Discover. Oncology最新文献

{"title":"Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling.","authors":"Mohamed J Saadh, Hanan Hassan Ahmed, Radhwan Abdul Kareem, Anupam Yadav, Subbulakshmi Ganesan, Aman Shankhyan, Girish Chandra Sharma, K Satyam Naidu, Akmal Rakhmatullaev, Hayder Naji Sameer, Ahmed Yaseen, Zainab H Athab, Mohaned Adil, Bagher Farhood","doi":"10.1007/s12672-025-02111-3","DOIUrl":"10.1007/s12672-025-02111-3","url":null,"abstract":"<p><strong>Purpose: </strong>This study proposes an advanced machine learning (ML) framework for breast cancer diagnostics by integrating transcriptomic profiling with optimized feature selection and classification techniques.</p><p><strong>Materials and methods: </strong>A dataset of 1759 samples (987 breast cancer patients, 772 healthy controls) was analyzed using Recursive Feature Elimination, Boruta, and ElasticNet for feature selection. Dimensionality reduction techniques, including Non-Negative Matrix Factorization (NMF), Autoencoders, and transformer-based embeddings (BioBERT, DNABERT), were applied to enhance model interpretability. Classifiers such as XGBoost, LightGBM, ensemble voting, Multi-Layer Perceptron, and Stacking were trained using grid search and cross-validation. Model evaluation was conducted using accuracy, AUC, MCC, Kappa Score, ROC, and PR curves, with external validation performed on an independent dataset of 175 samples.</p><p><strong>Results: </strong>XGBoost and LightGBM achieved the highest test accuracies (0.91 and 0.90) and AUC values (up to 0.92), particularly with NMF and BioBERT. The ensemble Voting method exhibited the best external accuracy (0.92), confirming its robustness. Transformer-based embeddings and advanced feature selection techniques significantly improved model performance compared to conventional approaches like PCA and Decision Trees.</p><p><strong>Conclusion: </strong>The proposed ML framework enhances diagnostic accuracy and interpretability, demonstrating strong generalizability on an external dataset. These findings highlight its potential for precision oncology and personalized breast cancer diagnostics.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"334"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647630","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":"Nomogram for predicting postoperative clinical remission of hypertension in patients with adrenal tumors.","authors":"YuanJian Liao, MingShun Zuo, YongPan Zhu, Te Xu, JiaJia Tang, LongMei Fan, Neng Zhang","doi":"10.1007/s12672-025-02108-y","DOIUrl":"10.1007/s12672-025-02108-y","url":null,"abstract":"<p><strong>Objective: </strong>Hypertension caused by adrenal tumors is a frequent cause of secondary hypertension. Treating primary adrenal disease can significantly improve or cure hypertension. However, hypertension may persist in some patients after surgery, leading to controversy over the choice of surgery or conservative treatment. The aim of this study is to construct and validate a model for predicting postoperative clinical remission of hypertension in patients with adrenal tumors to help surgeons make better surgical decisions.</p><p><strong>Patients and methods: </strong>A retrospective analysis was conducted on data pertaining to 336 patients diagnosed with adrenal tumors and hypertension between January 1, 2012 and December 31, 2022. Potential predictor variables were utilized to develop a nomogram, which were internally validated using a bootstrap resampling method. Clinical data from 141 patients from January 1, 2023 to December 31, 2023 were analyzed for external validation using the same criteria.</p><p><strong>Results: </strong>In patients with non-functioning adrenal tumors, lower age, body mass index, and hypertension grade were considered independent predictors of postoperative clinical remission of hypertension. In patients with functional adrenal tumors, absence of diabetes mellitus, lower systolic blood pressure, and duration of hypertension were considered independent predictors of postoperative clinical remission of hypertension. The area under the curve (AUC) for the nonfunctional adrenal tumor prediction model was 0.761, the AUC for internal validation using the bootstrap method (resampling = 1000) was 0.757, and the AUC for the external validation cohort was 0.837. The AUC for the functional adrenal tumor prediction model was 0.848, the AUC for internal validation using the bootstrap method (resampling = 1000) was 0.836, and the AUC for the external validation cohort was 0.836. The calibration curves demonstrated a satisfactory fit between the model and clinical utility, as evidenced by the decision curve analysis.