DNA Methylation, Unknown Primary Tumors
Conditions
Keywords
Unknown Primary Tumors, DNA methylation, Large Language Model, Pan-cancer
Brief summary
This study aims to initially utilize machine learning on pan-cancer DNA methylation data from public databases to construct a DNA methylation classification model (PaCIFiC-CUP, pan-cancer integrated fingerprinting classifier of CUP) for diagnosing various types of cancer, particularly the primary site of cancer of unknown primary. The goal is to achieve diagnosis of cancer pathology type by analyzing the DNA methylation patterns of cancer specimens, thereby guiding subsequent precision treatment for cancer.
Interventions
None listed
Sponsors
Sun Yat-sen University
First People's Hospital of Foshan
Third Affiliated Hospital, Sun Yat-Sen University
The First Affiliated Hospital of Anhui Medical University
Anhui Provincial Cancer Hospital
Dongguan People's Hospital
Beijing Tongren Hospital
Anhui Provincial Hospital
Peking University Third Hospital
Henan Cancer Hospital
Ganzhou Cancer Hospital
Guangzhou Chest Hospital.
First Affiliated Hospital of Jinan University
Peking University Shenzhen Hospital
Affiliated Cancer Hospital & Institute of Guangzhou Medical University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
1. The patient specimens were obtained from the Sun Yat-sen University Cancer Center and affiliated cooperating centers, with written consent from the patients authorizing the use of the specimens for research purposes. 2. Following standard assessments (medical history, physical examination, complete blood count, biochemistry, computed tomography scans of the neck, chest, abdomen, and pelvis, targeted evaluations of all symptomatic areas, pathology, and immunohistochemistry), the diagnosis was determined as a primary site unknown tumor (including adenocarcinoma, squamous cell carcinoma, undifferentiated carcinoma, neuroendocrine carcinoma, sarcoma, etc). 3. The diagnosis was confirmed at the participating institution and the patient had received systemic therapy. 4. Complete clinical, pathological, and follow-up data for the patients can be obtained. 5. ECOG performance status score: 0-2 points.
Exclusion criteria
1. Pregnant or lactating female patients. 2. Tumor tissue sample size is too small (tumor tissue accounts for \<70% in the biopsy or slice tissue). 3. Organ transplant or history of non-autologous (allogeneic) bone marrow or stem cell transplantation. 4. History of previous tumors, with the current condition being a recurrent tumor. 5. Hematological malignancies (excluding lymphoma). 6. Other diseases that may severely impact patient survival, such as severe cardiovascular or cerebrovascular diseases, sepsis, severe trauma or burns, etc.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Overall Survival | From date of diagnosis until the date of death from any cause, assessed up to 24 months | Overall survival refers to the length of time that a person lives after being diagnosed with cancer of unknown primary, without regard to any specific cause of death. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Progression-Free Survival | From date of diagnosis until the date of first documented progression or date of death from any cause, whichever came first, assessed up to 24 months | Progression-Free Survival refers to the length of time during which a patient with cancer of unknown primary remains alive without any signs or symptoms of disease progression |
Countries
China
Contacts
CONTACTYiyang Zhang
Outcome results
None listed