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

NCT ID: NCT07319949

Last Updated: 2026-01-06

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

30 participants

Study Classification

OBSERVATIONAL

Study Start Date

2026-01-15

Study Completion Date

2026-10-13

Brief Summary

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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.

Detailed Description

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This is a non-interventional, observational study. Through a single-center, prospective clinical trial, the study aims to utilize the transcriptomic profiling for tumor tissue origin identification to predict the tissue origin of primary sites in metastatic tumors and evaluate the accuracy and specificity of this prediction solution.

Primary Endpoint:

The accuracy of the transcriptomic profiling for tumor tissue origin identification in predicting the primary site of metastatic tumors (expressed as overall accuracy with its 95% confidence interval).

Secondary Endpoints:

1. The specificity and sensitivity of the transcriptomic profiling for tumor tissue origin identification in predicting the primary site of metastatic tumors.
2. Exploratory analysis of characteristic molecular markers expressed in metastatic lesions from different primary sites.

Conditions

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Malignant Tumor With Metastasis CUP

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

1. Clinically confirmed diagnosis of malignant tumor with metastasis;
2. Metastatic lesions confirmed as malignant by histopathology;
3. Sufficient surgical resection or biopsy specimens retained to meet the requirements for next-generation sequencing;
4. 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.

Exclusion Criteria

1\. The investigator deems the patient unable to provide informed consent.
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Fudan University

OTHER

Sponsor Role lead

Responsible Party

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Zhimin Shao

Director of the Department of Breast Surgery, Fudan University Shanghai Cancer Center

Responsibility Role PRINCIPAL_INVESTIGATOR

Central Contacts

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Zhiao Chen, Ph.D.

Role: CONTACT

008618017312074

References

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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.

Reference Type BACKGROUND
PMID: 37697439 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 35473935 (View on PubMed)

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

Reference Type BACKGROUND
PMID: 33288500 (View on PubMed)

Other Identifiers

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SRTCD-NO001

Identifier Type: -

Identifier Source: org_study_id

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