مقالات پذیرفته شده در نهمین کنگره بین المللی زیست پزشکی
Integrating Artificial Intelligence and CRISPR-Cas9 for Optimized gRNA Design and Mutation Detection in SLFN11 and KEAP1 Genes of Lung Cancer
Integrating Artificial Intelligence and CRISPR-Cas9 for Optimized gRNA Design and Mutation Detection in SLFN11 and KEAP1 Genes of Lung Cancer
Mehrnaz Hosseini,1,*
1. Department of Biotechnology, Faculty of Basic Sciences, University of Maragheh, Maragheh, Iran
Introduction: Lung cancer is one of the most lethal malignancies worldwide, largely due to its complex genetic landscape and resistance to conventional therapies. Identifying key genetic mutations is crucial for advancing precision medicine. Among them, SLFN11 and KEAP1 play essential roles in DNA damage response and NRF2 pathway regulation, respectively, and their mutations are strongly associated with lung cancer pathogenesis and prognosis. Integrating CRISPR-Cas9 with artificial intelligence (AI) offers a novel framework for optimized gRNA design and mutation detection.
Methods: Machine learning algorithms were applied to design targeted gRNAs for SLFN11 and KEAP1. Advanced AI models were then used to evaluate the specificity and predicted efficiency of each gRNA. High-scoring candidates were selected and tested against both wild-type and mutated sequences. Binding performance was compared, and AI-based analyses were conducted to classify mutation types and assess their potential roles in lung cancer progression.
Results: Initial results showed that multiple gRNAs with high predicted efficiency (>80%) and specificity were successfully designed for both genes. AI-driven analyses confirmed the ability of selected gRNAs to discriminate between wild-type and mutated sequences. Predicted mutation profiles aligned with reported lung cancer datasets, and several mutations were identified as potential drivers of drug resistance and oncogenic pathway activation.
Conclusion: This study demonstrates that combining CRISPR-Cas9 with AI algorithms provides a powerful and innovative approach for precise gRNA design and mutation detection in lung cancer. Targeting SLFN11 and KEAP1 highlights the potential of AI-CRISPR integration as a diagnostic and therapeutic strategy in precision medicine.