Evaluating the Predictive Capability of Transcriptomic Profiling for Identifying the Primary Site of Metastatic Tumors

NCT07319949 | Not Yet Recruiting

• 1 other identifier: SRTCD-NO001
• Study Type: observational
• Target: 30
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Malignant Tumor With Metastasis; CUP

Keywords

Cancer of unknown primary; Specific RNA transcript; Tissue origin identification; Machine learning; Cancer diagnosis

Outcome Measures

Primary Outcomes (1)

• The accuracy of the transcriptomic profiling for tumor tissue origin identification in predicting the primary site of metastatic tumors

  Overall accuracy of the tumor tissue origin tracing transcriptome test for predicting the primary site of metastatic tumors (number of correct predictions/total evaluable cases); Specificity \[true negatives/(true negatives + false positives)\]; Sensitivity \[true positives/(true positives + false negatives)\].

  through study completion, an average of 1 year

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients treated in Fudan University Shanghai Cancer Center, encompassing both outpatient and inpatient populations.

You may qualify if:

• Clinically confirmed diagnosis of malignant tumor with metastasis;
• Metastatic lesions confirmed as malignant by histopathology;
• Sufficient surgical resection or biopsy specimens retained to meet the requirements for next-generation sequencing;
• The participant (or their legal representative/guardian) has signed the informed consent form, confirming full understanding of the study's purpose and procedures, and voluntarily agrees to participate.

You may not qualify if:

• \. The investigator deems the patient unable to provide informed consent.

Sponsors & Collaborators

• Fudan University: lead

Related Publications (3)

• Shi Q, Li X, Liu Y, Chen Z, He X. FLIBase: a comprehensive repository of full-length isoforms across human cancers and tissues. Nucleic Acids Res. 2024 Jan 5;52(D1):D124-D133. doi: 10.1093/nar/gkad745.

  PMID: 37697439 BACKGROUND

• Shi Q, Liu T, Hu W, Chen Z, He X, Li S. SRTdb: an omnibus for human tissue and cancer-specific RNA transcripts. Biomark Res. 2022 Apr 26;10(1):27. doi: 10.1186/s40364-022-00377-1.

  PMID: 35473935 BACKGROUND

• Lee MS, Sanoff HK. Cancer of unknown primary. BMJ. 2020 Dec 7;371:m4050. doi: 10.1136/bmj.m4050.

  PMID: 33288500 BACKGROUND

Biospecimen

Retention: SAMPLES WITH DNA

Samples will be collected from patients who have been screened, enrolled, and have signed informed consent forms. Detectable sample types include fresh tissue samples, biopsy tissue samples, and formalin-fixed, paraffin-embedded (FFPE) tissue samples.

Central Study Contacts

Zhiao Chen, Ph.D.

CONTACT

008618017312074; zachen@fudan.edu.cn

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director of the Department of Breast Surgery, Fudan University Shanghai Cancer Center

Study Record Dates

• First Submitted: December 8, 2025
• First Posted: January 6, 2026
• Study Start: January 15, 2026
• Primary Completion (Estimated): October 13, 2026
• Study Completion (Estimated): October 13, 2026
• Last Updated: January 6, 2026

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share