Discover. Oncology最新文献

{"title":"Oncogenic EME1 promotes tumor progression and immune modulation in human cancers with therapeutic targeting potential.","authors":"Muhammad Alaa Eldeen, Abdelrahman Mostafa, Farag Mamdouh, Waleed K Abdulsahib, Dalal Sulaiman Alshaya, Eman Fayad, Hassan M Otifi, Hesham M Hassan, Mohammed Alshehri, Aiysha Althobaiti, Ghadi Alsharif, Mohamed A Soltan","doi":"10.1007/s12672-025-03631-8","DOIUrl":"10.1007/s12672-025-03631-8","url":null,"abstract":"<p><strong>Background: </strong>EME1, a critical DNA repair endonuclease, has emerged as a potential oncogene implicated in genome instability and cancer progression. However, its pan-cancer roles, prognostic significance, immune interactions, and therapeutic targeting remain underexplored.</p><p><strong>Methods: </strong>We conducted a comprehensive pan-cancer analysis integrating multi-omics data from public databases, including TIMER2.0, GEPIA2, TISIDB, and cBioPortal, to evaluate EME1 expression, genetic alterations, and their association with clinical outcomes, immune infiltration, and molecular pathways. Virtual screening of 3180 FDA-approved drugs and molecular dynamics (MD) simulations were employed to identify and validate potential EME1 inhibitors.</p><p><strong>Results: </strong>EME1 was significantly overexpressed in various human cancers and positively associated with advanced tumor grade and stage. High EME1 expression and mutations were linked to poor overall and disease-free survival. Immunogenomic profiling revealed strong positive correlations between EME1 and myeloid-derived suppressor cells (MDSCs), alongside a negative association with endothelial cell function, suggesting immunosuppressive roles. Machine learning models based on EME1-associated genes demonstrated high predictive accuracy for liver hepatocellular carcinoma (AUC > 0.90). Virtual screening identified eight promising drug candidates, including Everolimus and Dioscin, with strong binding affinities. MD simulations confirmed the stability of these interactions, particularly for Dioscin.</p><p><strong>Conclusion: </strong>This study reveals the multifaceted oncogenic roles of EME1 in tumor progression, immune evasion, and prognosis. It proposes EME1 as a promising biomarker and therapeutic target across multiple cancer types. The identified drug candidates warrant further in vitro and in vivo validation for potential repurposing in EME1-targeted cancer therapy.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1855"},"PeriodicalIF":2.9,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279232","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":"An exploratory study on the potential of angiogenic genes in early HNSCC detection.","authors":"Mao-Ling Huang, Fa-Zhang Lan, Lei Xu, Bin He, Long Huang, Zheng Tang, Zhen-Dong Jiang, Tao Liu, Wen-Jun Shan, Cheng Zhong","doi":"10.1007/s12672-025-03799-z","DOIUrl":"10.1007/s12672-025-03799-z","url":null,"abstract":"","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1854"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274045","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":"Comprehensive investigation identifies CPSF3 as a novel prognostic and oncogenic biomarker in bladder cancer.","authors":"Zhiyang Ma, Yining Hao, Wei He, Xin Xie, Danfeng Xu, Chenghe Wang","doi":"10.1007/s12672-025-03672-z","DOIUrl":"10.1007/s12672-025-03672-z","url":null,"abstract":"<p><strong>Background: </strong>Bladder cancer (BC) remains a prevalent malignancy worldwide, with rising incidence rates each year. Despite progress in therapeutic strategies, many patients suffer recurrence or progression, emphasizing the urgent need for novel prognostic biomarkers and therapeutic targets. This research evaluated the prognostic relevance and functional role of Cleavage and Polyadenylation Specificity Factor 3 (CPSF3) in BC.</p><p><strong>Methods: </strong>We analyzed CPSF3 expression using The Cancer Genome Atlas data and immunohistochemistry on a cohort of 203 BC patients. A nomogram incorporating CPSF3 expression was developed based on CPSF3 expression for prediction of overall survival and disease-free survival. Immune infiltration analyses and transcriptome sequencing were performed to explore underlying biological mechanisms. In vitro and in vivo experiments were utilized to examine the results of CPSF3 silencing on bladder cancer cell growth, colony-forming ability and cell cycle transitions.