Application of simulation-based CYP26 SNP-environment barcodes for evaluating the occurrence of oral malignant disorders by odds ratio-based binary particle swarm optimization: A case-control study in the Taiwanese population

Agent-based approaches for biological modeling in oncology: A literature review

CONTEXT

Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a promising approach to understand complex biological processes and predict their behavior under various conditions.

METHODOLOGY

This paper is a review of the recent literature on computational modeling of biological systems. Our study focuses on the field of oncology and the use of artificial intelligence (AI) and, in particular, agent-based modeling (ABM), between 2010 and May 2023.

RESULTS

Most of the articles studied focus on improving the diagnosis and understanding the behaviors of biological entities, with metaheuristic algorithms being the models most used. Several challenges are highlighted regarding increasing and structuring knowledge about biological systems, developing holistic models that capture multiple scales and levels of organization, reproducing emergent behaviors of biological systems, validating models with experimental data, improving computational performance of models and algorithms, and ensuring privacy and personal data protection are discussed.

Relationship between MEG3 gene polymorphism and risk of gastric cancer in Chinese population with high incidence of gastric cancer

Objective: Gastric cancer is the most common gastrointestinal malignancy in China and results from a combination of genetic and environmental factors. The present study was conducted to investigate the relationship between long noncoding RNA (lncRNA) materally expressed gene 3 (MEG3) single nucleotide polymorphisms (SNPs) and the risk of gastric cancer and to construct a genetic-environmental risk assessment model. Methods: A case–control study was conducted to include 474 patients with gastric cancer diagnosed by clinical and pathological examination and 543 healthy physical examination subjects. Blood samples, general demographic data and behavioral lifestyle of the subjects were collected. The TaqMan real-time PCR method was used for testing the genotypes of MEG3 rs7158663 and rs10132552. Results: The A allele at the rs7158663 loci of MEG3 was found to be risk factor for gastric cancer (odds ratio (OR) = 1.41, 95% confidence interval (95% CI) = 1.14–1.74, P=0.002). Yet, no significant association between rs10132552 polymorphisms and gastric cancer was observed. Drinking, tea drinking and preserved food eating were risk factors for gastric cancer (P<0.05). A genetic–environmental risk assessment model was established by using the logistic regression model to include MEG3 rs7158663, drinking, tea drinking, and preserved food eating. With the increase in risk score (RS), the risk of gastric cancer increased substantially (P<0.05). And the area under the receiver operating characteristic (ROC) curve was 0.745, which indicates a high diagnostic value. Conclusions:MEG3 rs7158663 might be associated with the risk of gastric cancer; the diagnostic ability of genetic–environmental risk assessment model for gastric cancer is better.