Discover. Oncology最新文献

{"title":"Enhanced multi-omics analysis reveals a lncRNA signature with 12 RNA modifications to predict tumor heterogeneity and potential therapy in non-small cell lung cancer.","authors":"Hua You, Mingyu Fan, Limei Yin, Feifei Na, Liting You","doi":"10.1007/s12672-025-03677-8","DOIUrl":"10.1007/s12672-025-03677-8","url":null,"abstract":"<p><p>This study delves into the landscape of RNA modification (RM)-related long non-coding RNAs (lncRNAs) within non-small cell lung cancer (NSCLC). We aim to uncover their significance in cancer biology and potential clinical implications. We utilized diverse datasets to identify 444 RM-related genes with 12 RMs. RM scores were computed, and associations with survival were analyzed. Weighted gene co-expression network analysis identified 730 RM-related lncRNAs. Univariate Cox regression identified 63 prognostically significant lncRNAs, leading to the classification of NSCLC samples into two clusters. Distinct differences in overall survival and disease-free interval were observed between the identified lncRNA clusters, showcasing their prognostic relevance. Molecular characterization uncovered mutation landscape variations, with cluster 2 displaying higher mutation rates in TP53 and TTN. Cluster-specific genomic alterations, immune cell infiltration, and immune checkpoint gene expression patterns were identified. Drug sensitivity analysis revealed distinct profiles, with cluster 1 showing potential resistance to a combined approach of certain chemotherapy and immunotherapy, while cluster 2 may be suitable for monotherapy with specific chemotherapeutic or targeted agents. In conclusion, this study stands as the first and most comprehensive exploration, elucidating the intricate connections between RM, lncRNAs, NSCLC, and tumor immunity. Its findings significantly enhance our comprehension of NSCLC heterogeneity, offering pivotal insights and paving the path toward personalized treatment strategies.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1873"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285884","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}

{"title":"LINC01123: a pan-cancer \"molecular commander\" deciphering the CeRNA network atlas for precision diagnosis and therapy.","authors":"Zheng Chen, Weijian Zhu, Qiang Yi, Xinting Ouyang, Kui Zhong, Qiong Ge, Mengying Zhang, Qinfu Yan, Jianing Zhong, Jinghua Zhong","doi":"10.1007/s12672-025-03685-8","DOIUrl":"10.1007/s12672-025-03685-8","url":null,"abstract":"","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1877"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285823","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}

{"title":"Integration of machine learning to reveal the correlation between ferroptosis and M2 macrophages in head and neck squamous cell carcinoma.","authors":"Juntao Huang, Ziqian Xu, Lixin Cheng, Chongchang Zhou, Zhenzhen Wang, Hong Zeng, Yi Shen","doi":"10.1007/s12672-025-03719-1","DOIUrl":"10.1007/s12672-025-03719-1","url":null,"abstract":"<p><strong>Objective: </strong>To investigate the correlation between ferroptosis and M2 macrophages (M2Ms) in head and neck squamous cell carcinoma on the basis of multiomics data and machine learning methods.</p><p><strong>Methods: </strong>M2M infiltration was assessed via the CIBERSORT algorithm, and Kaplan‒Meier (K‒M) survival analysis was conducted with the best cutoff value. The M2M-related genes (MRGs) were identified on the basis of the interactive results of weighted gene coexpression network analysis (WGCNA) and the Spearman test. The interactions between MRGs and ferroptosis genes were subsequently pooled to investigate their functions, and the hub genes were subsequently applied to establish a scoring system (MFRS) with 101 kinds of machine learning algorithms. The model with the highest concordance index was selected, and the predictive effect was assessed via the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The correlations of MFRS with immune infiltration, tumor mutation burden (TMB), copy number variation (CNV) and clinical treatment were analyzed, and the landscape of the model genes was displayed with multiomics data. Moreover, a pancancer analysis was conducted to reveal the roles of crucial model genes in different tumors.</p><p><strong>Results: </strong>Patients with low M2 infiltration had a better prognosis. According to Spearman and WGNCA, a total of 1551 interactive MRGs were identified, 40 of which were also associated with ferroptosis. After the 13 hub genes were obtained from STRING, 101 kinds of machine learning algorithms were applied to establish the predictive model. Among them, the model concerning lasso combined with plsRcox had the best predictive effects, with the highest average C-index value of 0.645, consisting of ALOX12B, CYBB, DDR2, DRD4, NOX4, PRKCA, RGS4, SLC2A3, SLC3A2, TIMP1 and ENPP2. Patients with low MFRSs presented longer survival times, a more active immune microenvironment and greater sensitivity to immunotherapy; nevertheless, those with high MFRSs presented better chemotherapeutic responses. PRKCA was considered a hub model gene on the basis of external validation of multiomics data, and the pancancer analysis subsequently revealed that it performs important roles in tumors.</p><p><strong>Conclusion: </strong>In this study, we constructed an MFRS model to predict patient prognosis and therapeutic response. This study also preliminarily reveals the roles of M2Ms and ferroptosis in HNSCC patients and provides potentially novel insight for treatment.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1860"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285867","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}

