Journal of Translational Medicine最新文献

{"title":"Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy.","authors":"Yuemin Wu, Wei Zhang, Xiao Liang, Pengpeng Zhang, Mengzhe Zhang, Yuqin Jiang, Yanan Cui, Yi Chen, Wenxin Zhou, Qi Liang, Jiali Dai, Chen Zhang, Jiali Xu, Jun Li, Tongfu Yu, Zhihong Zhang, Renhua Guo","doi":"10.1186/s12967-024-06057-y","DOIUrl":"10.1186/s12967-024-06057-y","url":null,"abstract":"<p><strong>Background: </strong>Non-small cell lung cancer (NSCLC) is highly heterogeneous, leading to varied treatment responses and immune-related adverse reactions (irAEs) among patients. Habitat radiomics allows non-invasive quantitative assessment of intratumor heterogeneity (ITH). Therefore, our objective is to employ habitat radiomics techniques to develop a robust approach for predicting the efficacy of Immune checkpoint inhibitors (ICIs) and the likelihood of irAEs in advanced NSCLC patients.</p><p><strong>Methods: </strong>In this retrospective two center study, two independent cohorts of patients with NSCLC were used to develop (n = 248) and validate signatures (n = 95). After applying four kinds of machine learning algorithms to select the key preoperative CT radiomic features, we used clinical, radiomics and habitat radiomic features to develop the clinical signature, radiomics signature and habitat radiomic signature for ICIs prognostics and irAEs prediction. By combining habitat radiomic features with corresponding clinicopathologic information, the nomogram signature was constructed in the training cohort. Next, the internal validation cohort (n = 75) of patients, and the external validation cohort (n = 20) of patients treated with ICIs were included to evaluate the predictive value of the four signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC).</p><p><strong>Results: </strong>Our study introduces a radiomic nomogram model that integrates clinical and habitat radiomic features to identify patients who may benefit from ICIs or experience irAEs. The Radiomics Nomogram model exhibited superior predictive performance in the training, validation, and external validation sets, with AUCs of 0.923, 0.817, and 0.899, respectively. This model outperformed both the Whole-tumor Radiomics Signature model (AUCs of 0.870, 0.736, and 0.626) and the Habitat Signature model (AUCs of 0.900, 0.804, and 0.808). The radiomics model focusing on tumor sub-regional habitat showed better predictive performance than the model derived from the entire tumor. Decision Curve Analysis (DCA) and calibration curves confirmed the nomogram's effectiveness.</p><p><strong>Conclusion: </strong>By leveraging machine learning to predict the outcomes of ICIs, we can move closer to achieving tailored ICIs for lung cancer. This advancement will assist physicians in selecting and managing subsequent treatment strategies, thereby facilitating clinical decision-making.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"393"},"PeriodicalIF":6.1,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11970015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.","authors":"Mingzhi Lin, Jiuqi Guo, Zhilin Gu, Wenyi Tang, Hongqian Tao, Shilong You, Dalin Jia, Yingxian Sun, Pengyu Jia","doi":"10.1186/s12967-025-06425-2","DOIUrl":"10.1186/s12967-025-06425-2","url":null,"abstract":"<p><p>The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection of the complex physiological and pathological changes in the body from a molecular perspective, providing new microscopic insights into cardiovascular diseases research. However, due to the vast volume and complexity of data, accurately describing, utilising, and translating these biomedical data demands substantial effort. Researchers and clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery and causal inference using various omics data. These AI approaches, integrated with multi-omics research, have shown promising outcomes in cardiovascular studies. In this review, we outline the methods for integrating machine learning, one of the most successful applications of AI, with omics data and summarise representative AI models developed that leverage various omics data to facilitate the exploration of cardiovascular diseases from underlying mechanisms to clinical practice. Particular emphasis is placed on the effectiveness of using AI to extract potential molecular information to address current knowledge gaps. We discuss the challenges and opportunities of integrating omics with AI into routine diagnostic and therapeutic practices and anticipate the future development of novel AI models for wider application in the field of cardiovascular diseases.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"388"},"PeriodicalIF":6.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11966820/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioinformatics analysis and experimental validation of potential targets and pathways in chronic kidney disease associated with renal fibrosis.","authors":"Cui Huimin, Zhao Yuxin, Wang Peng, Gong Wei, Lin Hong, Li Na, Yang Jianjun","doi":"10.1186/s12967-024-06058-x","DOIUrl":"10.1186/s12967-024-06058-x","url":null,"abstract":"<p><strong>Background: </strong>Chronic kidney disease (CKD) has emerged as a major health problem worldwide. Previous studies have shown that specific miRNA expression profiles of patients with CKD are significantly changed. In this study, we aim to elucidate the role of miRNAs as potential biomarkers in CKD progression by integrating bioinformatics analysis with experimental validation, thereby providing medical evidence for the prevention and treatment of CKD.