</p><p><strong>Results: </strong>Elevated CPSF3 expression was significantly linked to unfavorable overall survival and disease-free survival both in TCGA datasets and our cohort. The CPSF3-based nomogram outperformed conventional prognostic models. CPSF3 expression was associated with tumor-infiltrating immune cells and immune checkpoint markers. Enrichment analysis revealed CPSF3 enrichment in cell cycle-related pathways. Suppression of CPSF3 expression led to marked reductions in cell proliferation, colony formation, tumor growth in animal models and inhibited G1 to S phase progression.</p><p><strong>Conclusion: </strong>CPSF3 is a promising prognostic biomarker for BC and may play a crucial role in BC progression. Incorporating CPSF3 into clinical prognostic models may enhance prediction of patient outcomes. CPSF3 may represent a promising therapeutic target for BC management.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1847"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274002","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":"Impact of immune cells on renal cancer risk: a Mendelian randomization analysis with clinical cohort validation.","authors":"Shunqi Chen, Xiangkun Li, Chunyu Wu, Xiaoli Wang","doi":"10.1007/s12672-025-03687-6","DOIUrl":"10.1007/s12672-025-03687-6","url":null,"abstract":"<p><strong>Background: </strong>The causal relationship between immune cells and renal cancer risk remains unclear, as observational studies are susceptible to confounding factors.To evaluate the causal relationship between immune cells and renal cancer risk using Mendelian randomization (MR), and validate findings through clinical cohort studies.</p><p><strong>Methods: </strong>MR analysis was conducted based on UKB-b-1316 dataset using three methods to assess causal relationships, with focus on secreting CD4 regulatory T cells. A cohort study of 527 renal cancer patients analyzed clinical characteristics and prognostic factors.</p><p><strong>Results: </strong>MR analysis revealed no significant causal relationship between immune cell levels and renal cancer risk overall, with most genetic instrumental variables' 95% confidence intervals crossing 1.0. Analysis of secreting CD4 regulatory T cells showed effect estimates of 0.006070 (P = 0.053) by inverse variance weighted method and 0.000695 (P = 0.024) by weighted median method, suggesting weak positive association with limited evidence. In the clinical cohort, clear cell RCC accounted for 71.7%, with median follow-up of 62.5 months and overall recurrence rate of 28.3%. Multifactorial analysis identified TNM stage ≥ T3 (HR = 3.25), Fuhrman nuclear grade ≥ 3 (HR = 2.13), histological subtype (HR = 1.87), microvascular invasion (HR = 1.78), and tumor size > 7 cm (HR = 1.56) as independent recurrence risk factors.</p><p><strong>Conclusions: </strong>MR analysis found no strong causal association between immune cells and renal cancer risk, with only CD4 regulatory T cells showing weak association.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1849"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274164","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":"Transcriptomic landscape of NK cell-related genes in hepatocellular carcinoma: associations with prognosis and therapeutic response.","authors":"Yuxiang Wang, Wenqiang Liao, Jianbo Lin, Jun Li, Jian Gong","doi":"10.1007/s12672-025-03692-9","DOIUrl":"10.1007/s12672-025-03692-9","url":null,"abstract":"<p><p>Hepatocellular carcinoma (HCC) is a highly aggressive and heterogeneous malignancy, in which natural killer (NK) cells play a crucial role in tumor progression and immune surveillance. This study aimed to characterize the transcriptomic landscape of NK cell-associated genes (NAGs) and explore their associations with clinical outcomes and therapeutic responses in HCC. Using transcriptomic data from The Cancer Genome Atlas (TCGA), we identified key NAGs through comprehensive statistical analyses. Patients were stratified into distinct risk groups based on NAG expression profiles. Low-risk patients demonstrated better survival, higher immune infiltration, and greater predicted sensitivity to immunotherapy, whereas high-risk patients were associated with reduced chemotherapy responsiveness. These findings contribute to a deeper understanding of the immunogenomic features of HCC and provide a basis for developing personalized therapeutic approaches centered on NK cell-related mechanisms.