[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"conditionNormalized\":\"cup\",\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:cup":24},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":4,"eligibilityCriteria":15,"healthyVolunteers":11,"sex":16,"minAge":4,"maxAge":4,"enrollmentInfo":17,"targetDuration":4,"studyType":20,"phases":4,"briefSummary":21,"conditions":22,"keywords":25,"overallStatus":31,"whyStopped":4,"lastUpdateSubmitDate":32,"lastUpdatePostDateStruct":33,"startDateStruct":36,"completionDateStruct":38,"leadSponsor":40,"locationsCount":4},"100617540","evaluating-the-predictive-capability-of-transcriptomic-profiling-for-identifying-the-primary-site-of-metastatic-tumors-100617540",false,"NCT07319949","Evaluating the Predictive Capability of Transcriptomic Profiling for Identifying the Primary Site of Metastatic Tumors","Evaluative Study on Predicting the Primary Site of Metastatic Tumors Using Transcriptomic Profiling for Tumor Tissue Origin Identification","Inclusion Criteria\n\n1. Clinically confirmed diagnosis of malignant tumor with metastasis;\n2. Metastatic lesions confirmed as malignant by histopathology;\n3. Sufficient surgical resection or biopsy specimens retained to meet the requirements for next-generation sequencing;\n4. The participant (or their legal representative\u002Fguardian) has signed the informed consent form, confirming full understanding of the study's purpose and procedures, and voluntarily agrees to participate.\n\nExclusion Criteria:\n\n1\\. The investigator deems the patient unable to provide informed consent.","ALL",{"count":18,"type":19},30,"ESTIMATED","OBSERVATIONAL","This study will enroll patients with metastatic malignancies. Tumor samples (fresh or formalin-fixed paraffin-embedded tissue specimens) will undergo RNA extraction and next-generation sequencing (RNA-seq). Once the raw data is obtained, the system will analyze the transcriptomic feature values (cancer-specific RNA transcripts and tissue-specific RNA transcripts) expressed in the tumor tissue samples to further predict tissue origin using a machine learning model. The output includes probabilities and confidence intervals for tissue origin.",[23,24],"Malignant Tumor With Metastasis","CUP",[26,27,28,29,30],"Cancer of unknown primary","Specific RNA transcript","Tissue origin identification","Machine learning","Cancer diagnosis","NOT_YET_RECRUITING","2025-12-20",{"date":34,"type":35},"2026-01-06","ACTUAL",{"date":37,"type":19},"2026-01-15",{"date":39,"type":19},"2026-10-13",{"name":41,"class":42},"Fudan University","OTHER"]