{"title":"Interleukin-1 receptor-associated kinase-1 is a therapeutic target for gastric cancer.","authors":"Hui Shi, Jie Zhang, Xiaohua Bao","doi":"10.1007/s12672-025-03658-x","DOIUrl":"10.1007/s12672-025-03658-x","url":null,"abstract":"<p><p>Interleukin-1 receptor-associated kinase 1 (IRAK1), a pivotal mediator in innate immunity and inflammatory processes, exhibits constitutive activation across various cancers. Our study highlights the significant role of IRAK1 in gastric cancer progression. We observed elevated IRAK1 expression in both gastric cancer tissues and cells, which correlates with malignant phenotypes, including heightened proliferation, colony formation, and migratory capacity in normal gastric cells upon IRAK1 overexpression. Silencing IRAK1 genetically in gastric cancer cells effectively suppressed these oncogenic traits. Notably, we found that gastric cancer cells display marked sensitivity to IRAK1/4 inhibitor and pacritinib, the latter targeting JAK2 and IRAK1 specifically, over the pan-JAK inhibitor tofacitinib. In vivo experiments using a xenograft mouse model demonstrated the efficacy of pacritinib in arresting gastric cancer growth without eliciting significant systemic toxicity. Immunohistochemical analyses further confirmed pacritinib's ability to attenuate tumor progression by inhibiting IRAK1 activity. Collectively, our results underscore IRAK1 as a promising therapeutic target in gastric cancer. Furthermore, the pharmacological blockade of IRAK1 by pacritinib, an established drug for myelofibrosis and severe thrombocytopenia treatment, holds potential for repurposing in gastric cancer therapy.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1882"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285885","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}

{"title":"Mapping the global intellectual landscape of inflammatory tumor microenvironment in colorectal cancer pathogenesis and prognostic research since this century.","authors":"Hui Mu, Yushu Zhu, Feihu Yan, Can Lv, Xiaoyu Tu, Junyan He, Zhaoming Wang, Yi Zeng, Zhiwei Liu, Jiaojiao Chen, Bai Li, Enda Yu, Xuan Zheng","doi":"10.1007/s12672-025-03524-w","DOIUrl":"10.1007/s12672-025-03524-w","url":null,"abstract":"<p><strong>Background: </strong>Colorectal cancer (CRC) is one of the most prevalent gastrointestinal tumors worldwide. Complex interactions between tumor microenvironment (TME) and host inflammatory response influence CRC progression and recurrence. The current study provides a comprehensive bibliometric visualization of the current state and frontier trends of inflammatory TME in CRC.</p><p><strong>Materials and methods: </strong>Scientific publications on the inflammatory TME in CRC from 2000 to 2024 were retrieved from the Web of Science Core Collection (WoSCC) database. Biblioshiny software was mainly applied for visualization and analysis of literature. CiteSpace software and VOSviewer software were used to validate the results.</p><p><strong>Results: </strong>A total of 1593 articles related to CRC inflammatory TME have been retrieved since the 21st century. Cancers and Frontiers in Immunology were the two most popular journals, and Cancer Research is the most cited journal. Mcmillan DC, Park JH and Mantovani A were the most academic influential authors in inflammatory TME in CRC.</p><p><strong>Conclusions: </strong>This study preliminary visualize the association of inflammatory TME with CRC through bibliometric analysis. The immunomodulatory mechanisms in IBD-associated carcinogenesis, NF-κB-mediated TME remodeling in tumor progression, and CRC patient stratification and precision therapeutics were three hotspots for future research endeavors on the inflammatory TME in CRC research.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1884"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285899","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}