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1851"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274124","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":"Development and validation of a leukemia prognostic model through single-cell RNA sequencing and machine learning approaches.","authors":"Doujia Chen, Jie Yang, Mengting Wang, Tianye Jian","doi":"10.1007/s12672-025-03757-9","DOIUrl":"10.1007/s12672-025-03757-9","url":null,"abstract":"<p><strong>Background: </strong>Leukemia prognosis varies significantly among patients, highlighting the need for accurate prediction tools. Emerging evidence suggests that the immune microenvironment plays a crucial role in leukemia progression and treatment response.</p><p><strong>Methods: </strong>We analyzed RNA expression profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, supplemented by single-cell RNA sequencing datasets. Differential gene expression analysis was performed using stringent criteria (logFC > 1, FDR < 0.05) to identify leukemia-associated genes. Ten distinct machine learning algorithms, including Lasso, CoxBoost, and ensemble methods, were implemented for prognostic model development with cross-platform validation. Single-cell analysis employed Seurat for quality control and cell type annotation, while CellChat algorithm mapped intercellular communication networks. Experimental validation was conducted using quantitative RT-PCR analysis of key immune markers (TLR2, TLR4, CCR7, IL18) in U937 and K562 leukemia cell lines compared to normal peripheral blood mononuclear cells.</p><p><strong>Results: </strong>The machine learning-derived prognostic model demonstrated exceptional predictive performance with area under the curve values of 0.874, 0.891, and 0.925 for 1-, 2-, and 3-year survival endpoints, respectively. Six critical immune regulatory genes (TLR2, TLR4, CCR7, IL18, TIRAP, FOXP3) were identified as both differentially expressed and prognostically significant, with IL18 showing the highest discriminative capacity (AUC = 0.983). RT-PCR validation confirmed significant upregulation of all tested genes in leukemia cell lines: TLR2 (3.8-fold in U937, 2.2-fold in K562), TLR4 (3.4-fold in U937, 1.8-fold in K562), CCR7 (4.1-fold in U937, 2.7-fold in K562), and IL18 (5.2-fold in U937, 3.6-fold in K562) compared to normal controls (all p < 0.05). Single-cell analysis revealed substantial cellular heterogeneity with cell type-specific expression patterns and complex intercellular communication networks involving B cells, T cells, natural killer cells, and dendritic cells.</p><p><strong>Conclusion: </strong>This study provides a reliable prognostic tool for leukemia and offers insights into the critical role of the immune microenvironment in leukemia pathogenesis. Our findings may guide the development of personalized immunotherapy strategies for leukemia patients.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1846"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514105/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274077","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":"Glutaryl-CoA dehydrogenase (GCDH) enhances renal malignancy risk via modulating glutarylcarnitine levels.","authors":"Xuanshuo Liu, Bohua Chen, Jianghao Yuan, Shuhao Li, Lei Gao, Mingyan Chu, Shicang Fan, Weiwei Fan","doi":"10.1007/s12672-025-03700-y","DOIUrl":"10.1007/s12672-025-03700-y","url":null,"abstract":"<p><strong>Background: </strong>Crotonylation, a recently identified lysine acylation, plays a critical role in post-translational modifications [1]. It has been implicated in tumorigenesis by modulating metabolic reprogramming [2], DNA repair, immune evasion [3], and oncogenic signaling pathways, including PKA-FAK-AKT and androgen receptor signaling [4]. The specific role of crotonylation in renal malignancy (RM) remains poorly understood, especially in interaction with gene expression and metabolic pathway interactions.</p><p><strong>Methods: </strong>This study integrates genome-wide association study (GWAS) summary statistics for RM from the FinnGen database, data on crotonylation-associated gene expression obtained from the eQTLGen consortium, and metabolite GWAS data obtained from the GWAS Catalog. A combined two-sample Mendelian randomization (MR), summary data-based Mendelian randomization (SMR), and mediation analyses were performed to investigate the causal link between Glutaryl-CoA dehydrogenase (GCDH) and RM, with a specific focus on glutarylcarnitine metabolism.