Colorectal cancer (CRC) once predominantly affected older individuals, but in recent years has witnessed a progressive increase in incidence among young adults. Once rare, early-onset colorectal cancer (EOCRC, that is, a CRC diagnosed before the age of 50) now constitutes 10-15% of all newly diagnosed CRC cases and it stands as the first cause of cancer-related death in young men and the second for young women.

This study aims to detect EOCRC with a non-invasive test, using a blood-based molecular assay based on microRNA (ribonucleic acid)

The rising incidence of early-onset colorectal cancer (EOCRC) is a pressing clinical issue unique to our times, and it is expected to grow with an anticipated further 90% increase in incidence by the decade's end. Challenges persist even after reducing the CRC screening age to 45: under-45s lack routine screening and compliance in the 45-50 age group remains low, partly due to invasiveness and discomfort of standard screening methods. Urgent action is warranted to develop affordable, sensitive, and feasible screening for timely detection and improved participation. A non-invasive, patient-friendly screening test, like a blood-based assay, could address these epidemiological concerns and also attract underserved populations.

This study involves the development and validation of a liquid biopsy, assessing circulating cell-free and exosomal microRNAs (cf-miRNA and exo-miRNA, respectively) for indirect sampling of tumor tissue in the bloodstream. The researchers intend to harness machine learning and bioinformatics to create an integrated panel (with both cf-miRNAs and exo-miRNAs) to enhance the inherently high sensitivity of cf-miRNAs with the distinctive specificity of exo-miRNAs. This combined approach will not only improve the performance of a diagnostic model but will also tap into the diverse tumor biology aspects of EOCRC.

The study's core goal is to develop cost-efficient, non-invasive, clinic-friendly biomarkers with high sensitivity and specificity, aiding EOCRC detection.

The researchers intend to do so in three phases:

1. To perform comprehensive small RNA-Seq from matched cf-miRNA, exo-miRNA, cancer-derived miRNA, and mucosa-derived miRNA.
2. To develop and train two miRNA detection panels (cf-miRNA and exo-miRNA, respectively) based on advanced machine-learning models and, then, combine these two using several machine-learning models to obtain a final detection biomarker.
3. To validate the findings in an independent cohort of EOCRC and controls.

In summary, this proposal promises to improve patient care and compliance, and, ultimately, reduce mortality from EOCRC.

Inclusion Criteria:

• Stage I, II, III, IV colorectal cancer (TNM classification, 8th edition) diagnosed before the age of 50 (EOCRC cases)
• Received standard diagnostic and staging procedures as per local guidelines, and at least one sample was drawn before receiving any curative-intent treatment
• Colonoscopy-proven cancer-free status at the time of study inclusion (Non-disease controls)

Exclusion Criteria:

• Hereditary colorectal cancer syndromes (identified through genetic testing)
• Inflammatory bowel diseases
• Lack of written informed consent

Individuals younger than 50 years of age who were diagnosed with colorectal cancer before the age of 50 (cases) or were free from colorectal cancer at the time of study inclusion (controls)

Data collected for the study will be made available to others, including de-identified participant data, at publication, via a signed data access agreement and at the discretion of the investigators' approval of the proposed use of such data