{"title":"Mapping the future of early breast cancer diagnosis: a bibliometric analysis of AI innovations.","authors":"Şevki Pedük","doi":"10.1007/s12672-025-03495-y","DOIUrl":"10.1007/s12672-025-03495-y","url":null,"abstract":"<p><p>Breast cancer (BC) remains one of the most prevalent and challenging malignancies worldwide, affecting millions of women and shaping healthcare priorities across continents. Advances in early detection have significantly improved survival rates. In recent years, artificial intelligence (AI) has emerged as a powerful tool in this domain, transforming traditional diagnostic methods. Initially based on simple rule-based systems, AI has evolved into sophisticated deep learning models capable of analyzing complex medical data with remarkable accuracy. This bibliometric analysis examines the application of AI in the early diagnosis of breast cancer, aiming to understand not only the current state of the field but also its growth over the past decade. Publications indexed in Web of Science and Scopus from 2012 to March 2025 were systematically reviewed, while earlier literature (1994-2012) provided historical context. Tools such as Biblioshiny and VOSviewer were used to map research trends, collaboration patterns, and thematic evolution. Out of 1,436 initial documents, 1,293 high-quality studies were included. The results show a clear acceleration in AI-focused research after 2020, with increased global collaboration and a notable shift toward open-access publication. Recurring themes such as \"machine learning,\" \"diagnostic imaging,\" and \"clinical decision support\" highlight the field's direction. As AI becomes more integrated into clinical workflows, its potential to enhance diagnostic speed, consistency, and personalization is undeniable. However, key ethical issues such as bias, transparency, and patient data protection remain central to responsible implementation.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1870"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521688/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285913","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}

{"title":"Identification and validation of a prognostic model for HER2-low breast cancer based on unfolded protein response-related genes.","authors":"Yanjiao Zhao, Yuanyuan Gao, Hui Yan, Ping Hu","doi":"10.1007/s12672-025-03718-2","DOIUrl":"10.1007/s12672-025-03718-2","url":null,"abstract":"<p><strong>Objectives: </strong>Human epidermal growth factor receptor 2 (HER2) is down-regulated in approximately 45-55% of breast cancer patients. Cancer cells activate endoplasmic reticulum stress, which is counteracted by the unfolded protein response (UPR). The objective of the present research is to assess the predictive significance of UPR-related genes and investigate their effects on the immune landscape in HER2-low breast cancer patients.</p><p><strong>Methods: </strong>The prognostic UPR related genes in patients were identified by univariate Cox regression analysis, and the risk score model was created using LASSO Cox regression analysis. The nomogram model was built using RMS package, and the protein-protein interaction network was created via the STRING database.</p><p><strong>Results: </strong>Through comprehensive analysis, we identified four UPR-related genes (COPS5, DKC1, NOP56, and EIF4G1) that were significantly associated with HER2-low breast cancer prognosis. These genes were utilized to construct a robust risk score model, which effectively stratified patients into high- and low-risk groups. Patients in the high-risk group exhibited significantly worse clinical outcomes, confirming the independent prognostic value of the risk score in multivariate analysis. Furthermore, pathway enrichment analysis revealed significant suppression of immune-related signaling pathways (e.g. PI3K-AKT) in high-risk patients, alongside distinct tumor microenvironment profiles characterized by differential immune cell infiltration, altered expression of immune checkpoints, and significantly different TIDE scores, suggesting potential implications for immunotherapy response stratification.</p><p><strong>Conclusions: </strong>The prognostic model based on UPR related genes, COPS5, DKC1, NOP56, and EIF4G1, could predict the prognosis of HER2-low breast cancer patients.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1866"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285912","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}

{"title":"Predictive role of a novel nutrition and inflammation-based model in the short-term clinical outcomes and health-related quality of life of patients undergoing radical gastrectomy: a retrospective cohort study.","authors":"Pengfei Li, Chunhua Zhou","doi":"10.1007/s12672-025-03668-9","DOIUrl":"10.1007/s12672-025-03668-9","url":null,"abstract":"","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1883"},"PeriodicalIF":2.9,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145285886","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}

