Clinical Research Directory

New bioMarkers tO straTIfy cOlorectal caNcer Referrals

The goal of this observational study is to evaluate if a blood test for circulating progastrin (hPG80) and transposable elements (TEs) can accurately predict colorectal cancer (CRC) or polyps in adult patients referred to the 2-week wait (2WW) or Straight to Test (STT) pathways for suspected lower gastrointestinal cancer. The main questions it aims to answer are: Can plasma hPG80 levels accurately predict a diagnosis of CRC or polyps in patients undergoing standard 2WW investigations? Can transposable elements (TEs) in the plasma serve as predictive biomarkers for CRC diagnosis in these patients? What are the patient preferences for different diagnostic tests for CRC, particularly a blood-based test compared to more invasive methods? Participants will: Provide a 20ml blood sample during a routine hospital visit for their 2WW diagnostic test (e.g., colonoscopy, CT Colon). Undergo standard clinical investigations as determined by their treating clinicians. Have their final diagnosis (cancer, polyp, or normal) correlated with their plasma hPG80 levels. For a subset of 100 participants (25 with confirmed CRC, 75 non-cancer), have their plasma analyzed for circulating signatures using RNAseq and DNAseq. Complete an electronic post-study questionnaire to explore their preferences and experiences with different CRC diagnostic tests used within the 2WW pathway.

Early Diagnosis Model of Colorectal Adenoma Using Laboratory Examinations

Colorectal cancer has the third highest incidence and second highest mortality rate of all malignant tumors worldwide. Distinct from most other cancers, colorectal cancer can be prevented; colonoscopy-based identification and removal of adenomatous polyps is the most effective preventive measure. Early intestinal adenomas rarely cause specific symptoms, and many patients are diagnosed at advanced stages once symptoms emerge, leading to unsatisfactory treatment and prognosis. Colonoscopy, the main diagnostic tool for intestinal adenoma, is invasive, resulting in limited patient compliance, while grassroots hospitals face shortages of medical resources. There is an urgent demand for a convenient, affordable and well-tolerated early diagnostic method for intestinal adenoma. Artificial intelligence techniques can efficiently analyze routine clinical laboratory data. This study aims to establish an AI-based predictive model combining clinical information and laboratory test results to realize early identification of intestinal adenoma and optimize patient prognosis.