</p><p><strong>Conclusion: </strong>Nomograms have been demonstrated to perform well in predicting postoperative clinical remission of hypertension in patients with adrenal tumors. This may assist clinicians in distinguishing between patients with adrenal tumors who are likely to achieve clinical remission of hypertension after surgery at an early stage.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"341"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647636","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":"Pan-cancer analysis and experimental verification of cytochrome B561 as a prognostic and therapeutic biomarker in breast cancer.","authors":"Xiaoting Qiu, Peizhang Liu, Hongxiang Lin, Zeyi Peng, Xinhao Sun, Guanting Dong, Yuanyuan Han, Zhijian Huang","doi":"10.1007/s12672-025-02094-1","DOIUrl":"10.1007/s12672-025-02094-1","url":null,"abstract":"<p><strong>Objective: </strong>This study investigates Cytochrome B561 (CYB561) expression in Pan-Cancer, its relationship with immune invasion, and its prognostic value in Breast Cancer (BRCA) patients.</p><p><strong>Methods: </strong>Data from The Cancer Genome Atlas (TCGA) were analyzed. CYB561 expression in normal and tumor tissues was examined, with correlations to immune invasion, mutation, and immune checkpoints. Wilcoxon rank-sum test assessed expression differences. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Logistic regression, Kaplan-Meier, and Cox regression analyses evaluated clinicopathological features and survival outcomes. A Cox multivariate analysis-based Nomogram predicted CYB561's prognostic impact. CYB561 knockout in breast cancer cells assessed functional effects. Single-cell RNA sequencing identified prognostic biomarkers.</p><p><strong>Results: </strong>CYB561 was highly expressed in most tumors. BRCA showed the highest correlation with ESTIMATE scores and significant negative correlation with immune checkpoints. High CYB561 expression correlated with specific clinicopathological features and survival outcomes. The nomogram predicted BRCA prognosis. CYB561 knockout inhibited breast cancer cell proliferation. Seven predictive agents for CYB561 inhibition were identified.</p><p><strong>Conclusions: </strong>CYB561 exhibits aberrant expression in tumors, particularly in BRCA, and serves as a predictive marker for immune-related therapies and a prognostic indicator in BRCA.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"330"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11911281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647637","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 of GJC1 as a novel diagnostic marker for papillary thyroid carcinoma using weighted gene co-expression network analysis and machine learning algorithm.","authors":"Jingshu Zhang, Ping Sun","doi":"10.1007/s12672-025-02137-7","DOIUrl":"10.1007/s12672-025-02137-7","url":null,"abstract":"<p><strong>Background: </strong>The incidence of thyroid papillary carcinoma (PTC) is increasing annually, causing both physical and psychological pressure on patients. Therefore, early recognition and specific interventions for PTC are crucial. The objective of this study is to explore novel diagnostic marker and precise intervention targets for PTC.</p><p><strong>Methods: </strong>Based on a weighted gene co-expression network analysis (WGCNA), relevant datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were collected. Enrichment analysis was performed on differentially expressed genes (DEGs) using Gene Ontology (GO), Disease Ontology (DO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Subsequently, three machine learning algorithms Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) were used to identify the core genes. Finally, receiver operating characteristic (ROC) curves were used to analyze the clinical diagnostic value of the core genes.</p><p><strong>Results: </strong>We found, in total, 11,194 DEGs derived the TCGA and GEO datasets, that are primarily enriched in extracellular matrix (ECM) and inflammation related pathways, such as an ECM receptor interaction, cell adhesion molecules (CAMs), Tumor necrosis factor (TNF) signaling, and nucleotide-binding oligomerization domain (NOD) like receptor signaling pathways. Further analysis of the core genes, identified by the protein-protein interaction network, using three machine learning algorithms discovered three intersecting genes GJC1, KLHL4, and NOL4. Of which, GJC1 has good clinical diagnostic ability, which was verified using both the GEO (area under the ROC curve (AUC) = .982) and TCGA databases (AUC = .840).</p><p><strong>Conclusions: </strong>GJC1 is highly expressed in PTC. Therefore, it is considered as a potential biomarker and is expected to become a new target for PTC gene therapy. However, it still needs to be supported and verified by more clinical data.