[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100635520":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":12,"locations":18,"responsibleParty":125,"collaborators":127,"id":140,"slug":141,"hasResults":142,"nctId":143,"briefTitle":144,"officialTitle":145,"acronym":146,"eligibilityCriteria":147,"healthyVolunteers":142,"sex":148,"minAge":149,"maxAge":10,"enrollmentInfo":150,"targetDuration":10,"studyType":153,"phases":10,"briefSummary":154,"conditions":155,"keywords":163,"overallStatus":21,"whyStopped":10,"lastUpdateSubmitDate":166,"lastUpdatePostDateStruct":167,"startDateStruct":170,"completionDateStruct":172,"leadSponsor":174,"locationsCount":175},{"fullName":5,"class":6},"Xijing Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":10},"Prostate Cancer Cohort",null,"A single cohort of 1000 patients with suspected or histologically confirmed prostate cancer who undergo baseline treatment-naïve PSMA PET imaging. All patients provide blood samples for plasma exosomal RNA analysis, collected prior to any treatment and prior to prostate biopsy. This cohort is used for a four-phase biomarker study: Phase 1 (Discovery, n=250) for RNA sequencing to identify candidate biomarkers; Phase 2 (Model Development, n=300) for digital PCR-based signature development; Phase 3 (Internal Validation, n=300) for independent validation in a consecutive cohort; and Phase 4 (External Validation, n=150) for multi-center validation. Bone metastasis status is defined by PSMA PET. Phase 2 and Phase 3 cohorts are temporally and geographically independent. No patient is included in more than one phase",[13],{"name":14,"role":15,"phone":16,"phoneExt":10,"email":17},"Jianhua Jiao, MD.","CONTACT","+86 18700919857","1531769428@qq.com",[19,37,50,63,75,84,93,105,112],{"facility":20,"status":21,"city":22,"state":23,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":26,"geoPoint":31,"contacts":32},"The First Hospital of Lanzhou University","RECRUITING","Lanzhou","Gansu","China","CN",{"type":27,"coordinates":28},"Point",[29,30],103.83987,36.05701,{"lat":30,"lon":29},[33,35],{"name":34,"role":15,"phone":16,"phoneExt":10,"email":17},"Wei Zhang, MD.",{"name":34,"role":36,"phone":10,"phoneExt":10,"email":10},"PRINCIPAL_INVESTIGATOR",{"facility":38,"status":21,"city":39,"state":40,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":41,"geoPoint":45,"contacts":46},"General Hospital of Ningxia Medical University","Yinchuan","Ningxia",{"type":27,"coordinates":42},[43,44],106.27306,38.46806,{"lat":44,"lon":43},[47,49],{"name":48,"role":15,"phone":16,"phoneExt":10,"email":17},"Zhiyong Lv, MD.",{"name":48,"role":36,"phone":10,"phoneExt":10,"email":10},{"facility":51,"status":21,"city":52,"state":53,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":54,"geoPoint":58,"contacts":59},"Weinan Central Hospital","Weinan","Shaanxi",{"type":27,"coordinates":55},[56,57],109.50891,34.50355,{"lat":57,"lon":56},[60,62],{"name":61,"role":15,"phone":16,"phoneExt":10,"email":17},"Weihong Zhao, MD.",{"name":61,"role":36,"phone":10,"phoneExt":10,"email":10},{"facility":5,"status":21,"city":64,"state":53,"zip":65,"country":24,"countryCode":25,"cosmosGeoPoint":66,"geoPoint":70,"contacts":71},"Xi'an","710032",{"type":27,"coordinates":67},[68,69],108.92861,34.25833,{"lat":69,"lon":68},[72,73],{"name":14,"role":15,"phone":16,"phoneExt":10,"email":17},{"name":74,"role":36,"phone":10,"phoneExt":10,"email":10},"Weijun Qin, MD.",{"facility":76,"status":21,"city":64,"state":53,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":77,"geoPoint":79,"contacts":80},"Shaanxi Provincial People's Hospital",{"type":27,"coordinates":78},[68,69],{"lat":69,"lon":68},[81,83],{"name":82,"role":15,"phone":16,"phoneExt":10,"email":17},"Yi Sun, MD.",{"name":82,"role":36,"phone":10,"phoneExt":10,"email":10},{"facility":85,"status":21,"city":64,"state":53,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":86,"geoPoint":88,"contacts":89},"Xijing 