<p><strong>Purpose: </strong>Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicators. Metabolic reprogramming, a cancer hallmark, is linked to immune microenvironment remodeling, yet its role in LGG prognostic modeling remains underexplored. This study aims to develop a robust metabolism-related prognostic signature and elucidate its interaction with the immune microenvironment.</p><p><strong>Materials and methods: </strong>Multi-omics data from 1322 LGG patients were obtained from public databases, including the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and others. Metabolism-related genes were identified using three strategies: (1) differential expression analysis; (2) univariate Cox regression; and (3) weighted gene co-expression network analysis (WGCNA). Overlapping genes were further refined using protein-protein interaction (PPI) network analysis and four algorithms. We systematically compared 101 machine learning algorithms and selected the Cox model with likelihood-based boosting (CoxBoost) and Ridge regression (Ridge) to construct the hub metabolism-related gene risk score (HMRG-RS).</p><p><strong>Result: </strong>A total of 7 hub metabolic genes were identified (TYMS, PLA2G5, GPX7, GLRX, CYP17A1, ALOX15B, ACACB). HMRG-RS demonstrated good prognostic predictive performance across multiple external validation cohorts, with an average concordance index (C-index) of 0.723 and 1/3/5-year area under the receiver operating characteristic curve (AUC) of 0.778/0.797/0.745. Patients in the high-risk group exhibited significantly shorter survival and an immunosuppressive microenvironment characterized by M2 macrophage enrichment and increased tumor mutational burden(TMB). Notably, the prognostic value of HMRG-RS and the metabolic subtypes it characterizes were significantly dependent on Isocitrate dehydrogenase 1 (IDH1) mutation status. Drug sensitivity analysis revealed differential responsiveness to specific chemotherapeutic/targeted agents (e.g., AZD6482, fluvastatin) across risk groups. Molecular docking further predicted multiple therapeutic compounds (e.g., prunellin, mometasone, isoliquiritigenin) with high affinity for pivotal metabolic genes. Single-cell analysis confirmed high expression of hub metabolism-related genes (HMRGs) in myeloid cells (particularly metabolically active protumor M2 macrophages), implicating them in lipid metabolism reprogramming and immune evasion.</p><p><strong>Conclusion: </strong>This study constructed and validated a metabolism-driven prognostic model. The model enables prognostic stratification of LGG patients and links high-risk scores to metabolic dysregulation and an immunosuppressive microenvironment characterized by M2 macrophage enrichment, based on multi-omics data. Mechanistic exploration indicates this association is particularly pronounced in myeloid cells, predominantly within metaboli
{"title":"Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.","authors":"Haobin Liu, Yuxiao Wu, Haoyu Sun, Xiao Han, Qian Liu, Yuening Zhang, Jinling Zhang","doi":"10.1007/s12672-026-04635-8","DOIUrl":"https://doi.org/10.1007/s12672-026-04635-8","url":null,"abstract":"<p><strong>Purpose: </strong>Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicators. Metabolic reprogramming, a cancer hallmark, is linked to immune microenvironment remodeling, yet its role in LGG prognostic modeling remains underexplored. This study aims to develop a robust metabolism-related prognostic signature and elucidate its interaction with the immune microenvironment.</p><p><strong>Materials and methods: </strong>Multi-omics data from 1322 LGG patients were obtained from public databases, including the Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and others. Metabolism-related genes were identified using three strategies: (1) differential expression analysis; (2) univariate Cox regression; and (3) weighted gene co-expression network analysis (WGCNA). Overlapping genes were further refined using protein-protein interaction (PPI) network analysis and four algorithms. We systematically compared 101 machine learning algorithms and selected the Cox model with likelihood-based boosting (CoxBoost) and Ridge regression (Ridge) to construct the hub metabolism-related gene risk score (HMRG-RS).