</p><p><strong>Method: </strong>Bioinformatics analysis was used to identify potential targets and pathways in CKD-associated renal fibrosis through randomly obtaining miRNA microarray data related to CKD patients in the Gene Expression Omnibus (GEO) database according to the inclusion and exclusion criteria, conducting pathway enrichment analysis and constructing protein-protein interaction (PPI) networks and miRNA-mRNA network by Cytoscape 3.8.0. In vitro experiments were employed to verify the role and mechanism of miR-223-3p in human renal tubular epithelial cells (HK2) through Quantitative real-time PCR assays, Western blot, Immunofluorescence analysis and Double luciferase reporter gene experiment. Multi-group one-way analysis of variance (ANOVA) and the Dunnett-t test were uesd to analyze the results by SPSS24.0.</p><p><strong>Results: </strong>10 up-regulated and 11 down-regulated miRNAs of CKD patients were screened out. Phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) was the first pathway of pathway enrichment analysis. MiR-223-3p (logFC=-2.047, p = 0.002) was one of the four hub miRNAs. Furthermore, we observed a reduction in α-smooth muscle actin (α-SMA) (p = 0.001) and Collagen type I alpha 1 (Col1-a1) (p = 0.023) levels upon miR-223-3p overexpression, which aligned with our bioinformatics predictions. This downregulation was attributed to the inhibition of nuclear factor kappa-B (NF-κB) nuclear translocation and subsequent decrease in the secretion of inflammatory cytokines, such as interleukin-6 (IL-6) (p = 0.005). Conversely, when CHUK was further overexpressed, the inhibitory effect of miR-223-3p on epithelial-mesenchymal transition (EMT) was attenuated, confirming the specific interaction between miR-223-3p and CHUK.</p><p><strong>Conclusion: </strong>Our findings provide compelling evidence that miR-223-3p acts as a suppressor of EMT in CKD by specifically targeting the CHUK and modulating the PI3K/Akt pathway, which holds great promise as a novel therapeutic target for CKD treatment. Additionally, this study offers a potential avenue for the development of future interventions aimed at halting or reversing the progression of CKD.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"387"},"PeriodicalIF":6.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11967072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epithelial-mesenchymal transition orchestrates tumor microenvironment: current perceptions and challenges.","authors":"Yuqi Xie, Xuan Wang, Wenquan Wang, Ning Pu, Liang Liu","doi":"10.1186/s12967-025-06422-5","DOIUrl":"10.1186/s12967-025-06422-5","url":null,"abstract":"<p><p>The epithelial-mesenchymal transition (EMT) is a critical process in cancer progression, facilitating tumor cells to develop invasive traits and augmenting their migratory capabilities. EMT is primed by tumor microenvironment (TME)-derived signals, whereupon cancer cells undergoing EMT in turn remodel the TME, thereby modulating tumor progression and therapeutic response. This review discusses the mechanisms by which EMT coordinates TME dynamics, including secretion of soluble factors, direct cell contact, release of exosomes and enzymes, as well as metabolic reprogramming. Recent evidence also indicates that cells undergoing EMT may differentiate into cancer-associated fibroblasts, thereby establishing themselves as functional constituents of the TME. Elucidating the relationship between EMT and the TME offers novel perspectives for therapeutic strategies to enhance cancer treatment efficacy. Although EMT-directed therapies present significant therapeutic potential, the current lack of effective targeting approaches-attributable to EMT complexity and its microenvironmental context dependency-underscores the necessity for mechanistic investigations and translational clinical validation.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"386"},"PeriodicalIF":6.1,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11963649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143772591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CircPRKCA facilitates esophageal squamous cell carcinoma metastasis via m5C-dependent CSF2 mRNA stabilization.","authors":"Lixia Wu, Lina Gu, Yang Zheng, Jingjing Liu, Zishuan Wei, Fei Liu, Jiali Li, Lingjiao Meng, Yang Sang, Meixiang Sang, Lianmei Zhao, Baoen Shan","doi":"10.1186/s12967-025-06395-5","DOIUrl":"10.1186/s12967-025-06395-5","url":null,"abstract":"<p><strong>Background: </strong>Esophageal squamous cell carcinoma (ESCC) is a serious invasive malignancy with an ambiguous etiology. Evidence indicates that circular RNA (circRNA) is significantly involved in the regulatory processes associated with cancer development. Nevertheless, the specific molecular mechanisms through which circRNA facilitates the progression of ESCC are still largely undefined.