Introduction: Gastric cancer (GC) is known to be among the most prevalent types of cancer and a leading cause of cancer-related death in Iran. Currently, therapeutic strategies, selection of chemotherapy drugs, and treatment modalities in Iran are mainly based on the tumor histology type. This uniform strategy often leads to drug resistance, metastasis, and adverse therapeutic outcomes. In recent years, the dramatic advances in genomics and bioinformatics fields have allowed for personalized cancer treatment. The cancer patient’s exclusive genomic profile can now be identified in most countries using methods such as flow cytometry and new-generation sequencing (NGS). This valuable information allows for a more accurate selection of chemotherapy drugs and the design of targeted therapeutic strategies. The personalization of treatment based on the genomic profile can promise a better therapeutic response, reduction of side effects, and prolonged life of patients.
Methods: This study mainly aims to establish an all-inclusive database of GC patients’ genomic profiles, aiming at the personalized treatment and prediction of the patient’s response to various treatments, particularly concerning drug side effects. Through a comprehensive analysis of genomic and clinical data, this research seeks to identify genetic biomarkers related to response to treatment and the incidence of side effects in GC patients. To achieve the study goals, the comprehensive NCBI genomic database was used as a rich source of genetic information, and the genomic data of GC patients were extracted and analyzed from this database. The data were more accurately analyzed using the specialized pharmacogenetic software (Mega Gene), which allows for analyzing gene polymorphisms and predicting their impacts on drug metabolism and side effects.
Results: The comprehensive analysis of GC patients’ genomic data has manifested the pivotal role of gene polymorphisms in the development and progression of this disease. This article represents a wide range of multifactorial genes involved in GC pathogenesis. Our results indicate that the RS4987047 polymorphism in the BRCA2 gene is associated with an increased risk of breast and ovarian cancers. Similarly, the RS80357906 polymorphism in the BRCA1gene is linked to an elevated risk of neoplasm of ovary and pancreatic cancers, and the RS1800056 polymorphism in the ATM gene is related to a heightened risk of breast and carcinoma of colon cancers. These results demonstrate specific patterns of tumor metastasis and spread in GC patients.
Conclusion: Based on our results, it can be concluded that evaluating GC patients’ genomic profile, particularly the polymorphisms detected in this study, can help predict the risk of developing other cancers and play an effective role in selecting the chemotherapy drug type. Moreover, genetic tests are necessary to examine polymorphisms in common genes, including FTO, JAK2, TYR, and MLH, before prescribing the drugs for GC patients. This helps physicians to select a drug with minimal side effects based on each patient’s genetic profile, thereby improving treatment effectiveness.