مقالات پذیرفته شده در نهمین کنگره بین المللی زیست پزشکی
Study of common polymorphisms in melanoma cancer in Iranian genetic profiles and drug selection with minimal side effects
Study of common polymorphisms in melanoma cancer in Iranian genetic profiles and drug selection with minimal side effects
Majid Mesgar Tehrani,1,*Ayda Khatibi,2Mohammad Mehdi Eslami,3Reza Mirlohi,4
1. Member of the Core Committee of the National Genomics Hub, Shahid Beheshti University of Medical Sciences, Tehran, Iran 2. Department of Biological Sciences, Faculty of Basic Sciences, Institute of Higher Education of Nabi Akram, Tabriz, Iran. 3. Member of the Bioinformatics Research Group, Nasim Research Institute, Tehran, Iran 4. Member of the Bioinformatics Research Group, Nasim Research Institute, Tehran, Iran
Introduction: Melanoma is one of the most aggressive and life-threatening forms of skin cancer, with increasing incidence rates worldwide. Although it represents a smaller proportion of skin cancers compared to basal cell carcinoma and squamous cell carcinoma, melanoma accounts for the majority of skin cancer–related deaths due to its rapid metastatic potential. Recent advances in molecular genetics and bioinformatics have provided new opportunities for understanding the genetic background of melanoma, identifying key polymorphisms, and optimizing therapeutic strategies. Genetic heterogeneity plays a major role in the disease’s initiation, progression, and treatment response. Therefore, the study of common single-nucleotide polymorphisms (SNPs) across melanoma-associated genes is essential for uncovering disease mechanisms and designing personalized medicine approaches.
Methods: In this study, publicly available genomic datasets were retrieved from the NCBI database. Bioinformatic analyses were performed using the MegaGene web-based pharmacogenetic platform. This software allows the identification of disease-related polymorphisms, visualization of gene–disease interactions, and exploration of pharmacogenetic relationships between gene variants and drug responses. Within MegaGene, the MegaY and MegaX modules were used to map genetic contributors, analyze their prevalence in different populations, and assess their scientific impact through citation frequency. Graphical representations of SNP–disease associations and gene–drug adverse effect probabilities were downloaded and systematically reviewed. Particular attention was given to SNPs with high citation counts and strong associations with melanoma.
Results: The MegaGene analysis identified a total of 68 SNPs across multiple melanoma-related genes. Among these, the highest proportion was observed in BRAF (26.47%, 18 SNPs), highlighting its central role in melanoma pathogenesis and therapeutic targeting. Other genes with notable SNP representation included MC1R (16.17%, 11 SNPs), which is strongly linked to pigmentation and UV sensitivity, and NRAS (11.76%, 8 SNPs), a key regulator in MAPK signaling pathways. Additional variants were distributed across CDK4 (3 SNPs, 4.41%), CDKN2A (3 SNPs, 4.41%), EGF (3 SNPs, 4.41%), MMP2 (3 SNPs, 4.41%), and TERT (4 SNPs, 5.88%), genes that influence cell cycle regulation, growth factor signaling, and tumor invasion. Less frequent but relevant SNPs were also found in ASIP, ERCC2, IGF1R, KIT, MDM2, OCA2, RB1, TNF, TYR, TYRP1, and MDR, each contributing between 1.47% and 2.94% of the total SNPs.
These findings demonstrate that melanoma susceptibility and drug response are influenced by a broad genetic landscape, with BRAF, MC1R, and NRAS emerging as the most polymorphism-rich genes. This genetic variability supports the need for pharmacogenomic testing to tailor therapy and minimize adverse drug reactions.
Conclusion: Our bioinformatics-driven approach demonstrates that common genetic polymorphisms, particularly in TP53, CDKN2A, and MC1R, are major determinants of melanoma susceptibility and treatment outcomes. These findings support the integration of pharmacogenomics into clinical practice. Specifically, before prescribing melanoma therapies, genetic testing should be performed to identify the presence of high-risk polymorphisms. If detected, physicians can select alternative drugs with reduced genetic risk for adverse effects, thereby minimizing toxicity and improving therapeutic success. This personalized approach emphasizes the role of bioinformatics and pharmacogenetics in guiding clinical oncology toward safer and more effective treatment pathways.