</p><p><strong>Methods: </strong>Here, we identified that the expression of hsa_circ_0007580 (designated circPRKCA) was markedly elevated in ESCC. Fluorescence in situ hybridization (FISH) was conducted to verify the expression, intracellular localization, and potential prognostic value of circPRKCA based on the tissue microarray. Gain- and loss-of-function assays were employed to investigate the effects of circPRKCA both in vitro and in vivo. RNA pull-down and mass spectrometry (MS) were performed to identify the proteins bound to circPRKCA. mRNA sequencing was conducted to screen the downstream target genes of circPRKCA. Furthermore, immunoprecipitation and methylated RNA immunoprecipitation (MeRIP) analysis were used to explore the regulatory mechanisms.</p><p><strong>Results: </strong>We found that circPRKCA exhibited significant upregulation in ESCC tissues and correlated with unfavorable prognostic outcomes. Biological function experiments further confirmed that circPRKCA enhances the capabilities of migration, invasion, and angiogenesis in ESCC. Mechanistically, circPRKCA engages in interaction with Y-box binding protein 1 (YBX1) within the cytoplasmic milieu, consequently preventing the ubiquitination-mediated degradation of YBX1. Increased concentrations of YBX1 increase the stability of granulocyte-macrophage colony-stimulating factor (CSF2) mRNA in a 5-methylcytosine (m5C)-dependent manner. This process facilitates metastasis in ESCC.</p><p><strong>Conclusion: </strong>In this research, we identified a correlation between circPRKCA and unfavorable prognoses in patients with ESCC. It is instrumental in the metastatic progression of ESCC via the YBX1/CSF2 signaling pathway. Consequently, targeting circPRKCA may represent a promising therapeutic strategy for ESCC.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"385"},"PeriodicalIF":6.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11959849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of undernutrition and associated factors among children with congenital heart disease in Africa: a systemic review and meta-analysis.","authors":"Mequanint Ayehu Akele, Tenagnework Eseyneh Dagnaw, Molla Getie Mehari, Amare Mebrat Delie, Daniel Sisay W/Tsadik, Migbar Sibhat Mekonnen, Tamalew Alemie Tegegn, Dires Birhanu Mihretie, Kassa Genetu Alem","doi":"10.1186/s12967-024-05952-8","DOIUrl":"10.1186/s12967-024-05952-8","url":null,"abstract":"<p><strong>Background: </strong>Undernutrition is a major public health issue in children with congenital heart disease in Africa. In this continent, the degree of undernutrition also varies from country to country. Therefore, summarizing data concerning undernutrition in children with congenital heart disease is essential to refine treatment guidelines and policies. This meta-analysis aims to deliver pooled data concerning undernutrition among African children with congenital heart disease.</p><p><strong>Methods: </strong>In this review, relevant studies were searched via PubMed/MEDLINE online, Science Direct, Hinari, Web of Science, CINHAL, EMBASE, WHO database, Google, and Google Scholar. To conduct this review, PRISMA guidelines were used. STATA 17 was used to estimate the pooled prevalence of undernutrition in children. A random effect meta-analysis model was used to conduct this meta-analysis. The heterogeneity of the studies was evaluated by the I2 test. Publication bias was assessed via funnel plots supplemented with Egger's weighted regression test. Finally, for all analyses, p < 0.05 was considered statistically significant.</p><p><strong>Result: </strong>In this review, a total of 5898 studies were found. Among these, 5878 were excluded using PRISMA, and the remaining 20 studies were included in the final analysis. The prevalence of undernutrition, underweight, wasting, and stunting in children with congenital heart disease was 65.14% (95% CI 51.32-78.95, I² = 97.4%, p = 0.0001), 45.76% (95% CI 35.83-55.69, I² = 96.7, p < 0.0001), 39.37% (95% CI 29.55-49.19, I² = 97.4, p < 0.0001), and 39.38% (95% CI 33.02-45.72, I² = 92.4%, p < 0.0001), respectively. Anemia (OR = 4.5, 95% CI 1.60-12.68), CHF (OR = 5.98, 95% CI 3.09-11.57), pulmonary hypertension (OR = 2.76, 95% CI 1.89-4.04), and age (OR = 2.78, 95% CI 1.79-4.31) were associated with undernutrition in children with CHD.</p><p><strong>Conclusion: </strong>In this meta-analysis, the pooled prevalence of undernutrition and its indicators in children with CHD were high. As a result, there is still a need to improve early screening and treatment of undernutrition in children with congenital heart disease concomitant with early screening and treatment of congenital heart disease and its common complications in Africa.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"384"},"PeriodicalIF":6.