</p><p><strong>Result: </strong>A total of 7 hub metabolic genes were identified (TYMS, PLA2G5, GPX7, GLRX, CYP17A1, ALOX15B, ACACB). HMRG-RS demonstrated good prognostic predictive performance across multiple external validation cohorts, with an average concordance index (C-index) of 0.723 and 1/3/5-year area under the receiver operating characteristic curve (AUC) of 0.778/0.797/0.745. Patients in the high-risk group exhibited significantly shorter survival and an immunosuppressive microenvironment characterized by M2 macrophage enrichment and increased tumor mutational burden(TMB). Notably, the prognostic value of HMRG-RS and the metabolic subtypes it characterizes were significantly dependent on Isocitrate dehydrogenase 1 (IDH1) mutation status. Drug sensitivity analysis revealed differential responsiveness to specific chemotherapeutic/targeted agents (e.g., AZD6482, fluvastatin) across risk groups. Molecular docking further predicted multiple therapeutic compounds (e.g., prunellin, mometasone, isoliquiritigenin) with high affinity for pivotal metabolic genes. Single-cell analysis confirmed high expression of hub metabolism-related genes (HMRGs) in myeloid cells (particularly metabolically active protumor M2 macrophages), implicating them in lipid metabolism reprogramming and immune evasion.</p><p><strong>Conclusion: </strong>This study constructed and validated a metabolism-driven prognostic model. The model enables prognostic stratification of LGG patients and links high-risk scores to metabolic dysregulation and an immunosuppressive microenvironment characterized by M2 macrophage enrichment, based on multi-omics data. Mechanistic exploration indicates this association is particularly pronounced in myeloid cells, predominantly within metaboli","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-10DOI: 10.1007/s12672-026-04582-4
Yuxia Du, Weihong Qiu, Fei He, Zhibin Zhou, Weimin Ye
{"title":"A long-term survival prediction model for non-small cell lung cancer based on blood inflammatory biomarkers.","authors":"Yuxia Du, Weihong Qiu, Fei He, Zhibin Zhou, Weimin Ye","doi":"10.1007/s12672-026-04582-4","DOIUrl":"10.1007/s12672-026-04582-4","url":null,"abstract":"","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":" ","pages":"287"},"PeriodicalIF":2.9,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-10DOI: 10.1007/s12672-026-04483-6
Yueliang Xu, Chenhan Zhang, Yajun Fang, Yi Li
{"title":"Comprehensive analysis of diverse cytokine patterns in the prognosis and tumor microenvironment of lung adenocarcinoma.","authors":"Yueliang Xu, Chenhan Zhang, Yajun Fang, Yi Li","doi":"10.1007/s12672-026-04483-6","DOIUrl":"https://doi.org/10.1007/s12672-026-04483-6","url":null,"abstract":"","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1007/s12672-026-04626-9
Yundan You, Sha Huang, Jingjie Xu, Qifei Li, Yu Wang, Zhouwei Zhan, Yong Ye, Bin Lan, Xuefeng Wang, Zengqing Guo, Qiaoting Hu
Background: During chronic liver injury, hepatic stellate cells (HSCs) lose their vitamin-A-rich lipid droplets (LDs), yet whether these organelles are merely degraded or released via vesicles and functionally relevant remains unclear. We investigated the fate of HSCs LDs and their impact on hepatic macrophage phenotype and hepatocellular carcinoma (HCC) development.
Methods: Chronic liver injury was induced in C57BL/6 mice using carbon tetrachloride (CCl4) for up to 12 weeks. HSCs activation and lipid droplet dynamics were assessed by immunofluorescence, transmission electron microscopy, and flow cytometry. Single-cell RNA sequencing data from normal and inflamed livers were analyzed to characterize cell populations and interactions. HSC-derived LDs were isolated by gradient centrifugation and their effects on macrophage polarization were evaluated in vitro and in vivo. An orthotopic HCC model was used to assess the impact of lipid droplet-educated macrophages on tumor growth. Clinical relevance was validated using The Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) cohort data.
Results: Activated HSCs in fibrotic livers showed progressive fragmentation and release of LDs, which were subsequently internalized by hepatic macrophages. Single-cell transcriptomic analysis revealed enhanced HSC-macrophage interactions and upregulation of lipid metabolism pathways in both cell types during liver inflammation. HSC-derived LDs acted as a direct metabolic cue to induced M2 polarization of macrophages, characterized by elevated secretion of transforming growth factor-beta (TGF-β1), interleukin-10 (IL-10), and C-C Motif Chemokine Ligand 17 (CCL17). In orthotopic HCC models, co-injection of tumor cells with lipid droplet-educated macrophages significantly enhanced tumor growth compared to control macrophages. TCGA analysis showed that high CD163 expression correlated with poor overall survival in HCC patients.
