducted via AutoDock Vina. To validate the computational predictions, a quercetin intervention model was established. The optimal dose was determined through CCK-8 assays in HepG2 cells, and the expression of the five hub genes was examined in normal liver cells (LO2), HepG2 cells, and HepG2 cells treated with quercetin using RT-qPCR. The five hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were significantly overexpressed in both HCC tissues and cell lines. Enrichment analysis revealed that these genes were mainly involved in cancer-related pathways, including the cell cycle, p53 signaling pathway, and FoxO signaling pathway. Drug prediction analysis showed that quercetin exhibited a negative regulatory pattern with respect to HCC and displayed binding energies below - 5 kcal/mol with all five hub proteins. CCK-8 assays confirmed the dose-dependent inhibitory effect of quercetin on HepG2 cell viability. RT-qPCR results demonstrated that quercetin significantly downregulated the expression of the five hub genes, consistent with the bioinformatics predictions. This study integrated multi-omics analysis and experimental validation to identify five core genes closely associated with HCC and suggested that quercetin may exert anti-HCC effects partly associated with the regulation of these genes. Our findings offer new insights into the molecular mechanisms of HCC and provide a promising strategy for the development of targeted therapeutics.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10616-026-00993-x.

PMID:42145839 | PMC:PMC13176377 | DOI:10.1007/s10616-026-00993-x

J Ethnopharmacol. 2026 Mar 30;365:121612. doi: 10.1016/j.jep.2026.121612. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Airway inflammation is one of the primary pathological characteristics of asthma. Soufeng Yuchuan (SFYC) decoction, a compound formula derived from multiple traditional Chinese medicine prescriptions, is widely applied clinically and exhibits significant therapeutic efficacy against asthma. However, its anti-asthmatic mechanisms remain incompletely understood.

MATERIALS AND METHODS: Asthmatic rat models induced by ovalbumin (OVA) and ferroptosis models induced by erastin in BEAS-2B cells were established. Proteomics and metabolomics analyses were conducted on lung tissues and serum. Key ferroptosis-related targets (GPX4, SLC7A11/SLC3A2, GCLC, GSS, and VDAC2) were validated using Western blotting, RT-qPCR, and biochemical assays. The direct anti-ferroptosis effects of SFYC-containing serum were compared with ferrostatin-1 and blank serum in vitro.

RESULTS: Integrated omics analysis revealed that ferroptosis, glutathione metabolism, and ROS signaling pathways were the core targets modulated by SFYC. In vivo, SFYC significantly reduced airway inflammation and ROS accumulation, restored pulmonary GSH levels, upregulated the expression of GPX4, GCLC, GSS, SLC7A11, and SLC3A2, and downregulated VDAC2 expression (P < 0.05). In vitro, SFYC-containing serum effectively reversed erastin-induced lipid peroxidation, iron overload, GSH depletion, ROS elevation, and apoptosis in BEAS-2B cells, demonstrating comparable or superior efficacy to ferrostatin-1.

CONCLUSION: SFYC alleviates airway inflammation in asthma primarily by inhibiting ferroptosis. This study provides evidence that SFYC exerts anti-asthmatic effects, at least in part, via the regulation of ferroptosis pathways.

PMID:41921764 | DOI:10.1016/j.jep.2026.121612