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11959727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143764419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of enhanced MRI-based radiomics models using machine learning algorithms for non-invasive prediction of IL7R expression in high-grade gliomas and its prognostic value in clinical practice.","authors":"Jie Zhou","doi":"10.1186/s12967-025-06402-9","DOIUrl":"10.1186/s12967-025-06402-9","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;High-grade gliomas are among the most aggressive and deadly brain tumors, highlighting the critical need for improved prognostic markers and predictive models. Recent studies have identified the expression of IL7R as a significant risk factor that affects the prognosis of patients diagnosed with high-grade gliomas (HGG). This research focuses on investigating the prognostic significance of Interleukin 7 Receptor (IL7R) expression and aims to develop a noninvasive predictive model based on radiomics for HGG.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted an analysis using data from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA), focusing on a group of 310 patients diagnosed with high-grade gliomas. To evaluate prognosis, we applied both univariate and multivariate Cox regression analyses alongside Kaplan-Meier survival analysis. Radiomics features were extracted from specific regions of interest, which were outlined by two physicians using 3D Slicer software. For selecting the most relevant features, we utilized the Minimum Redundancy Maximum Relevance (mRMR) and Recursive Feature Elimination (RFE) algorithms. Following this, we developed and assessed Support Vector Machine (SVM) and Logistic Regression (LR) models, measuring their performance through various metrics such as accuracy, specificity, sensitivity, positive predictive value, calibration curves, the Hosmer-Lemeshow goodness-of-fit test, decision curve analysis (DCA), and Kaplan-Meier survival analysis.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The survival analysis encompassed a total of 310 patients diagnosed with high-grade glioma, sourced from the TCGA database. Patients were stratified into high and low expression groups based on the levels of IL7R expression. Kaplan-Meier survival curves and Cox regression analysis revealed that an increase in IL7R expression correlated with a decline in overall survival (OS). The median Intraclass Correlation Coefficient (ICC) for the assessed radiomic features was determined to be 0.869, with 93 features exhibiting an ICC of 0.75 or greater. Utilizing the mRMR and RFE methodologies led to the identification of a final set comprising eight features. The Support Vector Machine (SVM) model recorded an Area Under the Curve (AUC) value of 0.805, whereas the AUC derived from fivefold cross-validation was noted to be 0.768. Conversely, the Logistic Regression (LR) model produced an AUC of 0.85, with an internal fivefold cross-validation AUC of 0.779, indicating a more robust predictive capability. We developed Support Vector Machine (SVM) and Logistic Regression (LR) models, with the LR model demonstrating a more robust predictive capability. Further Kaplan-Meier analysis underscored a significant association between elevated risk scores from the LR model and OS malignancy, with a P value of less than 0.001. GSVA analysis showed the enrichment pathway of KEGG and Hallmark genes in the high RS group. Moreover, ex","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"383"},"PeriodicalIF":6.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11959755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach for the co-delivery of 5-fluorouracil and everolimus for breast cancer combination therapy: stimuli-responsive chitosan hydrogel embedded with mesoporous silica nanoparticles.","authors":"Pooria Mohammadi Arvejeh, Fatemeh Amini Chermahini, Francesco Marincola, Fatemeh Taheri, Seyed Abbas Mirzaei, Akram Alizadeh, Fatemeh Deris, Raziyeh Jafari, Niloufar Amiri, Amin Soltani, Elham Bijad, Ebrahim Soleiman Dehkordi, Pegah Khosravian","doi":"10.1186/s12967-025-06396-4","DOIUrl":"10.1186/s12967-025-06396-4","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer remains one of the leading causes of death among women globally, with traditional therapies often limited by challenges such as drug resistance and significant side effects. Combination therapies, coupled with nanotechnology-based co-delivery systems, offer enhanced efficacy by targeting multiple pathways in cancer progression. In this study, we developed an injectable, stimuli-responsive nanosystem using a chitosan hydrogel embedded with mesoporous silica nanoparticles for the co-administration of 5-fluorouracil and everolimus. This approach aims to optimize controlled drug release, enhance the synergistic anticancer effect, and overcome challenges associated with co-loading different therapeutic agents.</p><p><strong>Methods: </strong>Various techniques were employed to characterize the nanoparticles and the hydrogel. Cell uptake, apoptosis, and proliferation of 4T1 breast cancer cells were evaluated by flow cytometry and Resazurin assay, respectively. The Balb/C mice model of breast cancer, which received the therapeutical nanoplatforms subcutaneously near the tumoral region was used to examine tumor size and lung metastases.