</p><p><strong>Results: </strong>MR analysis demonstrated a significant association; increased expression of GCDH is likely to increase the risk of RM (OR = 1.25, P = 0.0045). Mediation analysis revealed that elevated GCDH expression significantly reduced glutarylcarnitine (C5-DC) levels, which in turn was inversely associated with RM risk. A three-step MR-based mediation confirmed a significant mediating effect of glutarylcarnitine (β₁₂ = 0.0680, P = 0.002), with 30.25% of the total effect attributable to it. The robustness of these findings was further demonstrated by sensitivity analyses and SMR results.</p><p><strong>Conclusion: </strong>This study represents the first evidence that GCDH might exert an indirect pro-RM effect via the downregulation of glutarylcarnitine, thus providing new insights into tumor metabolic pathways and positioning glutarylcarnitine as a potentially diagnostic biomarker and therapeutic target for RM.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1852"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514084/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274040","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":"Expression and prognosis of CXCL13 in uterine corpus endometrial carcinoma based on bioinformatics analysis.","authors":"Zhu Wang, Yongning Gao","doi":"10.1007/s12672-025-03684-9","DOIUrl":"10.1007/s12672-025-03684-9","url":null,"abstract":"<p><strong>Objective: </strong>The biological significance of the chemokine ligand C-X-C motif chemokine ligand 13 (CXCL13) may play a significant role in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). This study aims to identify and verify CXCL13 with predictive value for prognosis in UCEC.</p><p><strong>Methods: </strong>CXCL13 mRNA expression differences were analyzed using R software in three independent datasets: one each from The Cancer Genome Atlas (TCGA) and two from the Gene Expression Omnibus (GEO), namely GSE17025 and GSE106191. The correlation between CXCL13 expression and prognosis was evaluated by Kaplan-Meier analysis. Univariate and multivariate Cox analyses were utilized to construct a prognostic nomogram. Tumor Immune Estimation Resource (TIMER) and the Tumor and Immune System Interaction Database (TISIDB) were employed to assess the relationship between CXCL13 and tumor immune infiltration. Coexpressed genes with CXCL13 were identified by the Spearman correlation analysis. A CXCL13 protein-protein interaction (PPI) network was constructed with the STRING website tool and hub genes were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses were performed with the \"clusterProfiler\" R package. Gene set enrichment analysis (GSEA) was used to identify underlying biological mechanisms. A drug-gene interaction network was constructed in the Comparative Toxicogenomics Database (CTD).</p><p><strong>Results: </strong>High CXCL13 mRNA expression were validated in UCEC in the above three independent datasets. High CXCL13 expression was associated with favorable prognosis in UCEC. A nomogram for predicting the 1-, 3-, and 5-year survival probability in UCEC was construct based on CXCL13 expression and other clinical parameters. The use of Spearman correlation indicated certain correlation between CXCL13 and immune cells and immune checkpoint (ICP) genes. Seven hub genes were upregulated in UCEC, namely CXCL9, IFNG, CXCL10, CXCL11, GBP5, CCL18, and GZMB. The expression and prognostic relevance of CXCL9, IFNG, GBP5, and GZMB were in accordance with CXCL13. The main biological processes enriched were cytokine-cytokine receptor interaction and chemokine signaling pathway.</p><p><strong>Conclusions: </strong>The above comprehensive analyses suggest that CXCL13 may serve as a potential prognostic biomarker for UCEC, specifically for early-stage UCEC.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1848"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274028","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":"B cell-associated research in non-small cell lung cancer: a bibliometric analysis of current landscapes and future directions.","authors":"Yuxin Zhang, Xiuyu Chen, Wanzi Mai, Linxuan Wang, Yuli Wang, Chunzheng Ma, Honglin Li","doi":"10.1007/s12672-025-03544-6","DOIUrl":"10.1007/s12672-025-03544-6","url":null,"abstract":"<p><strong>Background: </strong>Recent years have witnessed growing interest in the interplay between B cells and non-small cell lung cancer (NSCLC) amid deepening investigations into cancer immunotherapy. B cells play pivotal roles in immunotherapeutic contexts by modulating tumor progression through immunoglobulin secretion, T-cell response activation, and direct tumoricidal activity. In this study, we employ bibliometric techniques to analyze research hotspots and evolutionary trends in NSCLC-associated B-cell investigations, aiming to inform future directions in this burgeoning field.