986 Hospital",{"type":27,"coordinates":87},[68,69],{"lat":69,"lon":68},[90,92],{"name":91,"role":15,"phone":16,"phoneExt":10,"email":17},"Wuhe Zhang, MD.",{"name":91,"role":36,"phone":10,"phoneExt":10,"email":10},{"facility":94,"status":21,"city":95,"state":53,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":96,"geoPoint":100,"contacts":101},"The Second Affiliated Hospital of Shaanxi University of Chinese Medicine","Xianyang",{"type":27,"coordinates":97},[98,99],108.70261,34.33778,{"lat":99,"lon":98},[102,104],{"name":103,"role":15,"phone":16,"phoneExt":10,"email":17},"Wei Zheng, MD.",{"name":103,"role":36,"phone":10,"phoneExt":10,"email":10},{"facility":106,"status":21,"city":107,"state":53,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":10,"geoPoint":10,"contacts":108},"Qinghai University Affiliated Hospital","Xining",[109,111],{"name":110,"role":15,"phone":16,"phoneExt":10,"email":17},"Guojun Chen, MD.",{"name":110,"role":36,"phone":10,"phoneExt":10,"email":10},{"facility":113,"status":21,"city":114,"state":53,"zip":10,"country":24,"countryCode":25,"cosmosGeoPoint":115,"geoPoint":119,"contacts":120},"Affiliated Hospital of Yan'an University","Yan’an",{"type":27,"coordinates":116},[117,118],109.49167,36.59889,{"lat":118,"lon":117},[121,123],{"name":122,"role":15,"phone":16,"phoneExt":10,"email":17},"Jixue Gao, MD.",{"name":124,"role":36,"phone":10,"phoneExt":10,"email":10},"Gao, MD.",{"type":126,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR",[128,130,131,133,134,135,137,139],{"name":129,"class":6},"Air Force Military Medical University, China",{"name":76,"class":6},{"name":132,"class":6},"Yan'an University Affiliated Hospital",{"name":51,"class":6},{"name":94,"class":6},{"name":136,"class":6},"LanZhou University",{"name":138,"class":6},"Qinghai University",{"name":38,"class":6},"100635520","plasma-exosomal-rna-signature-for-prostate-cancer-bone-metastasis-100635520",false,"NCT07553754","Plasma Exosomal RNA Signature for Prostate Cancer Bone Metastasis","Plasma Exosomal RNA Signature for Predicting PSMA PET-Defined Bone Metastasis in Prostate Cancer: A Prospective, Multicenter Discovery, Development, and Validation Study","EXO-MET","Inclusion Criteria\n\n1. Patients with histologically confirmed prostate cancer who are scheduled to undergo baseline PSMA PET imaging.\n2. Patients who undergo PSMA PET imaging prior to any prostate cancer-related treatment (including androgen deprivation therapy, radiotherapy, or surgery).\n3. Patients who provide blood samples for plasma exosomal RNA analysis collected prior to any treatment AND prior to prostate biopsy (if applicable).\n\n   Whole blood samples (approximately 10 mL) will be collected in EDTA tubes at this specified time point. Samples will be processed within 2 hours to obtain plasma and stored at -80°C until analysis.This timing ensures circulating exosomal RNA profiles reflect tumor biology without biopsy-induced contamination.\n4. Patients who are willing to undergo prostate biopsy if clinically indicated (biopsy performed after blood collection).\n5. Patients who provide written informed consent to participate in the study.\n6. Age ≥18 years.\n\nExclusion Criteria\n\n1. Patients who have received any prior prostate cancer-related treatment before the baseline PSMA PET scan (including hormonal therapy, radiotherapy, chemotherapy, or surgery).\n2. Patients whose blood samples were collected after prostate biopsy.\n3. Patients with a history of other active malignancies within the past two years (excluding non-melanoma skin cancer).\n4. Patients with inadequate blood sample quality or quantity for exosomal RNA extraction and analysis (e.g., hemolysis, insufficient volume \\\u003C8 mL).