Pub Date : 2026-02-10DOI: 10.1007/s12672-026-04632-x
Jinxiu Xie, Siying Chen, Shaoyu Fu, Jiayi Rao, Chenxi Wang, Yan Xiao, Kang Zou
{"title":"Bibliometric analysis reveals the current status and future trends of cancer associated photodynamic therapy in breast cancer.","authors":"Jinxiu Xie, Siying Chen, Shaoyu Fu, Jiayi Rao, Chenxi Wang, Yan Xiao, Kang Zou","doi":"10.1007/s12672-026-04632-x","DOIUrl":"https://doi.org/10.1007/s12672-026-04632-x","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":"146156190","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/s12672-026-04607-y
Lu Yang, Xuanjun Liu, Xiang Zhu, Baoshan Cao, Yangchun Gu, Wei Liu
{"title":"Clinical efficacy and cardiac toxicity associated with first-line dabrafenib plus trametinib in advanced BRAF V600_K601delinsE-mutated lung adenocarcinoma: a case report and literature review.","authors":"Lu Yang, Xuanjun Liu, Xiang Zhu, Baoshan Cao, Yangchun Gu, Wei Liu","doi":"10.1007/s12672-026-04607-y","DOIUrl":"https://doi.org/10.1007/s12672-026-04607-y","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":"146149351","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/s12672-026-04407-4
Aida Khademolhosseini, Mina Roshan Zamir, Ali Moazzeni, Zahra Mansourabadi, Elahe Safari
Tumor microenvironment (TME) is complicated by the interaction of different cells of immune system, stromal components, and tumor-associated elements. Immune cells largely influence tumor progression by the means of various activating and inhibitory mechanisms including, immune checkpoint molecules. These molecules have been targeted for treating different types of cancers. For instance, blocking antibodies against CTLA-4, PD-1, or PD-L1 have elicited durable clinical responses and remarkable efficacy. These antibodies have also led to long-term remissions in a subset of patients, especially when used in combination therapies. V-domain immunoglobulin suppressor of T cell activation (VISTA) as a negative regulator of the immune system is expressed on multiple immune cell subsets including, myeloid-derived suppressor cells (MDSCs), macrophages, and lymphocytes. VISTA exerts regulatory effects and modulates T cell function and has shown prognostic significance in different cancers, leading to an increased attention regarding its suppressive role in the context of cancer. In this review, we will summarize the VISTA structure, ligands, role in the TME, and expression on immune cells. Furthermore, the significance of VISTA expression in the prognosis of cancer and its role in cancer immunotherapy, tumor resistance and ongoing clinical trials will be discussed.
{"title":"VISTA: bridging gaps in cancer immunotherapy.","authors":"Aida Khademolhosseini, Mina Roshan Zamir, Ali Moazzeni, Zahra Mansourabadi, Elahe Safari","doi":"10.1007/s12672-026-04407-4","DOIUrl":"https://doi.org/10.1007/s12672-026-04407-4","url":null,"abstract":"<p><p>Tumor microenvironment (TME) is complicated by the interaction of different cells of immune system, stromal components, and tumor-associated elements. Immune cells largely influence tumor progression by the means of various activating and inhibitory mechanisms including, immune checkpoint molecules. These molecules have been targeted for treating different types of cancers. For instance, blocking antibodies against CTLA-4, PD-1, or PD-L1 have elicited durable clinical responses and remarkable efficacy. These antibodies have also led to long-term remissions in a subset of patients, especially when used in combination therapies. V-domain immunoglobulin suppressor of T cell activation (VISTA) as a negative regulator of the immune system is expressed on multiple immune cell subsets including, myeloid-derived suppressor cells (MDSCs), macrophages, and lymphocytes. VISTA exerts regulatory effects and modulates T cell function and has shown prognostic significance in different cancers, leading to an increased attention regarding its suppressive role in the context of cancer. In this review, we will summarize the VISTA structure, ligands, role in the TME, and expression on immune cells. Furthermore, the significance of VISTA expression in the prognosis of cancer and its role in cancer immunotherapy, tumor resistance and ongoing clinical trials will be discussed.</p>","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":"146149428","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}
<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":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12894479/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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