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"339"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647631","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":"Genetic association of lipids and lipid-lowering drug target genes with breast cancer.","authors":"Tianhua Wang, Yan Yao, Xinhai Gao, Hao Luan, Xue Wang, Lijuan Liu, Changgang Sun","doi":"10.1007/s12672-025-02041-0","DOIUrl":"10.1007/s12672-025-02041-0","url":null,"abstract":"<p><strong>Background: </strong>Although several preclinical and epidemiological studies have shown that blood lipids and lipid-lowering drugs can reduce the risk of breast cancer, this finding remains controversial. This study aimed to explore the causal relationship between dyslipidemia,lipid-lowering drugs, and breast cancer. We also aimed to evaluate the potential impact of lipid-lowering drug targets on breast cancer.</p><p><strong>Method: </strong>Data of 431 lipid- and lipid-related phenotypes were obtained from genome-wide association study (GWAS), and mendelian randomization (MR) analyses were performed using two independent breast cancer datasets as endpoints. Genetic variants associated with genes encoding lipid-lowering drug targets were extracted from the Global Lipid Genetics Consortium. Expression quantitative trait loci data in relevant tissues were used to further validate lipid-lowering drug targets that reached significance and combined with bioinformatics approaches for molecular expression and prognostic exploration. Further mediation analyses were performed to explore potential mediators.</p><p><strong>Result: </strong>In two independent datasets, phosphatidylcholine (18:1_0:0 levels) was associated with breast cancer risk (discovery: odds ratio (OR) = 1.255 [95% confidence interval (CI) 1.120-1.406]; p = 8.936 × 10^-5, replication: OR = 1.016 [95% CI, 1.003-1.030]; p = 0.017), HMG- CoA reductase (HMGCR) inhibition was genetically modeled and associated with a reduced risk of breast cancer (discovery: OR = 0.833 [95% CI 0.752-0.923], p = 5.12 × 10^-4; replication: OR = 0.975 [95% CI 0.960-0.990], p = 1.65 × 10^-3). There was a significant MR correlation between HMGCR expression in whole blood and breast cancer (OR = 1.11 [95% 1.01-1.22] p = 0.04). Bioinformatics analysis revealed that HMGCR expression higher in breast cancer tissues than in normal tissues, along with poor overall survival and relapse-free survival, and was associated with multiple immune cell infiltration. Finally, the mediation analysis showed that HMGCR inhibitors affected breast cancer through different immune cell phenotypes and C-reactive protein levels.</p><p><strong>Conclusion: </strong>In this study, we found for the first time that phosphatidylcholine (18:1_0:0) levels are associated with breast cancer risk. We found that HMGCR inhibitors are associated with a reduced risk of breast cancer, and part of their action may be through pathways other than lipid-lowering, including modulation of immune function and reduction of inflammation represented by C-reactive protein levels.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"331"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647617","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":"Exploring the combined roles of GALNT1 and GALNT2 in hepatocellular carcinoma malignancy and EGFR modulation.","authors":"Tagwa E Osman, Yanru Guo, Shijun Li","doi":"10.1007/s12672-025-02069-2","DOIUrl":"10.1007/s12672-025-02069-2","url":null,"abstract":"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC), the most formidable subtype of primary liver cancers, is becoming increasingly concerning due to its rising incidence worldwide. HCC ranks as the sixth most diagnosed cancer globally and is the third leading cause of cancer-related deaths. Glycosylation, a common post-translational modification of proteins, is frequently altered in tumors and is associated with the progression of malignancies. GALNT1 and GALNT2 are GalNAc-transferases that initiate protein O-glycosylation and are closely linked to cancer development. Investigating the relationship between GALNT1 and GALNT2 in HCC could provide new insights into the disease's pathogenesis. Thus, this study aimed to explore the combined effects of GALNT1 and GALNT2 transfection on HCC, compared to the effects of modifying each gene individually.</p><p><strong>Materials and methods: </strong>GALNT1 and GALNT2 were assessed by bioinformatics, qPCR, and Western blot analyses to detect their expression in HCC tissues and cell lines. The effects of GALNT1/GALNT2 overexpression and knockdown on cell viability, proliferation, migration, invasion, and apoptosis were evaluated in HCC cells using CCK8, colony formation, transwell migration and invasion, wound healing, TUNEL, and flow cytometry assays. EGFR protein levels were also analyzed by Western blotting.