Conclusion: Our findings identifies a distinct mechanism whereby activated HSCs transfer LDs to hepatic macrophages, inducing M2 polarization and creating a pro-tumorigenic microenvironment. This HSC-macrophage crosstalk represents a potential metabolic therapeutic target for preventing HCC development in patients with chronic liver disease.
{"title":"Hepatic stellate cell derived lipid droplets drive protumoral M2 macrophage polarization in hepatocellular carcinoma.","authors":"Yundan You, Sha Huang, Jingjie Xu, Qifei Li, Yu Wang, Zhouwei Zhan, Yong Ye, Bin Lan, Xuefeng Wang, Zengqing Guo, Qiaoting Hu","doi":"10.1007/s12672-026-04626-9","DOIUrl":"https://doi.org/10.1007/s12672-026-04626-9","url":null,"abstract":"<p><strong>Background: </strong>During chronic liver injury, hepatic stellate cells (HSCs) lose their vitamin-A-rich lipid droplets (LDs), yet whether these organelles are merely degraded or released via vesicles and functionally relevant remains unclear. We investigated the fate of HSCs LDs and their impact on hepatic macrophage phenotype and hepatocellular carcinoma (HCC) development.</p><p><strong>Methods: </strong>Chronic liver injury was induced in C57BL/6 mice using carbon tetrachloride (CCl<sub>4</sub>) for up to 12 weeks. HSCs activation and lipid droplet dynamics were assessed by immunofluorescence, transmission electron microscopy, and flow cytometry. Single-cell RNA sequencing data from normal and inflamed livers were analyzed to characterize cell populations and interactions. HSC-derived LDs were isolated by gradient centrifugation and their effects on macrophage polarization were evaluated in vitro and in vivo. An orthotopic HCC model was used to assess the impact of lipid droplet-educated macrophages on tumor growth. Clinical relevance was validated using The Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) cohort data.</p><p><strong>Results: </strong>Activated HSCs in fibrotic livers showed progressive fragmentation and release of LDs, which were subsequently internalized by hepatic macrophages. Single-cell transcriptomic analysis revealed enhanced HSC-macrophage interactions and upregulation of lipid metabolism pathways in both cell types during liver inflammation. HSC-derived LDs acted as a direct metabolic cue to induced M2 polarization of macrophages, characterized by elevated secretion of transforming growth factor-beta (TGF-β1), interleukin-10 (IL-10), and C-C Motif Chemokine Ligand 17 (CCL17). In orthotopic HCC models, co-injection of tumor cells with lipid droplet-educated macrophages significantly enhanced tumor growth compared to control macrophages. TCGA analysis showed that high CD163 expression correlated with poor overall survival in HCC patients.</p><p><strong>Conclusion: </strong>Our findings identifies a distinct mechanism whereby activated HSCs transfer LDs to hepatic macrophages, inducing M2 polarization and creating a pro-tumorigenic microenvironment. This HSC-macrophage crosstalk represents a potential metabolic therapeutic target for preventing HCC development in patients with chronic liver disease.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1007/s12672-026-04640-x
Daniel Shikun Zhou, Yao-Qi Lu
{"title":"Identification of a 32-gene signature that determines HPV status in head and neck cancer.","authors":"Daniel Shikun Zhou, Yao-Qi Lu","doi":"10.1007/s12672-026-04640-x","DOIUrl":"https://doi.org/10.1007/s12672-026-04640-x","url":null,"abstract":"","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146149486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Feasibility of an oral hydration regimen post high-dose methotrexate in children with acute leukemia: a pilot study.","authors":"Padma Sagarika Karri, Ruksana Sidhique Pr, Jagdish Prasad Meena, Rachna Seth, Aditya Kumar Gupta","doi":"10.1007/s12672-026-04608-x","DOIUrl":"https://doi.org/10.1007/s12672-026-04608-x","url":null,"abstract":"","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":" ","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146141429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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