Utilizing an In-silico Approach to Pinpoint Potential Biomarkers for Enhanced Early Detection of Colorectal Cancer.

Area of Science:

• Biomolecular research
• Genomics
• Cancer diagnostics

Background:

• Colorectal cancer (CRC) is a widespread disease with significant gene expression alterations.
• MicroRNAs (miRNAs) are increasingly recognized for their potential in non-invasive early CRC detection.
• Identifying specific miRNA-gene associations is crucial for developing effective diagnostic tools.

Purpose of the Study:

• To investigate the relationship between specific miRNAs and key genes implicated in colorectal cancer.
• To identify novel miRNA biomarkers for the early detection of CRC.
• To explore potential miRNA-target gene interactions in CRC pathogenesis.

Main Methods:

• Utilized two publicly available gene expression datasets (GSE106817, GSE23878) from the NCBI Gene Expression Omnibus.
• Employed penalized logistic regression (PLR) and artificial neural networks (ANN) for miRNA identification and classification accuracy assessment.
• Validated findings through analysis of bipartite miRNA-mRNA interactions.

Main Results:

• Identified three key miRNAs: miR-1228, miR-6765-5p, and miR-6787-5p, achieving a diagnostic accuracy exceeding 90%.
• Established CDK1 and MAD2L1 as target genes of miR-6787-5p through mRNA-miRNA interaction network analysis.
• Demonstrated the potential of these miRNAs in distinguishing CRC from non-cancerous samples.

Conclusions:

• The identified miRNAs (miR-1228, miR-6765-5p, miR-6787-5p) and their target genes (CDK1, MAD2L1) show promise as non-invasive biomarkers for colorectal cancer.
• Further laboratory validation is required to confirm the diagnostic utility of these molecular markers.
• This study contributes to the development of novel, non-invasive strategies for early CRC diagnosis.