</p><p><strong>Results: </strong>Co-transfection of GALNT1 knockdown with GALNT2 overexpression significantly suppressed proliferation, migration, and invasion, while promoting apoptosis in HCC cells. Conversely, co-transfection of GALNT1 overexpression with GALNT2 knockdown enhanced these malignant characteristics compared to the modified single gene. Notably, we observed that GALNT1 and GALNT2 modulated EGFR protein expression. Overall, our findings suggest that the combined activity of GALNT1 and GALNT2 is critical in regulating HCC malignant behaviors.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"337"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914428/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647558","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":"From cell lines to animal models: \"plant- derived chemotherapeutics unlocking new frontiers against oral squamous cell carcinoma\"-a comprehensive systematic review.","authors":"Saranya Ramsridhar, Chandini Rajkumar, Vishnu Priya Veeraraghavan, Arul Prakash Francis, Murali Balasubramaniam, Indu Bharkavi","doi":"10.1007/s12672-025-02057-6","DOIUrl":"10.1007/s12672-025-02057-6","url":null,"abstract":"<p><strong>Background and aim: </strong>Despite progress in traditional treatment methods, the overall survival rate for oral squamous cell carcinoma (OSCC) remains limited. Consequently, it is essential to investigate alternative therapeutic strategies to enhance patient outcomes. This review highlights the potential role of plant extracts as chemo preventive agents in oral cancer treatment.</p><p><strong>Methods: </strong>A systematic review was conducted following PRISMA guidelines, involving an extensive literature search from databases such as PubMed, Scopus, Embase, Web of science, Cochrane and CINAHL which included studies from 2010 to 2024 that explored the anticancer potential of medicinal plants for OSCC treatment. Data extraction focused on plant species, parts used, extract type, active components, dosage, and cancer cell lines or animal models used. Risk of bias was assessed using the OHAT tool for animal studies and the ROBINS-I tool for in vitro studies.</p><p><strong>Results: </strong>A total of 12 in vitro and animal studies were included, examining plants such as Allium sativum (garlic), Crocus sativus (saffron), Curcuma longa (turmeric), Scutellariabaicalensis (Baikal skullcap), etc., These studies demonstrated that bioactive components like allicin, curcumin, and baicalin significantly inhibited OSCC cell proliferation and induced apoptosis. However, there was substantial variability in the dose concentrations required, ranging from 1 µg/mL for garlic extract to 50 mg/mL for saffron nanoparticles. The risk of bias assessment indicated that four studies had a moderate risk, while one had a low risk of bias, indicating methodological rigor.</p><p><strong>Conclusion: </strong>Plant extracts such as Curcuma longa and Vitis vinifera present a promising, less toxic alternative for OSCC treatment, with the potential to be integrated into conventional chemotherapeutic regimens. While in-vitro and animal studies are encouraging, further clinical trials among humans are necessary to confirm their efficacy and safety in clinical settings.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"340"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647613","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":"97 Machine learning algorithms in the prognosis of cutaneous melanoma: a population-based study.","authors":"Tongtong Jin, Donggang Yao, Yan Xu, Xiaopeng Zhang, Xu Dong, Haiya Bai","doi":"10.1007/s12672-025-02129-7","DOIUrl":"10.1007/s12672-025-02129-7","url":null,"abstract":"<p><strong>Objectives: </strong>To establish a predictive model for prognosis of cutaneous melanoma using machine learning algorithms in large sample data.</p><p><strong>Methods: </strong>A retrospective analysis of patients diagnosed with cutaneous melanoma in the SEER database from 2010 to 2015 was performed using 12 different machine learning algorithms, for a total of 97 algorithm combinations, to screen for variables associated with cutaneous melanoma prognosis and to build predictive models.</p><p><strong>Results: </strong>A total of 24,457 cases were collected in this study, and 8,441 cases were finally included. Among them, 5908 cases in the training set and 2533 cases in the test set. The results of the study show that StepCox[both] + RSF is the best model. The variable features screened by the best model were Sex, Age, Marital, T stage, N stage, Ulcer, Site, Histologic, Surgery, Chemotherapy, Bone metastasis, Liver metastasis and Lung metastasis.