</p><p><strong>Methods: </strong>We conducted a systematic retrieval of peer-reviewed documents (original articles, reviews, editorials) from the Web of Science Core Collection (2004-2024). Bibliometric networks were constructed using CiteSpace 6.1.R6 and VOSviewer 1.6.18. With temporal productivity patterns analyzed through Microsoft Excel 2021.</p><p><strong>Results: </strong>This study analyzed 437 publications from 1,677 institutions across 156 countries/regions, encompassing 3,360 researchers. Annual publications showed sustained growth (2004-2021) followed by decline (2022-2024). China demonstrated highest productivity (n = 169), the United States dominated total citations (6,943). Keyword and highly cited literature analyses primarily focused on elucidating B cell heterogeneity and tertiary lymphoid structures, deciphering B cell-T cell interaction mechanisms, and exploring synergistic applications of B cells with other immunotherapeutic approaches.</p><p><strong>Conclusion: </strong>This study utilizes bibliometric analysis to explore research hotspots and developmental trends in B - cell - related studies of non - small cell lung cancer (NSCLC) over the past two decades. By elucidating the changing research landscape, this work aims to enhance researchers' comprehension of the immunological milieu and advancements in NSCLC immunotherapy, promote scientific advancement in this field, and provide directional guidance for future research.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1850"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274086","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":"Toxicity and mechanistic analysis of di(2-ethylhexyl)phthalate in renal cell carcinoma progression: a systematic study with network toxicology and molecular docking strategies.","authors":"Biao Ran, Xinyi Wang, Bohan Liu, Junjiang Ye, Liangren Liu, Zhaofa Yin, Zhongli Huang","doi":"10.1007/s12672-025-03543-7","DOIUrl":"10.1007/s12672-025-03543-7","url":null,"abstract":"<p><p>Millions of tons of unrecycled plastic pollute landfills and oceans, and di(2-ethylhexyl) phthalate (DEHP), a plasticizer classified as a possible carcinogen, is found in many environments, and poses health risks. Renal cell carcinoma (RCC), one of the most prevalent malignancies, accounted for approximately 434,419 new cases and 155,702 deaths in 2022. DEHP can cause developmental dysregulation, reproductive impairments, tumorigenesis, and transgenerational diseases; however, the mechanisms underlying these effects remain unclear. This study aimed to elucidate the toxic targets and molecular mechanisms associated with DEHP exposure and RCC occurrence by integrating network toxicology and molecular docking, utilizing multilevel bioinformatics data. By systematically utilizing diverse databases, 82 targets associated with both DEHP and RCC were identified. Subsequent screening with STRING and Cytoscape revealed 25 key targets, including CASP3, BCL2, MMP9, BCL2L1, CTSS, and APP. GO and KEGG enrichment analyses revealed that these targets are involved in apoptosis, abnormal hormone activity, cancer-related signaling cascades, ligand-receptor interactions, and endocrine system signaling pathways. Molecular docking simulations via CB-Dock confirmed the high-affinity binding interactions between DEHP and these key targets. These results suggest that DEHP exposure may promote the development of RCC by regulating apoptosis and proliferation through pathways such as neuroactive ligand-receptor interactions, pathways related to cancer, and apoptosis. This study provides a theoretical basis for understanding the molecular mechanisms of DEHP-induced renal carcinogenesis and serves as a foundation for future experimental studies to validate these computational predictions regarding DEHP-associated tumor risk from plastic products and environmental exposure.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"1853"},"PeriodicalIF":2.9,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12514089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145274178","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}