{"title":"Identifying the causal role and therapeutic potential of immune-related genes in bladder cancer: a Mendelian randomization study.","authors":"Yonggang Chen, Qingfeng Yu, Xiaomei Jiang, Lin Li, Le Kang","doi":"10.1007/s12672-025-03806-3","DOIUrl":"10.1007/s12672-025-03806-3","url":null,"abstract":"<p><strong>Background: </strong>Bladder cancer, a highly aggressive malignancy, necessitates effective therapeutic strategies due to the significant correlation between muscle invasion and prognosis. Despite advancements in immunotherapy, the mechanisms through which immune-related genes influence bladder cancer remain unclear.</p><p><strong>Methods: </strong>We conducted a comprehensive causal analysis of immune-related genes using GWAS data from the UK Biobank and FinnGen, and immune gene information from InnateDB. Mendelian Randomization (MR) analysis, supported by sensitivity, colocalization, and reverse causality analyses, was performed using eQTLGen Consortium and proteomics GWAS data. We analyzed protein-protein interaction networks with GeneMANIA and validated findings using mRNA expression and clinical survival data from the TCGA database.</p><p><strong>Results: </strong>The expression of the GSTM1 gene demonstrated an inverse correlation with cancer risk, suggesting a protective role in disease progression. Our study also identified OLFM4, NTN1, ITPR3, CLU, and CARD11 as genes significantly associated with immune cell composition and bladder cancer survival. Additionally, we uncovered protein interaction networks and key pathways involving these immune-related genes, providing new insights into bladder cancer intervention and treatment.</p><p><strong>Conclusion: </strong>This study elucidates the immune-related pathways influencing bladder cancer, highlighting GSTM1 and other key immune-related genes as potential biomarkers. These discoveries pave the way for innovative therapeutic approaches, advancing personalized medicine strategies in bladder cancer treatment.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1857"},"PeriodicalIF":2.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518733/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279217","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}

{"title":"PH responsive hydrogel nanocomposites for targeted Shikonin delivery enhance anti PDL1 immunotherapy in osteosarcoma.","authors":"Xiang-Yi Chen, Xian-Qin Yang, Ming-Yang Wang","doi":"10.1007/s12672-025-03742-2","DOIUrl":"10.1007/s12672-025-03742-2","url":null,"abstract":"<p><strong>Objective: </strong>To enhance the therapeutic effect of atezolizumab on osteosarcoma (OS) by constructing a pH-responsive hydrogel nanocomplex (Gel@PLGA@FA) as a delivery platform for Shikonin.</p><p><strong>Methods: </strong>First, Shikonin was initially employed to analyze the GSE14359 dataset, leading to the identification of 28 differentially expressed genes (DEGs). Based on this, a risk score model was constructed and molecular dynamics simulations were performed to assess the binding ability between Shikonin and cyclin-dependent kinase 1 (CDK1). In addition, the in vitro antiproliferative effect of Shikonin on MG63 and Saos-2 OS cell lines and its selective toxicity on normal cells were assessed. In order to overcome the disadvantages of poor water solubility and normal cytotoxicity towards Shikonin, a complex loaded with Shikonin by pH-responsive intelligent hydrogel nanomaterials was synthesized and its anti-programmed death ligand-1 (PD-L1) therapeutic effect on OS cells was evaluated.</p><p><strong>Results: </strong>Molecular dynamics simulation showed that Shikonin showed strong binding ability to CDK1, showing stable conformation, enhanced structural stability and other characteristics. In vitro experiments showed that Shikonin had a significant anti-proliferative effect on OS cells, while it had selective toxicity on normal liver, kidney and osteoblasts. The pH-responsive hydrogel nanomaterial (Gel@PLGA@FA) loaded with Shikonin showed good drug release characteristics at different pH conditions, especially in the tumor microenvironment to achieve controllable drug release. Combined use of Gel@PLGA@Shikonin@FA and atezolizumab effectively down-regulated CDK1 and PD-L1 expression, inhibited cell proliferation and promoted apoptosis, significantly enhancing the anti-PD-L1 therapeutic effect on OS cells. JC-1 staining experiments further confirmed that this combination therapy could perturb mitochondrial membrane potential and lead to stronger apoptosis.</p><p><strong>Conclusion: </strong>This study reveals the unique mechanism of action of Shikonin as a potential anticancer drug and demonstrates the potential of pH-responsive hydrogel nanomaterials as efficient and safe delivery systems for targeted cancer therapeutics. The strategy of Gel@PLGA@Shikonin@FA combined with atezolizumab provides a new idea and experimental basis for OS treatment.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1856"},"PeriodicalIF":2.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518731/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279289","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}