</p><p><strong>Conclusion: </strong>We have developed a predictive model with good accuracy for cutaneous melanoma prognosis using a combination of 97 machine learning algorithms in a large sample database.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"342"},"PeriodicalIF":2.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647627","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":"The SLC26A4-AS1/NTRK2 axis in breast cancer: insights into the ceRNA network and implications for prognosis and immune microenvironment.","authors":"Mengqiu Lan, Shuang Qin, Jingjing Wei, Lihong Wu, Zhenni Lu, Wenjie Huang","doi":"10.1007/s12672-025-02080-7","DOIUrl":"10.1007/s12672-025-02080-7","url":null,"abstract":"<p><p>Breast cancer is a leading malignancy in women, with mortality disparities between developed and underdeveloped regions. Accumulating evidence suggests that the competitive endogenous RNA (ceRNA) regulatory networks play paramount roles in various human cancers. However, the complexity and behavior characteristics of the ceRNA network in breast cancer progression have not been fully elucidated. The expression profiles of three RNAs (long non-coding RNAs [lncRNAs], microRNAs [miRNAs], and mRNAs) were extracted from breast cancer and adjacent samples were sourced from the TCGA database. The SLC26A4-AS1- hsa-miR-19a-3p-NTRK2 ceRNA network related to the prognosis of breast cancer was obtained by performing bioinformatics analysis. Importantly, we identified the SLC26A4-AS1/NTRK2 axis within the ceRNA network through correlation analysis and found it to be a potential prognostic model in clinical outcomes based on Cox regression analysis. Moreover, methylation analysis suggests that the aberrant downregulation of the SLC26A4-AS1/NTRK2 axis might be attributed to hypermethylation at specific sites. Immune infiltration analysis indicates that the SLC26A4-AS1/NTRK2 axis may have implications for the alteration of the tumor immune microenvironment and the emergence and progression of immune evasion in breast cancer. Finally, we validated the expression of SLC26A4-AS1-hsa-miR-19a-3p-NTRK2 in breast cancer cell lines. In summary, the present study posits that the SLC26A4-AS1/NTRK2 axis, based on the ceRNA network, could be a novel and significant prognostic factor associated with breast cancer diagnosis and outcomes.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"329"},"PeriodicalIF":2.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11911282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647641","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":"Multi-omics analysis identifies OSGEPL1 as an oncogene in hepatocellular carcinoma.","authors":"Sintim Mui, Juanyi Shi, Kai Wen, Yongcong Yan, Huoming Li, Weidong Wang, Zhenyu Zhou, Zhiyu Xiao","doi":"10.1007/s12672-025-02066-5","DOIUrl":"10.1007/s12672-025-02066-5","url":null,"abstract":"<p><strong>Purpose: </strong>N6-Threonylcarbamoyladenosine (t⁶A) modification irregularities and their associated enzymes genes (OSGEP, OSGEPL1, TPRKB, GON7, TP53RK, YRDC, and LAGE3) are linked to various malignancies development, including Hepatocellular Carcinoma (HCC), yet the specific mechanisms remain obscure. This gap in knowledge is significant, as understanding the mechanisms of t⁶A modification could reveal new insights into HCC pathogenesis and potentially identify novel therapeutic targets.</p><p><strong>Methods: </strong>We leveraged data from The Cancer Genome Atlas (TCGA) to analyze the expression of t⁶A-associated genes, with a focus on OSGEPL1 in HCC. Our analyses included survival outcome, gene expression, functional enrichment, immune cell infiltration, and somatic mutation data.</p><p><strong>Results: </strong>We discovered that OSGEPL1 is upregulated in HCC and is correlated with tumor grade, pathological T stage, and overall stage. It inversely impacts overall survival and immune cell infiltration. In vitro experiments confirmed the role of OSGEPL1 in promoting HCC cell proliferation.</p><p><strong>Conclusions: </strong>This study implicates t⁶A modification pathway dysregulation in HCC prognosis, identifying OSGEPL1 as a potential therapeutic target. These findings provide novel insights into HCC pathogenesis and may guide future treatment strategies.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"328"},"PeriodicalIF":2.8,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11911280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647634","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}