Journal of Clinical & Medical Genomics

Harnessing Artificial Intelligence for Predicting Microsatellite Instability and KRAS/BRAF Mutations in Cancer: A Revolutionary Approach

Abstract

Maria Getino

Cancer remains one of the most challenging health issues worldwide, with its complexity often defying conventional diagnostic and treatment approaches. In recent years, the integration of Artificial Intelligence (AI) into healthcare has shown promising results in various domains, including cancer diagnosis and prognosis. Among the critical molecular markers in cancer, Microsatellite Instability (MSI) and mutations in genes such as KRAS and BRAF hold significant prognostic and therapeutic implications. This article explores the revolutionary role of AI in predicting MSI, KRAS, and BRAF mutations, revolutionizing cancer management strategies, Microsatellites are short, repetitive DNA sequences scattered throughout the genome. MSI refers to the accumulation of errors (insertions or deletions) in these repetitive sequences due to impaired DNA mismatch repair mechanisms. MSI has emerged as a hallmark of several cancers, notably Colorectal Cancer (CRC), endometrial cancer, and gastric cancer, influencing prognosis and therapeutic response. KRAS and BRAF genes encode proteins involved in intracellular signalling pathways regulating cell growth and differentiation. Mutations in these genes are frequently encountered in various cancers, particularly CRC, impacting tumour behaviour and therapeutic susceptibility. For instance, KRAS mutations are associated with resistance to anti-EGFR (Epidermal Growth Factor Receptor) therapies in CRC patients