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Trial Title:
The Effectiveness of Liquid Biopsy in Differential Diagnosis and Early Screening of Epithelial Ovarian Cancer
NCT ID:
NCT05931055
Condition:
Ovarian Cancer
Differentiating
Diagnosis
Conditions: Official terms:
Ovarian Neoplasms
Carcinoma, Ovarian Epithelial
Disease
Study type:
Observational
Overall status:
Not yet recruiting
Study design:
Time perspective:
Prospective
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
Fragmentomics
Description:
The fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the
genome can indirectly reflect the state of gene expression regulation in vivo. Its
characteristics mainly include copy number variation (CNV), Nucleosome footprint,
fragment length and motif. Fragomics relies on WGS (Whole Genome Sequencing), and the
target covers the whole Genomics level. Thus, the cfDNA Fragmentomics testing can help
differentiation and early diagnosis.
Arm group label:
ovarian cancer group
Arm group label:
ovarian cyst group
Summary:
At present, there is a lack of effective screening methods. It is urgent to explore new
non-invasive detection methods for early diagnosis of epithelial ovarian cancer and
non-invasive differentiation methods for benign and malignant ovarian tumors.
Liquid biopsy technology has great potential for early screening of tumors. The
fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the genome
can indirectly reflect the state of gene expression regulation in vivo. Its
characteristics mainly include copy number variation (CNV), Nucleosome footprint,
fragment length and motif.
The number of proteins in a proteome can sometimes exceed the number of genomes. It
includes "structural Proteomics" and "functional Proteomics". At present, research has
explored the use of urinary protein biomarkers for early diagnosis of gastric cancer.
"Deep Visual Proteomics (DVP)" reveals the mechanism driving tumor evolution and new
therapeutic targets for tumors.
Using the currently mature low depth WGS sequencing technology, this study aims to
explore its clinical application in the differentiation and early screening of epithelial
ovarian cancer, as well as monitoring the course of epithelial ovarian cancer, including
the detection of minimal residual lesions (MRD) and monitoring of recurrence (MOR). This
study also explores the role of urine proteomics in the differentiation of benign and
malignant ovarian tumors, early screening and invasiveness of epithelial ovarian cancer,
and monitoring the course of epithelial ovarian cancer.
Detailed description:
At present, there is a lack of effective screening methods. Numerous studies and
practices have shown that tumor screening and early diagnosis and treatment can
effectively prolong the overall survival period of cancer patients and reduce the
economic burden of the disease. The traditional early screening methods for tumors in
clinical practice, including imaging screening, endoscopic screening, and tumor marker
screening, generally have defects such as strong invasiveness, significant discomfort
during the screening process, low accuracy (false negative, false positive), and poor
compliance. Therefore, it is urgent to explore new non-invasive detection methods for
early diagnosis of epithelial ovarian cancer and non-invasive differentiation methods for
benign and malignant ovarian tumors.
Liquid biopsy technology, as a non-invasive new detection technology, has great potential
for early screening of tumors. CfDNA is an important marker for liquid biopsy and has
been widely used in non-invasive prenatal examinations and cancer liquid biopsy research.
The fragmentation patterns of cfDNA fragments in plasma and the uneven coverage of the
genome can indirectly reflect the state of gene expression regulation in vivo. Its
characteristics mainly include copy number variation (CNV), Nucleosome footprint,
fragment length and motif.
Proteome changes with different tissue and even environmental states. During
transcription, a gene can be spliced in multiple mRNA forms, and a proteome is not a
direct product of a genome. The number of proteins in a proteome can sometimes exceed the
number of genomes. It includes "structural Proteomics" and "functional Proteomics". The
former is mainly the study of protein expression models, including protein amino acid
sequence, analysis and spatial structure analysis, type analysis and quantity
determination; The latter mainly focuses on the study of protein functional patterns,
including protein function and protein-protein interactions. At present, research has
explored the use of urinary protein biomarkers for early diagnosis of gastric cancer.
"Deep Visual Proteomics (DVP)" reveals the mechanism driving tumor evolution and new
therapeutic targets for tumors.
There is ample evidence to support the diagnostic value of fragment omics research in
tumors. Using the currently mature low depth WGS sequencing technology, this study aims
to explore its clinical application in the differentiation and early screening of
epithelial ovarian cancer, as well as monitoring the course of epithelial ovarian cancer,
including the detection of minimal residual lesions (MRD) and monitoring of recurrence
(MOR). In addition, there is currently no research on the use of urine proteomics in the
differentiation and early screening of ovarian cancer. This study also explores the role
of urine proteomics in the differentiation of benign and malignant ovarian tumors, early
screening and invasiveness of epithelial ovarian cancer, and monitoring the course of
epithelial ovarian cancer.
Criteria for eligibility:
Study pop:
female aged over 18 years old, having mass in accessory area, receiving surgery
Sampling method:
Probability Sample
Criteria:
Inclusion Criteria:
1. Age ≥ 18 years old, female;
2. Suspicious or considering mass in the accessory area, requiring surgical treatment
and obtaining pathology of ovarian tissue;
3. Having pelvic imaging evaluation results, including ultrasound, MRI, CT, or PET/CT;
4. Serum CA125 and HE4 tests are tested before surgery, and ROMA evaluation results is
obtained;
5. Volunteer to participate in this research and sign an informed consent form; (6)
Good compliance and regular follow-up.
Exclusion Criteria:
1. Patients with confirmed ovarian cancer who have undergone surgery and obtained
histopathology;
2. Patients who have received chemotherapy or pelvic radiation therapy within 6 months
prior to sample collection;
3. Researchers have confirmed patients with recurrent ovarian cancer, or ovarian cancer
patients who have received chemotherapy and/or surgical treatment;
4. Contraindications for surgical evaluation and inability to obtain ovarian pathology
or surgical pathological information;
5. Samples that do not meet the requirements for collecting and storing assessment
reagent samples; Or contaminated or suspected contaminated samples;
6. Samples with missing, incomplete, and untraceable clinical information of
corresponding patients;
7. Pregnant and lactating women;
8. Patients who cannot cooperate with examinations, have poor compliance, and cannot
follow up regularly.
Gender:
Female
Minimum age:
18 Years
Maximum age:
N/A
Start date:
January 1, 2024
Completion date:
January 1, 2032
Lead sponsor:
Agency:
Peking Union Medical College Hospital
Agency class:
Other
Source:
Peking Union Medical College Hospital
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05931055