\n5. Patients with severe comorbidities or conditions that, in the judgment of the investigator, could interfere with study compliance or pose significant risk.","MALE","18 Years",{"count":151,"type":152},1000,"ESTIMATED","OBSERVATIONAL","Brief Summary:\n\nThis prospective, multicenter study aims to discover, develop, and validate a plasma exosomal RNA-based signature as a rule-out test for predicting bone metastasis in prostate cancer, using baseline treatment-naïve PSMA PET as the gold standard. The study is designed in four sequential phases:\n\nPhase 1 (Discovery, n=250): High-throughput sequencing of plasma exosomal RNAs to identify differentially expressed candidate RNAs.\n\nPhase 2 (Model Development, n=300): Digital droplet PCR (ddPCR) analysis of candidates in an independent cohort to construct and lock the final multi-RNA predictive signature using appropriate machine learning methods.\n\nPhase 3 (Internal Validation, n=300): Independent validation of the locked signature in a consecutive cohort reflecting natural disease prevalence.\n\nPhase 4 (External Validation, n=150): Final independent validation in a multi-center cohort enriched for bone metastasis.\n\nPrimary Outcome:\n\nTo evaluate the diagnostic performance of the signature as a rule-out test for PSMA PET-defined bone metastasis. The primary performance metrics are:\n\nSensitivity, with a prespecified target of ≥95% (to ensure minimal false negatives).\n\nSpecificity at the threshold that achieves the ≥95% sensitivity. A specificity of ≥30% will be considered supportive of clinical utility. A specificity of ≥30% (or a lower bound of the 95% confidence interval exceeding 20%) will be considered supportive of clinical utility.\n\nNeed:\n\nCurrent biomarkers lack sensitivity and specificity for early detection of bone metastasis. More importantly, existing tools lack adequate negative predictive value to safely rule out bone metastasis in low-risk patients, leading to over-imaging or delayed detection. There is an urgent need for a non-invasive rule-out test to safely defer PSMA PET\u002FCT in very-low-risk patients. Plasma exosomal RNAs offer a promising liquid biopsy approach, but prospective multicenter studies with rigorous validation are lacking.\n\nSecondary Outcomes:\n\n1. Secondary metrics include negative predictive value (NPV), positive predictive value (PPV), area under the ROC curve (AUC), calibration, and decision curve analysis.\n2. Correlation between exosomal RNA levels and number of bone metastatic lesions (PSMA PET).\n3. Association with PSA, PSMA PET SUVmax, and MRI findings.\n4. Tissue-plasma correlation to confirm tumor origin (exploratory).\n5. Mechanistic exploration of key candidates via in vitro\u002Fin vivo assays (exploratory).\n6. Subgroup analyses by hormone sensitivity, metastatic pattern, Gleason grade (exploratory).\n\nInclusion Criteria:\n\n1. Histologically confirmed prostate cancer scheduled for baseline PSMA PET.\n2. PSMA PET performed prior to any prostate cancer-related treatment.\n3. Blood samples collected prior to any treatment AND prior to prostate biopsy.\n4. Willing to undergo prostate biopsy if clinically indicated (after blood collection).\n5. Written informed consent.\n6. Age ≥18 years.\n\nExclusion Criteria:\n\n1. Any prior prostate cancer treatment before baseline PSMA PET.\n2. Blood samples collected after prostate biopsy.\n3. Other active malignancy within past two years (excluding non-melanoma skin cancer).\n4. Inadequate blood sample quality or quantity.\n5. Severe comorbidities interfering with study conduct.",[156,157,158,159,160,161,162],"Prostate Cancer (Diagnosis)","Bone Metastasis","PSMA PET","Liquid Biopsy","Exosomal RNA","Biomarker Discovery and Validation","Treament-naive",[164,157,158,159,160,165],"Prostate Cancer","Predictive Signature","2026-04-20",{"date":168,"type":169},"2026-04-28","ACTUAL",{"date":171,"type":169},"2026-03-12",{"date":173,"type":152},"2027-12-31",{"name":5,"class":6},9]