</p><p><strong>Results: </strong>The results revealed that the nanoparticles had a suitable loading capacity and high cellular uptake. The drug release was pH-sensitive and synergistic. By incorporating nanoparticles into the hydrogel, the cell death rate and apoptosis of 4T1 breast cancer cells increased significantly, due to the synergistic effects of co-delivered drugs. Additionally, the combination treatment groups showed a significant reduction in tumor size and lung metastasis compared to the monotherapy and control groups.</p><p><strong>Conclusions: </strong>These findings underscore the potential of the nanocomposite used to develop a novel co-delivery system to enhance therapeutic outcomes, reduce side effects, and provide a promising new strategy for future cancer treatments.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"382"},"PeriodicalIF":6.1,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11956229/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized prediction of esophageal cancer risk based on virtually generated alcohol data.","authors":"Oswald Ndi Nfor, Pei-Ming Huang, Ming-Fang Wu, Ke-Cheng Chen, Ying-Hsiang Chou, Mong-Wei Lin, Ji-Han Zhong, Shuenn-Wen Kuo, Yu-Kwang Lee, Chih-Hung Hsu, Jang-Ming Lee, Yung-Po Liaw","doi":"10.1186/s12967-025-06383-9","DOIUrl":"https://doi.org/10.1186/s12967-025-06383-9","url":null,"abstract":"<p><strong>Background: </strong>Esophageal cancer (EC) presents a significant public health challenge globally, particularly in regions with high alcohol consumption. Its etiology is multifactorial, involving both genetic predispositions and lifestyle factors.</p><p><strong>Methods: </strong>This study aimed to develop a personalized risk prediction model for EC by integrating genetic polymorphisms (rs671 and rs1229984) with virtually generated alcohol consumption data, utilizing advanced artificial intelligence and machine learning techniques. We analyzed data from 86,845 individuals, including 763 diagnosed EC patients, sourced from the Taiwan Biobank. Eight machine learning models were employed: Bayesian Network, Decision Tree, Ensemble, Gradient Boosting, Logistic Regression, LASSO, Random Forest, and Support Vector Machines (SVM). A unique aspect of our approach was the virtual generation of alcohol consumption data, allowing us to evaluate risk profiles under both consuming and non-consuming scenarios.</p><p><strong>Results: </strong>Our analysis revealed that individuals with the genotypes rs671 = AG and rs1229984 = CC exhibited the highest probabilities of developing EC, with values ranging from 0.2041 to 0.9181. Notably, abstaining from alcohol could decrease their risk by approximately 16.29-49.58%. The Ensemble model demonstrated exceptional performance, achieving an area under the curve (AUC) of 0.9577 and a sensitivity of 0.9211. This transition from consumption to abstinence indicated a potential risk reduction of nearly 50% for individuals with high-risk genotypes.</p><p><strong>Conclusion: </strong>Overall, our findings highlight the importance of integrating virtually generated alcohol data for more precise personalized risk assessments for EC.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"379"},"PeriodicalIF":6.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in humanoid organoid-based research on inter-organ communications during cardiac organogenesis and cardiovascular diseases.","authors":"Baoqiang Ni, Lingqun Ye, Yan Zhang, Shijun Hu, Wei Lei","doi":"10.1186/s12967-025-06381-x","DOIUrl":"https://doi.org/10.1186/s12967-025-06381-x","url":null,"abstract":"<p><p>The intimate correlation between cardiovascular diseases and other organ pathologies, such as metabolic and kidney diseases, underscores the intricate interactions among these organs. Understanding inter-organ communications is crucial for developing more precise drugs and effective treatments for systemic diseases. While animal models have traditionally been pivotal in studying these interactions, human-induced pluripotent stem cells (hiPSCs) offer distinct advantages when constructing in vitro models. Beyond the conventional two-dimensional co-culture model, hiPSC-derived humanoid organoids have emerged as a substantial advancement, capable of replicating essential structural and functional attributes of internal organs in vitro. This breakthrough has spurred the development of multilineage organoids, assembloids, and organoids-on-a-chip technologies, which allow for enhanced physiological relevance. These technologies have shown great potential for mimicking coordinated organogenesis, exploring disease pathogenesis, and facilitating drug discovery. As the central organ of the cardiovascular system, the heart serves as the focal point of an extensively studied network of interactions. This review focuses on the advancements and challenges of hiPSC-derived humanoid organoids in studying interactions between the heart and other organs, presenting a comprehensive exploration of this cutting-edge approach in systemic disease research.</p>","PeriodicalId":17458,"journal":{"name":"Journal of Translational Medicine","volume":"23 1","pages":"380"},"PeriodicalIF":6.1,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11951738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143742596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}