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With high incidence and mortality rate, the effective therapeutic options of colorectal cancer remain limited. Up to 50% of patients with colorectal cancer will develop metastatic disease. Recurrent lesions can be diagnosed and characterized only when tumors have reached a certain volume by present radiologic imaging techniques such as CT and MRI. The exploration of differentiated clinical applications specifically for local recurrence versus distant metastasis remains an unmet need. The aim of this study is to explore methylation, fragmentomic, and cfRNA markers associated with local recurrence and distant metastasis of colorectal cancer by multi-omics approaches. By constructing a predictive model for the localization of post-treatment recurrence and metastasis, this study will compare the accuracy of different technical approaches in predicting the localization of recurrence and metastasis after colorectal cancer treatment.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Primary colorectal cancer | |||
| Locally recurrent colorectal cancer | |||
| Colorectal cancer liver metastasis | |||
| Colorectal cancer bone metastasis | |||
| Colorectal cancer lung metastasis | |||
| Colorectal cancer peritoneal metastasis | |||
| Primary lung cancer | |||
| Primary liver cancer | |||
| Colorectal cancer multi-organ metastasis | |||
| Primary bone tumor |
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| Measure | Description | Time Frame |
|---|---|---|
| Area Under the Receiver Operating Characteristic Curve (AUC) of the prediction models based on methylation, fragmentomics, and cfRNA data for local recurrence and distant metastasis localization in colorectal cancer | The AUC is calculated from the Receiver Operating Characteristic (ROC) curve to evaluate the discriminatory performance of the prediction model developed by use of methylation, fragmentomics, and cfRNA data. Model performance will be assessed on the validation cohort. | At baseline (at the time of study enrollment, prior to any surgical or anti-tumor treatment) |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of the prediction models based on methylation, fragmentomics, and cfRNA data for colorectal cancer recurrence and metastasis localization | Accuracy is defined as the proportion of correctly predicted samples (true positives + true negatives) among the total number of samples in the validation cohort. | Baseline (at enrollment) |
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Inclusion Criteria:
Exclusion Criteria:
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Patients meeting the following tumor types: primary colorectal cancer, locally recurrent colorectal cancer, colorectal cancer liver metastasis, colorectal cancer lung metastasis, colorectal cancer peritoneal metastasis, colorectal cancer bone metastasis, colorectal cancer multi-organ metastasis, primary liver cancer, primary lung cancer, primary peritoneal cancer, primary bone tumor.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Yanhong Gu, PhD | Contact | +025-68307881 | YanhongGu@njmu.edu.cn |
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| ID | Term |
|---|---|
| D015179 | Colorectal Neoplasms |
| ID | Term |
|---|---|
| D007414 | Intestinal Neoplasms |
| D005770 | Gastrointestinal Neoplasms |
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
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Peripheral Blood
| Primary peritoneal cancer |
| Sensitivity and specificity of individual omics technologies (methylation, fragmentomics, and cfRNA) |
Comparison of predictive performance of three individual technical approaches for localization of recurrent and metastatic lesions. |
| Baseline (at enrollment) |
| D009369 | Neoplasms |
| D004066 | Digestive System Diseases |
| D005767 | Gastrointestinal Diseases |
| D003108 | Colonic Diseases |
| D007410 | Intestinal Diseases |
| D012002 | Rectal Diseases |